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  • accessing parsed JSON on the iPhone SDK

    - by itai alter
    Hello All! I've been following the great tutorial about (iPhone, json and Flickr API and I did manage to access the parsed json info just fine. Now I'm trying to do the same thing with the Twitter API, and I am able to get the json info and parse it, but I can't seem to access it like in Flickr. I noticed that the json info that is retrieved from Twitter is a little different from Flickr. The Flickr json info starts straight with a curly braces ({), while the Twitter json info starts with a square bracket and then a curly braces ([{). I understand that it means it's an array inside the json info, but I don't know how to access it. In the Flickr example, I access the objects like so (the second line takes the number of pages Flickr has reported): NSDictionary *results = [jsonString JSONValue]; pagesString = [[results objectForKey:@"photos"] objectForKey:@"pages"]; but I can't seem to access the Twitter response in the same way... Does anyone know of a solution? (here's an example of the Twitter JSON response: api.twitter.com/1/statuses/public_timeline.json ) Thanks a bunch!

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  • NSTimer freezes the app until it gets fired again?

    - by itai alter
    Hello all, I have a simple app with a button, UIImageView and a NSTimer. The timer is fired up every 5 seconds repeatedly to update the ImageView with a new image, while the button simply stops the timer and switches to another View. The problem is that when I press the button, nothing happens for a few seconds (until the timer fires up again). Is there a way to cause the button to stop the timer and do its job at any given time instead of between intervals of the timer? Thanks!

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  • Saving a project as an .ipa

    - by itai alter
    Hello all! I wrote an app for the iPad, but I don't currently own an iPad. I would like to save my project as an .ipa file (assuming it's .ipa for the iPad, like the iPhone) so I could send it to a friend with a Jailbroken iPad to test it on an actual device before I release it to the App Store. Is there any way I can do this? Thanks a bunch!

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  • Stored procedure to remove FK of a given table

    - by Nicole
    I need to create a stored procedure that: Accepts a table name as a parameter Find its dependencies (FKs) Removes them Truncate the table I created the following so far based on http://www.mssqltips.com/sqlservertip/1376/disable-enable-drop-and-recreate-sql-server-foreign-keys/ . My problem is that the following script successfully does 1 and 2 and generates queries to alter tables but does not actually execute them. In another word how can execute the resulting "Alter Table ..." queries to actually remove FKs? CREATE PROCEDURE DropDependencies(@TableName VARCHAR(50)) AS BEGIN SELECT 'ALTER TABLE ' + OBJECT_SCHEMA_NAME(parent_object_id) + '.[' + OBJECT_NAME(parent_object_id) + '] DROP CONSTRAINT ' + name FROM sys.foreign_keys WHERE referenced_object_id=object_id(@TableName) END EXEC DropDependencies 'TableName' Any idea is appreciated! Update: I added the cursor to the SP but I still get and error: "Msg 203, Level 16, State 2, Procedure DropRestoreDependencies, Line 75 The name 'ALTER TABLE [dbo].[ChildTable] DROP CONSTRAINT [FK__ChileTable__ParentTable__745C7C5D]' is not a valid identifier." Here is the updated SP: CREATE PROCEDURE DropRestoreDependencies(@schemaName sysname, @tableName sysname) AS BEGIN SET NOCOUNT ON DECLARE @operation VARCHAR(10) SET @operation = 'DROP' --ENABLE, DISABLE, DROP DECLARE @cmd NVARCHAR(1000) DECLARE @FK_NAME sysname, @FK_OBJECTID INT, @FK_DISABLED INT, @FK_NOT_FOR_REPLICATION INT, @DELETE_RULE smallint, @UPDATE_RULE smallint, @FKTABLE_NAME sysname, @FKTABLE_OWNER sysname, @PKTABLE_NAME sysname, @PKTABLE_OWNER sysname, @FKCOLUMN_NAME sysname, @PKCOLUMN_NAME sysname, @CONSTRAINT_COLID INT DECLARE cursor_fkeys CURSOR FOR SELECT Fk.name, Fk.OBJECT_ID, Fk.is_disabled, Fk.is_not_for_replication, Fk.delete_referential_action, Fk.update_referential_action, OBJECT_NAME(Fk.parent_object_id) AS Fk_table_name, schema_name(Fk.schema_id) AS Fk_table_schema, TbR.name AS Pk_table_name, schema_name(TbR.schema_id) Pk_table_schema FROM sys.foreign_keys Fk LEFT OUTER JOIN sys.tables TbR ON TbR.OBJECT_ID = Fk.referenced_object_id --inner join WHERE TbR.name = @tableName AND schema_name(TbR.schema_id) = @schemaName OPEN cursor_fkeys FETCH NEXT FROM cursor_fkeys INTO @FK_NAME,@FK_OBJECTID, @FK_DISABLED, @FK_NOT_FOR_REPLICATION, @DELETE_RULE, @UPDATE_RULE, @FKTABLE_NAME, @FKTABLE_OWNER, @PKTABLE_NAME, @PKTABLE_OWNER WHILE @@FETCH_STATUS = 0 BEGIN -- create statement for dropping FK and also for recreating FK IF @operation = 'DROP' BEGIN -- drop statement SET @cmd = 'ALTER TABLE [' + @FKTABLE_OWNER + '].[' + @FKTABLE_NAME + '] DROP CONSTRAINT [' + @FK_NAME + ']' EXEC @cmd -- create process DECLARE @FKCOLUMNS VARCHAR(1000), @PKCOLUMNS VARCHAR(1000), @COUNTER INT -- create cursor to get FK columns DECLARE cursor_fkeyCols CURSOR FOR SELECT COL_NAME(Fk.parent_object_id, Fk_Cl.parent_column_id) AS Fk_col_name, COL_NAME(Fk.referenced_object_id, Fk_Cl.referenced_column_id) AS Pk_col_name FROM sys.foreign_keys Fk LEFT OUTER JOIN sys.tables TbR ON TbR.OBJECT_ID = Fk.referenced_object_id INNER JOIN sys.foreign_key_columns Fk_Cl ON Fk_Cl.constraint_object_id = Fk.OBJECT_ID WHERE TbR.name = @tableName AND schema_name(TbR.schema_id) = @schemaName AND Fk_Cl.constraint_object_id = @FK_OBJECTID -- added 6/12/2008 ORDER BY Fk_Cl.constraint_column_id OPEN cursor_fkeyCols FETCH NEXT FROM cursor_fkeyCols INTO @FKCOLUMN_NAME,@PKCOLUMN_NAME SET @COUNTER = 1 SET @FKCOLUMNS = '' SET @PKCOLUMNS = '' WHILE @@FETCH_STATUS = 0 BEGIN IF @COUNTER > 1 BEGIN SET @FKCOLUMNS = @FKCOLUMNS + ',' SET @PKCOLUMNS = @PKCOLUMNS + ',' END SET @FKCOLUMNS = @FKCOLUMNS + '[' + @FKCOLUMN_NAME + ']' SET @PKCOLUMNS = @PKCOLUMNS + '[' + @PKCOLUMN_NAME + ']' SET @COUNTER = @COUNTER + 1 FETCH NEXT FROM cursor_fkeyCols INTO @FKCOLUMN_NAME,@PKCOLUMN_NAME END CLOSE cursor_fkeyCols DEALLOCATE cursor_fkeyCols END FETCH NEXT FROM cursor_fkeys INTO @FK_NAME,@FK_OBJECTID, @FK_DISABLED, @FK_NOT_FOR_REPLICATION, @DELETE_RULE, @UPDATE_RULE, @FKTABLE_NAME, @FKTABLE_OWNER, @PKTABLE_NAME, @PKTABLE_OWNER END CLOSE cursor_fkeys DEALLOCATE cursor_fkeys END For running use: EXEC DropRestoreDependencies dbo, ParentTable

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  • __proto__ of a function

    - by alter
    if I have a class called Person. var Person = function(fname, lname){ this.fname = fname; this.lname = lname; } Person.prototype.mname = "Test"; var p = new Person('Alice','Bob'); Now, p.proto refers to prototype of Person but, when I try to do Person.proto , it points to function(), and Person.constructor points to Function(). can some1 explain what is the difference between function() and Function() and why prototype of a Function() class is a function()

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  • how does an object knows about its parent in javascript

    - by alter
    Lets suppose I made a class called Person. var Person = function(fname){this.fname = fname;}; pObj is the object I made from this class. var pObj = new Person('top'); now I add one property to Person class, say lname. Person.prototype.lname = "Thomsom"; now pObj.lname gets me "Thomson". My question is that, when pObj didn't find the property lname in it, how does it know where to look for.

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  • How do I update mysql database when posting form without using hidden inputs?

    - by user1322707
    I have a "members" table in mysql which has approximately 200 field names. Each user is given up to 7 website templates with 26 different values they can insert unique data into for each template. Each time they create a template, they post the form with the 26 associated values. These 26 field names are the same for each template, but are differentiated by an integer at the end, ie _1, _2, ... _7. In the form submitting the template, I have a variable called $pid_sum which is inserted at the end of each field name to identify which template they are creating. For instance: <form method='post' action='create.template.php'> <input type='hidden' name='address_1' value='address_1'> <input type='hidden' name='city_1' value='city_1'> <input type='hidden' name='state_1' value='state_1'> etc... <input type='hidden' name='address_1' value='address_2'> <input type='hidden' name='city_1' value='city_2'> <input type='hidden' name='state_1' value='state_2'> etc... <input type='hidden' name='address_2' value='address_3'> <input type='hidden' name='city_2' value='city_3'> <input type='hidden' name='state_2' value='state_3'> etc... <input type='hidden' name='address_2' value='address_4'> <input type='hidden' name='city_2' value='city_4'> <input type='hidden' name='state_2' value='state_4'> etc... <input type='hidden' name='address_2' value='address_5'> <input type='hidden' name='city_2' value='city_5'> <input type='hidden' name='state_2' value='state_5'> etc... <input type='hidden' name='address_2' value='address_6'> <input type='hidden' name='city_2' value='city_6'> <input type='hidden' name='state_2' value='state_6'> etc... <input type='hidden' name='address_2' value='address_7'> <input type='hidden' name='city_2' value='city_7'> <input type='hidden' name='state_2' value='state_7'> etc... // Visible form user fills out in creating their template ($pid_sum converts // into an integer 1-7, depending on what template they are filling out) <input type='' name='address_$pid_sum'> <input type='' name='city_$pid_sum'> <input type='' name='state_$pid_sum'> etc... <input type='submit' name='save_button' id='save_button' value='Save Settings'> <form> Each of these need updated in a hidden input tag with each form post, or the values in the database table (which aren't submitted with the form) get deleted. So I am forced to insert approximately 175 hidden input tags with every creation of 26 new values for one of the 7 templates. Is there a PHP function or command that would enable me to update all these values without inserting 175 hidden input tags within each form post? Here is the create.template.php file which the form action calls: <?php $q=new Cdb; $t->set_file("content", "create_template.html"); $q2=new CDB; $query="SELECT menu_category FROM menus WHERE link='create.template.ag.php'"; $q2->query($query); $toall=0; if ($q2->nf()<1) { $toall=1; } while ($q2->next_record()) { if ($q2->f('menu_category')=="main") { $toall=1; } } if ($toall==0) { get_logged_info(); $q2=new CDB; $query="SELECT id FROM menus WHERE link='create_template.php'"; $q2->query($query); $q2->next_record(); $query="SELECT membership_id FROM menu_permissions WHERE menu_item='".$q2->f("id")."'"; $q2->query($query); while ($q2->next_record()) { $permissions[]=$q2->f("membership_id"); } if (count($permissions)>0) { $error='<center><font color="red"><b>You do not have access to this area!<br><br>Upgrade your membership level!</b></font></center>'; foreach ($permissions as $value) { if ($value==$q->f("membership_id")) { $error=''; break; } } if ($error!="") { die("$error"); } } } $member_id=$q->f("id"); $pid=$q->f("pid"); $pid_sum = $pid +1; $first_name=$q->f("first_name"); $last_name=$q->f("last_name"); $email=$q->f("email"); echo " // THIS IS WHERE THE HTML FORM GOES "; replace_tags_t($q->f("id"), $t); ?>

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  • previousFailureCount always stays on 0 (zero)

    - by itai alter
    Hello all, First of all, I'd like to thank the good people who helped me around on this site. Thanks. I'm trying to detect a failed authentication attempt in my app... I'm using the didReceiveAuthenticationChallenge method, and the checking if [challenge previousFailureCount] is equal to 0. The problem is that it's always stays on zero, even if the username and password that I send with the credentials are incorrect. I couldn't find any info about this kind of issue, any help will be much appreciated. Thanks!

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  • Tutorial: Getting Started with the NoSQL JavaScript / Node.js API for MySQL Cluster

    - by Mat Keep
    Tutorial authored by Craig Russell and JD Duncan  The MySQL Cluster team are working on a new NoSQL JavaScript connector for MySQL. The objectives are simplicity and high performance for JavaScript users: - allows end-to-end JavaScript development, from the browser to the server and now to the world's most popular open source database - native "NoSQL" access to the storage layer without going first through SQL transformations and parsing. Node.js is a complete web platform built around JavaScript designed to deliver millions of client connections on commodity hardware. With the MySQL NoSQL Connector for JavaScript, Node.js users can easily add data access and persistence to their web, cloud, social and mobile applications. While the initial implementation is designed to plug and play with Node.js, the actual implementation doesn't depend heavily on Node, potentially enabling wider platform support in the future. Implementation The architecture and user interface of this connector are very different from other MySQL connectors in a major way: it is an asynchronous interface that follows the event model built into Node.js. To make it as easy as possible, we decided to use a domain object model to store the data. This allows for users to query data from the database and have a fully-instantiated object to work with, instead of having to deal with rows and columns of the database. The domain object model can have any user behavior that is desired, with the NoSQL connector providing the data from the database. To make it as fast as possible, we use a direct connection from the user's address space to the database. This approach means that no SQL (pun intended) is needed to get to the data, and no SQL server is between the user and the data. The connector is being developed to be extensible to multiple underlying database technologies, including direct, native access to both the MySQL Cluster "ndb" and InnoDB storage engines. The connector integrates the MySQL Cluster native API library directly within the Node.js platform itself, enabling developers to seamlessly couple their high performance, distributed applications with a high performance, distributed, persistence layer delivering 99.999% availability. The following sections take you through how to connect to MySQL, query the data and how to get started. Connecting to the database A Session is the main user access path to the database. You can get a Session object directly from the connector using the openSession function: var nosql = require("mysql-js"); var dbProperties = {     "implementation" : "ndb",     "database" : "test" }; nosql.openSession(dbProperties, null, onSession); The openSession function calls back into the application upon creating a Session. The Session is then used to create, delete, update, and read objects. Reading data The Session can read data from the database in a number of ways. If you simply want the data from the database, you provide a table name and the key of the row that you want. For example, consider this schema: create table employee (   id int not null primary key,   name varchar(32),   salary float ) ENGINE=ndbcluster; Since the primary key is a number, you can provide the key as a number to the find function. function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find('employee', 0, onData); }; function onData = function(err, data) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(data));   ... use data in application }; If you want to have the data stored in your own domain model, you tell the connector which table your domain model uses, by specifying an annotation, and pass your domain model to the find function. var annotations = new nosql.Annotations(); function Employee = function(id, name, salary) {   this.id = id;   this.name = name;   this.salary = salary;   this.giveRaise = function(percent) {     this.salary *= percent;   } }; annotations.mapClass(Employee, {'table' : 'employee'}); function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData); }; Updating data You can update the emp instance in memory, but to make the raise persistent, you need to write it back to the database, using the update function. function onData = function(err, emp) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp); // oops, session is out of scope here }; Using JavaScript can be tricky because it does not have the concept of block scope for variables. You can create a closure to handle these variables, or use a feature of the connector to remember your variables. The connector api takes a fixed number of parameters and returns a fixed number of result parameters to the callback function. But the connector will keep track of variables for you and return them to the callback. So in the above example, change the onSession function to remember the session variable, and you can refer to it in the onData function: function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData, session); }; function onData = function(err, emp, session) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp, onUpdate); // session is now in scope }; function onUpdate = function(err, emp) {   if (err) {     console.log(err);     ... error handling   } Inserting data Inserting data requires a mapped JavaScript user function (constructor) and a session. Create a variable and persist it: function onSession = function(err, session) {   var data = new Employee(999, 'Mat Keep', 20000000);   session.persist(data, onInsert);   } }; Deleting data To remove data from the database, use the session remove function. You use an instance of the domain object to identify the row you want to remove. Only the key field is relevant. function onSession = function(err, session) {   var key = new Employee(999);   session.remove(Employee, onDelete);   } }; More extensive queries We are working on the implementation of more extensive queries along the lines of the criteria query api. Stay tuned. How to evaluate The MySQL Connector for JavaScript is available for download from labs.mysql.com. Select the build: MySQL-Cluster-NoSQL-Connector-for-Node-js You can also clone the project on GitHub Since it is still early in development, feedback is especially valuable (so don't hesitate to leave comments on this blog, or head to the MySQL Cluster forum). Try it out and see how easy (and fast) it is to integrate MySQL Cluster into your Node.js platforms. You can learn more about other previewed functionality of MySQL Cluster 7.3 here

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  • I thought the new AUTO_SAMPLE_SIZE in Oracle Database 11g looked at all the rows in a table so why do I see a very small sample size on some tables?

    - by Maria Colgan
    I recently got asked this question and thought it was worth a quick blog post to explain in a little more detail what is going on with the new AUTO_SAMPLE_SIZE in Oracle Database 11g and what you should expect to see in the dictionary views. Let’s take the SH.CUSTOMERS table as an example.  There are 55,500 rows in the SH.CUSTOMERS tables. If we gather statistics on the SH.CUSTOMERS using the new AUTO_SAMPLE_SIZE but without collecting histogram we can check what sample size was used by looking in the USER_TABLES and USER_TAB_COL_STATISTICS dictionary views. The sample sized shown in the USER_TABLES is 55,500 rows or the entire table as expected. In USER_TAB_COL_STATISTICS most columns show 55,500 rows as the sample size except for four columns (CUST_SRC_ID, CUST_EFF_TO, CUST_MARTIAL_STATUS, CUST_INCOME_LEVEL ). The CUST_SRC_ID and CUST_EFF_TO columns have no sample size listed because there are only NULL values in these columns and the statistics gathering procedure skips NULL values. The CUST_MARTIAL_STATUS (38,072) and the CUST_INCOME_LEVEL (55,459) columns show less than 55,500 rows as their sample size because of the presence of NULL values in these columns. In the SH.CUSTOMERS table 17,428 rows have a NULL as the value for CUST_MARTIAL_STATUS column (17428+38072 = 55500), while 41 rows have a NULL values for the CUST_INCOME_LEVEL column (41+55459 = 55500). So we can confirm that the new AUTO_SAMPLE_SIZE algorithm will use all non-NULL values when gathering basic table and column level statistics. Now we have clear understanding of what sample size to expect lets include histogram creation as part of the statistics gathering. Again we can look in the USER_TABLES and USER_TAB_COL_STATISTICS dictionary views to find the sample size used. The sample size seen in USER_TABLES is 55,500 rows but if we look at the column statistics we see that it is same as in previous case except  for columns  CUST_POSTAL_CODE and  CUST_CITY_ID. You will also notice that these columns now have histograms created on them. The sample size shown for these columns is not the sample size used to gather the basic column statistics. AUTO_SAMPLE_SIZE still uses all the rows in the table - the NULL rows to gather the basic column statistics (55,500 rows in this case). The size shown is the sample size used to create the histogram on the column. When we create a histogram we try to build it on a sample that has approximately 5,500 non-null values for the column.  Typically all of the histograms required for a table are built from the same sample. In our example the histograms created on CUST_POSTAL_CODE and the CUST_CITY_ID were built on a single sample of ~5,500 (5,450 rows) as these columns contained only non-null values. However, if one or more of the columns that requires a histogram has null values then the sample size maybe increased in order to achieve a sample of 5,500 non-null values for those columns. n addition, if the difference between the number of nulls in the columns varies greatly, we may create multiple samples, one for the columns that have a low number of null values and one for the columns with a high number of null values.  This scheme enables us to get close to 5,500 non-null values for each column. +Maria Colgan

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  • SQL SERVER – Disable Clustered Index and Data Insert

    - by pinaldave
    Earlier today I received following email. “Dear Pinal, [Removed unrelated content] We looked at your script and found out that in your script of disabling indexes, you have only included non-clustered index during the bulk insert and missed to disabled all the clustered index. Our DBA[name removed] has changed your script a bit and included all the clustered indexes. Since our application is not working. When DBA [name removed] tried to enable clustered indexes again he is facing error incorrect syntax error. We are in deep problem [word replaced] [Removed Identity of organization and few unrelated stuff ]“ I have replied to my client and helped them fixed the problem. What really came to my attention is the concept of disabling clustered index. Let us try to learn a lesson from this experience. In this case, there was no need to disable clustered index at all. I had done necessary work when I was called in to work on tuning project. I had removed unused indexes, created few optimal indexes and wrote a script to disable few selected high cost indexes when bulk insert (and similar) operations are performed. There was another script which rebuild all the indexes as well. The solution worked till they included clustered index in disabling the script. Clustered indexes are in fact original table (or heap) physically ordered (any more things – not scope of this article) according to one or more keys(columns). When clustered index is disabled data rows of the disabled clustered index cannot be accessed. This means there will be no insert possible. When non clustered indexes are disabled all the data related to physically deleted but the definition of the index is kept in the system. Due to the same reason even reorganization of the index is not possible till the clustered index (which was disabled) is rebuild. Now let us come to the second part of the question, regarding receiving the error when clustered index is ‘enabled’. This is very common question I receive on the blog. (The following statement is written keeping the syntax of T-SQL in mind) Clustered indexes can be disabled but can not be enabled, they have to rebuild. It is intuitive to think that something which we have ‘disabled’ can be ‘enabled’ but the syntax for the same is ‘rebuild’. This issue has been explained here: SQL SERVER – How to Enable Index – How to Disable Index – Incorrect syntax near ‘ENABLE’. Let us go over this example where inserting the data is not possible when clustered index is disabled. USE AdventureWorks GO -- Create Table CREATE TABLE [dbo].[TableName]( [ID] [int] NOT NULL, [FirstCol] [varchar](50) NULL, CONSTRAINT [PK_TableName] PRIMARY KEY CLUSTERED ([ID] ASC) ) GO -- Create Nonclustered Index CREATE UNIQUE NONCLUSTERED INDEX [IX_NonClustered_TableName] ON [dbo].[TableName] ([FirstCol] ASC) GO -- Populate Table INSERT INTO [dbo].[TableName] SELECT 1, 'First' UNION ALL SELECT 2, 'Second' UNION ALL SELECT 3, 'Third' GO -- Disable Nonclustered Index ALTER INDEX [IX_NonClustered_TableName] ON [dbo].[TableName] DISABLE GO -- Insert Data should work fine INSERT INTO [dbo].[TableName] SELECT 4, 'Fourth' UNION ALL SELECT 5, 'Fifth' GO -- Disable Clustered Index ALTER INDEX [PK_TableName] ON [dbo].[TableName] DISABLE GO -- Insert Data will fail INSERT INTO [dbo].[TableName] SELECT 6, 'Sixth' UNION ALL SELECT 7, 'Seventh' GO /* Error: Msg 8655, Level 16, State 1, Line 1 The query processor is unable to produce a plan because the index 'PK_TableName' on table or view 'TableName' is disabled. */ -- Reorganizing Index will also throw an error ALTER INDEX [PK_TableName] ON [dbo].[TableName] REORGANIZE GO /* Error: Msg 1973, Level 16, State 1, Line 1 Cannot perform the specified operation on disabled index 'PK_TableName' on table 'dbo.TableName'. */ -- Rebuliding should work fine ALTER INDEX [PK_TableName] ON [dbo].[TableName] REBUILD GO -- Insert Data should work fine INSERT INTO [dbo].[TableName] SELECT 6, 'Sixth' UNION ALL SELECT 7, 'Seventh' GO -- Clean Up DROP TABLE [dbo].[TableName] GO I hope this example is clear enough. There were few additional posts I had written years ago, I am listing them here. SQL SERVER – Enable and Disable Index Non Clustered Indexes Using T-SQL SQL SERVER – Enabling Clustered and Non-Clustered Indexes – Interesting Fact Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Constraint and Keys, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SPARC T4-2 Produces World Record Oracle Essbase Aggregate Storage Benchmark Result

    - by Brian
    Significance of Results Oracle's SPARC T4-2 server configured with a Sun Storage F5100 Flash Array and running Oracle Solaris 10 with Oracle Database 11g has achieved exceptional performance for the Oracle Essbase Aggregate Storage Option benchmark. The benchmark has upwards of 1 billion records, 15 dimensions and millions of members. Oracle Essbase is a multi-dimensional online analytical processing (OLAP) server and is well-suited to work well with SPARC T4 servers. The SPARC T4-2 server (2 cpus) running Oracle Essbase 11.1.2.2.100 outperformed the previous published results on Oracle's SPARC Enterprise M5000 server (4 cpus) with Oracle Essbase 11.1.1.3 on Oracle Solaris 10 by 80%, 32% and 2x performance improvement on Data Loading, Default Aggregation and Usage Based Aggregation, respectively. The SPARC T4-2 server with Sun Storage F5100 Flash Array and Oracle Essbase running on Oracle Solaris 10 achieves sub-second query response times for 20,000 users in a 15 dimension database. The SPARC T4-2 server configured with Oracle Essbase was able to aggregate and store values in the database for a 15 dimension cube in 398 minutes with 16 threads and in 484 minutes with 8 threads. The Sun Storage F5100 Flash Array provides more than a 20% improvement out-of-the-box compared to a mid-size fiber channel disk array for default aggregation and user-based aggregation. The Sun Storage F5100 Flash Array with Oracle Essbase provides the best combination for large Oracle Essbase databases leveraging Oracle Solaris ZFS and taking advantage of high bandwidth for faster load and aggregation. Oracle Fusion Middleware provides a family of complete, integrated, hot pluggable and best-of-breed products known for enabling enterprise customers to create and run agile and intelligent business applications. Oracle Essbase's performance demonstrates why so many customers rely on Oracle Fusion Middleware as their foundation for innovation. Performance Landscape System Data Size(millions of items) Database Load(minutes) Default Aggregation(minutes) Usage Based Aggregation(minutes) SPARC T4-2, 2 x SPARC T4 2.85 GHz 1000 149 398* 55 Sun M5000, 4 x SPARC64 VII 2.53 GHz 1000 269 526 115 Sun M5000, 4 x SPARC64 VII 2.4 GHz 400 120 448 18 * – 398 mins with CALCPARALLEL set to 16; 484 mins with CALCPARALLEL threads set to 8 Configuration Summary Hardware Configuration: 1 x SPARC T4-2 2 x 2.85 GHz SPARC T4 processors 128 GB memory 2 x 300 GB 10000 RPM SAS internal disks Storage Configuration: 1 x Sun Storage F5100 Flash Array 40 x 24 GB flash modules SAS HBA with 2 SAS channels Data Storage Scheme Striped - RAID 0 Oracle Solaris ZFS Software Configuration: Oracle Solaris 10 8/11 Installer V 11.1.2.2.100 Oracle Essbase Client v 11.1.2.2.100 Oracle Essbase v 11.1.2.2.100 Oracle Essbase Administration services 64-bit Oracle Database 11g Release 2 (11.2.0.3) HP's Mercury Interactive QuickTest Professional 9.5.0 Benchmark Description The objective of the Oracle Essbase Aggregate Storage Option benchmark is to showcase the ability of Oracle Essbase to scale in terms of user population and data volume for large enterprise deployments. Typical administrative and end-user operations for OLAP applications were simulated to produce benchmark results. The benchmark test results include: Database Load: Time elapsed to build a database including outline and data load. Default Aggregation: Time elapsed to build aggregation. User Based Aggregation: Time elapsed of the aggregate views proposed as a result of tracked retrieval queries. Summary of the data used for this benchmark: 40 flat files, each of size 1.2 GB, 49.4 GB in total 10 million rows per file, 1 billion rows total 28 columns of data per row Database outline has 15 dimensions (five of them are attribute dimensions) Customer dimension has 13.3 million members 3 rule files Key Points and Best Practices The Sun Storage F5100 Flash Array has been used to accelerate the application performance. Setting data load threads (DLTHREADSPREPARE) to 64 and Load Buffer to 6 improved dataloading by about 9%. Factors influencing aggregation materialization performance are "Aggregate Storage Cache" and "Number of Threads" (CALCPARALLEL) for parallel view materialization. The optimal values for this workload on the SPARC T4-2 server were: Aggregate Storage Cache: 32 GB CALCPARALLEL: 16   See Also Oracle Essbase Aggregate Storage Option Benchmark on Oracle's SPARC T4-2 Server oracle.com Oracle Essbase oracle.com OTN SPARC T4-2 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 28 August 2012.

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  • Evaluating Oracle Data Mining Has Never Been Easier - Evaluation "Kit" Available

    - by chberger
    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;} Now you can quickly and easily get set up to starting using Oracle Data Mining for evaluation purposes. Just go to the Oracle Technology Network (OTN) and follow these simple steps. Oracle Data Mining Evaluation "Kit" Instructions Step 1: Download and Install the Oracle Database 11g Release 2 Anyone can download and install the Oracle Database for free for evaluation purposes. Read OTN web site for details. 11.2.0.1.0 DB is the minimum, 11.2.0.2 is better and naturally 11.2.0.3 is best if you are a current customer and on active support. Either 32-bit or 64-bit is fine. 4GB of RAM or more works fine for SQL Developer and the Oracle Data Miner GUI extension. Downloading the database and installing it should take just about an hour or so, depending on your network and computer. For more instructions on setting up Oracle Data Mining see: http://www.oracle.com/technetwork/database/options/odm/dataminerworkflow-168677.html When you install the Oracle Database, the Sample Examples data should also be installed e.g.:Release 2 Examples win32_11gR2_examples.zip (565,154,740 bytes). Contains examples of how to use the Oracle Database. Download if you are new to Oracle and want to try some of the examples presented in the Documentation Step 2: Install SQL Developer 3.1 (the Oracle Data Mining Extension installs automatically) Step 3. Follow the four free step-by-step Oracle-by-Examples e-training lessons: Setting Up Oracle Data Miner 11g Release 2 This tutorial covers the process of setting up Oracle Data Miner 11g Release 2 for use within Oracle SQL Developer 3.0. Using Oracle Data Miner 11g Release 2 This tutorial covers the use of Oracle Data Miner to perform data mining against Oracle Database 11g Release 2. In this lesson, you examine and solve a data mining business problem by using the Oracle Data Miner graphical user interface (GUI). Star Schema Mining Using Oracle Data Miner This tutorial covers the use of Oracle Data Miner to perform star schema mining against Oracle Database 11g Release 2. Text Mining Using Oracle Data Miner This tutorial covers the use of Oracle Data Miner to perform text mining against Oracle Database 11g Release 2. That’s it! Easy, fun and the fastest way to get started evaluating Oracle Data Mining. Enjoy! Charlie

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  • SQL Azure: Notes on Building a Shard Technology

    - by Herve Roggero
    In Chapter 10 of the book on SQL Azure (http://www.apress.com/book/view/9781430229612) I am co-authoring, I am digging deeper in what it takes to write a Shard. It's actually a pretty cool exercise, and I wanted to share some thoughts on how I am designing the technology. A Shard is a technology that spreads the load of database requests over multiple databases, as transparently as possible. The type of shard I am building is called a Vertical Partition Shard  (VPS). A VPS is a mechanism by which the data is stored in one or more databases behind the scenes, but your code has no idea at design time which data is in which database. It's like having a mini cloud for records instead of services. Imagine you have three SQL Azure databases that have the same schema (DB1, DB2 and DB3), you would like to issue a SELECT * FROM Users on all three databases, concatenate the results into a single resultset, and order by last name. Imagine you want to ensure your code doesn't need to change if you add a new database to the shard (DB4). Now imagine that you want to make sure all three databases are queried at the same time, in a multi-threaded manner so your code doesn't have to wait for three database calls sequentially. Then, imagine you would like to obtain a breadcrumb (in the form of a new, virtual column) that gives you a hint as to which database a record came from, so that you could update it if needed. Now imagine all that is done through the standard SqlClient library... and you have the Shard I am currently building. Here are some lessons learned and techniques I am using with this shard: Parellel Processing: Querying databases in parallel is not too hard using the Task Parallel Library; all you need is to lock your resources when needed Deleting/Updating Data: That's not too bad either as long as you have a breadcrumb. However it becomes more difficult if you need to update a single record and you don't know in which database it is. Inserting Data: I am using a round-robin approach in which each new insert request is directed to the next database in the shard. Not sure how to deal with Bulk Loads just yet... Shard Databases:  I use a static collection of SqlConnection objects which needs to be loaded once; from there on all the Shard commands use this collection Extension Methods: In order to make it look like the Shard commands are part of the SqlClient class I use extension methods. For example I added ExecuteShardQuery and ExecuteShardNonQuery methods to SqlClient. Exceptions: Capturing exceptions in a multi-threaded code is interesting... but I kept it simple for now. I am using the ConcurrentQueue to store my exceptions. Database GUID: Every database in the shard is given a GUID, which is calculated based on the connection string's values. DataTable. The Shard methods return a DataTable object which can be bound to objects.  I will be sharing the code soon as an open-source project in CodePlex. Please stay tuned on twitter to know when it will be available (@hroggero). Or check www.bluesyntax.net for updates on the shard. Thanks!

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  • October 2013 Oracle University Round-Up: New Training & Certifications

    - by Breanne Cooley
    Here are the highlights of what is happening this month at Oracle University.  New Technology Overview Courses: Cloud, Big Data and Security Learn about the latest technology solutions that can transform your business. These three Training On Demand courses are taught by industry experts. These courses help you develop an understanding of how Oracle technologies can make a positive impact on your organization.  Oracle Cloud Overview  Oracle Big Data Overview Oracle Security Overview  New Cloud Application Foundation Courses Check out our brand new 12c courses for WebLogic Server administrators and Coherence developers:  Oracle WebLogic Server 12c: Administration I Oracle WebLogic Server 12c: Administration II Oracle Coherence 12c: New Features  Oracle Database 12c Courses Our Oracle Database 12c training is becoming very popular. Here are this month's featured courses:  Oracle Database 12c: New Features for Administrators Oracle Database 12c: Administration Workshop  Oracle Database 12c: Install and Upgrade Workshop Oracle Database 12c: Admin, Install and Upgrade Accelerated  Validate your expertise and add value by earning an Oracle Database 12c Certification.  New Certifications for MySQL Watch our two new videos to find out what's new with Oracle MySQL Certifications. 1) Oracle MySQL 5.6 Certification: What's New for Database Administrators  Recommended training:  MySQL for Beginners MySQL for Database Administrators  2) Oracle MySQL 5.6 Certification: What's New for Developers Recommended training:  MySQL for Beginners MySQL for Developers New Training & Certification for Oracle Applications JD Edwards 9.1 Training Additional JD Edwards Enterprise One 9.1 training is now available for administrators, developers and implementation team members. Cross Application Training  JD Edwards Enterprise One Common Foundation Rel 9.x  Human Capital Management Training  JD Edwards EnterpriseOne Payroll for Canada Rel 9.x JD Edwards EnterpriseOne Payroll for US Rel 9.x JD Edwards EnterpriseOne Payroll Accelerated for Canada Rel 9.x JD Edwards EnterpriseOne Payroll Accelerated for US Rel 9.x  Financial  Management Training  JD Edwards EnterpriseOne Accounts Receivable Rel 9.x JD Edwards EnterpriseOne Financial Report Writing Rel 9.x  Knowledge Management 8.5 Training Oracle Knowledge 8.5 training is now available for analysts interested in learning how to quickly spot trends in content processing and system usage with analytics dashboards. Knowledge Analytics Rel 8.5  Taleo Training Updated Taleo training is now available. Taleo Business Edition (TEE) business users can learn how to create more efficient reports. Recruiters will learn how to efficiently and effectively use Taleo Business Edition (TBE) Recruit.  Taleo (TEE): Advanced Reporting Taleo (TBE): Recruit - End User Fundamentals  New Training for Oracle Retail 13.4.1 Updated training for Retail Predictive Application Server and Retail Demand Forecasting is now available.  RPAS Administration and Configuration Fundamentals RPAS Technical Essentials: Fusion Client 13.4.1 Retail Demand Forecasting (RDF) Business Essentials 13.4.1  View all available training courses, learning paths and certifications at education.oracle.com, or contact your local education representative to learn more about Oracle University's education solutions. See you in class!  -Oracle University Marketing Team 

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  • How Do You Actually Model Data?

    Since the 1970’s Developers, Analysts and DBAs have been able to represent concepts and relations in the form of data through the use of generic symbols.  But what is data modeling?  The first time I actually heard this term I could not understand why anyone would want to display a computer on a fashion show runway. Hey, what do you expect? At that time I was a freshman in community college, and obviously this was a long time ago.  I have since had the chance to learn what data modeling truly is through using it. Data modeling is a process of breaking down information and/or requirements in to common categories called objects. Once objects start being defined then relationships start to form based on dependencies found amongst other existing objects.  Currently, there are several tools on the market that help data designer actually map out objects and their relationships through the use of symbols and lines.  These diagrams allow for designs to be review from several perspectives so that designers can ensure that they have the optimal data design for their project and that the design is flexible enough to allow for potential changes and/or extension in the future. Additionally these basic models can always be further refined to show different levels of details depending on the target audience through the use of three different types of models. Conceptual Data Model(CDM)Conceptual Data Models include all key entities and relationships giving a viewer a high level understanding of attributes. Conceptual data model are created by gathering and analyzing information from various sources pertaining to a project during the typical planning phase of a project. Logical Data Model (LDM)Logical Data Models are conceptual data models that have been expanded to include implementation details pertaining to the data that it will store. Additionally, this model typically represents an origination’s business requirements and business rules by defining various attribute data types and relationships regarding each entity. This additional information can be directly translated to the Physical Data Model which reduces the actual time need to implement it. Physical Data Model(PDMs)Physical Data Model are transformed Logical Data Models that include the necessary tables, columns, relationships, database properties for the creation of a database. This model also allows for considerations regarding performance, indexing and denormalization that are applied through database rules, data integrity. Further expanding on why we actually use models in modern application/database development can be seen in the benefits that data modeling provides for data modelers and projects themselves, Benefits of Data Modeling according to Applied Information Science Abstraction that allows data designers remove concepts and ideas form hard facts in the form of data. This gives the data designers the ability to express general concepts and/or ideas in a generic form through the use of symbols to represent data items and the relationships between the items. Transparency through the use of data models allows complex ideas to be translated in to simple symbols so that the concept can be understood by all viewpoints and limits the amount of confusion and misunderstanding. Effectiveness in regards to tuning a model for acceptable performance while maintaining affordable operational costs. In addition it allows systems to be built on a solid foundation in terms of data. I shudder at the thought of a world without data modeling, think about it? Data is everywhere in our lives. Data modeling allows for optimizing a design for performance and the reduction of duplication. If one was to design a database without data modeling then I would think that the first things to get impacted would be database performance due to poorly designed database and there would be greater chances of unnecessary data duplication that would also play in to the excessive query times because unneeded records would need to be processed. You could say that a data designer designing a database is like a box of chocolates. You will never know what kind of database you will get until after it is built.

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  • To SYNC or not to SYNC – Part 4

    - by AshishRay
    This is Part 4 of a multi-part blog article where we are discussing various aspects of setting up Data Guard synchronous redo transport (SYNC). In Part 1 of this article, I debunked the myth that Data Guard SYNC is similar to a two-phase commit operation. In Part 2, I discussed the various ways that network latency may or may not impact a Data Guard SYNC configuration. In Part 3, I talked in details regarding why Data Guard SYNC is a good thing, and the distance implications you have to keep in mind. In this final article of the series, I will talk about how you can nicely complement Data Guard SYNC with the ability to failover in seconds. Wait - Did I Say “Seconds”? Did I just say that some customers do Data Guard failover in seconds? Yes, Virginia, there is a Santa Claus. Data Guard has an automatic failover capability, aptly called Fast-Start Failover. Initially available with Oracle Database 10g Release 2 for Data Guard SYNC transport mode (and enhanced in Oracle Database 11g to support Data Guard ASYNC transport mode), this capability, managed by Data Guard Broker, lets your Data Guard configuration automatically failover to a designated standby database. Yes, this means no human intervention is required to do the failover. This process is controlled by a low footprint Data Guard Broker client called Observer, which makes sure that the primary database and the designated standby database are behaving like good kids. If something bad were to happen to the primary database, the Observer, after a configurable threshold period, tells that standby, “Your time has come, you are the chosen one!” The standby dutifully follows the Observer directives by assuming the role of the new primary database. The DBA or the Sys Admin doesn’t need to be involved. And - in case you are following this discussion very closely, and are wondering … “Hmmm … what if the old primary is not really dead, but just network isolated from the Observer or the standby - won’t this lead to a split-brain situation?” The answer is No - It Doesn’t. With respect to why-it-doesn’t, I am sure there are some smart DBAs in the audience who can explain the technical reasons. Otherwise - that will be the material for a future blog post. So - this combination of SYNC and Fast-Start Failover is the nirvana of lights-out, integrated HA and DR, as practiced by some of our advanced customers. They have observed failover times (with no data loss) ranging from single-digit seconds to tens of seconds. With this, they support operations in industry verticals such as manufacturing, retail, telecom, Internet, etc. that have the most demanding availability requirements. One of our leading customers with massive cloud deployment initiatives tells us that they know about server failures only after Data Guard has automatically completed the failover process and the app is back up and running! Needless to mention, Data Guard Broker has the integration hooks for interfaces such as JDBC and OCI, or even for custom apps, to ensure the application gets automatically rerouted to the new primary database after the database level failover completes. Net Net? To sum up this multi-part blog article, Data Guard with SYNC redo transport mode, plus Fast-Start Failover, gives you the ideal triple-combo - that is, it gives you the assurance that for critical outages, you can failover your Oracle databases: very fast without human intervention, and without losing any data. In short, it takes the element of risk out of critical IT operations. It does require you to be more careful with your network and systems planning, but as far as HA is concerned, the benefits outweigh the investment costs. So, this is what we in the MAA Development Team believe in. What do you think? How has your deployment experience been? We look forward to hearing from you!

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  • Exadata X3, 11.2.3.2 and Oracle Platinum Services

    - by Rene Kundersma
    Oracle recently announced an Exadata Hardware Update. The overall architecture will remain the same, however some interesting hardware refreshes are done especially for the storage server (X3-2L). Each cell will now have 1600GB of flash, this means an X3-2 full rack will have 20.3 TB of total flash ! For all the details I would like to refer to the Oracle Exadata product page: www.oracle.com/exadata Together with the announcement of the X3 generation. A new Exadata release, 11.2.3.2 is made available. New Exadata systems will be shipped with this release and existing installations can be updated to that release. As always there is a storage cell patch and a patch for the compute node, which again needs to be applied using YUM. Instructions and requirements for patching existing Exadata compute nodes to 11.2.3.2 using YUM can be found in the patch README. Depending on the release you have installed on your compute nodes the README will direct you to a particular section in MOS note 1473002.1. MOS 1473002.1 should only be followed with the instructions from the 11.2.3.2 patch README. Like with 11.2.3.1.0 and 11.2.3.1.1 instructions are added to prepare your systems to use YUM for the first time in case you are still on release 11.2.2.4.2 and earlier. You will also find these One Time Setup instructions in MOS note 1473002.1 By default compute nodes that will be updated to 11.2.3.2.0 will have the UEK kernel. Before 11.2.3.2.0 the 'compatible kernel' was used for the compute nodes. For 11.2.3.2.0 customer will have the choice to replace the UEK kernel with the Exadata standard 'compatible kernel' which is also in the ULN 11.2.3.2 channel. Recommended is to use the kernel that is installed by default. One of the other great new things 11.2.3.2 brings is Writeback Flashcache (wbfc). By default wbfc is disabled after the upgrade to 11.2.3.2. Enable wbfc after patching on the storage servers of your test environment and see the improvements this brings for your applications. Writeback FlashCache can be enabled  by dropping the existing FlashCache, stopping the cellsrv process and changing the FlashCacheMode attribute of the cell. Of course stopping cellsrv can only be done in a controlled manner. Steps: drop flashcache alter cell shutdown services cellsrv again, cellsrv can only be stopped in a controlled manner alter cell flashCacheMode = WriteBack alter cell startup services cellsrv create flashcache all Going back to WriteThrough FlashCache is also possible, but only after flushing the FlashCache: alter cell flashcache all flush Last item I like to highlight in particular is already from a while ago, but a great thing to emphasis: Oracle Platinum Services. On top of the remote fault monitoring with faster response times Oracle has included update and patch deployment services.These services are delivered by Oracle Advanced Customer Support at no additional costs for qualified Oracle Premier Support customers. References: 11.2.3.2.0 README Exadata YUM Repository Population, One-Time Setup Configuration and YUM upgrades  1473002.1 Oracle Platinum Services

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  • Documentation and Test Assertions in Databases

    - by Phil Factor
    When I first worked with Sybase/SQL Server, we thought our databases were impressively large but they were, by today’s standards, pathetically small. We had one script to build the whole database. Every script I ever read was richly annotated; it was more like reading a document. Every table had a comment block, and every line would be commented too. At the end of each routine (e.g. procedure) was a quick integration test, or series of test assertions, to check that nothing in the build was broken. We simply ran the build script, stored in the Version Control System, and it pulled everything together in a logical sequence that not only created the database objects but pulled in the static data. This worked fine at the scale we had. The advantage was that one could, by reading the source code, reach a rapid understanding of how the database worked and how one could interface with it. The problem was that it was a system that meant that only one developer at the time could work on the database. It was very easy for a developer to execute accidentally the entire build script rather than the selected section on which he or she was working, thereby cleansing the database of everyone else’s work-in-progress and data. It soon became the fashion to work at the object level, so that programmers could check out individual views, tables, functions, constraints and rules and work on them independently. It was then that I noticed the trend to generate the source for the VCS retrospectively from the development server. Tables were worst affected. You can, of course, add or delete a table’s columns and constraints retrospectively, which means that the existing source no longer represents the current object. If, after your development work, you generate the source from the live table, then you get no block or line comments, and the source script is sprinkled with silly square-brackets and other confetti, thereby rendering it visually indigestible. Routines, too, were affected. In our system, every routine had a directly attached string of unit-tests. A retro-generated routine has no unit-tests or test assertions. Yes, one can still commit our test code to the VCS but it’s a separate module and teams end up running the whole suite of tests for every individual change, rather than just the tests for that routine, which doesn’t scale for database testing. With Extended properties, one can get the best of both worlds, and even use them to put blame, praise or annotations into your VCS. It requires a lot of work, though, particularly the script to generate the table. The problem is that there are no conventional names beyond ‘MS_Description’ for the special use of extended properties. This makes it difficult to do splendid things such ensuring the integrity of the build by running a suite of tests that are actually stored in extended properties within the database and therefore the VCS. We have lost the readability of database source code over the years, and largely jettisoned the use of test assertions as part of the database build. This is not unexpected in view of the increasing complexity of the structure of databases and number of programmers working on them. There must, surely, be a way of getting them back, but I sometimes wonder if I’m one of very few who miss them.

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  • MySQL for Excel new features (1.2.0): Save and restore Edit sessions

    - by Javier Rivera
    Today we are going to talk about another new feature included in the latest MySQL for Excel release to date (1.2.0) which can be Installed directly from our MySQL Installer downloads page.Since the first release you were allowed to open a session to directly edit data from a MySQL table at Excel on a worksheet and see those changes reflected immediately on the database. You were also capable of opening multiple sessions to work with different tables at the same time (when they belong to the same schema). The problem was that if for any reason you were forced to close Excel or the Workbook you were working on, you had no way to save the state of those open sessions and to continue where you left off you needed to reopen them one by one. Well, that's no longer a problem since we are now introducing a new feature to save and restore active Edit sessions. All you need to do is in click the options button from the main MySQL for Excel panel:  And make sure the Edit Session Options (highlighted in yellow) are set correctly, specially that Restore saved Edit sessions is checked: Then just begin an Edit session like you would normally do, select the connection and schema on the main panel and then select table you want to edit data from and click over Edit MySQL Data. and just import the MySQL data into Excel:You can edit data like you always did with the previous version. To test the save and restore saved sessions functionality, first we need to save the workbook while at least one Edit session is opened and close the file.Then reopen the workbook. Depending on your version of Excel is where the next steps are going to differ:Excel 2013 extra step (first): In Excel 2013 you first need to open the workbook with saved edit sessions, then click the MySQL for Excel Icon on the the Data menu (notice how in this version, every time you open or create a new file the MySQL for Excel panel is closed in the new window). Please note that if you work on Excel 2013 with several workbooks with open edit sessions each at the same time, you'll need to repeat this step each time you open one of them: Following steps:  In Excel 2010 or previous, you just need to make sure the MySQL for Excel panel is already open at this point, if its not, please do the previous step specified above (Excel 2013 extra step). For Excel 2010 or older versions you will only need to do this previous step once.  When saved sessions are detected, you will be prompted what to do with those sessions, you can click Restore to continue working where you left off, click Discard to delete the saved sessions (All edit session information for this file will be deleted from your computer, so you will no longer be prompted the next time you open this same file) or click Nothing to continue without opening saved sessions (This will keep the saved edit sessions intact, to be prompted again about them the next time you open this workbook): And there you have it, now you will be able to save your Edit sessions, close your workbook or turn off your computer and you will still be able to reopen them in the future, to continue working right where you were. Today we talked about how you can save your active Edit sessions and restore them later, this is another feature included in the latest MySQL for Excel release (1.2.0). Please remember you can try this product and many others for free downloading the installer directly from our MySQL Installer downloads page.Happy editing !

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  • Editing files without race conditions?

    - by user2569445
    I have a CSV file that needs to be edited by multiple processes at the same time. My question is, how can I do this without introducing race conditions? It's easy to write to the end of the file without race conditions by open(2)ing it in "a" (O_APPEND) mode and simply write to it. Things get more difficult when removing lines from the file. The easiest solution is to read the file into memory, make changes to it, and overwrite it back to the file. If another process writes to it after it is in memory, however, that new data will be lost upon overwriting. To further complicate matters, my platform does not support POSIX record locks, checking for file existence is a race condition waiting to happen, rename(2) replaces the destination file if it exists instead of failing, and editing files in-place leaves empty bytes in it unless the remaining bytes are shifted towards the beginning of the file. My idea for removing a line is this (in pseudocode): filename = "/home/user/somefile"; file = open(filename, "r"); tmp = open(filename+".tmp", "ax") || die("could not create tmp file"); //"a" is O_APPEND, "x" is O_EXCL|O_CREAT while(write(tmp, read(file)); //copy the $file to $file+".new" close(file); //edit tmp file unlink(filename) || die("could not unlink file"); file = open(filename, "wx") || die("another process must have written to the file after we copied it."); //"w" is overwrite, "x" is force file creation while(write(file, read(tmp))); //copy ".tmp" back to the original file unlink(filename+".tmp") || die("could not unlink tmp file"); Or would I be better off with a simple lock file? Appender process: lock = open(filename+".lock", "wx") || die("could not lock file"); file = open(filename, "a"); write(file, "stuff"); close(file); close(lock); unlink(filename+".lock"); Editor process: lock = open(filename+".lock", "wx") || die("could not lock file"); file = open(filename, "rw"); while(contents += read(file)); //edit "contents" write(file, contents); close(file); close(lock); unlink(filename+".lock"); Both of these rely on an additional file that will be left over if a process terminates before unlinking it, causing other processes to refuse to write to the original file. In my opinion, these problems are brought on by the fact that the OS allows multiple writable file descriptors to be opened on the same file at the same time, instead of failing if a writable file descriptor is already open. It seems that O_CREAT|O_EXCL is the closest thing to a real solution for preventing filesystem race conditions, aside from POSIX record locks. Another possible solution is to separate the file into multiple files and directories, so that more granular control can be gained over components (lines, fields) of the file using O_CREAT|O_EXCL. For example, "file/$id/$field" would contain the value of column $field of the line $id. It wouldn't be a CSV file anymore, but it might just work. Yes, I know I should be using a database for this as databases are built to handle these types of problems, but the program is relatively simple and I was hoping to avoid the overhead. So, would any of these patterns work? Is there a better way? Any insight into these kinds of problems would be appreciated.

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  • 11??OTN????????

    - by OTN-J Master
    11??OTN???????????????????????????????????????????????????????????????????????????????????????URL?????????????????https://blogs.oracle.com/otnjp/category/Event ????????????? [11/14(?)??]  WebLogic Server??????????? & ???????????????? [11/21(?)??] Oracle Database Appliance ???????? [11/22(?)??] ?30? WebLogic Server???? 11?20?????DBA & Developer Day 2012?????????????????????????????????OTN???????????????????????????????????????????????????OTN???????????????????????????????>>??????????????????(oracle.com???)??????????????? [11/ 9(?)??]  JavaOne 2012 San Francisco ???  (??Java????????) [11/10(?)??] JJUG ???????????????? 2012 Fall (??Java????????)[11/28(?)??] 90?????!Oracle Database??????????????? (????????) JavaOne 2012 San Francisco ??? (??Java????????) ???: 11?9?(?)13:00~19:00???: ??(???·???????) ???: ???2012?9?30???10?4?????????????????JavaOne 2012?????????JavaOne?????????????????????????????????!>> ??????????? ?????? JJUG ???????????????? 2012 Fall (??Java????????)???: 11?10?(?) 10:00~19:15???: ??(??:???????) ???: ??Java??????????????????????????? 2012 Fall(??:JJUG CCC 2012 Fall)?????????Java????????????????????????????????????????????CCC?????????????????????????????????????????????! >>??·???????? ?????? ?93? ????! ???????? -WebLogic Server??????????? & ???????????????? ???: 11?14?(?)18:30 ~20:30???: ??(?????? ????????????) ???: 18?????????????????????! ????????????????????????????!???????????WebLogic Server ??????????????????Java???????????????WebLogic Server?JRockit????????????????????????????????????????????????????????????????????????????????WebLogic Server????????????????????????????????2???????????????WebLogic Server???????????????????????????????????????JDBC?????????????WebLogic Server??????????????????????????????????????????????????????????????????????????????????????????????????????????????????iPad??????????????????????????????????WebLogic Server???Oracle JDeveloper????? ?Oracle Application Development Framework (ADF)????????? ?WebCenter Framework ????????????????????????????????????????????????????????????????????Java EE??????WebLogic Server?????????????????????????????????????????????????????????????????????????????????????????????????????Java EE6???????????? >> ??·???????? ??????  Oracle Database Appliance ???????? ???: 11?21?(?)15:30 ~ 17:00???: ??(?????????? ?? 13F???????) ???:??????????????????????????????????·?????Oracle Database Appliance??????????????????????????????????????????????????????????????????·?????Oracle Database Appliance ?????????????????????? >>??????????? ?????? ?30? WebLogic Server??? ???: 11?22?(?)18:30~20:40???: ??(????????????) ???:?????WebLogic Server?????:??????JSF2.0????2???????????????WebLogic Server????????????????2?????????????????????·???????????????????? WebLogic Server???????????????????????????????WebLogic Server????????????????TIPS?????????WebLogic Server???????????????????????????JSF2.0???????Java EE 6?????JSF2.0???????????????JSF2.0????????????????JSF2.0????????RIA(??????????????????)????????????????JSF2.0??????Java EE 6?????????Web???????????????????????????????????????????????????WebLogic Server????????????????????????????????????WebLogic Server????????????WebLogic Server?????????????????????????????????????!>>??????????? ?????? 90?????!Oracle Database??????????????? [????????] ???: 11?28?(?) 19:00~20:30???: ??(??????????) ???:Oracle Database????????·?????????????????????Oracle Database??????/????????????????- ???????????????????????????????????? ?????- ???????????????????Oracle Database???????? ?????????????¦???????????¦???????????¦???????????(NetCA, Net Manager)¦???????¦Oracle?????????¦??????????????>> ??·???????? ??????

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  • Fulltext search for django : Mysql not so bad ? (vs sphinx, xapian)

    - by Eric
    I am studying fulltext search engines for django. It must be simple to install, fast indexing, fast index update, not blocking while indexing, fast search. After reading many web pages, I put in short list : Mysql MYISAM fulltext, djapian/python-xapian, and django-sphinx I did not choose lucene because it seems complex, nor haystack as it has less features than djapian/django-sphinx (like fields weighting). Then I made some benchmarks, to do so, I collected many free books on the net to generate a database table with 1 485 000 records (id,title,body), each record is about 600 bytes long. From the database, I also generated a list of 100 000 existing words and shuffled them to create a search list. For the tests, I made 2 runs on my laptop (4Go RAM, Dual core 2.0Ghz): the first one, just after a server reboot to clear all caches, the second is done juste after in order to test how good are cached results. Here are the "home made" benchmark results : 1485000 records with Title (150 bytes) and body (450 bytes) Mysql 5.0.75/Ubuntu 9.04 Fulltext : ========================================================================== Full indexing : 7m14.146s 1 thread, 1000 searchs with single word randomly taken from database : First run : 0:01:11.553524 next run : 0:00:00.168508 Mysql 5.5.4 m3/Ubuntu 9.04 Fulltext : ========================================================================== Full indexing : 6m08.154s 1 thread, 1000 searchs with single word randomly taken from database : First run : 0:01:11.553524 next run : 0:00:00.168508 1 thread, 100000 searchs with single word randomly taken from database : First run : 9m09s next run : 5m38s 1 thread, 10000 random strings (random strings should not be found in database) : just after the 100000 search test : 0:00:15.007353 1 thread, boolean search : 1000 x (+word1 +word2) First run : 0:00:21.205404 next run : 0:00:00.145098 Djapian Fulltext : ========================================================================== Full indexing : 84m7.601s 1 thread, 1000 searchs with single word randomly taken from database with prefetch : First run : 0:02:28.085680 next run : 0:00:14.300236 python-xapian Fulltext : ========================================================================== 1 thread, 1000 searchs with single word randomly taken from database : First run : 0:01:26.402084 next run : 0:00:00.695092 django-sphinx Fulltext : ========================================================================== Full indexing : 1m25.957s 1 thread, 1000 searchs with single word randomly taken from database : First run : 0:01:30.073001 next run : 0:00:05.203294 1 thread, 100000 searchs with single word randomly taken from database : First run : 12m48s next run : 9m45s 1 thread, 10000 random strings (random strings should not be found in database) : just after the 100000 search test : 0:00:23.535319 1 thread, boolean search : 1000 x (word1 word2) First run : 0:00:20.856486 next run : 0:00:03.005416 As you can see, Mysql is not so bad at all for fulltext search. In addition, its query cache is very efficient. Mysql seems to me a good choice as there is nothing to install (I need just to write a small script to synchronize an Innodb production table to a MyISAM search table) and as I do not really need advanced search feature like stemming etc... Here is the question : What do you think about Mysql fulltext search engine vs sphinx and xapian ?

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