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  • Basics of Join Predicate Pushdown in Oracle

    - by Maria Colgan
    Happy New Year to all of our readers! We hope you all had a great holiday season. We start the new year by continuing our series on Optimizer transformations. This time it is the turn of Predicate Pushdown. I would like to thank Rafi Ahmed for the content of this blog.Normally, a view cannot be joined with an index-based nested loop (i.e., index access) join, since a view, in contrast with a base table, does not have an index defined on it. A view can only be joined with other tables using three methods: hash, nested loop, and sort-merge joins. Introduction The join predicate pushdown (JPPD) transformation allows a view to be joined with index-based nested-loop join method, which may provide a more optimal alternative. In the join predicate pushdown transformation, the view remains a separate query block, but it contains the join predicate, which is pushed down from its containing query block into the view. The view thus becomes correlated and must be evaluated for each row of the outer query block. These pushed-down join predicates, once inside the view, open up new index access paths on the base tables inside the view; this allows the view to be joined with index-based nested-loop join method, thereby enabling the optimizer to select an efficient execution plan. The join predicate pushdown transformation is not always optimal. The join predicate pushed-down view becomes correlated and it must be evaluated for each outer row; if there is a large number of outer rows, the cost of evaluating the view multiple times may make the nested-loop join suboptimal, and therefore joining the view with hash or sort-merge join method may be more efficient. The decision whether to push down join predicates into a view is determined by evaluating the costs of the outer query with and without the join predicate pushdown transformation under Oracle's cost-based query transformation framework. The join predicate pushdown transformation applies to both non-mergeable views and mergeable views and to pre-defined and inline views as well as to views generated internally by the optimizer during various transformations. The following shows the types of views on which join predicate pushdown is currently supported. UNION ALL/UNION view Outer-joined view Anti-joined view Semi-joined view DISTINCT view GROUP-BY view Examples Consider query A, which has an outer-joined view V. The view cannot be merged, as it contains two tables, and the join between these two tables must be performed before the join between the view and the outer table T4. A: SELECT T4.unique1, V.unique3 FROM T_4K T4,            (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3) VWHERE T4.unique3 = V.hundred(+) AND       T4.ten = V.ten(+) AND       T4.thousand = 5; The following shows the non-default plan for query A generated by disabling join predicate pushdown. When query A undergoes join predicate pushdown, it yields query B. Note that query B is expressed in a non-standard SQL and shows an internal representation of the query. B: SELECT T4.unique1, V.unique3 FROM T_4K T4,           (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3             AND T4.unique3 = V.hundred(+)             AND T4.ten = V.ten(+)) V WHERE T4.thousand = 5; The execution plan for query B is shown below. In the execution plan BX, note the keyword 'VIEW PUSHED PREDICATE' indicates that the view has undergone the join predicate pushdown transformation. The join predicates (shown here in red) have been moved into the view V; these join predicates open up index access paths thereby enabling index-based nested-loop join of the view. With join predicate pushdown, the cost of query A has come down from 62 to 32.  As mentioned earlier, the join predicate pushdown transformation is cost-based, and a join predicate pushed-down plan is selected only when it reduces the overall cost. Consider another example of a query C, which contains a view with the UNION ALL set operator.C: SELECT R.unique1, V.unique3 FROM T_5K R,            (SELECT T1.unique3, T2.unique1+T1.unique1             FROM T_5K T1, T_10K T2             WHERE T1.unique1 = T2.unique1             UNION ALL             SELECT T1.unique3, T2.unique2             FROM G_4K T1, T_10K T2             WHERE T1.unique1 = T2.unique1) V WHERE R.unique3 = V.unique3 and R.thousand < 1; The execution plan of query C is shown below. In the above, 'VIEW UNION ALL PUSHED PREDICATE' indicates that the UNION ALL view has undergone the join predicate pushdown transformation. As can be seen, here the join predicate has been replicated and pushed inside every branch of the UNION ALL view. The join predicates (shown here in red) open up index access paths thereby enabling index-based nested loop join of the view. Consider query D as an example of join predicate pushdown into a distinct view. We have the following cardinalities of the tables involved in query D: Sales (1,016,271), Customers (50,000), and Costs (787,766).  D: SELECT C.cust_last_name, C.cust_city FROM customers C,            (SELECT DISTINCT S.cust_id             FROM sales S, costs CT             WHERE S.prod_id = CT.prod_id and CT.unit_price > 70) V WHERE C.cust_state_province = 'CA' and C.cust_id = V.cust_id; The execution plan of query D is shown below. As shown in XD, when query D undergoes join predicate pushdown transformation, the expensive DISTINCT operator is removed and the join is converted into a semi-join; this is possible, since all the SELECT list items of the view participate in an equi-join with the outer tables. Under similar conditions, when a group-by view undergoes join predicate pushdown transformation, the expensive group-by operator can also be removed. With the join predicate pushdown transformation, the elapsed time of query D came down from 63 seconds to 5 seconds. Since distinct and group-by views are mergeable views, the cost-based transformation framework also compares the cost of merging the view with that of join predicate pushdown in selecting the most optimal execution plan. Summary We have tried to illustrate the basic ideas behind join predicate pushdown on different types of views by showing example queries that are quite simple. Oracle can handle far more complex queries and other types of views not shown here in the examples. Again many thanks to Rafi Ahmed for the content of this blog post.

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  • TFS 2010 Basic Concepts

    - by jehan
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Here, I’m going to discuss some key Architectural changes and concepts that have taken place in TFS 2010 when compared to TFS 2008. In TFS 2010 Installation, First you need to do the Installation and then you have to configure the Installation Feature from the available features. This is bit similar to SharePoint Installation, where you will first do the Installation and then configure the SharePoint Farms. 1) Installation Features available in TFS2010: a) Basic: It is the most compact TFS installation possible. It will install and configure Source Control, Work Item tracking and Build Services only. (SharePoint and Reporting Integration will not be possible). b) Standard Single Server: This is suitable for Single Server deployment of TFS. It will install and configure Windows SharePoint Services for you and will use the default instance of SQL Server. c) Advanced: It is suitable, if you want use Remote Servers for SQL Server Databases, SharePoint Products and Technologies and SQL Server Reporting Services. d) Application Tier Only: If you want to configure high availability for Team Foundation Server in a Load Balanced Environment (NLB) or you want to move Team Foundation Server from one server to other or you want to restore TFS. e) Upgrade: If you want to upgrade from a prior version of TFS. Note: One more important thing to know here about  TFS 2010 Basic is that,  it can be installed on Client Operations Systems(Windows 7 and Windows Vista SP3), Where as  earlier you cannot Install previous version of TFS (2008 and 2005) on client OS. 2) Team Project Collections: Connect to TFS dialog box in TFS 2008:  In TFS 2008, the TFS Server contains a set of Team Projects and each project may or may not be independent of other projects and every checkin gets a ever increasing  changeset ID  irrespective of the team project in which it is checked in and the same applies to work items  also, who also gets unique Work Item Ids.The main problem with this approach was that there are certain things which were impossible to do; those were required as per the Application Development Process. a)      If something has gone wrong in one team project and now you want to restore it back to earlier state where it was working properly then it requires you to restore the Database of Team Foundation Server from the backup you have taken as per your Maintenance plans and because of this the other team projects may lose out on the work which is not backed up. b)       Your company had a merge with some other company and now you have two TFS servers. One TFS Server which you are working on and other TFS server which other company was working and now after the merge you want to integrate the team projects from two TFS servers into one, which is almost impossible to achieve in TFS 2008. Though you can create the Team Projects in one server manually (In Source Control) which you want to integrate from the other TFS Server, but will lose out on History of Change Sets and Work items and others which are very important. There were few more issues of this sort, which were difficult to resolve in TFS 2008. To resolve issues related to above kind of scenarios which were mainly related TFS Maintenance, Integration, migration and Security,  Microsoft has come up with Team Project Collections concept in TFS 2010.This concept is similar to SharePoint Site Collections and if you are familiar with SharePoint Architecture, then it will help you to understand TFS 2010 Architecture easily. Connect to TFS dialog box in TFS 2010: In above dialog box as you can see there are two Team Project Collections, each team project can contain any number of team projects as you can see on right side it shows the two Team Projects in Team Project Collection (Default Collection) which I have chosen. Note: You can connect to only one Team project Collection at a time using an instance of  TFS Team Explorer. How does it work? To introduce Team Project Collections, changes have been done in reorganization of TFS databases. TFS 2008 was composed of 5-7 databases partitioned by subsystem (each for Version Control, Work Item Tracking, Build, Integration, Project Management...) New TFS 2010 database architecture: TFS_Config: It’s the root database and it contains centralized TFS configuration data, including the list of all team projects exist in TFS server. TFS_Warehouse: The data warehouse contains all the reporting data of served by this server (farm). TFS_* : This contains individual team project collection data. This database contains all the operational data of team project collection regardless of subsystem.In additional to this, you will have databases for SharePoint and Report Server. 3) TFS Farms:  As TFS 2010 is more flexible to configure as multiple Application tiers and multiple Database tiers, so it will be more appropriate to call as TFS Farm if you going for multi server installation of TFS. NLB support for TFS application tiers – With TFS 2010: you can configure multiple TFS application tier machines to serve the same set of Team Project Collections. The primary purpose of NLB support is to enable a cleaner and more complete high availability than in TFS 2008. Even if any application tier in the farm fails then farm will automatically continue to work with hardly any indication to end users of a problem. SQL data tiers: With 2010 you can configure many SQL Servers. Each Database can be configured to be on any SQL Server because each Team Project Collection is an independent database. This feature can also be used to load balance databases across SQL Servers.These new capabilities will significantly change the way enterprises manage their TFS installations in the future. With Team Project Collections and TFS farms, you can create a single, arbitrarily large TFS installation. You can grow it incrementally by adding ATs and SQL Servers as needed.

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  • SQL Server 2008 R2 Reporting Services - The Word is But a Stage (T-SQL Tuesday #006)

    - by smisner
    Host Michael Coles (blog|twitter) has selected LOB data as the topic for this month's T-SQL Tuesday, so I'll take this opportunity to post an overview of reporting with spatial data types. As part of my work with SQL Server 2008 R2 Reporting Services, I've been exploring the use of spatial data types in the new map data region. You can create a map using any of the following data sources: Map Gallery - a set of Shapefiles for the United States only that ships with Reporting Services ESRI Shapefile - a .shp file conforming to the Environmental Systems Research Institute, Inc. (ESRI) shapefile spatial data format SQL Server spatial data - a query that includes SQLGeography or SQLGeometry data types Rob Farley (blog|twitter) points out today in his T-SQL Tuesday post that using the SQL geography field is a preferable alternative to ESRI shapefiles for storing spatial data in SQL Server. So how do you get spatial data? If you don't already have a GIS application in-house, you can find a variety of sources. Here are a few to get you started: US Census Bureau Website, http://www.census.gov/geo/www/tiger/ Global Administrative Areas Spatial Database, http://biogeo.berkeley.edu/gadm/ Digital Chart of the World Data Server, http://www.maproom.psu.edu/dcw/ In a recent post by Pinal Dave (blog|twitter), you can find a link to free shapefiles for download and a tutorial for using Shape2SQL, a free tool to convert shapefiles into SQL Server data. In my post today, I'll show you how to use combine spatial data that describes boundaries with spatial data in AdventureWorks2008R2 that identifies stores locations to embed a map in a report. Preparing the spatial data First, I downloaded Shapefile data for the administrative boundaries in France and unzipped the data to a local folder. Then I used Shape2SQL to upload the data into a SQL Server database called Spatial. I'm not sure of the reason why, but I had to uncheck the option to create a spatial index to upload the data. Otherwise, the upload appeared to run successfully, but no table appeared in my database. The zip file that I downloaded contained three files, but I didn't know what was in them until I used Shape2SQL to upload the data into tables. Then I found that FRA_adm0 contains spatial data for the country of France, FRA_adm1 contains spatial data for each region, and FRA_adm2 contains spatial data for each department (a subdivision of region). Next I prepared my SQL query containing sales data for fictional stores selling Adventure Works products in France. The Person.Address table in the AdventureWorks2008R2 database (which you can download from Codeplex) contains a SpatialLocation column which I joined - along with several other tables - to the Sales.Customer and Sales.Store tables. I'll be able to superimpose this data on a map to see where these stores are located. I included the SQL script for this query (as well as the spatial data for France) in the downloadable project that I created for this post. Step 1: Using the Map Wizard to Create a Map of France You can build a map without using the wizard, but I find it's rather useful in this case. Whether you use Business Intelligence Development Studio (BIDS) or Report Builder 3.0, the map wizard is the same. I used BIDS so that I could create a project that includes all the files related to this post. To get started, I added an empty report template to the project and named it France Stores. Then I opened the Toolbox window and dragged the Map item to the report body which starts the wizard. Here are the steps to perform to create a map of France: On the Choose a source of spatial data page of the wizard, select SQL Server spatial query, and click Next. On the Choose a dataset with SQL Server spatial data page, select Add a new dataset with SQL Server spatial data. On the Choose a connection to a SQL Server spatial data source page, select New. In the Data Source Properties dialog box, on the General page, add a connecton string like this (changing your server name if necessary): Data Source=(local);Initial Catalog=Spatial Click OK and then click Next. On the Design a query page, add a query for the country shape, like this: select * from fra_adm1 Click Next. The map wizard reads the spatial data and renders it for you on the Choose spatial data and map view options page, as shown below. You have the option to add a Bing Maps layer which shows surrounding countries. Depending on the type of Bing Maps layer that you choose to add (from Road, Aerial, or Hybrid) and the zoom percentage you select, you can view city names and roads and various boundaries. To keep from cluttering my map, I'm going to omit the Bing Maps layer in this example, but I do recommend that you experiment with this feature. It's a nice integration feature. Use the + or - button to rexize the map as needed. (I used the + button to increase the size of the map until its edges were just inside the boundaries of the visible map area (which is called the viewport). You can eliminate the color scale and distance scale boxes that appear in the map area later. Select the Embed map data in this report for faster rendering. The spatial data won't be changing, so there's no need to leave it in the database. However, it does increase the size of the RDL. Click Next. On the Choose map visualization page, select Basic Map. We'll add data for visualization later. For now, we have just the outline of France to serve as the foundation layer for our map. Click Next, and then click Finish. Now click the color scale box in the lower left corner of the map, and press the Delete key to remove it. Then repeat to remove the distance scale box in the lower right corner of the map. Step 2: Add a Map Layer to an Existing Map The map data region allows you to add multiple layers. Each layer is associated with a different data set. Thus far, we have the spatial data that defines the regional boundaries in the first map layer. Now I'll add in another layer for the store locations by following these steps: If the Map Layers windows is not visible, click the report body, and then click twice anywhere on the map data region to display it. Click on the New Layer Wizard button in the Map layers window. And then we start over again with the process by choosing a spatial data source. Select SQL Server spatial query, and click Next. Select Add a new dataset with SQL Server spatial data, and click Next. Click New, add a connection string to the AdventureWorks2008R2 database, and click Next. Add a query with spatial data (like the one I included in the downloadable project), and click Next. The location data now appears as another layer on top of the regional map created earlier. Use the + button to resize the map again to fill as much of the viewport as possible without cutting off edges of the map. You might need to drag the map within the viewport to center it properly. Select Embed map data in this report, and click Next. On the Choose map visualization page, select Basic Marker Map, and click Next. On the Choose color theme and data visualization page, in the Marker drop-down list, change the marker to diamond. There's no particular reason for a diamond; I think it stands out a little better than a circle on this map. Clear the Single color map checkbox as another way to distinguish the markers from the map. You can of course create an analytical map instead, which would change the size and/or color of the markers according to criteria that you specify, such as sales volume of each store, but I'll save that exploration for another post on another day. Click Finish and then click Preview to see the rendered report. Et voilà...c'est fini. Yes, it's a very simple map at this point, but there are many other things you can do to enhance the map. I'll create a series of posts to explore the possibilities. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Bye Bye Year of the Dragon, Hello BPM

    - by Ajay Khanna
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} As 2012 fades and we usher in a New Year, let’s look back at some of the hottest BPM trends and those we’ll be seeing more of in the coming months. BPM is as much about people as it is about technology. As people adopt new ways of engagement, new channels of communications and new devices to interact , the changes are reflected in BPM practices. As Social and Mobile have become an integral part of our personal and professional lives, we’ll see tighter integration of social and mobile with BPM, and more use cases emerging for smarter process management in 2013. And with products and services becoming less differentiated, organizations will strive to differentiate on Customer Experience. Concepts like Pace Layered Architecture and Dynamic Case Management will provide more flexibility and agility to IT groups and knowledge workers. Take a look at some of these capabilities we showcased (see video) at Oracle OpenWorld 2012. Some of these trends that will continue to gain momentum in 2013: Social networks and social media have provided a new way for businesses to engage with customers. A prospect is likely to reach out to their social network before making any purchase. Companies are increasingly engaging with customers in social networks to influence their purchasing decisions, as well as listening to customers via tools like sentiment analysis to see what customers think about a particular product or process. These insights are valuable as companies look to improve their processes. Inside organizations, workers are using social tools to engage with each other to design new products and processes. Social collaboration tools are being used to resolve issues where an employee needs consultation to reach a decision. Oracle BPM Suite includes social interaction as an integral part of its process design and work management to empower today’s business users. Ubiquitous smart mobile devices are trending as a tool of choice for many workers. Many companies are adopting the policy of “Bring Your Own Device,” and the device of choice is a tablet. Devices like smart phones and tablets not only provide mobility to workers and customers, but they also provide additional important information – the context. By integrating the mobile context (location, photos, and preferences) into your processes, organizations can make much more informed decisions, as well as offer more personalized service to customers. Using Oracle ADF Mobile, you can easily create user interfaces for mobile devices and also capture location data for process execution. Customer experience was at the forefront of trending topics in 2012. Organizations are trying to understand their customers better and offer them more personalized and differentiated services. Customer experience is paramount when companies design sales and support processes. Companies are looking to BPM to consistently and efficiently orchestrate customer facing processes across disparate systems, departments and channels of communication. Oracle BPM Suite provides just the right capabilities for organizations to design and deliver an excellent customer experience. Pace Layered Architecture strategy is gaining traction as a way to maximize agility and minimize disruption in organizations. It provides a framework to manage the evolution of your information system when different pieces of it are changing at different rates and need to be updated independent of one another. Oracle Fusion Middleware and Oracle BPM Suite are designed with this in mind. The database layer, integration layer, application layer, and process layer should not be required to change at the same time. Most of the business changes to policy or process can be done at the process layer without disrupting the whole infrastructure. By understanding the type of change needed at a particular level, organizations can become much more agile and efficient. Adaptive Case Management proposes more flexibility to manage processes or cases that do not follow a structured process flow. In such situations, the knowledge worker managing the case needs to evaluate what step should occur next because the sequence of steps can’t be predetermined. Another characteristic is that it requires much more collaboration than straight-through process. As simple processes become automated, and customers adopt more and more self-service, cases that reach the case workers are much more complex and need more investigation. Oracle BPM suite includes comprehensive adaptive case management capability to manage such unstructured and complex processes. Smart BPM or making your BPM intelligent has been the holy grail for BPM practitioners who imagined that one day BPM would become one with Business Intelligence, Business Activity Monitoring and Complex Event Processing, making it much more responsive and helpful in organizational decision making. In 2013, organizations will begin to deploy these intelligent BPM solutions. Oracle offers an integrated solution that brings together the powerful functionality of BI, BAM, event processing, and Real Time Decisions to help organizations create smart process based solutions. In order to help customers reach their BPM goals faster and remove risks associated with BPM initiatives, Oracle has introduced Oracle Process Accelerators, pre-built best practices applications built on Oracle BPM Suite that are fully production grade and ready to deploy. These are exiting times for BPM practitioners and there is so much to look forward to in 2013. We wish you a very happy and prosperous New Year 2013. Happy BPMing!

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  • SSIS Lookup component tuning tips

    - by jamiet
    Yesterday evening I attended a London meeting of the UK SQL Server User Group at Microsoft’s offices in London Victoria. As usual it was both a fun and informative evening and in particular there seemed to be a few questions arising about tuning the SSIS Lookup component; I rattled off some comments and figured it would be prudent to drop some of them into a dedicated blog post, hence the one you are reading right now. Scene setting A popular pattern in SSIS is to use a Lookup component to determine whether a record in the pipeline already exists in the intended destination table or not and I cover this pattern in my 2006 blog post Checking if a row exists and if it does, has it changed? (note to self: must rewrite that blog post for SSIS2008). Fundamentally the SSIS lookup component (when using FullCache option) sucks some data out of a database and holds it in memory so that it can be compared to data in the pipeline. One of the big benefits of using SSIS dataflows is that they process data one buffer at a time; that means that not all of the data from your source exists in the dataflow at the same time and is why a SSIS dataflow can process data volumes that far exceed the available memory. However, that only applies to data in the pipeline; for reasons that are hopefully obvious ALL of the data in the lookup set must exist in the memory cache for the duration of the dataflow’s execution which means that any memory used by the lookup cache will not be available to be used as a pipeline buffer. Moreover, there’s an obvious correlation between the amount of data in the lookup cache and the time it takes to charge that cache; the more data you have then the longer it will take to charge and the longer you have to wait until the dataflow actually starts to do anything. For these reasons your goal is simple: ensure that the lookup cache contains as little data as possible. General tips Here is a simple tick list you can follow in order to tune your lookups: Use a SQL statement to charge your cache, don’t just pick a table from the dropdown list made available to you. (Read why in SELECT *... or select from a dropdown in an OLE DB Source component?) Only pick the columns that you need, ignore everything else Make the database columns that your cache is populated from as narrow as possible. If a column is defined as VARCHAR(20) then SSIS will allocate 20 bytes for every value in that column – that is a big waste if the actual values are significantly less than 20 characters in length. Do you need DT_WSTR typed columns or will DT_STR suffice? DT_WSTR uses twice the amount of space to hold values that can be stored using a DT_STR so if you can use DT_STR, consider doing so. Same principle goes for the numerical datatypes DT_I2/DT_I4/DT_I8. Only populate the cache with data that you KNOW you will need. In other words, think about your WHERE clause! Thinking outside the box It is tempting to build a large monolithic dataflow that does many things, one of which is a Lookup. Often though you can make better use of your available resources by, well, mixing things up a little and here are a few ideas to get your creative juices flowing: There is no rule that says everything has to happen in a single dataflow. If you have some particularly resource intensive lookups then consider putting that lookup into a dataflow all of its own and using raw files to pass the pipeline data in and out of that dataflow. Know your data. If you think, for example, that the majority of your incoming rows will match with only a small subset of your lookup data then consider chaining multiple lookup components together; the first would use a FullCache containing that data subset and the remaining data that doesn’t find a match could be passed to a second lookup that perhaps uses a NoCache lookup thus negating the need to pull all of that least-used lookup data into memory. Do you need to process all of your incoming data all at once? If you can process different partitions of your data separately then you can partition your lookup cache as well. For example, if you are using a lookup to convert a location into a [LocationId] then why not process your data one region at a time? This will mean your lookup cache only has to contain data for the location that you are currently processing and with the ability of the Lookup in SSIS2008 and beyond to charge the cache using a dynamically built SQL statement you’ll be able to achieve it using the same dataflow and simply loop over it using a ForEach loop. Taking the previous data partitioning idea further … a dataflow can contain more than one data path so why not split your data using a conditional split component and, again, charge your lookup caches with only the data that they need for that partition. Lookups have two uses: to (1) find a matching row from the lookup set and (2) put attributes from that matching row into the pipeline. Ask yourself, do you need to do these two things at the same time? After all once you have the key column(s) from your lookup set then you can use that key to get the rest of attributes further downstream, perhaps even in another dataflow. Are you using the same lookup data set multiple times? If so, consider the file caching option in SSIS 2008 and beyond. Above all, experiment and be creative with different combinations. You may be surprised at what works. Final  thoughts If you want to know more about how the Lookup component differs in SSIS2008 from SSIS2005 then I have a dedicated blog post about that at Lookup component gets a makeover. I am on a mini-crusade at the moment to get a BULK MERGE feature into the database engine, the thinking being that if the database engine can quickly merge massive amounts of data in a similar manner to how it can insert massive amounts using BULK INSERT then that’s a lot of work that wouldn’t have to be done in the SSIS pipeline. If you think that is a good idea then go and vote for BULK MERGE on Connect. If you have any other tips to share then please stick them in the comments. Hope this helps! @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • How Mary Meeker’s Latest Findings May Make You Re-Imagine Commerce

    - by Brenna Johnson-Oracle
    0 0 1 954 5439 Endeca Technologies 45 12 6381 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin;} Today, Mary Meeker released her highly anticipated annual “Internet Trends” presentation for 2014. All 164 slides are jam-packed with pretty much everything you need to know about the state of the Internet. And as luck would have it, Oracle is staying ahead of these trends (but we’ll talk about that later). There were a few surprises, some stats to solidify what you likely already know, and Meeker’s novel observations about where we are all going. What interested me the most is not only how people are engaging in their personal lives, but how they engage with brands. As you could probably predict, Internet usage growth is slowing while tablet user and mobile data traffic growth continue their meteoric rise around the globe, with tremendous growth in underpenetrated markets like China, India, Brazil and Indonesia. Now hold those the “Internet is dead” comments. Keep in mind there’s still plenty of room to grow, and a multiscreen model is Meeker’s vision for our future. Despite 1.5x YOY growth for mobile traffic, mobile still only makes up about 23% of all traffic today. With tablet shipments easily outpacing figures for PCs even at their height (in 2007), mobile will only continue on it’s path, but won’t be everything to everyone. Mobile won’t replace every touchpoint, it’s just created our shorter attention spans and demand for simpler, more personal experiences. As Meeker points out TVs, tablets, PCs, and smartphones are used for different activities at present, but lines will blur (for example, 84% of smartphones owners use their device while watching TV). Day-to-day activities are being re-imagining through simple, beautiful user experiences. It seems like every day I discover a new way a brand/site/app made the most mundane or mounting task enjoyable and frictionless – and I’m not alone. Meeker points out the evolution of how we do everything from how we communicate, get information, use money, meet someone, get places, order a meal, and consume media is all done through new user interfaces that make day-to-day tasks simpler. This movement has caused just about everyone’s patience for a poor UX to take a nosedive. And it’s not just the digital user experience, technology is making a lot of people’s offline lives easier, and less expensive. Today 47% of online shopping utilizes free shipping— nearly half. And Meeker predicts same day local delivery will be the “next big thing” (and you can take a guess on who will own that). Content, Community and Commerce creates the “Internet Trifecta.” Meeker pointed out that when content, communities and commerce occur in a single experience it’s embraced by consumers, which translates to big dollars for brands. The magic happens when consumers can get inspired, research, and buy in a single experience. As the buying cycle has changed and touchpoints (Web, mobile, social, store) are no longer tied to “roles” or steps in the customer journey, brands must make all experiences (content and commerce) available in a single, adaptable experience. (We at Oracle Commerce have a lot to say on this topic – stay tuned!) And in what Meeker calls the “biggest re-imagination of all:” consumers enabled with smartphones and sensors are creating troves of findable and sharable data, which she says is in the early stages, by growing rapidly. She notes that transparency and patterns of consumers with this hardware (FYI - there are up to 10 sensors embedded in smartphones now) has created a Big Data treasure chest to be mined to improve business and the life of the consumer. The opportunities are endless. So what does it all mean for a company doing business online? Start thinking about how you can: Re-imagine your experience. Not your online experience and your mobile experience and your social experience – your overall experience. When consumers can research, buy, and advocate from anywhere (and their attention spans are at an all-time low) channels don’t exist. Enable simple and beautiful interactions informed by all of the online and offline data you leverage across your enterprise. Ethically leverage the endless supply of data (user generated content, clicks, purchases, in-store behavior, social activity) to make experiences more beautiful, more accurate, and more personalized (not to mention, more lucrative for you). Re-imagine content and commerce. Content and commerce must co-exist in a single destination where shoppers can get inspired, explore, research, share, and purchase in a collective experience. Think of how you can deliver an experience where all types of experiences (brand stories and commerce) adapt to every customer need. (Look for more on this topic coming soon). Re-imagine your reach. Look to Meeker’s findings to see how the global appetite for digital experiences is growing, but under-served in many places (i.e.: India, Mexico, Indonesia, Brazil, Philippines, etc.). Growing your online business to a new geography doesn’t have to mean starting from scratch or having an entirely new team manage the new endeavor. Expand using what you’ve already built in a multisite framework, with global language support. And of course, make sure it’s optimized for mobile! Re-imagine the possible. After every Meeker report, I’m always left with the thought “we are just at the beginning.” Everyday there is more data, more possibilities, more online consumers, and more opportunities to use new latest technology to get closer to your customers and be more successful. There’s a lot going on in our Product Development and Product Innovations groups to automate innovation for our customers, so that they can continue to stay ahead of these trends, without disrupting their business. Check out a recent interview with our Innovations Team on some of these new possibilities. Staying on track despite the seemingly endless possibilities out there is the hard part. Prioritizing where you will focus based on your unique brand promise, customer and goals is what you do best. To learn how Oracle Commerce can help your business achieve your goals check out oracle.com/commerce. Check out Meeker’s entire report here.

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  • Psychonauts crashes right after entering load save door

    - by user67974
    Psychonauts crashes right after entering the 'Load Save' door. Here is the terminal output: Shader assembly time: 0.88 seconds Found OpenAL device: 'Simple Directmedia Layer' Found OpenAL device: 'ALSA Software' Found OpenAL device: 'OSS Software' Found OpenAL device: 'PulseAudio Software' Opened OpenAL Device: '(null)' ERROR: CAudioDrv::CAudioDrv->alGenSources reports AL_INVALID_VALUE error. PSYCHONAUTS UNIX FILENAME: corrected 'workresource/sounds/commonfx.isb' to 'WorkResource/Sounds/commonfx.isb' PSYCHONAUTS UNIX FILENAME: corrected 'workresource/sounds/commonvoice.isb' to 'WorkResource/Sounds/commonvoice.isb' PSYCHONAUTS UNIX FILENAME: corrected 'workresource/sounds/commonmusic.isb' to 'WorkResource/Sounds/commonmusic.isb' PSYCHONAUTS UNIX FILENAME: corrected 'workresource/sounds/commonmentalfx.isb' to 'WorkResource/Sounds/commonmentalfx.isb' PSYCHONAUTS UNIX FILENAME: corrected 'workresource/sounds/commonmenfxmem.isb' to 'WorkResource/Sounds/commonmenfxmem.isb' PSYCHONAUTS UNIX FILENAME: corrected 'workresource/sounds/commonfxmem.isb' to 'WorkResource/Sounds/commonfxmem.isb' GameApp::StartUp InitSoundFiles() completed in 0.15 seconds GameApp::StartUp Load some common textures completed in 0.00 seconds WARN: ENGINE: Lua garbage collection starting FreeUnusedBlocksInBuckets released 0 Kb GameApp::StartUp InitEntities() completed in 0.02 seconds PSYCHONAUTS UNIX FILENAME: corrected 'WorkResource/SavedGames/savegameprefs.ini' to 'WorkResource/SAVEDGAMES/savegameprefs.ini' PSYCHONAUTS UNIX FILENAME: corrected 'WorkResource/SavedGames/savegameprefs.ini' to 'WorkResource/SAVEDGAMES/savegameprefs.ini' GameApp::StartUp m_pSaveLoadInterface->Startup() completed in 0.00 seconds GameApp::StartUp m_UserInterface.Setup() completed in 0.00 seconds STUBBED: multisample at EDisplayOptionsWidget (/home/icculus/projects/psychonauts/Source/game/luatest/Game/UIPCDisplayOptions.cpp:97) STUBBED: VK_* at CheckVirtualKey (/home/icculus/projects/psychonauts/Source/CommonLibs/DirectX/SDLInput.cpp:1443) Game: Engine Running hook startup Game: Engine -> SetupGlobalObjects Game: Engine -> SetupLevelMenu Game: Engine -> InitMath GameApp::StartUp InitLua2() completed in 0.00 seconds GameApp::StartUp SetupLevelMenu() completed in 0.00 seconds STUBBED: do we even use this? at InitSocket (/home/icculus/projects/psychonauts/Source/game/luatest/Game/Gameplaylogger.cpp:210) GameApp::StartUp Post-Install total completed in 0.20 seconds Start Up completed in 1.57 seconds UnixMain: StartUp successful.. Working directory: /opt/psychonauts STUBBED: dispatch SDL events at PCMainHandleAnyWindowsMessages (/home/icculus/projects/psychonauts/Source/game/luatest/UnixMain.cpp:56) STUBBED: write me at GetJoystickInput (/home/icculus/projects/psychonauts/Source/CommonLibs/DirectX/SDLInput.cpp:428) STUBBED: write me at GetJoystickActionValue (/home/icculus/projects/psychonauts/Source/CommonLibs/DirectX/SDLInput.cpp:613) PSYCHONAUTS UNIX FILENAME: corrected 'workresource/cutScenes/prerendered/dflogo.bik' to 'WorkResource/cutscenes/prerendered/DFLogo.bik' Prerender subtitle file: workresource\cutScenes\prerendered\dflogo.dfs not found PSYCHONAUTS UNIX FILENAME: corrected 'workresource/cutScenes/prerendered/dflogo.bik' to 'WorkResource/cutscenes/prerendered/DFLogo.bik' STUBBED: fixed function pipeline? at setColorOp (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Texture.cpp:2097) STUBBED: fixed function pipeline? at setColorArg1 (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Texture.cpp:2106) STUBBED: fixed function pipeline? at setColorArg2 (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Texture.cpp:2115) STUBBED: fixed function pipeline? at setAlphaOp (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Texture.cpp:2124) STUBBED: fixed function pipeline? at setAlphaArg1 (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Texture.cpp:2133) STUBBED: fixed function pipeline? at setAlphaArg2 (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Texture.cpp:2142) STUBBED: fixed function pipeline? at setProjected (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Texture.cpp:2223) LOC WARN: Could not open Localization file 'Localization/English/_StringTable.lub' STUBBED: memory status at UpdateMemoryTracking (/home/icculus/projects/psychonauts/Source/game/luatest/Game/GameApp.cpp:4884) WARN: Couldn't resize array to 128; out-of-bounds elements are still in use: Vertex Pool, 188 Loading new level 'STMU' STUBBED: Need multithreaded GL at DisplayLoadingScreen (/home/icculus/projects/psychonauts/Source/game/luatest/Game/LoadingScreen.cpp:83) ========================= Memory post unload level ========================= ========================= LOC WARN: Could not open Localization file 'Localization/English/ST_StringTable.lub' DaveD: Info: Texture pack file contains 137 textures Doing a texture readback for locking! Game: Engine Saved[GLOBAL]: InstaHintFord_HostileRecord = [table] Game: Engine Saved[GLOBAL]: InstaHintFord_HostileOrder = [table] WARN: Redundant packfile read: anims\thought_bubble\bubblefirestarting.jan WARN: Redundant packfile read: anims\thought_bubble\bubbleintothemind.jan WARN: Redundant packfile read: anims\thought_bubble\bubbleinvisibility.jan WARN: Redundant packfile read: anims\thought_bubble\bubblepopperfill.jan WARN: Redundant packfile read: anims\thought_bubble\bubbletelekinesis.jan Initializing level script (if there is one) PSYCHONAUTS UNIX FILENAME: corrected 'workresource/sounds/stfx.isb' to 'WorkResource/Sounds/stfx.isb' Game: Engine Reloading goals: Game: Engine Saved[GLOBAL]: NextEncouragement = '/GLZF014TO/ 10' Game: Engine Saved[GLOBAL]: bUsedSalts = 0 Game: Engine Saved[GLOBAL]: bSTEntered = 1 Game: Engine Saved[GLOBAL]: memoriesST = 1 Game: Engine Saved[GLOBAL]: PsiBallColor = 'red' Game: Engine Saved[ST]: lastSubLevel = 'STMU' Game: Engine LOADING LEVEL st.STMU Game: Engine Saved[CA]: CALevelState = 1 Game: Engine Cutscene progression: CS Script moving from state nil to state nil, resultant state nil. Time: 0.124746672809124. * Stack Trace 1: (null) (line -1, file '(none)) () 2: SpawnScript (line -1, file 'C) (global) 3: onBeginLevel (line -1, file '(none)) (field) 4: (null) (line -1, file '(none)) () WARN: Cannot call GetDirectoryListing when running from the DVD Game: Engine Raz spawning at DartStart startpoint VM : LevelScript could not find script 'doorrimlight1' * Stack Trace 1: (null) (line -1, file '(none)) () WARN: (none(-1) SetEntityAlpha LevelScript: NULL script object passed Game: Engine Saved[GLOBAL]: bLoadedFromMainMenu = 1 Game: Engine Saved[GLOBAL]: NextEncouragement = '/GLZF014TO/ 10' Game: Engine Saved[GLOBAL]: NeedRankIncrement = 0 STUBBED: Need multithreaded GL at HideLoadingScreen (/home/icculus/projects/psychonauts/Source/game/luatest/Game/LoadingScreen.cpp:110) WARN: ENGINE: Lua garbage collection starting FreeUnusedBlocksInBuckets released 0 Kb Game: Engine Saved[GLOBAL]: SplineFigmentTVSizex = 4.51434326171875 Game: Engine Saved[GLOBAL]: SplineFigmentTVSizey = 46.38104248046875 Game: Engine Saved[GLOBAL]: SplineFigmentTVSizez = 47.08810424804688 WARN: (none(-1) SetNewAction LevelScript: no string passed ====================== Asset load progression ====================== Initial: 2.518 MB Vertex, 8.688 MB Texture Level : 3.719 MB Vertex, 22.535 MB Texture Scripts: 3.747 MB Vertex, 22.848 MB Texture ====================== ====================== Memory post level load ====================== ====================== WARN: ENGINE: Lua garbage collection starting FreeUnusedBlocksInBuckets released 0 Kb DaveD: Level loaded in 0.14 seconds Anim: anims\objects\tk_arrow_idle.jan: loaded (1 frames latency) Anim: anims\dartnew\helmet\darthelmetdn.jan: loaded (1 frames latency) Anim: anims\thought_bubble\shieldloop.jan: loaded (1 frames latency) Anim: anims\dartnew\standready.jan: loaded (1 frames latency) Anim: anims\dartnew\walkmove.jan: loaded (1 frames latency) Anim: anims\janitor\hint_end.jan: loaded (1 frames latency) Anim: anims\thought_bubble\ballstatic.jan: loaded (1 frames latency) Anim: anims\dartnew\actionfall.jan: loaded (1 frames latency) Anim: anims\dartnew\standstill.jan: loaded (1 frames latency) Anim: anims\dartnew\pack\packbounce_lf_rt.jan: loaded (1 frames latency) Anim: anims\dartnew\pack\packbounce_up_dn.jan: loaded (1 frames latency) Anim: anims\dartnew\helmet\darthelmetdefpose.jan: loaded (1 frames latency) 1: 1 (number) 1: 1 (number) STUBBED: This is probably wrong at GetDt (/home/icculus/projects/psychonauts/Source/CommonLibs/DFUtil/Profiler.cpp:181) STUBBED: set specular highlights at setSpecularEnable (/home/icculus/projects/psychonauts/Source/CommonLibs/DFGraphics/Renderer.cpp:2035) Anim: anims\dartnew\trnrtcycle.jan: loaded (1 frames latency) Anim: anims\dartnew\run.jan: loaded (1 frames latency) Anim: anims\dartnew\walk.jan: loaded (1 frames latency) Anim: anims\thought_bubble\bubbledoublejump.jan: loaded (1 frames latency) Anim: anims\dartnew\longjump.jan: loaded (1 frames latency) Anim: anims\menubrain\door1crack.jan: loaded (1 frames latency) Anim: anims\menubrain\door1crackedidle.jan: loaded (1 frames latency) Anim: anims\menubrain\door1closedidle.jan: loaded (1 frames latency) Anim: anims\dartnew\180.jan: loaded (1 frames latency) Anim: anims\menubrain\door3crack.jan: loaded (1 frames latency) Anim: anims\menubrain\door3crackedidle.jan: loaded (1 frames latency) Anim: anims\menubrain\door3closedidle.jan: loaded (1 frames latency) Anim: anims\dartnew\railslide45angle.jan: loaded (1 frames latency) Anim: anims\dartnew\railslideflat.jan: loaded (1 frames latency) Anim: anims\dartnew\trnlfcycle.jan: loaded (1 frames latency) WARN: (none(-1) SetNewAction LevelScript: no string passed Anim: anims\dartnew\mainmenu_jump.jan: loaded (1 frames latency) Anim: anims\menubrain\door1open.jan: loaded (1 frames latency) ERROR: Assert in /home/icculus/projects/psychonauts/Source/game/luatest/../../CommonLibs/Include/../DFGraphics/Color.h, line 96 v.x >= 0.0f && v.x <= 1.0f && v.y >= 0.0f && v.y <= 1.0f && v.z >= 0.0f && v.z <= 1.0f && v.w >= 0.0f && v.w <= 1.0f Encountered Error: Psychonauts has encountered an error /home/icculus/projects/psychonauts/Source/game/luatest/../../CommonLibs/Include/../DFGraphics/Color.h, line 96 v.x >= 0.0f && v.x <= 1.0f && v.y >= 0.0f && v.y <= 1.0f && v.z >= 0.0f && v.z <= 1.0f && v.w >= 0.0f && v.w <= 1.0f Please contact technical support at http://www.doublefine.com. I am currently using Bumblebee for hybrid graphics, if that helps in any way.

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  • Listing common SQL Code Smells.

    - by Phil Factor
    Once you’ve done a number of SQL Code-reviews, you’ll know those signs in the code that all might not be well. These ’Code Smells’ are coding styles that don’t directly cause a bug, but are indicators that all is not well with the code. . Kent Beck and Massimo Arnoldi seem to have coined the phrase in the "OnceAndOnlyOnce" page of www.C2.com, where Kent also said that code "wants to be simple". Bad Smells in Code was an essay by Kent Beck and Martin Fowler, published as Chapter 3 of the book ‘Refactoring: Improving the Design of Existing Code’ (ISBN 978-0201485677) Although there are generic code-smells, SQL has its own particular coding habits that will alert the programmer to the need to re-factor what has been written. See Exploring Smelly Code   and Code Deodorants for Code Smells by Nick Harrison for a grounding in Code Smells in C# I’ve always been tempted by the idea of automating a preliminary code-review for SQL. It would be so useful to trawl through code and pick up the various problems, much like the classic ‘Lint’ did for C, and how the Code Metrics plug-in for .NET Reflector by Jonathan 'Peli' de Halleux is used for finding Code Smells in .NET code. The problem is that few of the standard procedural code smells are relevant to SQL, and we need an agreed list of code smells. Merrilll Aldrich made a grand start last year in his blog Top 10 T-SQL Code Smells.However, I'd like to make a start by discovering if there is a general opinion amongst Database developers what the most important SQL Smells are. One can be a bit defensive about code smells. I will cheerfully write very long stored procedures, even though they are frowned on. I’ll use dynamic SQL occasionally. You can only use them as an aid for your own judgment and it is fine to ‘sign them off’ as being appropriate in particular circumstances. Also, whole classes of ‘code smells’ may be irrelevant for a particular database. The use of proprietary SQL, for example, is only a ‘code smell’ if there is a chance that the database will have to be ported to another RDBMS. The use of dynamic SQL is a risk only with certain security models. As the saying goes,  a CodeSmell is a hint of possible bad practice to a pragmatist, but a sure sign of bad practice to a purist. Plamen Ratchev’s wonderful article Ten Common SQL Programming Mistakes lists some of these ‘code smells’ along with out-and-out mistakes, but there are more. The use of nested transactions, for example, isn’t entirely incorrect, even though the database engine ignores all but the outermost: but it does flag up the possibility that the programmer thinks that nested transactions are supported. If anything requires some sort of general agreement, the definition of code smells is one. I’m therefore going to make this Blog ‘dynamic, in that, if anyone twitters a suggestion with a #SQLCodeSmells tag (or sends me a twitter) I’ll update the list here. If you add a comment to the blog with a suggestion of what should be added or removed, I’ll do my best to oblige. In other words, I’ll try to keep this blog up to date. The name against each 'smell' is the name of the person who Twittered me, commented about or who has written about the 'smell'. it does not imply that they were the first ever to think of the smell! Use of deprecated syntax such as *= (Dave Howard) Denormalisation that requires the shredding of the contents of columns. (Merrill Aldrich) Contrived interfaces Use of deprecated datatypes such as TEXT/NTEXT (Dave Howard) Datatype mis-matches in predicates that rely on implicit conversion.(Plamen Ratchev) Using Correlated subqueries instead of a join   (Dave_Levy/ Plamen Ratchev) The use of Hints in queries, especially NOLOCK (Dave Howard /Mike Reigler) Few or No comments. Use of functions in a WHERE clause. (Anil Das) Overuse of scalar UDFs (Dave Howard, Plamen Ratchev) Excessive ‘overloading’ of routines. The use of Exec xp_cmdShell (Merrill Aldrich) Excessive use of brackets. (Dave Levy) Lack of the use of a semicolon to terminate statements Use of non-SARGable functions on indexed columns in predicates (Plamen Ratchev) Duplicated code, or strikingly similar code. Misuse of SELECT * (Plamen Ratchev) Overuse of Cursors (Everyone. Special mention to Dave Levy & Adrian Hills) Overuse of CLR routines when not necessary (Sam Stange) Same column name in different tables with different datatypes. (Ian Stirk) Use of ‘broken’ functions such as ‘ISNUMERIC’ without additional checks. Excessive use of the WHILE loop (Merrill Aldrich) INSERT ... EXEC (Merrill Aldrich) The use of stored procedures where a view is sufficient (Merrill Aldrich) Not using two-part object names (Merrill Aldrich) Using INSERT INTO without specifying the columns and their order (Merrill Aldrich) Full outer joins even when they are not needed. (Plamen Ratchev) Huge stored procedures (hundreds/thousands of lines). Stored procedures that can produce different columns, or order of columns in their results, depending on the inputs. Code that is never used. Complex and nested conditionals WHILE (not done) loops without an error exit. Variable name same as the Datatype Vague identifiers. Storing complex data  or list in a character map, bitmap or XML field User procedures with sp_ prefix (Aaron Bertrand)Views that reference views that reference views that reference views (Aaron Bertrand) Inappropriate use of sql_variant (Neil Hambly) Errors with identity scope using SCOPE_IDENTITY @@IDENTITY or IDENT_CURRENT (Neil Hambly, Aaron Bertrand) Schemas that involve multiple dated copies of the same table instead of partitions (Matt Whitfield-Atlantis UK) Scalar UDFs that do data lookups (poor man's join) (Matt Whitfield-Atlantis UK) Code that allows SQL Injection (Mladen Prajdic) Tables without clustered indexes (Matt Whitfield-Atlantis UK) Use of "SELECT DISTINCT" to mask a join problem (Nick Harrison) Multiple stored procedures with nearly identical implementation. (Nick Harrison) Excessive column aliasing may point to a problem or it could be a mapping implementation. (Nick Harrison) Joining "too many" tables in a query. (Nick Harrison) Stored procedure returning more than one record set. (Nick Harrison) A NOT LIKE condition (Nick Harrison) excessive "OR" conditions. (Nick Harrison) User procedures with sp_ prefix (Aaron Bertrand) Views that reference views that reference views that reference views (Aaron Bertrand) sp_OACreate or anything related to it (Bill Fellows) Prefixing names with tbl_, vw_, fn_, and usp_ ('tibbling') (Jeremiah Peschka) Aliases that go a,b,c,d,e... (Dave Levy/Diane McNurlan) Overweight Queries (e.g. 4 inner joins, 8 left joins, 4 derived tables, 10 subqueries, 8 clustered GUIDs, 2 UDFs, 6 case statements = 1 query) (Robert L Davis) Order by 3,2 (Dave Levy) MultiStatement Table functions which are then filtered 'Sel * from Udf() where Udf.Col = Something' (Dave Ballantyne) running a SQL 2008 system in SQL 2000 compatibility mode(John Stafford)

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  • How to Upload a file from client to server using OFBIZ?

    - by SIVAKUMAR.J
    I'm new to ofbiz so try to keep your answer as simple as possibly. If you can give examples that would be kind. My problem is I created a project inside the ofbiz/hot-deploy folder namely productionmgntSystem. Inside the folder ofbiz\hot-deploy\productionmgntSystem\webapp\productionmgntSystem I created a file app_details_1.ftl. The following are the code of this file <html> <head> <meta http-equiv="Content-Type" content="text/html; charset=ISO-8859-1"> <title>Insert title here</title> <script TYPE="TEXT/JAVASCRIPT" language=""JAVASCRIPT"> function uploadFile() { //alert("Before calling upload.jsp"); window.location='<@ofbizUrl>testing_service1</@ofbizUrl>' } </script> </head> <!-- <form action="<@ofbizUrl>testing_service1</@ofbizUrl>" enctype="multipart/form-data" name="app_details_frm"> --> <form action="<@ofbizUrl>logout1</@ofbizUrl>" enctype="multipart/form-data" name="app_details_frm"> <center style="height: 299px; "> <table border="0" style="height: 177px; width: 788px"> <tr style="height: 115px; "> <td style="width: 103px; "> <td style="width: 413px; "><h1>APPLICATION DETAILS</h1> <td style="width: 55px; "> </tr> <tr> <td style="width: 125px; ">Application name : </td> <td> <input name="app_name_txt" id="txt_1" value=" " /> </td> </tr> <tr> <td style="width: 125px; ">Excell sheet &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;: </td> <td> <input type="file" name="filename"/> </td> </tr> <tr> <td> <!-- <input type="button" name="logout1_cmd" value="Logout" onclick="logout1()"/> --> <input type="submit" name="logout_cmd" value="logout"/> </td> <td> <!-- <input type="submit" name="upload_cmd" value="Submit" /> --> <input type="button" name="upload1_cmd" value="Upload" onclick="uploadFile()"/> </td> </tr> </table> </center> </form> </html> the following coding is present in the file ofbiz\hot-deploy\productionmgntSystem\webapp\productionmgntSystem\WEB-INF\controller.xml ...... ....... ........ <request-map uri="testing_service1"> <security https="true" auth="true"/> <event type="java" path="org.ofbiz.productionmgntSystem.web_app_req.WebServices1" invoke="testingService"/> <response name="ok" type="view" value="ok_view"/> <response name="exception" type="view" value="exception_view"/> </request-map> .......... ............ .......... <view-map name="ok_view" type="ftl" page="ok_view.ftl"/> <view-map name="exception_view" type="ftl" page="exception_view.ftl"/> ................ ............. ............. The following are the coding present in the file ofbiz\hot-deploy\productionmgntSystem\src\org\ofbiz\productionmgntSystem\web_app_req\WebServices1.java package org.ofbiz.productionmgntSystem.web_app_req; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpServletResponse; import java.io.DataInputStream; import java.io.FileOutputStream; import java.io.IOException; public class WebServices1 { public static String testingService(HttpServletRequest request, HttpServletResponse response) { //int i=0; String result="ok"; System.out.println("\n\n\t*************************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response)- Start"); String contentType=request.getContentType(); System.out.println("\n\n\t*************************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response)- contentType : "+contentType); String str=new String(); // response.setContentType("text/html"); //PrintWriter writer; if ((contentType != null) && (contentType.indexOf("multipart/form-data") >= 0)) { System.out.println("\n\n\t**********************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response) after if (contentType != null)"); try { // writer=response.getWriter(); System.out.println("\n\n\t**********************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response) - try Start"); DataInputStream in = new DataInputStream(request.getInputStream()); int formDataLength = request.getContentLength(); byte dataBytes[] = new byte[formDataLength]; int byteRead = 0; int totalBytesRead = 0; //this loop converting the uploaded file into byte code while (totalBytesRead < formDataLength) { byteRead = in.read(dataBytes, totalBytesRead,formDataLength); totalBytesRead += byteRead; } String file = new String(dataBytes); //for saving the file name String saveFile = file.substring(file.indexOf("filename=\"") + 10); saveFile = saveFile.substring(0, saveFile.indexOf("\n")); saveFile = saveFile.substring(saveFile.lastIndexOf("\\")+ 1,saveFile.indexOf("\"")); int lastIndex = contentType.lastIndexOf("="); String boundary = contentType.substring(lastIndex + 1,contentType.length()); int pos; //extracting the index of file pos = file.indexOf("filename=\""); pos = file.indexOf("\n", pos) + 1; pos = file.indexOf("\n", pos) + 1; pos = file.indexOf("\n", pos) + 1; int boundaryLocation = file.indexOf(boundary, pos) - 4; int startPos = ((file.substring(0, pos)).getBytes()).length; int endPos = ((file.substring(0, boundaryLocation)).getBytes()).length; //creating a new file with the same name and writing the content in new file FileOutputStream fileOut = new FileOutputStream("/"+saveFile); fileOut.write(dataBytes, startPos, (endPos - startPos)); fileOut.flush(); fileOut.close(); System.out.println("\n\n\t**********************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response) - try End"); } catch(IOException ioe) { System.out.println("\n\n\t*********************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response) - Catch IOException"); //ioe.printStackTrace(); return("exception"); } catch(Exception ex) { System.out.println("\n\n\t*********************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response) - Catch Exception"); return("exception"); } } else { System.out.println("\n\n\t********************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response) else part"); result="exception"; } System.out.println("\n\n\t*************************************\n\tInside WebServices1.testingService(HttpServletRequest request, HttpServletResponse response)- End"); return(result); } } I want to upload a file to the server. The file is get from user " tag in the "app_details_1.ftl" file & it is updated into the server by using the method "testingService(HttpServletRequest request, HttpServletResponse response)" in the class "WebServices1". But the file is not uploaded. Give me a good solution for uploading a file to the server.

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  • SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS

    - by pinaldave
    Data Quality Services is a very important concept of SQL Server. I have recently started to explore the same and I am really learning some good concepts. Here are two very important blog posts which one should go over before continuing this blog post. Installing Data Quality Services (DQS) on SQL Server 2012 Connecting Error to Data Quality Services (DQS) on SQL Server 2012 This article is introduction to Data Quality Services for beginners. We will be using an Excel file Click on the image to enlarge the it. In the first article we learned to install DQS. In this article we will see how we can learn about building Knowledge Base and using it to help us identify the quality of the data as well help correct the bad quality of the data. Here are the two very important steps we will be learning in this tutorial. Building a New Knowledge Base  Creating a New Data Quality Project Let us start the building the Knowledge Base. Click on New Knowledge Base. In our project we will be using the Excel as a knowledge base. Here is the Excel which we will be using. There are two columns. One is Colors and another is Shade. They are independent columns and not related to each other. The point which I am trying to show is that in Column A there are unique data and in Column B there are duplicate records. Clicking on New Knowledge Base will bring up the following screen. Enter the name of the new knowledge base. Clicking NEXT will bring up following screen where it will allow to select the EXCE file and it will also let users select the source column. I have selected Colors and Shade both as a source column. Creating a domain is very important. Here you can create a unique domain or domain which is compositely build from Colors and Shade. As this is the first example, I will create unique domain – for Colors I will create domain Colors and for Shade I will create domain Shade. Here is the screen which will demonstrate how the screen will look after creating domains. Clicking NEXT it will bring you to following screen where you can do the data discovery. Clicking on the START will start the processing of the source data provided. Pre-processed data will show various information related to the source data. In our case it shows that Colors column have unique data whereas Shade have non-unique data and unique data rows are only two. In the next screen you can actually add more rows as well see the frequency of the data as the values are listed unique. Clicking next will publish the knowledge base which is just created. Now the knowledge base is created. We will try to take any random data and attempt to do DQS implementation over it. I am using another excel sheet here for simplicity purpose. In reality you can easily use SQL Server table for the same. Click on New Data Quality Project to see start DQS Project. In the next screen it will ask which knowledge base to use. We will be using our Colors knowledge base which we have recently created. In the Colors knowledge base we had two columns – 1) Colors and 2) Shade. In our case we will be using both of the mappings here. User can select one or multiple column mapping over here. Now the most important phase of the complete project. Click on Start and it will make the cleaning process and shows various results. In our case there were two columns to be processed and it completed the task with necessary information. It demonstrated that in Colors columns it has not corrected any value by itself but in Shade value there is a suggestion it has. We can train the DQS to correct values but let us keep that subject for future blog posts. Now click next and keep the domain Colors selected left side. It will demonstrate that there are two incorrect columns which it needs to be corrected. Here is the place where once corrected value will be auto-corrected in future. I manually corrected the value here and clicked on Approve radio buttons. As soon as I click on Approve buttons the rows will be disappeared from this tab and will move to Corrected Tab. If I had rejected tab it would have moved the rows to Invalid tab as well. In this screen you can see how the corrected 2 rows are demonstrated. You can click on Correct tab and see previously validated 6 rows which passed the DQS process. Now let us click on the Shade domain on the left side of the screen. This domain shows very interesting details as there DQS system guessed the correct answer as Dark with the confidence level of 77%. It is quite a high confidence level and manual observation also demonstrate that Dark is the correct answer. I clicked on Approve and the row moved to corrected tab. On the next screen DQS shows the summary of all the activities. It also demonstrates how the correction of the quality of the data was performed. The user can explore their data to a SQL Server Table, CSV file or Excel. The user also has an option to either explore data and all the associated cleansing info or data only. I will select Data only for demonstration purpose. Clicking explore will generate the files. Let us open the generated file. It will look as following and it looks pretty complete and corrected. Well, we have successfully completed DQS Process. The process is indeed very easy. I suggest you try this out yourself and you will find it very easy to learn. In future we will go over advanced concepts. Are you using this feature on your production server? If yes, would you please leave a comment with your environment and business need. It will be indeed interesting to see where it is implemented. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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  • SQL SERVER – Guest Post – Architecting Data Warehouse – Niraj Bhatt

    - by pinaldave
    Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group. Here is the guest post by Niraj Bhatt. As data in your applications grows it’s the database that usually becomes a bottleneck. It’s hard to scale a relational DB and the preferred approach for large scale applications is to create separate databases for writes and reads. These databases are referred as transactional database and reporting database. Though there are tools / techniques which can allow you to create snapshot of your transactional database for reporting purpose, sometimes they don’t quite fit the reporting requirements of an enterprise. These requirements typically are data analytics, effective schema (for an Information worker to self-service herself), historical data, better performance (flat data, no joins) etc. This is where a need for data warehouse or an OLAP system arises. A Key point to remember is a data warehouse is mostly a relational database. It’s built on top of same concepts like Tables, Rows, Columns, Primary keys, Foreign Keys, etc. Before we talk about how data warehouses are typically structured let’s understand key components that can create a data flow between OLTP systems and OLAP systems. There are 3 major areas to it: a) OLTP system should be capable of tracking its changes as all these changes should go back to data warehouse for historical recording. For e.g. if an OLTP transaction moves a customer from silver to gold category, OLTP system needs to ensure that this change is tracked and send to data warehouse for reporting purpose. A report in context could be how many customers divided by geographies moved from sliver to gold category. In data warehouse terminology this process is called Change Data Capture. There are quite a few systems that leverage database triggers to move these changes to corresponding tracking tables. There are also out of box features provided by some databases e.g. SQL Server 2008 offers Change Data Capture and Change Tracking for addressing such requirements. b) After we make the OLTP system capable of tracking its changes we need to provision a batch process that can run periodically and takes these changes from OLTP system and dump them into data warehouse. There are many tools out there that can help you fill this gap – SQL Server Integration Services happens to be one of them. c) So we have an OLTP system that knows how to track its changes, we have jobs that run periodically to move these changes to warehouse. The question though remains is how warehouse will record these changes? This structural change in data warehouse arena is often covered under something called Slowly Changing Dimension (SCD). While we will talk about dimensions in a while, SCD can be applied to pure relational tables too. SCD enables a database structure to capture historical data. This would create multiple records for a given entity in relational database and data warehouses prefer having their own primary key, often known as surrogate key. As I mentioned a data warehouse is just a relational database but industry often attributes a specific schema style to data warehouses. These styles are Star Schema or Snowflake Schema. The motivation behind these styles is to create a flat database structure (as opposed to normalized one), which is easy to understand / use, easy to query and easy to slice / dice. Star schema is a database structure made up of dimensions and facts. Facts are generally the numbers (sales, quantity, etc.) that you want to slice and dice. Fact tables have these numbers and have references (foreign keys) to set of tables that provide context around those facts. E.g. if you have recorded 10,000 USD as sales that number would go in a sales fact table and could have foreign keys attached to it that refers to the sales agent responsible for sale and to time table which contains the dates between which that sale was made. These agent and time tables are called dimensions which provide context to the numbers stored in fact tables. This schema structure of fact being at center surrounded by dimensions is called Star schema. A similar structure with difference of dimension tables being normalized is called a Snowflake schema. This relational structure of facts and dimensions serves as an input for another analysis structure called Cube. Though physically Cube is a special structure supported by commercial databases like SQL Server Analysis Services, logically it’s a multidimensional structure where dimensions define the sides of cube and facts define the content. Facts are often called as Measures inside a cube. Dimensions often tend to form a hierarchy. E.g. Product may be broken into categories and categories in turn to individual items. Category and Items are often referred as Levels and their constituents as Members with their overall structure called as Hierarchy. Measures are rolled up as per dimensional hierarchy. These rolled up measures are called Aggregates. Now this may seem like an overwhelming vocabulary to deal with but don’t worry it will sink in as you start working with Cubes and others. Let’s see few other terms that we would run into while talking about data warehouses. ODS or an Operational Data Store is a frequently misused term. There would be few users in your organization that want to report on most current data and can’t afford to miss a single transaction for their report. Then there is another set of users that typically don’t care how current the data is. Mostly senior level executives who are interesting in trending, mining, forecasting, strategizing, etc. don’t care for that one specific transaction. This is where an ODS can come in handy. ODS can use the same star schema and the OLAP cubes we saw earlier. The only difference is that the data inside an ODS would be short lived, i.e. for few months and ODS would sync with OLTP system every few minutes. Data warehouse can periodically sync with ODS either daily or weekly depending on business drivers. Data marts are another frequently talked about topic in data warehousing. They are subject-specific data warehouse. Data warehouses that try to span over an enterprise are normally too big to scope, build, manage, track, etc. Hence they are often scaled down to something called Data mart that supports a specific segment of business like sales, marketing, or support. Data marts too, are often designed using star schema model discussed earlier. Industry is divided when it comes to use of data marts. Some experts prefer having data marts along with a central data warehouse. Data warehouse here acts as information staging and distribution hub with spokes being data marts connected via data feeds serving summarized data. Others eliminate the need for a centralized data warehouse citing that most users want to report on detailed data. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Business Intelligence, Data Warehousing, Database, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – A Quick Look at Logging and Ideas around Logging

    - by pinaldave
    This blog post is written in response to the T-SQL Tuesday post on Logging. When someone talks about logging, personally I get lots of ideas about it. I have seen logging as a very generic term. Let me ask you this question first before I continue writing about logging. What is the first thing comes to your mind when you hear word “Logging”? Now ask the same question to the guy standing next to you. I am pretty confident that you will get  a different answer from different people. I decided to do this activity and asked 5 SQL Server person the same question. Question: What is the first thing comes to your mind when you hear the word “Logging”? Strange enough I got a different answer every single time. Let me just list what answer I got from my friends. Let us go over them one by one. Output Clause The very first person replied output clause. Pretty interesting answer to start with. I see what exactly he was thinking. SQL Server 2005 has introduced a new OUTPUT clause. OUTPUT clause has access to inserted and deleted tables (virtual tables) just like triggers. OUTPUT clause can be used to return values to client clause. OUTPUT clause can be used with INSERT, UPDATE, or DELETE to identify the actual rows affected by these statements. Here are some references for Output Clause: OUTPUT Clause Example and Explanation with INSERT, UPDATE, DELETE Reasons for Using Output Clause – Quiz Tips from the SQL Joes 2 Pros Development Series – Output Clause in Simple Examples Error Logs I was expecting someone to mention Error logs when it is about logging. The error log is the most looked place when there is any error either with the application or there is an error with the operating system. I have kept the policy to check my server’s error log every day. The reason is simple – enough time in my career I have figured out that when I am looking at error logs I find something which I was not expecting. There are cases, when I noticed errors in the error log and I fixed them before end user notices it. Other common practices I always tell my DBA friends to do is that when any error happens they should find relevant entries in the error logs and document the same. It is quite possible that they will see the same error in the error log  and able to fix the error based on the knowledge base which they have created. There can be many different kinds of error log files exists in SQL Server as well – 1) SQL Server Error Logs 2) Windows Event Log 3) SQL Server Agent Log 4) SQL Server Profile Log 5) SQL Server Setup Log etc. Here are some references for Error Logs: Recycle Error Log – Create New Log file without Server Restart SQL Error Messages Change Data Capture I got surprised with this answer. I think more than the answer I was surprised by the person who had answered me this one. I always thought he was expert in HTML, JavaScript but I guess, one should never assume about others. Indeed one of the cool logging feature is Change Data Capture. Change Data Capture records INSERTs, UPDATEs, and DELETEs applied to SQL Server tables, and makes a record available of what changed, where, and when, in simple relational ‘change tables’ rather than in an esoteric chopped salad of XML. These change tables contain columns that reflect the column structure of the source table you have chosen to track, along with the metadata needed to understand the changes that have been made. Here are some references for Change Data Capture: Introduction to Change Data Capture (CDC) in SQL Server 2008 Tuning the Performance of Change Data Capture in SQL Server 2008 Download Script of Change Data Capture (CDC) CDC and TRUNCATE – Cannot truncate table because it is published for replication or enabled for Change Data Capture Dynamic Management View (DMV) I like this answer. If asked I would have not come up with DMV right away but in the spirit of the original question, I think DMV does log the data. DMV logs or stores or records the various data and activity on the SQL Server. Dynamic management views return server state information that can be used to monitor the health of a server instance, diagnose problems, and tune performance. One can get plethero of information from DMVs – High Availability Status, Query Executions Details, SQL Server Resources Status etc. Here are some references for Dynamic Management View (DMV): SQL SERVER – Denali – DMV Enhancement – sys.dm_exec_query_stats – New Columns DMV – sys.dm_os_windows_info – Information about Operating System DMV – sys.dm_os_wait_stats Explanation – Wait Type – Day 3 of 28 DMV sys.dm_exec_describe_first_result_set_for_object – Describes the First Result Metadata for the Module Transaction Log Impact Detection Using DMV – dm_tran_database_transactions Log Files I almost flipped with this final answer from my friend. This should be probably the first answer. Yes, indeed log file logs the SQL Server activities. One can write infinite things about log file. SQL Server uses log file with the extension .ldf to manage transactions and maintain database integrity. Log file ensures that valid data is written out to database and system is in a consistent state. Log files are extremely useful in case of the database failures as with the help of full backup file database can be brought in the desired state (point in time recovery is also possible). SQL Server database has three recovery models – 1) Simple, 2) Full and 3) Bulk Logged. Each of the model uses the .ldf file for performing various activities. It is very important to take the backup of the log files (along with full backup) as one never knows when backup of the log file come into the action and save the day! How to Stop Growing Log File Too Big Reduce the Virtual Log Files (VLFs) from LDF file Log File Growing for Model Database – model Database Log File Grew Too Big master Database Log File Grew Too Big SHRINKFILE and TRUNCATE Log File in SQL Server 2008 Can I just say I loved this month’s T-SQL Tuesday Question. It really provoked very interesting conversation around me. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • How to launch LOV and Date dialogs using the keyboard

    - by frank.nimphius
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Using the ADF Faces JavaScript API, developers can listen for user keyboard input in input components to filter or respond to specific characters or key combination. The JavaScript shown below can be used with an af:clientListener tag on af:inputListOfValues or af:inputDate. At runtime, the JavaScript code determines the component type it is executed on and either opens the LOV dialog or the input Date popup.   <af:resource type="javascript">     /**     * function to launch dialog if cursor is in LOV or     * input date field     * @param evt argument to capture the AdfUIInputEvent object     */   function launchPopUpUsingF8(evt) {      var component = evt.getSource();      if (evt.getKeyCode() == AdfKeyStroke.F8_KEY) {      //check for input LOV component        if (component.getTypeName() == 'AdfRichInputListOfValues') {            AdfLaunchPopupEvent.queue(component, true);            //event is handled on the client. Server does not need            //to be notified            evt.cancel();          }         //check for input Date component               else if (component.getTypeName() == 'AdfRichInputDate') {           //the inputDate af:popup component ID always is ::pop           var popupClientId = component.getAbsoluteLocator() + '::pop';           var popup = component.findComponent(popupClientId);           var hints = {align : AdfRichPopup.ALIGN_END_AFTER,                        alignId : component.getAbsoluteLocator()};           popup.show(hints);           //event is handled on the client. Server does not need           //to be notified           evt.cancel();        }              } } </af:resource> The af:clientListener that calls the JavaScript is added as shown below. <af:inputDate label="Label 1" id="id1">    <af:clientListener method="launchPopUpUsingF8" type="keyDown"/> </af:inputDate> As you may have noticed, the call to open the popup is different for the af:inputListOfValues and the af:inputDate. For the list of values component, an ADF Faces AdfLaunchPopupEvent is queued with the LOV component passed s an argument. Launching the input date popup is a bit more complicate and requires you to lookup the implicit popup dialog and to open it manually. Because the popup is opened manually using the show() method on the af:popup component, the alignment of the dialog also needs to be handled manually. For this, the popup component specifies alignment hints, that for the ALIGN_END_AFTER hint aligns the dialog at the end and below the date component. The align Id hint specifies the component the dialog is relatively positioned to, which of course should be the input date field. The ADF Faces JavaScript API and how to use it is further explained in the Using JavaScript in ADF Faces Rich Client Applications whitepaper available from the Oracle Technology Network (OTN) http://www.oracle.com/technetwork/developer-tools/jdev/1-2011-javascript-302460.pdf An ADF Insider recording about JavaScript in ADF Faces can be watched from here http://download.oracle.com/otn_hosted_doc/jdeveloper/11gdemos/adf-insider-javascript/adf-insider-javascript.html

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  • Life Technologies: Making Life Easier to Manage

    - by Michael Snow
    12.00 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} When we’re thinking about customer engagement, we’re acutely aware of all the forces at play competing for our customer’s attention. Solutions that make life easier for our customers draw attention to themselves. We tend to engage more when there is a distinct benefit and we can take a deep breath and accept that there is hope in the world and everything isn’t designed to frustrate us and make our lives miserable. (sigh…) When products are designed to automate processes that were consuming hours of our time with no relief in sight, they deserve to be recognized. One of our recent Oracle Fusion Middleware Innovation Award Winners in the WebCenter category, Life Technologies, has recently posted a video promoting their “award winning” solution. The Oracle Innovation Awards are part of the overall Oracle Excellence awards given to customers for innovation with Oracle products. More info here. Their award nomination included this description: Life Technologies delivered the My Life Service Portal as part of a larger Digital Hub strategy. This Portal is the first of its kind in the biotechnology service providing industry. The Portal provides access to Life Technologies cloud based service monitoring system where all customer deployed instruments can be remotely monitored and proactively repaired. The portal provides alerts from these cloud based monitoring services directly to the customer and to Life Technologies Field Engineers. The Portal provides insight into the instruments and services customers purchased for the purpose of analyzing and anticipating future customer needs and creating targeted sales and service programs. This portal not only provides benefits for Life Technologies internal sales and service teams but provides customers a central place to track all pertinent instrument information including: instrument service history instrument status and previous activities instrument performance analytics planned service visits warranty/contract information discussion forums social networks for lab management and collaboration alerts and notifications on all of the above team scheduling for instrument usage promote optional reagents required to keep instruments performing From their website The Life Technologies Instruments & Services Portal Helps You Save Time and Gain Peace of Mind Introducing the new, award-winning, free online tool that enables easier management of your instrument use and care, faster response to requests for service or service quotes, and instant sharing of key instrument and service information with your colleagues. Now – this unto itself is obviously beneficial for their customers who were previously burdened with having to do all of these tasks separately, manually and inconsistently by nature. Now – all in one place and free to their customers – a portal that ties it all together. They now have built the platform to give their customers yet another reason to do business with them – Their headline on their product page says it all: “Life is now easier to manage - All your instrument use and care in one place – the no-cost, no-hassle Instruments and Services Portal.” Of course – it’s very convenient that the company name includes “Life” and now can also promote to their clients and prospects that doing business with them is easy and their sophisticated lab equipment is easy to manage. In an industry full of PhD’s – “Easy” isn’t usually the first word that comes to mind, but Life Technologies has now tied the word to their brand in a very eloquent way. Between our work lives and family or personal lives, getting any mono-focused minutes of dedicated attention has become such a rare occurrence in our current era of multi-tasking that those moments of focus are highly prized. So – when something is done really well – so well that it becomes captivating and urges sharing impulses – I take notice and dig deeper and most of the time I discover other gems not so hidden below the surface. And then I share with those I know would enjoy and understand. In the spirit of full disclosure, I must admit here that the first person I shared the videos below with was my daughter. She’s in her senior year of high school in the midst of her college search. She’s passionate about her academics and has already decided that she wants to study Neuroscience in college and like her mother will be in for the long haul to a PhD eventually. In a summer science program at Smith College 2 summers ago – she sent the family famous text to me – “I just dissected a sheep’s brain – wicked cool!” – This was followed by an equally memorable text this past summer in a research mentorship in Neuroscience at UConn – “Just sliced up some rat brain. Reminded me of a deli slicer at the supermarket… sorry I forgot to call last night…” So… needless to say – I knew I had an audience that would enjoy and understand these videos below and are now being shared among her science classmates and faculty. And evidently - so does Life Technologies! They’ve done a great job on these making them fun and something that will easily be shared among their customers social networks. They’ve created a neuro-archetypal character, “Ph.Diddy” and know that their world of clients in academics, research, and other institutions would understand and enjoy the “edutainment” value in this series of videos on their YouTube channel that pokes fun at the stereotypes while also promoting their products at the same time. They use their Facebook page for additional engagement with their clients and as another venue to promote these videos. Enjoy this one as well! More to be found here: http://www.youtube.com/lifetechnologies Stay tuned to this Oracle WebCenter blog channel. Tomorrow we'll be taking a look at another winner of the Innovation Awards, LADWP - helping to keep the citizens of Los Angeles engaged with their Water and Power provider.

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  • Best of Breed vs. Suite – Oracle’s SaaS Delivers Both

    - by yaldahhakim
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The debate of which is better: “best of breed” business applications vs. an integrated suite is certainly not a new conversation. This has been argued between IT vendors and CIOs for years. It’s also important to clarify that “best of breed” does not necessarily translate into being the richest functionality; rather it’s often about just having the best fit solution to solve a specific business problem or need. So what does cloud have to do with the niche vs. suite debate? Consuming business applications in a cloud or SaaS deployment model can change the best of breed vs. suite discussion - if the cloud is done right. It’s having your cake and eating it too only better: you don’t have to gather all the ingredients or wait to bake your cake, and you can adjust how big of slice you take. Before you eat, it’s worth pausing to recall much of what we learned about IT over the last decade. These basic IT principles still hold true even though the financial model has changed from buying to renting. In other words, what’s under the technology hood still matters. Architecture and development methodologies like building an application based on open standards so it works with other systems - is still important. Data and information silos, complex integrations, and proprietary technologies that lock you in, are still bad. While some may argue that IT no longer matters with cloud, the opposite is actually true. If anything cloud can help return IT back to its rightful place as key strategic asset vs. a liability on the balance sheet. The “I” in CIO was never meant to stand for “integration” yet it’s amazing how much time and money is poured into these types of initiatives for most organizations each year. Rather the “I” needs to stand for “innovation”. This is where Oracle SaaS can uniquely help. Oracle’s application strategy has not really changed over the years. It’s always been about bringing the best and richest functionality across the enterprise to our customers while leveraging a common, standards-based, and enterprise-grade platform. So not jut best fit, but the best capabilities based on the input of thousands of enterprise customers across the globe. Oracle invests billions in R&D every year to add new capabilities to the broadest cloud portfolio in the industry, spanning across functional pillars like CRM, HCM, ERP, etc. And where it makes sense, Oracle combines key strategic acquisitions to complement organic functionality. The result is best of breed delivered in a suite. Again this is not something new. The game changer now with cloud is that it impacts HOW Oracle customers adopt the richest, most modern applications across the business – and continue on getting it. Consuming oracle applications in the cloud means you can adopt new capabilities and updates very quickly and easily. There’s no hardware to buy or software to manage. Oracle does it for you. Low upfront costs and an OpEx financial model is the easy part. Oracle Cloud Applications take it a big step further. For organizations that demand having the latest and richest functionality and accelerating the time to value from their IT investment, Oracle Cloud is the right path. It’s about holistically changing the “hows” and the “whys” of the organization by leveraging transformational innovations like social, mobile, and big data in a consistent and more powerful way. Not just about sales force automation or talent management. These technologies should impact all parts of the company and Oracle Cloud is the enterprise-grade delivery vehicle. Oracle SaaS helps break down barriers of adoption and is eases the headache of upgrades, investing in new supporting hardware, or adding internal expertise to manage it all. With Oracle Cloud, customers can get best of breed capabilities in either a full suite model or a la carte. And because it’s entirely built on open standards, it’s built to co-exist with existing IT investments. Updates can be automatic or delayed based on a customer’s requirements. And it’s complete – a full suite of cross pillar functionality. Even better, if you don’t like it, need more or less, just turn the dial up or down. Just like your utility bill, you pay for what you use, and can consume more or less power whenever you need it. Lower cost, lower investment risk, without compromising on functionality, security, or performance. Technology still matters in the cloud. So our cloud customers also like that when they adopt our cloud applications, they also get the best underlying technology, from the middleware and database platform down to infrastructure and Oracle’s engineered systems. Therefore it’s not just the greatest and latest in application functionality, but everything underneath that makes it work is also the latest and greatest. The best of breed technology stack powering best of breed business applications, and all delivered in a subscription based model. The best of both worlds. Yep, that’s the idea.

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  • Using SQL Source Control with Fortress or Vault &ndash; Part 2

    - by AjarnMark
    In Part 1, I started talking about using Red-Gate’s newest version of SQL Source Control and how I really like it as a viable method to source control your database development.  It looks like this is going to turn into a little series where I will explain how we have done things in the past, and how life is different with SQL Source Control.  I will also explain some of my philosophy and methodology around deployment with these tools.  But for now, let’s talk about some of the good and the bad of the tool itself. More Kudos and Features I mentioned previously how impressed I was with the responsiveness of Red-Gate’s team.  I have been having an ongoing email conversation with Gyorgy Pocsi, and as I have run into problems or requested things behave a little differently, it has not been more than a day or two before a new Build is ready for me to download and test.  Quite impressive! I’m sure much of the requests I put in were already in the plans, so I can’t really take credit for them, but throughout this conversation, Red-Gate has implemented several features that were not in the first Early Access version.  Those include: Honoring the Fortress configuration option to require Work Item (Bug) IDs on check-ins. Adding the check-in comment text as a comment to the Work Item. Adding the list of checked-in files, along with the Fortress links for automatic History and DIFF view Updating the status of a Work Item on check-in (e.g. setting the item to Complete or, in our case “Dev-Complete”) Support for the Fortress 2.0 API, and not just the Vault Pro 5.1 API.  (See later notes regarding support for Fortress 2.0). These were all features that I felt we really needed to have in-place before I could honestly consider converting my team to using SQL Source Control on a regular basis.  Now that I have those, my only excuse is not wanting to switch boats on the team mid-stream.  So when we wrap up our current release in a few weeks, we will make the jump.  In the meantime, I will continue to bang on it to make sure it is stable.  It passed one test for stability when I did a test load of one of our larger database schemas into Fortress with SQL Source Control.  That database has about 150 tables, 200 User-Defined Functions and nearly 900 Stored Procedures.  The initial load to source control went smoothly and took just a brief amount of time. Warnings Remember that this IS still in pre-release stage and while I have not had any problems after that first hiccup I wrote about last time, you still need to treat it with a healthy respect.  As I understand it, the RTM is targeted for February.  There are a couple more features that I hope make it into the final release version, but if not, they’ll probably be coming soon thereafter.  Those are: A Browse feature to let me lookup the Work Item ID instead of having to remember it or look back in my Item details.  This is just a matter of convenience. I normally have my Work Item list open anyway, so I can easily look it up, but hey, why not make it even easier. A multi-line comment area.  The current space for writing check-in comments is a single-line text box.  I would like to have a multi-line space as I sometimes write lengthy commentary.  But I recognize that it is a struggle to get most developers to put in more than the word “fixed” as their comment, so this meets the need of the majority as-is, and it’s not a show-stopper for us. Merge.  SQL Source Control currently does not have a Merge feature.  If two or more people make changes to the same database object, you will get a warning of the conflict and have to choose which one wins (and then manually edit to include the others’ changes).  I think it unlikely you will run into actual conflicts in Stored Procedures and Functions, but you might with Views or Tables.  This will be nice to have, but I’m not losing any sleep over it.  And I have multiple tools at my disposal to do merges manually, so really not a show-stopper for us. Automation has its limits.  As cool as this automation is, it has its limits and there are some changes that you will be better off scripting yourself.  For example, if you are refactoring table definitions, and want to change a column name, you can write that as a quick sp_rename command and preserve the data within that column.  But because this tool is looking just at a before and after picture, it cannot tell that you just renamed a column.  To the tool, it looks like you dropped one column and added another.  This is not a knock against Red-Gate.  All automated scripting tools have this issue, unless the are actively monitoring your every step to know exactly what you are doing.  This means that when you go to Deploy your changes, SQL Compare will script the change as a column drop and add, or will attempt to rebuild the entire table.  Unfortunately, neither of these approaches will preserve the existing data in that column the way an sp_rename will, and so you are better off scripting that change yourself.  Thankfully, SQL Compare will produce warnings about the potential loss of data before it does the actual synchronization and give you a chance to intercept the script and do it yourself. Also, please note that the current official word is that SQL Source Control supports Vault Professional 5.1 and later.  Vault Professional is the new name for what was previously known as Fortress.  (You can read about the name change on SourceGear’s site.)  The last version of Fortress was 2.x, and the API for Fortress 2.x is different from the API for Vault Pro.  At my company, we are currently running Fortress 2.0, with plans to upgrade to Vault Pro early next year.  Gyorgy was able to come up with a work-around for me to be able to use SQL Source Control with Fortress 2.0, even though it is not officially supported.  If you are using Fortress 2.0 and want to use SQL Source Control, be aware that this is not officially supported, but it is working for us, and you can probably get the work-around instructions from Red-Gate if you’re really, really nice to them. Upcoming Topics Some of the other topics I will likely cover in this series over the next few weeks are: How we used to do source control back in the old days (a few weeks ago) before SQL Source Control was available to Vault users What happens when you restore a database that is linked to source control Handling multiple development branches of source code Concurrent Development practices and handling Conflicts Deployment Tips and Best Practices A recap after using the tool for a while

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  • Time Tracking on an Agile Team

    - by Stephen.Walther
    What’s the best way to handle time-tracking on an Agile team? Your gut reaction to this question might be to resist any type of time-tracking at all. After all, one of the principles of the Agile Manifesto is “Individuals and interactions over processes and tools”.  Forcing the developers on your team to track the amount of time that they devote to completing stories or tasks might seem like useless bureaucratic red tape: an impediment to getting real work done. I completely understand this reaction. I’ve been required to use time-tracking software in the past to account for each hour of my workday. It made me feel like Fred Flintstone punching in at the quarry mine and not like a professional. Why You Really Do Need Time-Tracking There are, however, legitimate reasons to track time spent on stories even when you are a member of an Agile team.  First, if you are working with an outside client, you might need to track the number of hours spent on different stories for the purposes of billing. There might be no way to avoid time-tracking if you want to get paid. Second, the Product Owner needs to know when the work on a story has gone over the original time estimated for the story. The Product Owner is concerned with Return On Investment. If the team has gone massively overtime on a story, then the Product Owner has a legitimate reason to halt work on the story and reconsider the story’s business value. Finally, you might want to track how much time your team spends on different types of stories or tasks. For example, if your team is spending 75% of their time doing testing then you might need to bring in more testers. Or, if 10% of your team’s time is expended performing a software build at the end of each iteration then it is time to consider better ways of automating the build process. Time-Tracking in SonicAgile For these reasons, we added time-tracking as a feature to SonicAgile which is our free Agile Project Management tool. We were heavily influenced by Jeff Sutherland (one of the founders of Scrum) in the way that we implemented time-tracking (see his article http://scrum.jeffsutherland.com/2007/03/time-tracking-is-anti-scrum-what-do-you.html). In SonicAgile, time-tracking is disabled by default. If you want to use this feature then the project owner must enable time-tracking in Project Settings. You can choose to estimate using either days or hours. If you are estimating at the level of stories then it makes more sense to choose days. Otherwise, if you are estimating at the level of tasks then it makes more sense to use hours. After you enable time-tracking then you can assign three estimates to a story: Original Estimate – This is the estimate that you enter when you first create a story. You don’t change this estimate. Time Spent – This is the amount of time that you have already devoted to the story. You update the time spent on each story during your daily standup meeting. Time Left – This is the amount of time remaining to complete the story. Again, you update the time left during your daily standup meeting. So when you first create a story, you enter an original estimate that becomes the time left. During each daily standup meeting, you update the time spent and time left for each story on the Kanban. If you had perfect predicative power, then the original estimate would always be the same as the sum of the time spent and the time left. For example, if you predict that a story will take 5 days to complete then on day 3, the story should have 3 days spent and 2 days left. Unfortunately, never in the history of mankind has anyone accurately predicted the exact amount of time that it takes to complete a story. For this reason, SonicAgile does not update the time spent and time left automatically. Each day, during the daily standup, your team should update the time spent and time left for each story. For example, the following table shows the history of the time estimates for a story that was originally estimated to take 3 days but, eventually, takes 5 days to complete: Day Original Estimate Time Spent Time Left Day 1 3 days 0 days 3 days Day 2 3 days 1 day 2 days Day 3 3 days 2 days 2 days Day 4 3 days 3 days 2 days Day 5 3 days 4 days 0 days In the table above, everything goes as predicted until you reach day 3. On day 3, the team realizes that the work will require an additional two days. The situation does not improve on day 4. All of the sudden, on day 5, all of the remaining work gets done. Real work often follows this pattern. There are long periods when nothing gets done punctuated by occasional and unpredictable bursts of progress. We designed SonicAgile to make it as easy as possible to track the time spent and time left on a story. Detecting when a Story Goes Over the Original Estimate Sometimes, stories take much longer than originally estimated. There’s a surprise. For example, you discover that a new software component is incompatible with existing software components. Or, you discover that you have to go through a month-long certification process to finish a story. In those cases, the Product Owner has a legitimate reason to halt work on a story and re-evaluate the business value of the story. For example, the Product Owner discovers that a story will require weeks to implement instead of days, then the story might not be worth the expense. SonicAgile displays a warning on both the Backlog and the Kanban when the time spent on a story goes over the original estimate. An icon of a clock is displayed. Time-Tracking and Tasks Another optional feature of SonicAgile is tasks. If you enable Tasks in Project Settings then you can break stories into one or more tasks. You can perform time-tracking at the level of a story or at the level of a task. If you don’t break a story into tasks then you can enter the time left and time spent for the story. As soon as you break a story into tasks, then you can no longer enter the time left and time spent at the level of the story. Instead, the time left and time spent for a story is rolled up from its tasks. On the Kanban, you can see how the time left and time spent for each task gets rolled up into each story. The progress bar for the story is rolled up from the progress bars for each task. The original estimate is never rolled up – even when you break a story into tasks. A story’s original estimate is entered separately from the original estimates of each of the story’s tasks. Summary Not every Agile team can avoid time-tracking. You might be forced to track time to get paid, to detect when you are spending too much time on a particular story, or to track the amount of time that you are devoting to different types of tasks. We designed time-tracking in SonicAgile to require the least amount of work to track the information that you need. Time-tracking is an optional feature. If you enable time-tracking then you can track the original estimate, time left, and time spent for each story and task. You can use time-tracking with SonicAgile for free. Register at http://SonicAgile.com.

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  • Why It Is So Important to Know Your Customer

    - by Christie Flanagan
    Over the years, I endured enough delayed flights, air turbulence and misadventures in airport security clearance to watch my expectations for the air travel experience fall to abysmally low levels. The extent of my loyalty to any one carrier had more to do with the proximity of the airport parking garage to their particular gate than to any effort on the airline’s part to actually earn and retain my business. That all changed one day when I found myself at the airport hoping to catch a return flight home a few hours earlier than expected, using an airline I had flown with for the first time just that week.  When you travel regularly for business, being able to catch a return flight home that’s even an hour or two earlier than originally scheduled is a big deal. It can mean the difference between having a normal evening with your family and having to sneak in like a cat burglar after everyone is fast asleep. And so I found myself on this particular day hoping to catch an earlier flight home. I approached the gate agent and was told that I could go on standby for their next flight out. Then I asked how much it was going to cost to change the flight, knowing full well that I wouldn’t get reimbursed by my company for any change fees. “Oh, there’s no charge to fly on standby,” the gate agent told me. I made a funny look. I couldn’t believe what I was hearing. This airline was going to let my fly on standby, at no additional charge, even though I was a new customer with no status or points. It had been years since I’d seen an airline pass up a short term revenue generating opportunity in favor of a long term loyalty generating one.  At that moment, this particular airline gained my loyal business. Since then, this airline has had the opportunity to learn a lot about me. They know where I live, where I fly from, where I usually fly to, and where I like to sit on the plane. In general, I’ve found their customer service to be quite good whether at the airport, via call center and even through social channels. They email me occasionally, and when they do, they demonstrate that they know me by promoting deals for flights from where I live to places that I’d be interested in visiting. And that’s part of why I’m always so puzzled when I visit their website.Does this company with the great service, customer friendly policies, and clean planes demonstrate that they know me at all when I visit their website? The answer is no. Even when I log in using my loyalty program credentials, it’s pretty obvious that they’re presenting the same old home page and same old offers to every single one of their site visitors. I mean, those promotional offers that they’re featuring so prominently  -- they’re for flights that originate thousands of miles from where I live! There’s no way I’d ever book one of those flights and I’m sure I’m not the only one of their customers to feel that way.My reason for recounting this story is not to pick on the one customer experience flaw I've noticed with this particular airline, in fact, they do so many things right that I’ll continue to fly with them. But I did want to illustrate just how glaringly obvious it is to customers today when a touch point they have with a brand is impersonal, unconnected and out of sync. As someone who’s spent a number of years in the web experience management and online marketing space, it particularly peeves me when that out of sync touch point is a brand’s website, perhaps because I know how important it is to make a customer’s online experience relevant and how many powerful tools are available for making a relevant experience a reality. The fact is, delivering a one-size-fits-all online customer experience is no longer acceptable or particularly effective in today’s world. Today’s savvy customers expect you to know who they are and to understand their preferences, behavior and relationship with your brand. Not only do they expect you to know about them, but they also expect you to demonstrate this knowledge across all of their touch points with your brand in a consistent and compelling fashion, whether it be on your traditional website, your mobile web presence or through various social channels.Delivering the kind of personalized online experiences that customers want can have tremendous business benefits. This is not just about generating feelings of goodwill and higher customer satisfaction ratings either. More relevant and personalized online experiences boost the effectiveness of online marketing initiatives and the statistics prove this out. Personalized web experiences can help increase online conversion rates by 70% -- that’s a huge number.1  And more than three quarters of consumers indicate that they’ve made additional online purchases based on personalized product recommendations.2Now if only this airline would get on board with delivering a more personalized online customer experience. I’d certainly be happier and more likely to spring for one of their promotional offers. And by targeting relevant offers on their home page to appropriate segments of their site visitors, I bet they’d be happier and generating additional revenue too. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}  ***** If you're interested in hearing more perspectives on the benefits of demonstrating that you know your customers by delivering a more personalized experience, check out this white paper on creating a successful and meaningful customer experience on the web.  Also catch the video below on the business value of CX in attracting new customers featuring Oracle's VP of Customer Experience Strategy, Brian Curran. 1 Search Engine Watch 2 Marketing Charts

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  • Amazon Web Services (AWS) Plug-in for Oracle Enterprise Manager

    - by Anand Akela
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Contributed by Sunil Kunisetty and Daniel Chan Introduction and ArchitectureAs more and more enterprises deploy some of their non-critical workload on Amazon Web Services (AWS), it’s becoming critical to monitor those public AWS resources along side with their on-premise resources. Oracle recently announced Oracle Enterprise Manager Plug-in for Amazon Web Services (AWS) allows you to achieve that goal. The on-premise Oracle Enterprise Manager (EM12c) acts as a single tool to get a comprehensive view of your public AWS resources as well as your private cloud resources.  By deploying the plug-in within your Cloud Control environment, you gain the following management features: Monitor EBS, EC2 and RDS instances on Amazon Web Services Gather performance metrics and configuration details for AWS instances Raise alerts and violations based on thresholds set on monitoring Generate reports based on the gathered data Users of this Plug-in can leverage the rich Enterprise Manager features such as system promotion, incident generation based on thresholds, integration with 3rd party ticketing applications etc. AWS Monitoring via this Plug-in is enabled via Amazon CloudWatch API and the users of this Plug-in are responsible for supplying credentials for accessing AWS and the CloudWatch API. This Plug-in can only be deployed on an EM12C R2 platform and agent version should be at minimum 12c R2.Here is a pictorial view of the overall architecture: Amazon Elastic Block Store (EBS) Amazon Elastic Compute Cloud (EC2) Amazon Relational Database Service (RDS) Here are a few key features: Rich and exhaustive list of metrics. Metrics can be gathered from an Agent running outside AWS. Critical configuration information. Custom Home Pages with charts and AWS configuration information. Generate incidents based on thresholds set on monitoring data. Discovery and Monitoring AWS instances can be added to EM12C either via the EM12c User Interface (UI) or the EM12c Command Line Interface ( EMCLI)  by providing the AWS credentials (Secret Key and Access Key Id) as well as resource specific properties as target properties. Here is a quick mapping of target types and properties for each AWS resources AWS Resource Type Target Type Resource specific properties EBS Resource Amazon EBS Service CloudWatch base URI, EC2 Base URI, Period, Volume Id, Proxy Server and Port EC2 Resource Amazon EC2 Service CloudWatch base URI, EC2 Base URI, Period, Instance  Id, Proxy Server and Port RDS Resource Amazon RDS Service CloudWatch base URI, RDS Base URI, Period, Instance  Id, Proxy Server and Port Proxy server and port are optional and are only needed if the agent is within the firewall. Here is an emcli example to add an EC2 target. Please read the Installation and Readme guide for more details and step-by-step instructions to deploy  the plugin and adding the AWS the instances. ./emcli add_target \       -name="<target name>" \       -type="AmazonEC2Service" \       -host="<host>" \       -properties="ProxyHost=<proxy server>;ProxyPort=<proxy port>;EC2_BaseURI=http://ec2.<region>.amazonaws.com;BaseURI=http://monitoring.<region>.amazonaws.com;InstanceId=<EC2 instance Id>;Period=<data point periond>"  \     -subseparator=properties="=" ./emcli set_monitoring_credential \                 -set_name="AWSKeyCredentialSet"  \                 -target_name="<target name>"  \                 -target_type="AmazonEC2Service" \                 -cred_type="AWSKeyCredential"  \                 -attributes="AccessKeyId:<access key id>;SecretKey:<secret key>" Emcli utility is found under the ORACLE_HOME of EM12C install. Once the instance is discovered, the target will show up under the ‘All Targets’ list under “Amazon EC2 Service’. Once the instances are added, one can navigate to the custom homepages for these resource types. The custom home pages not only include critical metrics, but also vital configuration parameters and incidents raised for these instances.  By mapping the configuration parameters as instance properties, we can slice-and-dice and group various AWS instance by leveraging the EM12C Config search feature. The following configuration properties and metrics are collected for these Resource types. Resource Type Configuration Properties Metrics EBS Resource Volume Id, Volume Type, Device Name, Size, Availability Zone Response: Status Utilization: QueueLength, IdleTime Volume Statistics: ReadBrandwith, WriteBandwidth, ReadThroughput, WriteThroughput Operation Statistics: ReadSize, WriteSize, ReadLatency, WriteLatency EC2 Resource Instance ID, Owner Id, Root Device type, Instance Type. Availability Zone Response: Status CPU Utilization: CPU Utilization Disk I/O:  DiskReadBytes, DiskWriteBytes, DiskReadOps, DiskWriteOps, DiskReadRate, DiskWriteRate, DiskIOThroughput, DiskReadOpsRate, DiskWriteOpsRate, DiskOperationThroughput Network I/O : NetworkIn, NetworkOut, NetworkInRate, NetworkOutRate, NetworkThroughput RDS Resource Instance ID, Database Engine Name, Database Engine Version, Database Instance Class, Allocated Storage Size, Availability Zone Response: Status Disk I/O:  ReadIOPS, WriteIOPS, ReadLatency, WriteLatency, ReadThroughput, WriteThroughput DB Utilization:  BinLogDiskUsage, CPUUtilization, DatabaseConnections, FreeableMemory, ReplicaLag, SwapUsage Custom Home Pages As mentioned above, we have custom home pages for these target types that include basic configuration information,  last 24 hours availability, top metrics and the incidents generated. Here are few snapshots. EBS Instance Home Page: EC2 Instance Home Page: RDS Instance Home Page: Further Reading: 1)      AWS Plugin download 2)      Installation and  Read Me. 3)      Screenwatch on SlideShare 4)      Extensibility Programmer's Guide 5)      Amazon Web Services

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • Java EE 6 and NoSQL/MongoDB on GlassFish using JPA and EclipseLink 2.4 (TOTD #175)

    - by arungupta
    TOTD #166 explained how to use MongoDB in your Java EE 6 applications. The code in that tip used the APIs exposed by the MongoDB Java driver and so requires you to learn a new API. However if you are building Java EE 6 applications then you are already familiar with Java Persistence API (JPA). Eclipse Link 2.4, scheduled to release as part of Eclipse Juno, provides support for NoSQL databases by mapping a JPA entity to a document. Their wiki provides complete explanation of how the mapping is done. This Tip Of The Day (TOTD) will show how you can leverage that support in your Java EE 6 applications deployed on GlassFish 3.1.2. Before we dig into the code, here are the key concepts ... A POJO is mapped to a NoSQL data source using @NoSQL or <no-sql> element in "persistence.xml". A subset of JPQL and Criteria query are supported, based upon the underlying data store Connection properties are defined in "persistence.xml" Now, lets lets take a look at the code ... Download the latest EclipseLink 2.4 Nightly Bundle. There is a Installer, Source, and Bundle - make sure to download the Bundle link (20120410) and unzip. Download GlassFish 3.1.2 zip and unzip. Install the Eclipse Link 2.4 JARs in GlassFish Remove the following JARs from "glassfish/modules": org.eclipse.persistence.antlr.jar org.eclipse.persistence.asm.jar org.eclipse.persistence.core.jar org.eclipse.persistence.jpa.jar org.eclipse.persistence.jpa.modelgen.jar org.eclipse.persistence.moxy.jar org.eclipse.persistence.oracle.jar Add the following JARs from Eclipse Link 2.4 nightly build to "glassfish/modules": org.eclipse.persistence.antlr_3.2.0.v201107111232.jar org.eclipse.persistence.asm_3.3.1.v201107111215.jar org.eclipse.persistence.core.jpql_2.4.0.v20120407-r11132.jar org.eclipse.persistence.core_2.4.0.v20120407-r11132.jar org.eclipse.persistence.jpa.jpql_2.0.0.v20120407-r11132.jar org.eclipse.persistence.jpa.modelgen_2.4.0.v20120407-r11132.jar org.eclipse.persistence.jpa_2.4.0.v20120407-r11132.jar org.eclipse.persistence.moxy_2.4.0.v20120407-r11132.jar org.eclipse.persistence.nosql_2.4.0.v20120407-r11132.jar org.eclipse.persistence.oracle_2.4.0.v20120407-r11132.jar Start MongoDB Download latest MongoDB from here (2.0.4 as of this writing). Create the default data directory for MongoDB as: sudo mkdir -p /data/db/sudo chown `id -u` /data/db Refer to Quickstart for more details. Start MongoDB as: arungup-mac:mongodb-osx-x86_64-2.0.4 <arungup> ->./bin/mongod./bin/mongod --help for help and startup optionsMon Apr  9 12:56:02 [initandlisten] MongoDB starting : pid=3124 port=27017 dbpath=/data/db/ 64-bit host=arungup-mac.localMon Apr  9 12:56:02 [initandlisten] db version v2.0.4, pdfile version 4.5Mon Apr  9 12:56:02 [initandlisten] git version: 329f3c47fe8136c03392c8f0e548506cb21f8ebfMon Apr  9 12:56:02 [initandlisten] build info: Darwin erh2.10gen.cc 9.8.0 Darwin Kernel Version 9.8.0: Wed Jul 15 16:55:01 PDT 2009; root:xnu-1228.15.4~1/RELEASE_I386 i386 BOOST_LIB_VERSION=1_40Mon Apr  9 12:56:02 [initandlisten] options: {}Mon Apr  9 12:56:02 [initandlisten] journal dir=/data/db/journalMon Apr  9 12:56:02 [initandlisten] recover : no journal files present, no recovery neededMon Apr  9 12:56:02 [websvr] admin web console waiting for connections on port 28017Mon Apr  9 12:56:02 [initandlisten] waiting for connections on port 27017 Check out the JPA/NoSQL sample from SVN repository. The complete source code built in this TOTD can be downloaded here. Create Java EE 6 web app Create a Java EE 6 Maven web app as: mvn archetype:generate -DarchetypeGroupId=org.codehaus.mojo.archetypes -DarchetypeArtifactId=webapp-javaee6 -DgroupId=model -DartifactId=javaee-nosql -DarchetypeVersion=1.5 -DinteractiveMode=false Copy the model files from the checked out workspace to the generated project as: cd javaee-nosqlcp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/model src/main/java Copy "persistence.xml" mkdir src/main/resources cp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/META-INF ./src/main/resources Add the following dependencies: <dependency> <groupId>org.eclipse.persistence</groupId> <artifactId>org.eclipse.persistence.jpa</artifactId> <version>2.4.0-SNAPSHOT</version> <scope>provided</scope></dependency><dependency> <groupId>org.eclipse.persistence</groupId> <artifactId>org.eclipse.persistence.nosql</artifactId> <version>2.4.0-SNAPSHOT</version></dependency><dependency> <groupId>org.mongodb</groupId> <artifactId>mongo-java-driver</artifactId> <version>2.7.3</version></dependency> The first one is for the EclipseLink latest APIs, the second one is for EclipseLink/NoSQL support, and the last one is the MongoDB Java driver. And the following repository: <repositories> <repository> <id>EclipseLink Repo</id> <url>http://www.eclipse.org/downloads/download.php?r=1&amp;nf=1&amp;file=/rt/eclipselink/maven.repo</url> <snapshots> <enabled>true</enabled> </snapshots> </repository>  </repositories> Copy the "Test.java" to the generated project: mkdir src/main/java/examplecp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/example/Test.java ./src/main/java/example/ This file contains the source code to CRUD the JPA entity to MongoDB. This sample is explained in detail on EclipseLink wiki. Create a new Servlet in "example" directory as: package example;import java.io.IOException;import java.io.PrintWriter;import javax.servlet.ServletException;import javax.servlet.annotation.WebServlet;import javax.servlet.http.HttpServlet;import javax.servlet.http.HttpServletRequest;import javax.servlet.http.HttpServletResponse;/** * @author Arun Gupta */@WebServlet(name = "TestServlet", urlPatterns = {"/TestServlet"})public class TestServlet extends HttpServlet { protected void processRequest(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { response.setContentType("text/html;charset=UTF-8"); PrintWriter out = response.getWriter(); try { out.println("<html>"); out.println("<head>"); out.println("<title>Servlet TestServlet</title>"); out.println("</head>"); out.println("<body>"); out.println("<h1>Servlet TestServlet at " + request.getContextPath() + "</h1>"); try { Test.main(null); } catch (Exception ex) { ex.printStackTrace(); } out.println("</body>"); out.println("</html>"); } finally { out.close(); } } @Override protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); } @Override protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); }} Build the project and deploy it as: mvn clean packageglassfish3/bin/asadmin deploy --force=true target/javaee-nosql-1.0-SNAPSHOT.war Accessing http://localhost:8080/javaee-nosql/TestServlet shows the following messages in the server.log: connecting(EISLogin( platform=> MongoPlatform user name=> "" MongoConnectionSpec())) . . .Connected: User: Database: 2.7  Version: 2.7 . . .Executing MappedInteraction() spec => null properties => {mongo.collection=CUSTOMER, mongo.operation=INSERT} input => [DatabaseRecord( CUSTOMER._id => 4F848E2BDA0670307E2A8FA4 CUSTOMER.NAME => AMCE)]. . .Data access result: [{TOTALCOST=757.0, ORDERLINES=[{DESCRIPTION=table, LINENUMBER=1, COST=300.0}, {DESCRIPTION=balls, LINENUMBER=2, COST=5.0}, {DESCRIPTION=rackets, LINENUMBER=3, COST=15.0}, {DESCRIPTION=net, LINENUMBER=4, COST=2.0}, {DESCRIPTION=shipping, LINENUMBER=5, COST=80.0}, {DESCRIPTION=handling, LINENUMBER=6, COST=55.0},{DESCRIPTION=tax, LINENUMBER=7, COST=300.0}], SHIPPINGADDRESS=[{POSTALCODE=L5J1H7, PROVINCE=ON, COUNTRY=Canada, CITY=Ottawa,STREET=17 Jane St.}], VERSION=2, _id=4F848E2BDA0670307E2A8FA8,DESCRIPTION=Pingpong table, CUSTOMER__id=4F848E2BDA0670307E2A8FA7, BILLINGADDRESS=[{POSTALCODE=L5J1H8, PROVINCE=ON, COUNTRY=Canada, CITY=Ottawa, STREET=7 Bank St.}]}] You'll not see any output in the browser, just the output in the console. But the code can be easily modified to do so. Once again, the complete Maven project can be downloaded here. Do you want to try accessing relational and non-relational (aka NoSQL) databases in the same PU ?

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  • OS Analytics - Deep Dive Into Your OS

    - by Eran_Steiner
    Enterprise Manager Ops Center provides a feature called "OS Analytics". This feature allows you to get a better understanding of how the Operating System is being utilized. You can research the historical usage as well as real time data. This post will show how you can benefit from OS Analytics and how it works behind the scenes. We will have a call to discuss this blog - please join us!Date: Thursday, November 1, 2012Time: 11:00 am, Eastern Daylight Time (New York, GMT-04:00)1. Go to https://oracleconferencing.webex.com/oracleconferencing/j.php?ED=209833067&UID=1512092402&PW=NY2JhMmFjMmFh&RT=MiMxMQ%3D%3D2. If requested, enter your name and email address.3. If a password is required, enter the meeting password: oracle1234. Click "Join". To join the teleconference:Call-in toll-free number:       1-866-682-4770  (US/Canada)      Other countries:                https://oracle.intercallonline.com/portlets/scheduling/viewNumbers/viewNumber.do?ownerNumber=5931260&audioType=RP&viewGa=true&ga=ONConference Code:       7629343#Security code:            7777# Here is quick summary of what you can do with OS Analytics in Ops Center: View historical charts and real time value of CPU, memory, network and disk utilization Find the top CPU and Memory processes in real time or at a certain historical day Determine proper monitoring thresholds based on historical data View Solaris services status details Drill down into a process details View the busiest zones if applicable Where to start To start with OS Analytics, choose the OS asset in the tree and click the Analytics tab. You can see the CPU utilization, Memory utilization and Network utilization, along with the current real time top 5 processes in each category (click the image to see a larger version):  In the above screen, you can click each of the top 5 processes to see a more detailed view of that process. Here is an example of one of the processes: One of the cool things is that you can see the process tree for this process along with some port binding and open file descriptors. On Solaris machines with zones, you get an extra level of tabs, allowing you to get more information on the different zones: This is a good way to see the busiest zones. For example, one zone may not take a lot of CPU but it can consume a lot of memory, or perhaps network bandwidth. To see the detailed Analytics for each of the zones, simply click each of the zones in the tree and go to its Analytics tab. Next, click the "Processes" tab to see real time information of all the processes on the machine: An interesting column is the "Target" column. If you configured Ops Center to work with Enterprise Manager Cloud Control, then the two products will talk to each other and Ops Center will display the correlated target from Cloud Control in this table. If you are only using Ops Center - this column will remain empty. Next, if you view a Solaris machine, you will have a "Services" tab: By default, all services will be displayed, but you can choose to display only certain states, for example, those in maintenance or the degraded ones. You can highlight a service and choose to view the details, where you can see the Dependencies, Dependents and also the location of the service log file (not shown in the picture as you need to scroll down to see the log file). The "Threshold" tab is particularly helpful - you can view historical trends of different monitored values and based on the graph - determine what the monitoring values should be: You can ask Ops Center to suggest monitoring levels based on the historical values or you can set your own. The different colors in the graph represent the current set levels: Red for critical, Yellow for warning and Blue for Information, allowing you to quickly see how they're positioned against real data. It's important to note that when looking at longer periods, Ops Center smooths out the data and uses averages. So when looking at values such as CPU Usage, try shorter time frames which are more detailed, such as one hour or one day. Applying new monitoring values When first applying new values to monitored attributes - a popup will come up asking if it's OK to get you out of the current Monitoring Policy. This is OK if you want to either have custom monitoring for a specific machine, or if you want to use this current machine as a "Gold image" and extract a Monitoring Policy from it. You can later apply the new Monitoring Policy to other machines and also set it as a default Monitoring Profile. Once you're done with applying the different monitoring values, you can review and change them in the "Monitoring" tab. You can also click the "Extract a Monitoring Policy" in the actions pane on the right to save all the new values to a new Monitoring Policy, which can then be found under "Plan Management" -> "Monitoring Policies". Visiting the past Under the "History" tab you can "go back in time". This is very helpful when you know that a machine was busy a few hours ago (perhaps in the middle of the night?), but you were not around to take a look at it in real time. Here's a view into yesterday's data on one of the machines: You can see an interesting CPU spike happening at around 3:30 am along with some memory use. In the bottom table you can see the top 5 CPU and Memory consumers at the requested time. Very quickly you can see that this spike is related to the Solaris 11 IPS repository synchronization process using the "pkgrecv" command. The "time machine" doesn't stop here - you can also view historical data to determine which of the zones was the busiest at a given time: Under the hood The data collected is stored on each of the agents under /var/opt/sun/xvm/analytics/historical/ An "os.zip" file exists for the main OS. Inside you will find many small text files, named after the Epoch time stamp in which they were taken If you have any zones, there will be a file called "guests.zip" containing the same small files for all the zones, as well as a folder with the name of the zone along with "os.zip" in it If this is the Enterprise Controller or the Proxy Controller, you will have folders called "proxy" and "sat" in which you will find the "os.zip" for that controller The actual script collecting the data can be viewed for debugging purposes as well: On Linux, the location is: /opt/sun/xvmoc/private/os_analytics/collect On Solaris, the location is /opt/SUNWxvmoc/private/os_analytics/collect If you would like to redirect all the standard error into a file for debugging, touch the following file and the output will go into it: # touch /tmp/.collect.stderr   The temporary data is collected under /var/opt/sun/xvm/analytics/.collectdb until it is zipped. If you would like to review the properties for the Analytics, you can view those per each agent in /opt/sun/n1gc/lib/XVM.properties. Find the section "Analytics configurable properties for OS and VSC" to view the Analytics specific values. I hope you find this helpful! Please post questions in the comments below. Eran Steiner

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  • MySQL Cluster 7.2: Over 8x Higher Performance than Cluster 7.1

    - by Mat Keep
    0 0 1 893 5092 Homework 42 11 5974 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Summary The scalability enhancements delivered by extensions to multi-threaded data nodes enables MySQL Cluster 7.2 to deliver over 8x higher performance than the previous MySQL Cluster 7.1 release on a recent benchmark What’s New in MySQL Cluster 7.2 MySQL Cluster 7.2 was released as GA (Generally Available) in February 2012, delivering many enhancements to performance on complex queries, new NoSQL Key / Value API, cross-data center replication and ease-of-use. These enhancements are summarized in the Figure below, and detailed in the MySQL Cluster New Features whitepaper Figure 1: Next Generation Web Services, Cross Data Center Replication and Ease-of-Use Once of the key enhancements delivered in MySQL Cluster 7.2 is extensions made to the multi-threading processes of the data nodes. Multi-Threaded Data Node Extensions The MySQL Cluster 7.2 data node is now functionally divided into seven thread types: 1) Local Data Manager threads (ldm). Note – these are sometimes also called LQH threads. 2) Transaction Coordinator threads (tc) 3) Asynchronous Replication threads (rep) 4) Schema Management threads (main) 5) Network receiver threads (recv) 6) Network send threads (send) 7) IO threads Each of these thread types are discussed in more detail below. MySQL Cluster 7.2 increases the maximum number of LDM threads from 4 to 16. The LDM contains the actual data, which means that when using 16 threads the data is more heavily partitioned (this is automatic in MySQL Cluster). Each LDM thread maintains its own set of data partitions, index partitions and REDO log. The number of LDM partitions per data node is not dynamically configurable, but it is possible, however, to map more than one partition onto each LDM thread, providing flexibility in modifying the number of LDM threads. The TC domain stores the state of in-flight transactions. This means that every new transaction can easily be assigned to a new TC thread. Testing has shown that in most cases 1 TC thread per 2 LDM threads is sufficient, and in many cases even 1 TC thread per 4 LDM threads is also acceptable. Testing also demonstrated that in some instances where the workload needed to sustain very high update loads it is necessary to configure 3 to 4 TC threads per 4 LDM threads. In the previous MySQL Cluster 7.1 release, only one TC thread was available. This limit has been increased to 16 TC threads in MySQL Cluster 7.2. The TC domain also manages the Adaptive Query Localization functionality introduced in MySQL Cluster 7.2 that significantly enhanced complex query performance by pushing JOIN operations down to the data nodes. Asynchronous Replication was separated into its own thread with the release of MySQL Cluster 7.1, and has not been modified in the latest 7.2 release. To scale the number of TC threads, it was necessary to separate the Schema Management domain from the TC domain. The schema management thread has little load, so is implemented with a single thread. The Network receiver domain was bound to 1 thread in MySQL Cluster 7.1. With the increase of threads in MySQL Cluster 7.2 it is also necessary to increase the number of recv threads to 8. This enables each receive thread to service one or more sockets used to communicate with other nodes the Cluster. The Network send thread is a new thread type introduced in MySQL Cluster 7.2. Previously other threads handled the sending operations themselves, which can provide for lower latency. To achieve highest throughput however, it has been necessary to create dedicated send threads, of which 8 can be configured. It is still possible to configure MySQL Cluster 7.2 to a legacy mode that does not use any of the send threads – useful for those workloads that are most sensitive to latency. The IO Thread is the final thread type and there have been no changes to this domain in MySQL Cluster 7.2. Multiple IO threads were already available, which could be configured to either one thread per open file, or to a fixed number of IO threads that handle the IO traffic. Except when using compression on disk, the IO threads typically have a very light load. Benchmarking the Scalability Enhancements The scalability enhancements discussed above have made it possible to scale CPU usage of each data node to more than 5x of that possible in MySQL Cluster 7.1. In addition, a number of bottlenecks have been removed, making it possible to scale data node performance by even more than 5x. Figure 2: MySQL Cluster 7.2 Delivers 8.4x Higher Performance than 7.1 The flexAsynch benchmark was used to compare MySQL Cluster 7.2 performance to 7.1 across an 8-node Intel Xeon x5670-based cluster of dual socket commodity servers (6 cores each). As the results demonstrate, MySQL Cluster 7.2 delivers over 8x higher performance per data nodes than MySQL Cluster 7.1. More details of this and other benchmarks will be published in a new whitepaper – coming soon, so stay tuned! In a following blog post, I’ll provide recommendations on optimum thread configurations for different types of server processor. You can also learn more from the Best Practices Guide to Optimizing Performance of MySQL Cluster Conclusion MySQL Cluster has achieved a range of impressive benchmark results, and set in context with the previous 7.1 release, is able to deliver over 8x higher performance per node. As a result, the multi-threaded data node extensions not only serve to increase performance of MySQL Cluster, they also enable users to achieve significantly improved levels of utilization from current and future generations of massively multi-core, multi-thread processor designs.

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  • SQL SERVER – Faster SQL Server Databases and Applications – Power and Control with SafePeak Caching Options

    - by Pinal Dave
    Update: This blog post is written based on the SafePeak, which is available for free download. Today, I’d like to examine more closely one of my preferred technologies for accelerating SQL Server databases, SafePeak. Safepeak’s software provides a variety of advanced data caching options, techniques and tools to accelerate the performance and scalability of SQL Server databases and applications. I’d like to look more closely at some of these options, as some of these capabilities could help you address lagging database and performance on your systems. To better understand the available options, it is best to start by understanding the difference between the usual “Basic Caching” vs. SafePeak’s “Dynamic Caching”. Basic Caching Basic Caching (or the stale and static cache) is an ability to put the results from a query into cache for a certain period of time. It is based on TTL, or Time-to-live, and is designed to stay in cache no matter what happens to the data. For example, although the actual data can be modified due to DML commands (update/insert/delete), the cache will still hold the same obsolete query data. Meaning that with the Basic Caching is really static / stale cache.  As you can tell, this approach has its limitations. Dynamic Caching Dynamic Caching (or the non-stale cache) is an ability to put the results from a query into cache while maintaining the cache transaction awareness looking for possible data modifications. The modifications can come as a result of: DML commands (update/insert/delete), indirect modifications due to triggers on other tables, executions of stored procedures with internal DML commands complex cases of stored procedures with multiple levels of internal stored procedures logic. When data modification commands arrive, the caching system identifies the related cache items and evicts them from cache immediately. In the dynamic caching option the TTL setting still exists, although its importance is reduced, since the main factor for cache invalidation (or cache eviction) become the actual data updates commands. Now that we have a basic understanding of the differences between “basic” and “dynamic” caching, let’s dive in deeper. SafePeak: A comprehensive and versatile caching platform SafePeak comes with a wide range of caching options. Some of SafePeak’s caching options are automated, while others require manual configuration. Together they provide a complete solution for IT and Data managers to reach excellent performance acceleration and application scalability for  a wide range of business cases and applications. Automated caching of SQL Queries: Fully/semi-automated caching of all “read” SQL queries, containing any types of data, including Blobs, XMLs, Texts as well as all other standard data types. SafePeak automatically analyzes the incoming queries, categorizes them into SQL Patterns, identifying directly and indirectly accessed tables, views, functions and stored procedures; Automated caching of Stored Procedures: Fully or semi-automated caching of all read” stored procedures, including procedures with complex sub-procedure logic as well as procedures with complex dynamic SQL code. All procedures are analyzed in advance by SafePeak’s  Metadata-Learning process, their SQL schemas are parsed – resulting with a full understanding of the underlying code, objects dependencies (tables, views, functions, sub-procedures) enabling automated or semi-automated (manually review and activate by a mouse-click) cache activation, with full understanding of the transaction logic for cache real-time invalidation; Transaction aware cache: Automated cache awareness for SQL transactions (SQL and in-procs); Dynamic SQL Caching: Procedures with dynamic SQL are pre-parsed, enabling easy cache configuration, eliminating SQL Server load for parsing time and delivering high response time value even in most complicated use-cases; Fully Automated Caching: SQL Patterns (including SQL queries and stored procedures) that are categorized by SafePeak as “read and deterministic” are automatically activated for caching; Semi-Automated Caching: SQL Patterns categorized as “Read and Non deterministic” are patterns of SQL queries and stored procedures that contain reference to non-deterministic functions, like getdate(). Such SQL Patterns are reviewed by the SafePeak administrator and in usually most of them are activated manually for caching (point and click activation); Fully Dynamic Caching: Automated detection of all dependent tables in each SQL Pattern, with automated real-time eviction of the relevant cache items in the event of “write” commands (a DML or a stored procedure) to one of relevant tables. A default setting; Semi Dynamic Caching: A manual cache configuration option enabling reducing the sensitivity of specific SQL Patterns to “write” commands to certain tables/views. An optimization technique relevant for cases when the query data is either known to be static (like archive order details), or when the application sensitivity to fresh data is not critical and can be stale for short period of time (gaining better performance and reduced load); Scheduled Cache Eviction: A manual cache configuration option enabling scheduling SQL Pattern cache eviction based on certain time(s) during a day. A very useful optimization technique when (for example) certain SQL Patterns can be cached but are time sensitive. Example: “select customers that today is their birthday”, an SQL with getdate() function, which can and should be cached, but the data stays relevant only until 00:00 (midnight); Parsing Exceptions Management: Stored procedures that were not fully parsed by SafePeak (due to too complex dynamic SQL or unfamiliar syntax), are signed as “Dynamic Objects” with highest transaction safety settings (such as: Full global cache eviction, DDL Check = lock cache and check for schema changes, and more). The SafePeak solution points the user to the Dynamic Objects that are important for cache effectiveness, provides easy configuration interface, allowing you to improve cache hits and reduce cache global evictions. Usually this is the first configuration in a deployment; Overriding Settings of Stored Procedures: Override the settings of stored procedures (or other object types) for cache optimization. For example, in case a stored procedure SP1 has an “insert” into table T1, it will not be allowed to be cached. However, it is possible that T1 is just a “logging or instrumentation” table left by developers. By overriding the settings a user can allow caching of the problematic stored procedure; Advanced Cache Warm-Up: Creating an XML-based list of queries and stored procedure (with lists of parameters) for periodically automated pre-fetching and caching. An advanced tool allowing you to handle more rare but very performance sensitive queries pre-fetch them into cache allowing high performance for users’ data access; Configuration Driven by Deep SQL Analytics: All SQL queries are continuously logged and analyzed, providing users with deep SQL Analytics and Performance Monitoring. Reduce troubleshooting from days to minutes with database objects and SQL Patterns heat-map. The performance driven configuration helps you to focus on the most important settings that bring you the highest performance gains. Use of SafePeak SQL Analytics allows continuous performance monitoring and analysis, easy identification of bottlenecks of both real-time and historical data; Cloud Ready: Available for instant deployment on Amazon Web Services (AWS). As you can see, there are many options to configure SafePeak’s SQL Server database and application acceleration caching technology to best fit a lot of situations. If you’re not familiar with their technology, they offer free-trial software you can download that comes with a free “help session” to help get you started. You can access the free trial here. Also, SafePeak is available to use on Amazon Cloud. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Upgrading from 12.10 to 13.04 -> dpkg: error processing sudo (--configure)

    - by Korrigan Nagirrok
    Here's the deal and reason I'm asking for your help. Last night I went on upgrading my Xubuntu 12.10 installation to 13.04, so at tty1 I run the command sudo do-release-upgrade and everything seemed to went well except that after rebooting and when I run sudo apt-get update && sudo apt-get upgrade I get this error: sudo apt-get update && sudo apt-get upgrade Hit http://pt.archive.ubuntu.com raring Release.gpg Hit http://pt.archive.ubuntu.com raring-updates Release.gpg Hit http://dl.google.com stable Release.gpg Hit http://pt.archive.ubuntu.com raring-backports Release.gpg Hit http://pt.archive.ubuntu.com raring Release Hit http://archive.canonical.com raring Release.gpg Hit http://ppa.launchpad.net raring Release.gpg Hit http://pt.archive.ubuntu.com raring-updates Release Hit http://extras.ubuntu.com raring Release.gpg Hit http://pt.archive.ubuntu.com raring-backports Release Hit http://dl.google.com stable Release Hit http://pt.archive.ubuntu.com raring/main Sources Hit http://pt.archive.ubuntu.com raring/restricted Sources Hit http://extras.ubuntu.com raring Release Hit http://archive.canonical.com raring Release Hit http://ppa.launchpad.net raring Release.gpg Hit http://pt.archive.ubuntu.com raring/universe Sources Hit http://pt.archive.ubuntu.com raring/multiverse Sources Hit http://dl.google.com stable/main i386 Packages Get:1 http://security.ubuntu.com raring-security Release.gpg [933 B] Hit http://pt.archive.ubuntu.com raring/main i386 Packages Hit http://extras.ubuntu.com raring/main Sources Hit http://ppa.launchpad.net raring Release Hit http://archive.canonical.com raring/partner i386 Packages Hit http://pt.archive.ubuntu.com raring/restricted i386 Packages Hit http://pt.archive.ubuntu.com raring/universe i386 Packages Hit http://extras.ubuntu.com raring/main i386 Packages Hit http://pt.archive.ubuntu.com raring/multiverse i386 Packages Hit http://ppa.launchpad.net raring Release Hit http://pt.archive.ubuntu.com raring/main Translation-en Hit http://ppa.launchpad.net raring/main Sources Hit http://ppa.launchpad.net raring/main i386 Packages Hit http://pt.archive.ubuntu.com raring/multiverse Translation-en Hit http://pt.archive.ubuntu.com raring/restricted Translation-en Hit http://pt.archive.ubuntu.com raring/universe Translation-en Hit http://pt.archive.ubuntu.com raring-updates/main Sources Hit http://pt.archive.ubuntu.com raring-updates/restricted Sources Hit http://ppa.launchpad.net raring/main Sources Hit http://pt.archive.ubuntu.com raring-updates/universe Sources Hit http://pt.archive.ubuntu.com raring-updates/multiverse Sources Hit http://pt.archive.ubuntu.com raring-updates/main i386 Packages Hit http://ppa.launchpad.net raring/main i386 Packages Hit http://pt.archive.ubuntu.com raring-updates/restricted i386 Packages Hit http://pt.archive.ubuntu.com raring-updates/universe i386 Packages Hit http://pt.archive.ubuntu.com raring-updates/multiverse i386 Packages Ign http://dl.google.com stable/main Translation-en_US Hit http://pt.archive.ubuntu.com raring-updates/main Translation-en Ign http://archive.canonical.com raring/partner Translation-en_US Ign http://extras.ubuntu.com raring/main Translation-en_US Ign http://dl.google.com stable/main Translation-en Ign http://archive.canonical.com raring/partner Translation-en Hit http://pt.archive.ubuntu.com raring-updates/multiverse Translation-en Ign http://extras.ubuntu.com raring/main Translation-en Hit http://pt.archive.ubuntu.com raring-updates/restricted Translation-en Hit http://pt.archive.ubuntu.com raring-updates/universe Translation-en Hit http://pt.archive.ubuntu.com raring-backports/main Sources Hit http://pt.archive.ubuntu.com raring-backports/restricted Sources Hit http://pt.archive.ubuntu.com raring-backports/universe Sources Hit http://pt.archive.ubuntu.com raring-backports/multiverse Sources Hit http://pt.archive.ubuntu.com raring-backports/main i386 Packages Hit http://pt.archive.ubuntu.com raring-backports/restricted i386 Packages Hit http://pt.archive.ubuntu.com raring-backports/universe i386 Packages Hit http://pt.archive.ubuntu.com raring-backports/multiverse i386 Packages Hit http://pt.archive.ubuntu.com raring-backports/main Translation-en Hit http://pt.archive.ubuntu.com raring-backports/multiverse Translation-en Get:2 http://security.ubuntu.com raring-security Release [40.8 kB] Hit http://pt.archive.ubuntu.com raring-backports/restricted Translation-en Hit http://pt.archive.ubuntu.com raring-backports/universe Translation-en Ign http://ppa.launchpad.net raring/main Translation-en_US Ign http://ppa.launchpad.net raring/main Translation-en Get:3 http://security.ubuntu.com raring-security/main Sources [2,109 B] Ign http://ppa.launchpad.net raring/main Translation-en_US Ign http://ppa.launchpad.net raring/main Translation-en Get:4 http://security.ubuntu.com raring-security/restricted Sources [14 B] Get:5 http://security.ubuntu.com raring-security/universe Sources [14 B] Get:6 http://security.ubuntu.com raring-security/multiverse Sources [14 B] Get:7 http://security.ubuntu.com raring-security/main i386 Packages [3,670 B] Get:8 http://security.ubuntu.com raring-security/restricted i386 Packages [14 B] Get:9 http://security.ubuntu.com raring-security/universe i386 Packages [2,824 B] Get:10 http://security.ubuntu.com raring-security/multiverse i386 Packages [14 B] Ign http://pt.archive.ubuntu.com raring/main Translation-en_US Ign http://pt.archive.ubuntu.com raring/multiverse Translation-en_US Ign http://pt.archive.ubuntu.com raring/restricted Translation-en_US Ign http://pt.archive.ubuntu.com raring/universe Translation-en_US Ign http://pt.archive.ubuntu.com raring-updates/main Translation-en_US Ign http://pt.archive.ubuntu.com raring-updates/multiverse Translation-en_US Hit http://security.ubuntu.com raring-security/main Translation-en Ign http://pt.archive.ubuntu.com raring-updates/restricted Translation-en_US Ign http://pt.archive.ubuntu.com raring-updates/universe Translation-en_US Ign http://pt.archive.ubuntu.com raring-backports/main Translation-en_US Ign http://pt.archive.ubuntu.com raring-backports/multiverse Translation-en_US Ign http://pt.archive.ubuntu.com raring-backports/restricted Translation-en_US Hit http://security.ubuntu.com raring-security/multiverse Translation-en Ign http://pt.archive.ubuntu.com raring-backports/universe Translation-en_US Hit http://security.ubuntu.com raring-security/restricted Translation-en Hit http://security.ubuntu.com raring-security/universe Translation-en Ign http://security.ubuntu.com raring-security/main Translation-en_US Ign http://security.ubuntu.com raring-security/multiverse Translation-en_US Ign http://security.ubuntu.com raring-security/restricted Translation-en_US Ign http://security.ubuntu.com raring-security/universe Translation-en_US Fetched 50.4 kB in 6s (7,454 B/s) Reading package lists... Done Reading package lists... Done Building dependency tree Reading state information... Done 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 2 not fully installed or removed. Need to get 0 B/373 kB of archives. After this operation, 0 B of additional disk space will be used. Do you want to continue [Y/n]? Y dpkg: error processing sudo (--configure): Package is in a very bad inconsistent state - you should reinstall it before attempting configuration. No apport report written because MaxReports is reached already dpkg: dependency problems prevent configuration of ubuntu-minimal: ubuntu-minimal depends on sudo; however: Package sudo is not configured yet. dpkg: error processing ubuntu-minimal (--configure): dependency problems - leaving unconfigured No apport report written because MaxReports is reached already Errors were encountered while processing: sudo ubuntu-minimal E: Sub-process /usr/bin/dpkg returned an error code (1) I've tried everything I thought logical, like sudo dpkg --configure -a dpkg: error processing sudo (--configure): Package is in a very bad inconsistent state - you should reinstall it before attempting configuration. dpkg: dependency problems prevent configuration of ubuntu-minimal: ubuntu-minimal depends on sudo; however: Package sudo is not configured yet. dpkg: error processing ubuntu-minimal (--configure): dependency problems - leaving unconfigured Errors were encountered while processing: sudo ubuntu-minimal sudo apt-get install -f Reading package lists... Done Building dependency tree Reading state information... Done 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 2 not fully installed or removed. Need to get 0 B/373 kB of archives. After this operation, 0 B of additional disk space will be used. dpkg: error processing sudo (--configure): Package is in a very bad inconsistent state - you should reinstall it before attempting configuration. dpkg: dependency problems prevent configuration of ubuntu-minimal: ubuntu-minimal depends on sudo; however: Package sudo is not configured yet. dpkg: error processing ubuntu-minimal (--configure): dependency problems - leaving unconfigured No apport report written because MaxReports is reached already No apport report written because MaxReports is reached already Errors were encountered while processing: sudo ubuntu-minimal E: Sub-process /usr/bin/dpkg returned an error code (1) Can someone help me, please. Edit: Here's some more info that could be of help for anyone. The output of apt-cache policy linux-image-generic-pae linux-generic-pae is linux-image-generic-pae: Installed: (none) Candidate: 3.8.0.19.35 Version table: 3.8.0.19.35 0 500 http://pt.archive.ubuntu.com/ubuntu/ raring/main i386 Packages linux-generic-pae: Installed: (none) Candidate: 3.8.0.19.35 Version table: 3.8.0.19.35 0 500 http://pt.archive.ubuntu.com/ubuntu/ raring/main i386 Packages

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