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  • Sales & Technical Tutorials: Updated for OBI, BI-Apps and Hyperion EPM

    - by Mike.Hallett(at)Oracle-BI&EPM
      To get the latest updated OBI, BI-Apps and Hyperion EPM Sales & Technical Tutorials, goto the Oracle Business Intelligence and Enterprise Performance Management library for Partners, a compilation of pre-recorded Oracle BI & EPM online tutorials and webinars that have been delivered recently from Oracle: that you can replay at any time. Sales & Technical Tutorials for OBI, BI-Apps and Hyperion EPM.

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  • 9/18 Live Webcast: Three Compelling Reasons to Upgrade to Oracle Database 11g - Still time to register

    - by jgelhaus
    If you or your organization is still working with Oracle Database 10g or an even older version, now is the time to upgrade. Oracle Database 11g offers a wide variety of advantages to enhance your operation. Join us 10 am PT / 1pm ET September 18th for this live Webcast and learn about what you’re missing: the business, operational, and technical benefits. With Oracle Database 11g, you can: Upgrade with zero downtime Improve application performance and database security Reduce the amount of storage required Save time and money Register today 

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  • On-Demand Webcast: Managing Oracle Exadata with Oracle Enterprise Manager 11g

    - by Scott McNeil
    Watch this on-demand webcast and discover how Oracle Enterprise Manager 11g's unique management capabilities allow you to efficiently manage all stages of Oracle Exadata's lifecycle, from testing applications on Exadata to deployment. You'll learn how to: Maximize and predict database performance Drive down IT operational costs through automation Ensure service quality with proactive management Register today and unlock the potential of Oracle Exadata for your enterprise. Register Now!

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  • Stairway to Server-side Tracing - Level 10: Profiler versus Server-Side tracing

    Compares and contrasts tracing using Profiler with server-side tracing, illustrating important performance differences so that one can choose the right tool for the task at hand. Make working with SQL a breezeSQL Prompt 5.3 is the effortless way to write, edit, and explore SQL. It's packed with features such as code completion, script summaries, and SQL reformatting, that make working with SQL a breeze. Try it now.

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  • Stairway to PowerPivot and DAX - Level 3: The DAX DISTINCT() Function and Basic Distinct Counts

    Bill Pearson, Business Intelligence architect and author, exposes the DAX DISTINCT() function, and then provides some hands-on exposure to its use in generating distinct counts. Moreover, he further explores working with measures in the PivotTable in this, the third Level of our new Stairway to PowerPivot and DAX series. Optimize SQL Server performance“With SQL Monitor, we can be proactive in our optimization process, instead of waiting until a customer reports a problem,” John Trumbul, Sr. Software Engineer. Optimize your servers with a free trial.

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  • A Real-Time HPC Approach for Optimizing Multicore Architectures

    Complex math is at the heart of many of the biggest technical challenges. With multicore processors, the type of calculations that would have required a supercomputer can now be performed in real-time, embedded environments. High-performance computing - Supercomputer - Real-time computing - Operating system - Companies

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  • VirtualBox 4.2.14 is now available

    - by user12611829
    The VirtualBox development team has just released version 4.2.14, and it is now available for download. This is a maintenance release for version 4.2 and contains quite a few fixes. Here is the list from the official Changelog. VMM: another TLB invalidation fix for non-present pages VMM: fixed a performance regression (4.2.8 regression; bug #11674) GUI: fixed a crash on shutdown GUI: prevent stuck keys under certain conditions on Windows hosts (bugs #2613, #6171) VRDP: fixed a rare crash on the guest screen resize VRDP: allow to change VRDP parameters (including enabling/disabling the server) if the VM is paused USB: fixed passing through devices on Mac OS X host to a VM with 2 or more virtual CPUs (bug #7462) USB: fixed hang during isochronous transfer with certain devices (4.1 regression; Windows hosts only; bug #11839) USB: properly handle orphaned URBs (bug #11207) BIOS: fixed function for returning the PCI interrupt routing table (fixes NetWare 6.x guests) BIOS: don't use the ENTER / LEAVE instructions in the BIOS as these don't work in the real mode as set up by certain guests (e.g. Plan 9 and QNX 4) DMI: allow to configure DmiChassisType (bug #11832) Storage: fixed lost writes if iSCSI is used with snapshots and asynchronous I/O (bug #11479) Storage: fixed accessing certain VHDX images created by Windows 8 (bug #11502) Storage: fixed hang when creating a snapshot using Parallels disk images (bug #9617) 3D: seamless + 3D fixes (bug #11723) 3D: version 4.2.12 was not able to read saved states of older versions under certain conditions (bug #11718) Main/Properties: don't create a guest property for non-running VMs if the property does not exist and is about to be removed (bug #11765) Main/Properties: don't forget to make new guest properties persistent after the VM was terminated (bug #11719) Main/Display: don't lose seamless regions during screen resize Main/OVF: don't crash during import if the client forgot to call Appliance::interpret() (bug #10845) Main/OVF: don't create invalid appliances by stripping the file name if the VM name is very long (bug #11814) Main/OVF: don't fail if the appliance contains multiple file references (bug #10689) Main/Metrics: fixed Solaris file descriptor leak Settings: limit depth of snapshot tree to 250 levels, as more will lead to decreased performance and may trigger crashes VBoxManage: fixed setting the parent UUID on diff images using sethdparentuuid Linux hosts: work around for not crashing as a result of automatic NUMA balancing which was introduced in Linux 3.8 (bug #11610) Windows installer: force the installation of the public certificate in background (i.e. completely prevent user interaction) if the --silent command line option is specified Windows Additions: fixed problems with partial install in the unattended case Windows Additions: fixed display glitch with the Start button in seamless mode for some themes Windows Additions: Seamless mode and auto-resize fixes Windows Additions: fixed trying to to retrieve new auto-logon credentials if current ones were not processed yet Windows Additions installer: added the /with_wddm switch to select the experimental WDDM driver by default Linux Additions: fixed setting own timed out and aborted texts in information label of the lightdm greeter Linux Additions: fixed compilation against Linux 3.2.0 Ubuntu kernels (4.2.12 regression as a side effect of the Debian kernel build fix; bug #11709) X11 Additions: reduced the CPU load of VBoxClient in drag'and'drop mode OS/2 Additions: made the mouse wheel work (bug #6793) Guest Additions: fixed problems copying and pasting between two guests on an X11 host (bug #11792) The full changelog can be found here. You can download binaries for Solaris, Linux, Windows and MacOS hosts at http://www.virtualbox.org/wiki/Downloads Technocrati Tags: Oracle Virtualization VirtualBox

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  • SQLAuthority News Three Posts on Reporting T-SQL Tuesday #005

    If you are following my blog, you already know that I am more of T-SQL and Performance Tuning type of person. I do have a good understanding of Business Intelligence suit and I also do certain training sessions on the same subject. When I was writing the blog post for T-SQL Tuesday #005 Reporting, [...]...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Heroku Postgres: A New SQL Database-as-a-Service

    Idera, a Houston-based company known worldwide for its SQL Server solutions in the realms of backup and recovery, performance monitoring, auditing, security, and more, recently announced that it had won five of SQL Server Magazine's 2011 Community Choice Awards. SQL Server Magazine, a publication produced by Penton Media, offers SQL Server users, both beginning and advanced, a host of hands-on information delivered by SQL Server experts. The magazine presented Idera with 2011 Community Choice Awards for five separate products which will only serve to boost the already strong reputation of it...

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  • SQL SERVER – Get 2 of My Books FREE at Koenig Tech Day – Where Technologies Converge!

    - by pinaldave
    As a regular reader of my blog – you must be aware of that I love to write books and talk about various subjects of my book. The founders of Koenig Solutions are my very old friends, I know them for many years. They have been my biggest supporter of my books. Coming weekend they have a technology event at their Bangalore Location. Every attendee of the technology event will get a set of two books worth Rs. 450 – ‘SQL Server Interview Questions And Answers‘ and ‘SQL Wait Stats Joes 2 Pros‘. I am going to cover a couple of topics of the books and present  as well. I am very confident that every attendee will be having a great time. I will be covering following subjects: SQL Server Tricks and Tips for Blazing Fast Performance Slow Running Queries (SQL) are the most common problem that developers face while working with SQL Server. While it is easy to blame the SQL Server for unsatisfactory performance, however the issue often persists with the way queries have been written, and how SQL Server has been set up. The session will focus on the ways of identifying problems that slow down SQL Servers, and tricks to fix them. Developers will walk out with scripts and knowledge that can be applied to their servers, immediately post the session. After the session is over – I will point to what exact location in the book where you can continue for the further learning. I am pretty excited, this is more like book reading but in entire different format. The one day event will cover four technologies in four separate interactive sessions on: Microsoft SQL Server Security VMware/Virtualization ASP.NET MVC Date of the event: Dec 15, 2012 9 AM to 6PM. Location of the event:  Koenig Solutions Ltd. # 47, 4th Block, 100 feet Road, 3rd Floor, Opp to Shanthi Sagar, Koramangala, Bangalore- 560034 Mobile : 09008096122 Office : 080- 41127140 Organizers have informed me that there are very limited seats for this event and technical session based on my book will start at Sharp 9 AM. If you show up late there are chances that you will not get any seats. Registration for the event is a MUST. Please visit this link for further information. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, SQLAuthority News, T SQL, Technology

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  • Differences between TypeScript and Dart

    - by margabit
    Microsoft recently unveiled Typescript, a new JavaScript-like programming language. Some time ago, I heard about Dart, a new programming language created by Google to solve problems related to Javascript like performance, scalability, etc.. The purpose of both new languages seem the same to me.. What do you think? Are the purposes of the languages the same? What are the real differences about them?

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  • Business Strategy - Google Case Study

    Business strategy defined by SMBTN.com is a term used in business planning that implies a careful selection and application of resources to obtain a competitive advantage in anticipation of future events or trends. In more general terms business strategy is positioning a company so that it has the greatest competitive advantage over others in the markets and industries that they participate in. This process involves making corporate decisions regarding which markets to provide goods and services, pricing, acceptable quality levels, and how to interact with others in the marketplace. The primary objective of business strategy is to create and increase value for all of its shareholders and stakeholders through the creation of customer value. According to InformationWeek.com, Google has a distinctive technology advantage over its competitors like Microsoft, eBay, Amazon, Yahoo. Google utilizes custom high-performance systems which are cost efficient because they can scale to extreme workloads. This hardware allows for a huge cost advantage over its competitors. In addition, InformationWeek.com interviewed Stephen Arnold who stated that Google’s programmers are 50%-100% more productive compared to programmers working for their competitors.  He based this theory on Google’s competitors having to spend up to four times as much just to keep up. In addition to Google’s technological advantage, they also have developed a decentralized management schema where employees report directly to multiple managers and team project leaders. This allows for the responsibility of the technology department to be shared amongst multiple senior level engineers and removes the need for a singular department head to oversee the activities of the department.  This is a unique approach from the standard management style. Typically a department head like a CIO or CTO would oversee the department’s global initiatives and business functionality.  This would then be passed down and administered through middle management and implemented by programmers, business analyst, network administrators and Database administrators. It goes without saying that an IT professional’s responsibilities would be directed by Google’s technological advantage and management strategy.  Simply because they work within the department, and would have to design, develop, and support the high-performance systems and would have to report multiple managers and project leaders on a regular basis. Since Google was established and driven by new and immerging technology, all other departments would be directly impacted by the technology department.  In fact, they would have to cater to the technology department since it is a huge driving for in the success of Google. Reference: http://www.smbtn.com/smallbusinessdictionary/#b http://www.informationweek.com/news/software/linux/showArticle.jhtml?articleID=192300292&pgno=1&queryText=&isPrev=

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  • A System Monitoring Tool Primer

    <b>CertCities:</b> "Linux comes with a number of utilities that can be used to monitor one or more of these performance parameters. The following sections introduce a few of these utilities and show how to understand the information presented by them"

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  • Outstanding SQL Saturday

    - by merrillaldrich
    I had the privilege to attend the SQL Saturday held in Redmond today, and it was really outstanding. Among the many sessions, I especially enjoyed and took a lot of useful information away from Greg Larsen’s Dynamic Management Views session, Kalen Delaney’s Compression Session – I am planning to implement 2008 Enterprise compression on my company’s data warehouse later this year – Remus Rusanu’s session on Service Broker to process NAP data, and Matt Masson’s presentation on high performance SSIS...(read more)

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  • NUMA-aware placement of communication variables

    - by Dave
    For classic NUMA-aware programming I'm typically most concerned about simple cold, capacity and compulsory misses and whether we can satisfy the miss by locally connected memory or whether we have to pull the line from its home node over the coherent interconnect -- we'd like to minimize channel contention and conserve interconnect bandwidth. That is, for this style of programming we're quite aware of where memory is homed relative to the threads that will be accessing it. Ideally, a page is collocated on the node with the thread that's expected to most frequently access the page, as simple misses on the page can be satisfied without resorting to transferring the line over the interconnect. The default "first touch" NUMA page placement policy tends to work reasonable well in this regard. When a virtual page is first accessed, the operating system will attempt to provision and map that virtual page to a physical page allocated from the node where the accessing thread is running. It's worth noting that the node-level memory interleaving granularity is usually a multiple of the page size, so we can say that a given page P resides on some node N. That is, the memory underlying a page resides on just one node. But when thinking about accesses to heavily-written communication variables we normally consider what caches the lines underlying such variables might be resident in, and in what states. We want to minimize coherence misses and cache probe activity and interconnect traffic in general. I don't usually give much thought to the location of the home NUMA node underlying such highly shared variables. On a SPARC T5440, for instance, which consists of 4 T2+ processors connected by a central coherence hub, the home node and placement of heavily accessed communication variables has very little impact on performance. The variables are frequently accessed so likely in M-state in some cache, and the location of the home node is of little consequence because a requester can use cache-to-cache transfers to get the line. Or at least that's what I thought. Recently, though, I was exploring a simple shared memory point-to-point communication model where a client writes a request into a request mailbox and then busy-waits on a response variable. It's a simple example of delegation based on message passing. The server polls the request mailbox, and having fetched a new request value, performs some operation and then writes a reply value into the response variable. As noted above, on a T5440 performance is insensitive to the placement of the communication variables -- the request and response mailbox words. But on a Sun/Oracle X4800 I noticed that was not the case and that NUMA placement of the communication variables was actually quite important. For background an X4800 system consists of 8 Intel X7560 Xeons . Each package (socket) has 8 cores with 2 contexts per core, so the system is 8x8x2. Each package is also a NUMA node and has locally attached memory. Every package has 3 point-to-point QPI links for cache coherence, and the system is configured with a twisted ladder "mobius" topology. The cache coherence fabric is glueless -- there's not central arbiter or coherence hub. The maximum distance between any two nodes is just 2 hops over the QPI links. For any given node, 3 other nodes are 1 hop distant and the remaining 4 nodes are 2 hops distant. Using a single request (client) thread and a single response (server) thread, a benchmark harness explored all permutations of NUMA placement for the two threads and the two communication variables, measuring the average round-trip-time and throughput rate between the client and server. In this benchmark the server simply acts as a simple transponder, writing the request value plus 1 back into the reply field, so there's no particular computation phase and we're only measuring communication overheads. In addition to varying the placement of communication variables over pairs of nodes, we also explored variations where both variables were placed on one page (and thus on one node) -- either on the same cache line or different cache lines -- while varying the node where the variables reside along with the placement of the threads. The key observation was that if the client and server threads were on different nodes, then the best placement of variables was to have the request variable (written by the client and read by the server) reside on the same node as the client thread, and to place the response variable (written by the server and read by the client) on the same node as the server. That is, if you have a variable that's to be written by one thread and read by another, it should be homed with the writer thread. For our simple client-server model that means using split request and response communication variables with unidirectional message flow on a given page. This can yield up to twice the throughput of less favorable placement strategies. Our X4800 uses the QPI 1.0 protocol with source-based snooping. Briefly, when node A needs to probe a cache line it fires off snoop requests to all the nodes in the system. Those recipients then forward their response not to the original requester, but to the home node H of the cache line. H waits for and collects the responses, adjudicates and resolves conflicts and ensures memory-model ordering, and then sends a definitive reply back to the original requester A. If some node B needed to transfer the line to A, it will do so by cache-to-cache transfer and let H know about the disposition of the cache line. A needs to wait for the authoritative response from H. So if a thread on node A wants to write a value to be read by a thread on node B, the latency is dependent on the distances between A, B, and H. We observe the best performance when the written-to variable is co-homed with the writer A. That is, we want H and A to be the same node, as the writer doesn't need the home to respond over the QPI link, as the writer and the home reside on the very same node. With architecturally informed placement of communication variables we eliminate at least one QPI hop from the critical path. Newer Intel processors use the QPI 1.1 coherence protocol with home-based snooping. As noted above, under source-snooping a requester broadcasts snoop requests to all nodes. Those nodes send their response to the home node of the location, which provides memory ordering, reconciles conflicts, etc., and then posts a definitive reply to the requester. In home-based snooping the snoop probe goes directly to the home node and are not broadcast. The home node can consult snoop filters -- if present -- and send out requests to retrieve the line if necessary. The 3rd party owner of the line, if any, can respond either to the home or the original requester (or even to both) according to the protocol policies. There are myriad variations that have been implemented, and unfortunately vendor terminology doesn't always agree between vendors or with the academic taxonomy papers. The key is that home-snooping enables the use of a snoop filter to reduce interconnect traffic. And while home-snooping might have a longer critical path (latency) than source-based snooping, it also may require fewer messages and less overall bandwidth. It'll be interesting to reprise these experiments on a platform with home-based snooping. While collecting data I also noticed that there are placement concerns even in the seemingly trivial case when both threads and both variables reside on a single node. Internally, the cores on each X7560 package are connected by an internal ring. (Actually there are multiple contra-rotating rings). And the last-level on-chip cache (LLC) is partitioned in banks or slices, which with each slice being associated with a core on the ring topology. A hardware hash function associates each physical address with a specific home bank. Thus we face distance and topology concerns even for intra-package communications, although the latencies are not nearly the magnitude we see inter-package. I've not seen such communication distance artifacts on the T2+, where the cache banks are connected to the cores via a high-speed crossbar instead of a ring -- communication latencies seem more regular.

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  • External File Upload Optimizations for Windows Azure

    - by rgillen
    [Cross posted from here: http://rob.gillenfamily.net/post/External-File-Upload-Optimizations-for-Windows-Azure.aspx] I’m wrapping up a bit of the work we’ve been doing on data movement optimizations for cloud computing and the latest set of data yielded some interesting points I thought I’d share. The work done here is not really rocket science but may, in some ways, be slightly counter-intuitive and therefore seemed worthy of posting. Summary: for those who don’t like to read detailed posts or don’t have time, the synopsis is that if you are uploading data to Azure, block your data (even down to 1MB) and upload in parallel. Set your block size based on your source file size, but if you must choose a fixed value, use 1MB. Following the above will result in significant performance gains… upwards of 10x-24x and a reduction in overall file transfer time of upwards of 90% (eg, uploading a 1GB file averaged 46.37 minutes prior to optimizations and averaged 1.86 minutes afterwards). Detail: For those of you who want more detail, or think that the claims at the end of the preceding paragraph are over-reaching, what follows is information and code supporting these claims. As the title would indicate, these tests were run from our research facility pointing to the Azure cloud (specifically US North Central as it is physically closest to us) and do not represent intra-cloud results… we have performed intra-cloud tests and the overall results are similar in notion but the data rates are significantly different as well as the tipping points for the various block sizes… this will be detailed separately). We started by building a very simple console application that would loop through a directory and upload each file to Azure storage. This application used the shipping storage client library from the 1.1 version of the azure tools. The only real variation from the client library is that we added code to collect and record the duration (in ms) and size (in bytes) for each file transferred. The code is available here. We then created a directory that had a collection of files for the following sizes: 2KB, 32KB, 64KB, 128KB, 512KB, 1MB, 5MB, 10MB, 25MB, 50MB, 100MB, 250MB, 500MB, 750MB, and 1GB (50 files for each size listed). These files contained randomly-generated binary data and do not benefit from compression (a separate discussion topic). Our file generation tool is available here. The baseline was established by running the application described above against the directory containing all of the data files. This application uploads the files in a random order so as to avoid transferring all of the files of a given size sequentially and thereby spreading the affects of periodic Internet delays across the collection of results.  We then ran some scripts to split the resulting data and generate some reports. The raw data collected for our non-optimized tests is available via the links in the Related Resources section at the bottom of this post. For each file size, we calculated the average upload time (and standard deviation) and the average transfer rate (and standard deviation). As you likely are aware, transferring data across the Internet is susceptible to many transient delays which can cause anomalies in the resulting data. It is for this reason that we randomized the order of source file processing as well as executed the tests 50x for each file size. We expect that these steps will yield a sufficiently balanced set of results. Once the baseline was collected and analyzed, we updated the test harness application with some methods to split the source file into user-defined block sizes and then to upload those blocks in parallel (using the PutBlock() method of Azure storage). The parallelization was handled by simply relying on the Parallel Extensions to .NET to provide a Parallel.For loop (see linked source for specific implementation details in Program.cs, line 173 and following… less than 100 lines total). Once all of the blocks were uploaded, we called PutBlockList() to assemble/commit the file in Azure storage. For each block transferred, the MD5 was calculated and sent ensuring that the bits that arrived matched was was intended. The timer for the blocked/parallelized transfer method wraps the entire process (source file splitting, block transfer, MD5 validation, file committal). A diagram of the process is as follows: We then tested the affects of blocking & parallelizing the transfers by running the updated application against the same source set and did a parameter sweep on the block size including 256KB, 512KB, 1MB, 2MB, and 4MB (our assumption was that anything lower than 256KB wasn’t worth the trouble and 4MB is the maximum size of a block supported by Azure). The raw data for the parallel tests is available via the links in the Related Resources section at the bottom of this post. This data was processed and then compared against the single-threaded / non-optimized transfer numbers and the results were encouraging. The Excel version of the results is available here. Two semi-obvious points need to be made prior to reviewing the data. The first is that if the block size is larger than the source file size you will end up with a “negative optimization” due to the overhead of attempting to block and parallelize. The second is that as the files get smaller, the clock-time cost of blocking and parallelizing (overhead) is more apparent and can tend towards negative optimizations. For this reason (and is supported in the raw data provided in the linked worksheet) the charts and dialog below ignore source file sizes less than 1MB. (click chart for full size image) The chart above illustrates some interesting points about the results: When the block size is smaller than the source file, performance increases but as the block size approaches and then passes the source file size, you see decreasing benefit to the point of negative gains (see the values for the 1MB file size) For some of the moderately-sized source files, small blocks (256KB) are best As the size of the source file gets larger (see values for 50MB and up), the smallest block size is not the most efficient (presumably due, at least in part, to the increased number of blocks, increased number of individual transfer requests, and reassembly/committal costs). Once you pass the 250MB source file size, the difference in rate for 1MB to 4MB blocks is more-or-less constant The 1MB block size gives the best average improvement (~16x) but the optimal approach would be to vary the block size based on the size of the source file.    (click chart for full size image) The above is another view of the same data as the prior chart just with the axis changed (x-axis represents file size and plotted data shows improvement by block size). It again highlights the fact that the 1MB block size is probably the best overall size but highlights the benefits of some of the other block sizes at different source file sizes. This last chart shows the change in total duration of the file uploads based on different block sizes for the source file sizes. Nothing really new here other than this view of the data highlights the negative affects of poorly choosing a block size for smaller files.   Summary What we have found so far is that blocking your file uploads and uploading them in parallel results in significant performance improvements. Further, utilizing extension methods and the Task Parallel Library (.NET 4.0) make short work of altering the shipping client library to provide this functionality while minimizing the amount of change to existing applications that might be using the client library for other interactions.   Related Resources Source code for upload test application Source code for random file generator ODatas feed of raw data from non-optimized transfer tests Experiment Metadata Experiment Datasets 2KB Uploads 32KB Uploads 64KB Uploads 128KB Uploads 256KB Uploads 512KB Uploads 1MB Uploads 5MB Uploads 10MB Uploads 25MB Uploads 50MB Uploads 100MB Uploads 250MB Uploads 500MB Uploads 750MB Uploads 1GB Uploads Raw Data OData feeds of raw data from blocked/parallelized transfer tests Experiment Metadata Experiment Datasets Raw Data 256KB Blocks 512KB Blocks 1MB Blocks 2MB Blocks 4MB Blocks Excel worksheet showing summarizations and comparisons

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  • StreamInsight is in all editions (except express)

    - by simonsabin
    Contrary to many posts and even press releases from Microsoft StreamInsight is not just for Data Center edition. It is available in all paid for editions. If you read the license terms http://go.microsoft.com/fwlink/?LinkID=186261&clcid=0x409 you will see you get StreamInsight in all paid editions. Whats confusing is the performance/limitations in each edition. The only reference I could find of these limitations is here http://blogs.msdn.com/b/streaminsight/archive/2010/02/10/streaminsight-versions...(read more)

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  • T-SQL User-Defined Functions: the good, the bad, and the ugly (part 4)

    - by Hugo Kornelis
    Scalar user-defined functions are bad for performance. I already showed that for T-SQL scalar user-defined functions without and with data access, and for most CLR scalar user-defined functions without data access , and in this blog post I will show that CLR scalar user-defined functions with data access fit into that picture. First attempt Sticking to my simplistic example of finding the triple of an integer value by reading it from a pre-populated lookup table and following the standard recommendations...(read more)

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  • What is new in Oracle SOA Suite 11g R1 PS6? by Shanny Anoep

    - by JuergenKress
    Oracle has released a new version 11.1.1.7.0 for their Oracle Fusion Middleware product line. This version includes Patch Set #6 (PS6) for Oracle SOA Suite 11g R1, with a big list of improvements and fixes for each component in that suite. In this post we will highlight some of the interesting updates with regards to troubleshooting, performance, reliability and scalability. Infrastructure/Purging scripts Database growth is a common problem for large-scale Oracle SOA Suite deployments. Oracle already provides multiple purging strategies for the SOA Suite runtime database. This patch set includes two new scripts for purging most of the runtime data: Table Recreation Script (TRS): This script can be used to reclaim as much database space as possible, while still retaining the open instances. It can be used as a corrective action for databases that grew excessively, for example when purging was not performed at all. This should be used as a single corrective action only; the script does not replace the normal purging scripts. Truncate script: Remove all records from the SOA Suite runtime tables without dropping the tables. This script can be used for cloning SOA Suite environments without copying the instance data, or for recreating test scenarios by cleaning all the runtime data. The Oracle SOA Suite Administrator's guide contains a table with the available purging strategies. Diagnostic dumps Using WLST you could already dump diagnostic information about various components of the SOA Suite. This version adds support to retrieve more information on BPEL and Adapters from the command-line. Diagnostic dumps for BPEL New diagnostic dumps are available for BPEL to get information on thread pools, average processing time for BPEL components, and average waiting times for asynchronous instances. This information can be very useful for performance analysis or troubleshooting. With WLST this information can be retrieved from the command-line and included for monitoring or reporting. Read the full article here. SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Facebook Wiki Mix Forum Technorati Tags: SOA Suite PS6,SOA Community,Oracle SOA,Oracle BPM,Community,OPN,Jürgen Kress

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  • PASS Data Architecture VC presents Neil Hambly on Improve Data Quality & Integrity using Constraints

    On Tuesday June 19th 12PM noon Central, Neil Hambly will discuss "Leveraging the power of constraints to improve both data quality and performance of your databases." What are your servers really trying to tell you? Find out with new SQL Monitor 3.0, an easy-to-use tool built for no-nonsense database professionals.For effortless insights into SQL Server, download a free trial today.

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  • Google I/O 2010 - Google Analytics APIs: End to end

    Google I/O 2010 - Google Analytics APIs: End to end Google I/O 2010 - Google Analytics APIs: End to end Google APIs 201 Nick Mihailovski Google Analytics measures performance of your website. Learn advanced techniques on how to use our tracking, processing and data export APIs as we walk you through an example of creating a most visited pages web element for your website. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 6 0 ratings Time: 55:42 More in Science & Technology

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  • Move Data into the grid for scalable, predictable response times

    - by JuergenKress
    CloudTran is pleased to introduce the availability of the CloudTran Transaction and Persistence Manager for creating scalable, reliable data services on the Oracle Coherence In-Memory Data Grid (IMDG). Use of IMDG architectures has been key to handling today’s web-scale loads because it eliminates database latency by storing important and frequently access data in memory instead of on disk. The CloudTran product lets developers easily use an IMDG for full ACID-compliant transactions without having to be concerned about the location or spread of data. The system has its own implementation of fast, scalable distributed transactions that does NOT depend on XA protocols but still guarantees all ACID properties. Plus, CloudTran asynchronously replicates data going into the IMDG to back-end datastores and back-up data centers, again ensuring ACID properties. CloudTran can be accessed through Java Persistence API (JPA via TopLink Grid) and now, through a new Low-Level API, or LLAPI. This is ideal for use in SOA applications that need data reliability, high availability, performance, and scalability. It is still in its limited beta release, the LLAPI gives developers the ability to use standard put/remove logic available in Coherence and then wrap logic with simple Spring annotations or XML+AspectJ to start transactions. An important feature of LLAPI is the ability to join transactions. This is a common outcome for SOA applications that need to reduce network traffic by aggregating data into single cache entries and then doing SOA service processing in the node holding the data. This results in the need to orchestrate transaction processing across multiple service calls. CloudTran has the capability to handle these “multi-client” transactions at speed with no loss in ACID properties. Developing software around an IMDG like Oracle Coherence is an important choice for today’s web-scale applications and services. But this introduces new architectural considerations to maintain scalability in light of increased network loads and data movement. Without using CloudTran, developers are faced with an incredibly difficult task to ensure data reliability, availability, performance, and scalability when working with an IMDG. Working with highly distributed data that is entirely volatile while stored in memory presents numerous edge cases where failures can result in data loss. The CloudTran product takes care of all of this, leaving developers with the confidence and peace of mind that all data is processed correctly. For those interested in evaluating the CloudTran product and IMDGs, take a look at this link for more information: http://www.CloudTran.com/downloadAPI.ph , or send your questions to [email protected]. SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: CloudTran,data grid,M,SOA Community,Oracle SOA,Oracle BPM,BPM,Community,OPN,Jürgen Kress

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  • Operations Manager SQL monitoring issue?

    - by merrillaldrich
    We're in the early stages of implementing System Center Operations Manager 2007 R2, and from what I've see so far it looks really good. I am still interested to see the depth of performance counter information that it'll collect and store, but haven't been able to really dig into that just yet. There is one issue I am seeing and I don't know if others have come across this (could not find much online about it either): computing a database file free space alert rule is a little complicated, and it...(read more)

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