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• Oracle’s New Memory-Optimized x86 Servers: Getting the Most Out of Oracle Database In-Memory

  - by Josh Rosen, x86 Product Manager-Oracle

  With the launch of Oracle Database In-Memory, it is now possible to perform real-time analytics operations on your business data as it exists at that moment – in the DRAM of the server – and immediately return completely current and consistent data. The Oracle Database In-Memory option dramatically accelerates the performance of analytics queries by storing data in a highly optimized columnar in-memory format.  This is a truly exciting advance in database technology.As Larry Ellison mentioned in his recent webcast about Oracle Database In-Memory, queries run 100 times faster simply by throwing a switch.  But in order to get the most from the Oracle Database In-Memory option, the underlying server must also be memory-optimized. This week Oracle announced new 4-socket and 8-socket x86 servers, the Sun Server X4-4 and Sun Server X4-8, both of which have been designed specifically for Oracle Database In-Memory.  These new servers use the fastest Intel® Xeon® E7 v2 processors and each subsystem has been designed to be the best for Oracle Database, from the memory, I/O and flash technologies right down to the system firmware.Amongst these subsystems, one of the most important aspects we have optimized with the Sun Server X4-4 and Sun Server X4-8 are their memory subsystems.  The new In-Memory option makes it possible to select which parts of the database should be memory optimized.  You can choose to put a single column or table in memory or, if you can, put the whole database in memory.  The more, the better.  With 3 TB and 6 TB total memory capacity on the Sun Server X4-4 and Sun Server X4-8, respectively, you can memory-optimize more, if not your entire database.   Sun Server X4-8 CMOD with 24 DIMM slots per socket (up to 192 DIMM slots per server) But memory capacity is not the only important factor in selecting the best server platform for Oracle Database In-Memory.  As you put more of your database in memory, a critical performance metric known as memory bandwidth comes into play.  The total memory bandwidth for the server will dictate the rate in which data can be stored and retrieved from memory.  In order to achieve real-time analysis of your data using Oracle Database In-Memory, even under heavy load, the server must be able to handle extreme memory workloads.  With that in mind, the Sun Server X4-8 was designed with the maximum possible memory bandwidth, providing over a terabyte per second of total memory bandwidth.  Likewise, the Sun Server X4-4 also provides extreme memory bandwidth in an even more compact form factor with over half a terabyte per second, providing customers with scalability and choice depending on the size of the database.Beyond the memory subsystem, Oracle’s Sun Server X4-4 and Sun Server X4-8 systems provide other key technologies that enable Oracle Database to run at its best.  The Sun Server X4-4 allows for up 4.8 TB of internal, write-optimized PCIe flash while the Sun Server X4-8 allows for up to 6.4 TB of PCIe flash.  This enables dramatic acceleration of data inserts and updates to Oracle Database.  And with the new elastic computing capability of Oracle’s new x86 servers, server performance can be adapted to your specific Oracle Database workload to ensure that every last bit of processing power is utilized.Because Oracle designs and tests its x86 servers specifically for Oracle workloads, we provide the highest possible performance and reliability when running Oracle Database.  To learn more about Sun Server X4-4 and Sun Server X4-8, you can find more details including data sheets and white papers here. Josh Rosen is a Principal Product Manager for Oracle’s x86 servers, focusing on Oracle’s operating systems and software.  He previously spent more than a decade as a developer and architect of system management software. Josh has worked on system management for many of Oracle's hardware products ranging from the earliest blade systems to the latest Oracle x86 servers. 

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• SQL SERVER – Weekend Project – Experimenting with ACID Transactions, SQL Compliant, Elastically Scalable Database

  - by pinaldave

  Database technology is huge and big world. I like to explore always beyond what I know and share the learning. Weekend is the best time when I sit around download random software on my machine which I like to call as a lab machine (it is a pretty old laptop, hardly a quality as lab machine) and experiment it. There are so many free betas available for download that it’s hard to keep track and even harder to find the time to play with very many of them.  This blog is about one you shouldn’t miss if you are interested in the learning various relational databases. NuoDB just released their Beta 7.  I had already downloaded their Beta 6 and yesterday did the same for 7.   My impression is that they are onto something very very interesting.  In fact, it might be something really promising in terms of database elasticity, scale and operational cost reduction. The folks at NuoDB say they are working on the world’s first “emergent” database which they tout as a brand new transitional database that is intended to dramatically change what’s possible with OLTP.  It is SQL compliant, guarantees ACID transactions, yet scales elastically on heterogeneous and decentralized cloud-based resources. Interesting note for sure, making me explore more. Based on what I’ve seen so far, they are solving the architectural challenge that exists between elastic, cloud-based compute infrastructures designed to scale out in response to workload requirements versus the traditional relational database management system’s architecture of central control. Here’s my experience with the NuoDB Beta 6 so far: First they pretty much threw away all the features you’d associate with existing RDBMS architectures except the SQL and ACID transactions which they were smart to keep.  It looks like they have incorporated a number of the big ideas from various algorithms, systems and techniques to achieve maximum DB scalability. From a user’s perspective, the NuoDB Beta software behaves like any other traditional SQL database and seems to offer all the benefits users have come to expect from standards-based SQL solutions. One of the interesting feature is that one can run a transactional node and a storage node on my Windows laptop as well on other platforms – indeed interesting for sure. It’s quite amazing to see a database elastically scale across machine boundaries. So, one of the basic NuoDB concepts is that as you need to scale out, you can easily use more inexpensive hardware when/where you need it.  This is unlike what we have traditionally done to scale a database for an application – we replace the hardware with something more powerful (faster CPU and Disks). This is where I started to feel like NuoDB is on to something that has the potential to elastically scale on commodity hardware while reducing operational expense for a big OLTP database to a degree we’ve never seen before. NuoDB is able to fully leverage the cloud in an asynchronous and highly decentralized manner – while providing both SQL compliance and ACID transactions. Basically what NuoDB is doing is so new that it is all hard to believe until you’ve experienced it in action.  I will keep you up to date as I test the NuoDB Beta 7 but if you are developing a web-scale application or have an on-premise app you are thinking of moving to the cloud, testing this beta is worth your time. If you do try it, let me know what you think.  Before I say anything more, I am going to do more experiments and more test on this product and compare it with other existing similar products. For me it was a weekend worth spent on learning something new. I encourage you to download Beta 7 version and share your opinions here. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• SQL SERVER – CXPACKET – Parallelism – Advanced Solution – Wait Type – Day 7 of 28

  - by pinaldave

  Earlier we discussed about the what is the common solution to solve the issue with CXPACKET wait time. Today I am going to talk about few of the other suggestions which can help to reduce the CXPACKET wait. If you are going to suggest that I should focus on MAXDOP and COST THRESHOLD – I totally agree. I have covered them in details in yesterday’s blog post. Today we are going to discuss few other way CXPACKET can be reduced. Potential Reasons: If data is heavily skewed, there are chances that query optimizer may estimate the correct amount of the data leading to assign fewer thread to query. This can easily lead to uneven workload on threads and may create CXPAKCET wait. While retrieving the data one of the thread face IO, Memory or CPU bottleneck and have to wait to get those resources to execute its tasks, may create CXPACKET wait as well. Data which is retrieved is on different speed IO Subsystem. (This is not common and hardly possible but there are chances). Higher fragmentations in some area of the table can lead less data per page. This may lead to CXPACKET wait. As I said the reasons here mentioned are not the major cause of the CXPACKET wait but any kind of scenario can create the probable wait time. Best Practices to Reduce CXPACKET wait: Refer earlier article regarding MAXDOP and Cost Threshold. De-fragmentation of Index can help as more data can be obtained per page. (Assuming close to 100 fill-factor) If data is on multiple files which are on multiple similar speed physical drive, the CXPACKET wait may reduce. Keep the statistics updated, as this will give better estimate to query optimizer when assigning threads and dividing the data among available threads. Updating statistics can significantly improve the strength of the query optimizer to render proper execution plan. This may overall affect the parallelism process in positive way. Bad Practice: In one of the recent consultancy project, when I was called in I noticed that one of the ‘experienced’ DBA noticed higher CXPACKET wait and to reduce them, he has increased the worker threads. The reality was increasing worker thread has lead to many other issues. With more number of the threads, more amount of memory was used leading memory pressure. As there were more threads CPU scheduler faced higher ‘Context Switching’ leading further degrading performance. When I explained all these to ‘experienced’ DBA he suggested that now we should reduce the number of threads. Not really! Lower number of the threads may create heavy stalling for parallel queries. I suggest NOT to touch the setting of number of the threads when dealing with CXPACKET wait. Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest reading book on-line for further clarification. All the discussion of Wait Stats over here is generic and it varies by system to system. You are recommended to test this on development server before implementing to production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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• Why your Netapp is so slow...

  - by Darius Zanganeh

  Have you ever wondered why your Netapp FAS box is slow and doesn't perform well at large block workloads?  In this blog entry I will give you a little bit of information that will probably help you understand why it’s so slow, why you shouldn't use it for applications that read and write in large blocks like 64k, 128k, 256k ++ etc..  Of course since I work for Oracle at this time, I will show you why the ZS3 storage boxes are excellent choices for these types of workloads. Netapp’s Fundamental Problem The fundamental problem you have running these workloads on Netapp is the backend block size of their WAFL file system.  Every application block on a Netapp FAS ends up in a 4k chunk on a disk. Reference:  Netapp TR-3001 Whitepaper Netapp has proven this lacking large block performance fact in at least two different ways. They have NEVER posted an SPC-2 Benchmark yet they have posted SPC-1 and SPECSFS, both recently. In 2011 they purchased Engenio to try and fill this GAP in their portfolio. Block Size Matters So why does block size matter anyways?  Many applications use large block chunks of data especially in the Big Data movement.  Some examples are SAS Business Analytics, Microsoft SQL, Hadoop HDFS is even 64MB! Now let me boil this down for you.  If an application such MS SQL is writing data in a 64k chunk then before Netapp actually writes it on disk it will have to split it into 16 different 4k writes and 16 different disk IOPS.  When the application later goes to read that 64k chunk the Netapp will have to again do 16 different disk IOPS.  In comparison the ZS3 Storage Appliance can write in variable block sizes ranging from 512b to 1MB.  So if you put the same MSSQL database on a ZS3 you can set the specific LUNs for this database to 64k and then when you do an application read/write it requires only a single disk IO.  That is 16x faster!  But, back to the problem with your Netapp, you will VERY quickly run out of disk IO and hit a wall.  Now all arrays will have some fancy pre fetch algorithm and some nice cache and maybe even flash based cache such as a PAM card in your Netapp but with large block workloads you will usually blow through the cache and still need significant disk IO.  Also because these datasets are usually very large and usually not dedupable they are usually not good candidates for an all flash system.  You can do some simple math in excel and very quickly you will see why it matters.  Here are a couple of READ examples using SAS and MSSQL.  Assume these are the READ IOPS the application needs even after all the fancy cache and algorithms.   Here is an example with 128k blocks.  Notice the numbers of drives on the Netapp! Here is an example with 64k blocks You can easily see that the Oracle ZS3 can do dramatically more work with dramatically less drives.  This doesn't even take into account that the ONTAP system will likely run out of CPU way before you get to these drive numbers so you be buying many more controllers.  So with all that said, lets look at the ZS3 and why you should consider it for any workload your running on Netapp today.  ZS3 World Record Price/Performance in the SPC-2 benchmark ZS3-2 is #1 in Price Performance $12.08ZS3-2 is #3 in Overall Performance 16,212 MBPS Note: The number one overall spot in the world is held by an AFA 33,477 MBPS but at a Price Performance of $29.79.  A customer could purchase 2 x ZS3-2 systems in the benchmark with relatively the same performance and walk away with $600,000 in their pocket.

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• SQL SERVER – Simple Demo of New Cardinality Estimation Features of SQL Server 2014

  - by Pinal Dave

  SQL Server 2014 has new cardinality estimation logic/algorithm. The cardinality estimation logic is responsible for quality of query plans and majorly responsible for improving performance for any query. This logic was not updated for quite a while, but in the latest version of SQL Server 2104 this logic is re-designed. The new logic now incorporates various assumptions and algorithms of OLTP and warehousing workload. Cardinality estimates are a prediction of the number of rows in the query result. The query optimizer uses these estimates to choose a plan for executing the query. The quality of the query plan has a direct impact on improving query performance. ~ Souce MSDN Let us see a quick example of how cardinality improves performance for a query. I will be using the AdventureWorks database for my example. Before we start with this demonstration, remember that even though you have SQL Server 2014 to see the effect of new cardinality estimates, you will need your database compatibility mode set to 120 which is for SQL Server 2014. If your server instance of SQL Server 2014 but you have set up your database compatibility mode to 110 or any other earlier version, you will get performance from your query like older version of SQL Server. Now we will execute following query in two different compatibility mode and see its performance. (Note that my SQL Server instance is of version 2014). USE AdventureWorks2014 GO -- ------------------------------- -- NEW Cardinality Estimation ALTER DATABASE AdventureWorks2014 SET COMPATIBILITY_LEVEL = 120 GO EXEC [dbo].[uspGetManagerEmployees] 44 GO -- ------------------------------- -- Old Cardinality Estimation ALTER DATABASE AdventureWorks2014 SET COMPATIBILITY_LEVEL = 110 GO EXEC [dbo].[uspGetManagerEmployees] 44 GO Result of Statistics IO Compatibility level 120 Table ‘Person’. Scan count 0, logical reads 6, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table ‘Employee’. Scan count 2, logical reads 7, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table ‘Worktable’. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table ‘Worktable’. Scan count 2, logical reads 7, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Compatibility level 110 Table ‘Worktable’. Scan count 2, logical reads 7, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table ‘Person’. Scan count 0, logical reads 137, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table ‘Employee’. Scan count 2, logical reads 7, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table ‘Worktable’. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. You will notice in the case of compatibility level 110 there 137 logical read from table person where as in the case of compatibility level 120 there are only 6 physical reads from table person. This drastically improves the performance of the query. If we enable execution plan, we can see the same as well. I hope you will find this quick example helpful. You can read more about this in my latest Pluralsight Course. Reference: Pinal Dave (http://blog.SQLAuthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Part 9: EBS Customizations, how to track

  - by volker.eckardt(at)oracle.com

  In the previous blogs we were concentrating on the preparation tasks. We have defined standards, we know about the tools and techniques we will start with. Additionally, we have defined the modification strategy, and how to handle such topics best. Now we are ready to take the requirements! Such requirements coming over in spreadsheets, word files (like GAP documents), or in any other format. As we have to assign some attributes, we start numbering all that and assign a short name to each of these requirements (=CEMLI reference). We may also have already a Functional person assigned, and we might involve someone from the tech team to estimate, and we like to assign a status such as 'planned', 'estimated' etc. All these data are usually kept in spreadsheets, but I would put them into a database (yes, I am from Oracle :). If you don't have any good looking and centralized application already, please give a try with Oracle APEX. It should be up and running in a day and the imported sheets are than manageable concurrently!  For one of my clients I have created this CEMLI-DB; in between enriched with a lot of additional functionality, but initially it was just a simple centralized CEMLI tracking application. Why I am pointing out again the centralized method to manage such data? Well, your data quality will dramatically increase, if you let your project members see (also review and update) "your" data.  APEX allows you to filter, sort, print, and also export. And if you can spend some time to define proper value lists, everyone will gain from. APEX allows you to work in 'agile' mode, means you can improve your application step by step. Let's say you like to reference a document, or even upload the same, you can do that. Or, you need to classify the CEMLIs by release, just add this release field, same for business area or CEMLI type. One CEMLI record may then look like this: Prepare one or two (online) reports, to be ready to present your "workload" to the project management. Use such extracts also when you work offline (to prioritize etc.). But as soon as you are again connected, feed the data back into the central application. Note: I have combined this application with an additional issue tracker.  Here the most important element is the CEMLI reference, which acts as link to any other application (if you are not using APEX also as issue tracker :).  Please spend a minute to define such a reference (see blog Part 8: How to name Customizations).   Summary: Building the bridge from Gap analyse to the development has to be done in a controlled way. Usually the information is provided differently, but it is suggested to collect all requirements centrally. Oracle APEX is a great solution to enter and maintain such information in a structured, but flexible way. APEX helped me a lot to work with distributed development teams during the complete development cycle.

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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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• Threading Overview

  - by ACShorten

  One of the major features of the batch framework is the ability to support multi-threading. The multi-threading support allows a site to increase throughput on an individual batch job by splitting the total workload across multiple individual threads. This means each thread has fine level control over a segment of the total data volume at any time. The idea behind the threading is based upon the notion that "many hands make light work". Each thread takes a segment of data in parallel and operates on that smaller set. The object identifier allocation algorithm built into the product randomly assigns keys to help ensure an even distribution of the numbers of records across the threads and to minimize resource and lock contention. The best way to visualize the concept of threading is to use a "pie" analogy. Imagine the total workset for a batch job is a "pie". If you split that pie into equal sized segments, each segment would represent an individual thread. The concept of threading has advantages and disadvantages: Smaller elapsed runtimes - Jobs that are multi-threaded finish earlier than jobs that are single threaded. With smaller amounts of work to do, jobs with threading will finish earlier. Note: The elapsed runtime of the threads is rarely proportional to the number of threads executed. Even though contention is minimized, some contention does exist for resources which can adversely affect runtime. Threads can be managed individually – Each thread can be started individually and can also be restarted individually in case of failure. If you need to rerun thread X then that is the only thread that needs to be resubmitted. Threading can be somewhat dynamic – The number of threads that are run on any instance can be varied as the thread number and thread limit are parameters passed to the job at runtime. They can also be configured using the configuration files outlined in this document and the relevant manuals.Note: Threading is not dynamic after the job has been submitted Failure risk due to data issues with threading is reduced – As mentioned earlier individual threads can be restarted in case of failure. This limits the risk to the total job if there is a data issue with a particular thread or a group of threads. Number of threads is not infinite – As with any resource there is a theoretical limit. While the thread limit can be up to 1000 threads, the number of threads you can physically execute will be limited by the CPU and IO resources available to the job at execution time. Theoretically with the objects identifiers evenly spread across the threads the elapsed runtime for the threads should all be the same. In other words, when executing in multiple threads theoretically all the threads should finish at the same time. Whilst this is possible, it is also possible that individual threads may take longer than other threads for the following reasons: Workloads within the threads are not always the same - Whilst each thread is operating on the roughly the same amounts of objects, the amount of processing for each object is not always the same. For example, an account may have a more complex rate which requires more processing or a meter has a complex amount of configuration to process. If a thread has a higher proportion of objects with complex processing it will take longer than a thread with simple processing. The amount of processing is dependent on the configuration of the individual data for the job. Data may be skewed – Even though the object identifier generation algorithm attempts to spread the object identifiers across threads there are some jobs that use additional factors to select records for processing. If any of those factors exhibit any data skew then certain threads may finish later. For example, if more accounts are allocated to a particular part of a schedule then threads in that schedule may finish later than other threads executed. Threading is important to the success of individual jobs. For more guidelines and techniques for optimizing threading refer to Multi-Threading Guidelines in the Batch Best Practices for Oracle Utilities Application Framework based products (Doc Id: 836362.1) whitepaper available from My Oracle Support

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• Updated SOA Documents now available in ITSO Reference Library

  - by Bob Rhubart

  Nine documents within the IT Strategies from Oracle (ITSO) reference library have recently been updated. (Access to the ITSO collection is free to registered Oracle.com members -- and that membership is free.) All nine documents fall within the Service Oriented Architecture section of the ITSO collection, and cover the following topics: SOA Practitioner Guides Creating an SOA Roadmap (PDF, 54 pages, published: February 2012) The secret to successful SOA is to build a roadmap that can be successfully executed. SOA offers an opportunity to adopt an iterative technique to deliver solutions incrementally. This document offers a structured, iterative methodology to help you stay focused on business results, mitigate technology and organizational risk, and deliver successful SOA projects. A Framework for SOA Governance (PDF, 58 pages, published: February 2012) Successful SOA requires a strong governance strategy that designs-in measurement, management, and enforcement procedures. Enterprise SOA adoption introduces new assets, processes, technologies, standards, roles, etc. which require application of appropriate governance policies and procedures. This document offers a framework for defining and building a proper SOA governance model. Determining ROI of SOA through Reuse (PDF, 28 pages, published: February 2012) SOA offers the opportunity to save millions of dollars annually through reuse. Sharing common services intuitively reduces workload, increases developer productivity, and decreases maintenance costs. This document provides an approach for estimating the reuse value of the various software assets contained in a typical portfolio. Identifying and Discovering Services (PDF, 64 pages, published: March 2012) What services should we build? How can we promote the reuse of existing services? A sound approach to answer these questions is a primary measure for the success of a SOA initiative. This document describes a pragmatic approach for collecting the necessary information for identifying proper services and facilitating service reuse. Software Engineering in an SOA Environment (PDF, 66 pages, published: March 2012) Traditional software delivery methods are too narrowly focused and need to be adjusted to enable SOA. This document describes an engineering approach for delivering projects within an SOA environment. It identifies the unique software engineering challenges faced by enterprises adopting SOA and provides a framework to remove the hurdles and improve the efficiency of the SOA initiative. SOA Reference Architectures SOA Foundation (PDF, 70 pages, published: February 2012) This document describes they key tenets for SOA design, development, and execution environments. Topics include: service definition, service layering, service types, the service model, composite applications, invocation patterns, and standards. SOA Infrastructure (PDF, 86 pages, published: February 2012) Properly architected, SOA provides a robust and manageable infrastructure that enables faster solution delivery. This document describes the role of infrastructure and its capabilities. Topics include: logical architecture, deployment views, and Oracle product mapping. SOA White Papers and Data Sheets Oracle's Approach to SOA (white paper) (PDF, 14 pages, published: February 2012) Oracle has developed a pragmatic, holistic approach, based on years of experience with numerous companies to help customers successfully adopt SOA and realize measureable business benefits. This executive datasheet and whitepaper describe Oracle's proven approach to SOA. Oracle's Approach to SOA (data sheet) (PDF, 3 pages, published: March 2012) SOA adoption is complex and success is far from assured. This is why Oracle has developed a pragmatic, holistic approach, based on years of experience with numerous companies, to help customers successfully adopt SOA and realize measurable business benefits. This data sheet provides an executive overview of Oracle's proven approach to SOA.

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• Oracle MAA Part 1: When One Size Does Not Fit All

  - by JoeMeeks

  The good news is that Oracle Maximum Availability Architecture (MAA) best practices combined with Oracle Database 12c (see video) introduce first-in-the-industry database capabilities that truly make unplanned outages and planned maintenance transparent to users. The trouble with such good news is that Oracle’s enthusiasm in evangelizing its latest innovations may leave some to wonder if we’ve lost sight of the fact that not all database applications are created equal. Afterall, many databases don’t have the business requirements for high availability and data protection that require all of Oracle’s ‘stuff’. For many real world applications, a controlled amount of downtime and/or data loss is OK if it saves money and effort. Well, not to worry. Oracle knows that enterprises need solutions that address the full continuum of requirements for data protection and availability. Oracle MAA accomplishes this by defining four HA service level tiers: BRONZE, SILVER, GOLD and PLATINUM. The figure below shows the progression in service levels provided by each tier. Each tier uses a different MAA reference architecture to deploy the optimal set of Oracle HA capabilities that reliably achieve a given service level (SLA) at the lowest cost.  Each tier includes all of the capabilities of the previous tier and builds upon the architecture to handle an expanded fault domain. Bronze is appropriate for databases where simple restart or restore from backup is ‘HA enough’. Bronze is based upon a single instance Oracle Database with MAA best practices that use the many capabilities for data protection and HA included with every Oracle Enterprise Edition license. Oracle-optimized backups using Oracle Recovery Manager (RMAN) provide data protection and are used to restore availability should an outage prevent the database from being able to restart. Silver provides an additional level of HA for databases that require minimal or zero downtime in the event of database instance or server failure as well as many types of planned maintenance. Silver adds clustering technology - either Oracle RAC or RAC One Node. RMAN provides database-optimized backups to protect data and restore availability should an outage prevent the cluster from being able to restart. Gold raises the game substantially for business critical applications that can’t accept vulnerability to single points-of-failure. Gold adds database-aware replication technologies, Active Data Guard and Oracle GoldenGate, which synchronize one or more replicas of the production database to provide real time data protection and availability. Database-aware replication greatly increases HA and data protection beyond what is possible with storage replication technologies. It also reduces cost while improving return on investment by actively utilizing all replicas at all times. Platinum introduces all of the sexy new Oracle Database 12c capabilities that Oracle staff will gush over with great enthusiasm. These capabilities include Application Continuity for reliable replay of in-flight transactions that masks outages from users; Active Data Guard Far Sync for zero data loss protection at any distance; new Oracle GoldenGate enhancements for zero downtime upgrades and migrations; and Global Data Services for automated service management and workload balancing in replicated database environments. Each of these technologies requires additional effort to implement. But they deliver substantial value for your most critical applications where downtime and data loss are not an option. The MAA reference architectures are inherently designed to address conflicting realities. On one hand, not every application has the same objectives for availability and data protection – the Not One Size Fits All title of this blog post. On the other hand, standard infrastructure is an operational requirement and a business necessity in order to reduce complexity and cost. MAA reference architectures address both realities by providing a standard infrastructure optimized for Oracle Database that enables you to dial-in the level of HA appropriate for different service level requirements. This makes it simple to move a database from one HA tier to the next should business requirements change, or from one hardware platform to another – whether it’s your favorite non-Oracle vendor or an Oracle Engineered System. Please stay tuned for additional blog posts in this series that dive into the details of each MAA reference architecture. Meanwhile, more information on Oracle HA solutions and the Maximum Availability Architecture can be found at: Oracle Maximum Availability Architecture - Webcast Maximize Availability with Oracle Database 12c - Technical White Paper

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• World Record Siebel PSPP Benchmark on SPARC T4 Servers

  - by Brian

  Oracle's SPARC T4 servers set a new World Record for Oracle's Siebel Platform Sizing and Performance Program (PSPP) benchmark suite. The result used Oracle's Siebel Customer Relationship Management (CRM) Industry Applications Release 8.1.1.4 and Oracle Database 11g Release 2 running Oracle Solaris on three SPARC T4-2 and two SPARC T4-1 servers. The SPARC T4 servers running the Siebel PSPP 8.1.1.4 workload which includes Siebel Call Center and Order Management System demonstrates impressive throughput performance of the SPARC T4 processor by achieving 29,000 users. This is the first Siebel PSPP 8.1.1.4 benchmark supporting 29,000 concurrent users with a rate of 239,748 Business Transactions/hour. The benchmark demonstrates vertical and horizontal scalability of Siebel CRM Release 8.1.1.4 on SPARC T4 servers. Performance Landscape Systems Txn/hr Users Call Center Order Management Response Times (sec) 1 x SPARC T4-1 (1 x SPARC T4 2.85 GHz) – Web 3 x SPARC T4-2 (2 x SPARC T4 2.85 GHz) – App/Gateway 1 x SPARC T4-1 (1 x SPARC T4 2.85 GHz) – DB 239,748 29,000 0.165 0.925 Oracle: Call Center + Order Management Transactions: 197,128 + 42,620 Users: 20300 + 8700 Configuration Summary Web Server Configuration: 1 x SPARC T4-1 server 1 x SPARC T4 processor, 2.85 GHz 128 GB memory Oracle Solaris 10 8/11 iPlanet Web Server 7 Application Server Configuration: 3 x SPARC T4-2 servers, each with 2 x SPARC T4 processor, 2.85 GHz 256 GB memory 3 x 300 GB SAS internal disks Oracle Solaris 10 8/11 Siebel CRM 8.1.1.5 SIA Database Server Configuration: 1 x SPARC T4-1 server 1 x SPARC T4 processor, 2.85 GHz 128 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 (11.2.0.2) Storage Configuration: 1 x Sun Storage F5100 Flash Array 80 x 24 GB flash modules Benchmark Description Siebel 8.1 PSPP benchmark includes Call Center and Order Management: Siebel Financial Services Call Center – Provides the most complete solution for sales and service, allowing customer service and telesales representatives to provide superior customer support, improve customer loyalty, and increase revenues through cross-selling and up-selling. High-level description of the use cases tested: Incoming Call Creates Opportunity, Quote and Order and Incoming Call Creates Service Request . Three complex business transactions are executed simultaneously for specific number of concurrent users. The ratios of these 3 scenarios were 30%, 40%, 30% respectively, which together were totaling 70% of all transactions simulated in this benchmark. Between each user operation and the next one, the think time averaged approximately 10, 13, and 35 seconds respectively. Siebel Order Management – Oracle's Siebel Order Management allows employees such as salespeople and call center agents to create and manage quotes and orders through their entire life cycle. Siebel Order Management can be tightly integrated with back-office applications allowing users to perform tasks such as checking credit, confirming availability, and monitoring the fulfillment process. High-level description of the use cases tested: Order & Order Items Creation and Order Updates. Two complex Order Management transactions were executed simultaneously for specific number of concurrent users concurrently with aforementioned three Call Center scenarios above. The ratio of these 2 scenarios was 50% each, which together were totaling 30% of all transactions simulated in this benchmark. Between each user operation and the next one, the think time averaged approximately 20 and 67 seconds respectively. Key Points and Best Practices No processor cores or cache were activated or deactivated on the SPARC T-Series systems to achieve special benchmark effects. See Also Siebel White Papers SPARC T4-1 Server oracle.com OTN SPARC T4-2 Server oracle.com OTN Siebel CRM 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 30 September 2012.

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• Seperation of project responsibilities in new project

  - by dreza

  We have very recently started a new project (MVC 3.0) and some of our early discussion has been around how the work and development will be split amongst the team members to ensure we get the least amount of overlap of work and so help make it a bit easier for each developer to get on and do their work. The project is expected to take about 6 months - 1 year (although not all developers are likely to be on and might filter off towards the end), Our team is going to be small so this will help out a bit I believe. The team will essentially consist of: 3 x developers (1 a slightly more experienced and will be the lead) 1 x project manager / product owner / tester An external company responsbile for doing our design work General project/development decisions so far have included: Develop in an Agile way using SCRUM techniques (We are still very much learning this approach as a company) Use MVVM archectecture Use Ninject and DI where possible Attempt to use as TDD as much as possible to drive development. Keep our controllers as skinny as possible Keep our views as simple as possible During our discussions two approaches have been broached as too how to seperate the workload given our objectives outlined above. OPTION 1: A framework seperation where each person is responsible for conceptual areas with overlap and discussion primarily in the integration areas. The integration areas would the responsibily of both developers as required. View prototypes (**Graphic designer**) | - Mockups | Views (Razor and view helpers etc) & Javascript (**Developer 1**) | - View models (Integration point) | Controllers and Application logic (**Developer 2**) | - Models (Integration point) | Domain model and persistence (**Developer 3**) PROS: Integration points are quite clear and so developers can work without dependencies on others fairly easily Code practices such as naming conventions and style is more easily managed in regards to consistancy as primarily only one developer will be handling an area CONS: Completion of an entire feature becomes a bit grey as no single person is responsible for an entire feature (story?) A person might not have a full appreciation for all areas of the project and so code overlap might be lacking if suddenly that person left. OPTION 2: A more task orientated approach where each person is responsible for the completion of the entire task from view - controller - model. PROS: A person is responsible for one entire feature so it's "complete" state can be clearly defined Code overlap into different areas will occur so each individual has good coverage over the entire application CONS: Overlap of development will occur in all the modules and developers can develop/extend without a true understanding of what the original code owner was intending. This could potentially lead more easily to code bloat? Following a convention might be harder as developers are adding to all areas of the project If a developer sets up a way of doing things would it be harder to enforce the other developers to follow that convention or even build on it (or even discuss it?). Dunno.. Bugs could more easily be introduced into areas not thought about by the developer It's easier to possibly to carry a team member in so far as one member just hacks code together to complete a task whilst another takes time to build a foundation that could be used by others and so help make future tasks easier i.e. starts building a framework? QUESTION: As it might appear I'm more in favor of option 1, however I'm interested to see how others might have approached this or what is the standard or best or preferred way of undertaking a project. Or indeed any different approach to handling this?

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• Using WKA in Large Coherence Clusters (Disabling Multicast)

  - by jpurdy

  Disabling hardware multicast (by configuring well-known addresses aka WKA) will place significant stress on the network. For messages that must be sent to multiple servers, rather than having a server send a single packet to the switch and having the switch broadcast that packet to the rest of the cluster, the server must send a packet to each of the other servers. While hardware varies significantly, consider that a server with a single gigabit connection can send at most ~70,000 packets per second. To continue with some concrete numbers, in a cluster with 500 members, that means that each server can send at most 140 cluster-wide messages per second. And if there are 10 cluster members on each physical machine, that number shrinks to 14 cluster-wide messages per second (or with only mild hyperbole, roughly zero). It is also important to keep in mind that network I/O is not only expensive in terms of the network itself, but also the consumption of CPU required to send (or receive) a message (due to things like copying the packet bytes, processing a interrupt, etc). Fortunately, Coherence is designed to rely primarily on point-to-point messages, but there are some features that are inherently one-to-many: Announcing the arrival or departure of a member Updating partition assignment maps across the cluster Creating or destroying a NamedCache Invalidating a cache entry from a large number of client-side near caches Distributing a filter-based request across the full set of cache servers (e.g. queries, aggregators and entry processors) Invoking clear() on a NamedCache The first few of these are operations that are primarily routed through a single senior member, and also occur infrequently, so they usually are not a primary consideration. There are cases, however, where the load from introducing new members can be substantial (to the point of destabilizing the cluster). Consider the case where cluster in the first paragraph grows from 500 members to 1000 members (holding the number of physical machines constant). During this period, there will be 500 new member introductions, each of which may consist of several cluster-wide operations (for the cluster membership itself as well as the partitioned cache services, replicated cache services, invocation services, management services, etc). Note that all of these introductions will route through that one senior member, which is sharing its network bandwidth with several other members (which will be communicating to a lesser degree with other members throughout this process). While each service may have a distinct senior member, there's a good chance during initial startup that a single member will be the senior for all services (if those services start on the senior before the second member joins the cluster). It's obvious that this could cause CPU and/or network starvation. In the current release of Coherence (3.7.1.3 as of this writing), the pure unicast code path also has less sophisticated flow-control for cluster-wide messages (compared to the multicast-enabled code path), which may also result in significant heap consumption on the senior member's JVM (from the message backlog). This is almost never a problem in practice, but with sufficient CPU or network starvation, it could become critical. For the non-operational concerns (near caches, queries, etc), the application itself will determine how much load is placed on the cluster. Applications intended for deployment in a pure unicast environment should be careful to avoid excessive dependence on these features. Even in an environment with multicast support, these operations may scale poorly since even with a constant request rate, the underlying workload will increase at roughly the same rate as the underlying resources are added. Unless there is an infrastructural requirement to the contrary, multicast should be enabled. If it can't be enabled, care should be taken to ensure the added overhead doesn't lead to performance or stability issues. This is particularly crucial in large clusters.

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• When good programmers go bad!

  - by Ed Bloom

  Hi, I'm a team lead/dev who manages a team of 10 programmers. Most of them are hard working talented guys. But of late, I've got this one person who while highly talented and has delivered great work for me in the past, has just become completely unreliable. It's not his ability - that is not in question - he's proven that many times. He just looks bored now. Is blatantly not doing much work (despite a LOT of pressure being put on the team to meet tight deadlines etc.) He just doesn't seem to care and looks bored. I'm partially guilty for not having addressed this before now - I was afraid to have to lose a talented guy given the workload I've got on. But at this stage it's becoming a problem and affecting those around him. Can anyone spare their thoughts or words of wisdom on how I should go about dealing this. I want the talented AND motivated guy back. Otherwise he's gonna have to go. Thanks, Ed

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• HTTP 1.0 vs 1.1

  - by Jason Baker

  Could somebody give me a brief overview of the differences between HTTP 1.0 and HTTP 1.1? I've spent some time with both of the RFCs, but haven't been able to pull out a lot of difference between them. Wikipedia says this: HTTP/1.1 (1997-1999) Current version; persistent connections enabled by default and works well with proxies. Also supports request pipelining, allowing multiple requests to be sent at the same time, allowing the server to prepare for the workload and potentially transfer the requested resources more quickly to the client. But that doesn't mean a lot to me. I realize this is a somewhat complicated subject, so I'm not expecting a full answer, but can someone give me a brief overview of the differences at a bit lower level? By this I mean that I'm looking for the info I would need to know to implement either an HTTP server or application. I realize that this can be a somewhat complicated subject (based on what I know about HTTP as of right now), so I'm not necessarily looking for a full answer. I'm really more looking for a nudge in the right direction so that I can figure it out on my own.

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• How can I test this SQL Server performance Utility?

  - by Martin Smith

  As part of my MSc I need to do a three month project later this year. I have decided to do something which will likely be useful for me in the workplace and spend the time getting to understand SQL Server internals. The deliverable for this project will be a performance advisor looking at a variety of different rules. Some static such as finding redundant indexes, some more dynamic such as using XEvents to find outlying invocations of stored procedure execution times when certain parameters are passed. I am struggling to come up with a good way of testing this though. I can obviously design a "bad" database and a synthetic workload that my tool will pick up issues on but I also need to demonstrate that it has real world utility. Looking at the self tuning database literature it is common to use TPC benchmarks but I've had a look at the TPCC site and it looks very time consuming to implement and not that good a fit to my project's testing needs in any event (I would still be able to "rig" it by the decisions I made on indexing or physical architecture). Plan A would be to find willing beta tester(s) but in the event that isn't possible I will need a fallback plan. The best idea I have come up with so far is to use the various MS sample applications as examples of real world applications. e.g. http://msftdpprodsamples.codeplex.com/ http://www.asp.net/community/projects/ Does anyone have any better suggestions?

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• How can I 'transpose' my data using SQL and remove duplicates at the same time?

  - by Remnant

  I have the following data structure in my database: LastName FirstName CourseName John Day Pricing John Day Marketing John Day Finance Lisa Smith Marketing Lisa Smith Finance etc... The data shows employess within a business and which courses they have shown a preference to attend. The number of courses per employee will vary (i.e. as above, John has 3 courses and Lisa 2). I need to take this data from the database and pass it to a webpage view (asp.net mvc). I would like the data that comes out of my database to match the view as much as possible and want to transform the data using SQl so that it looks like the following: LastName FirstName Course1 Course2 Course3 John Day Pricing Marketing Finance Lisa Smith Marketing Finance Any thoughts on how this may be achieved? Note: one of the reasons I am trying this approach is that the original data structure does not easily lend itself to be iterated over using the typical mvc syntax: <% foreach (var item in Model.courseData) { %> Because of the duplication of names in the orignal data I would end up with lots of conditionals in my View which I would like to avoid. I have tried transforming the data using c# in my ViewModel but have found it tough going and feel that I could lighten the workload by leveraging SQL before I return the data. Thanks.

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• C# Multithreaded Domain Design

  - by Thijs Cramer

  Let's say i have a Domain Model that im trying to make compatible with multithreading. The prototype domain is a game domain that consists of Space, SpaceObject, and Location objects. A SpaceObject has the Move method and Asteroid and Ship extend this object with specific properties for the object (Ship has a name and Asteroid has a color) Let's say i want to make the Move method for each object run in a seperate thread. That would be stupid because with 10000 objects, i would have 10000 threads. What would be the best way to seperate the workload between cores/threads? I'm trying to learn the basics of concurrency, and building a small game to prototype a lot of concepts. What i've already done, is build a domain, and a threading model with a timer that launches events based on intervals. If the event occurs i want to update my entire model with the new locations of any SpaceObject. But i don't know how and when to launch new threads with workloads when the event occurs. Some people at work told me that u can't update your core domain multithreaded, because you have to synch everything. But in that case i can't run my game on a dual quadcore server, because it would only use 1 CPU for the hardest tasks. Anyone know what to do here?

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• Why is my GUI unresponsive while a SwingWorker thread runs?

  - by Starchy

  Hello, I have a SwingWorker thread with an IOBound task which is totally locking up the interface while it runs. Swapping out the normal workload for a counter loop has the same result. The SwingWorker looks basically like this: public class BackupWorker extends SwingWorker<String, String> { private static String uname = null; private static String pass = null; private static String filename = null; static String status = null; BackupWorker (String uname, String pass, String filename) { this.uname = uname; this.pass = pass; this.filename = filename; } @Override protected String doInBackground() throws Exception { BackupObject bak = newBackupObject(uname,pass,filename); return "Done!"; } } The code that kicks it off lives in a class that extends JFrame: public void actionPerformed(ActionEvent event) { String cmd = event.getActionCommand(); if (BACKUP.equals(cmd)) { SwingUtilities.invokeLater(new Runnable() { public void run() { final StatusFrame statusFrame = new StatusFrame(); statusFrame.setVisible(true); SwingUtilities.invokeLater(new Runnable() { public void run () { statusFrame.beginBackup(uname,pass,filename); } }); } }); } } Here's the interesting part of StatusFrame: public void beginBackup(final String uname, final String pass, final String filename) { worker = new BackupWorker(uname, pass, filename); worker.execute(); try { System.out.println(worker.get()); } catch (InterruptedException e) { e.printStackTrace(); } catch (ExecutionException e) { e.printStackTrace(); } } } So far as I can see, everything "long-running" is handled by the worker, and everything that touches the GUI on the EDT. Have I tangled things up somewhere, or am I expecting too much of SwingWorker?

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• OpenSource Projects - Is there a site which lists projecs that need more developers?

  - by Jamie

  Morning/Afternoon/Evening all, Do any of you know of a website which lists opensource projects which are in need of more help? Let me elaborate, I would like to work on another open source project (I already work on a couple), however, it would be nice to have a site which lists lots of OS projects, their aims, deadlines, workload, how many more developers they are in need of etc. Of course, I could just pick a topic i'm interested in, find an OS project and then work on it, however, it would be nice to see a diversified list of projects. Primarily because some little known awesome projects get little attention and big projects such as jQuery forks, adium, gimp etc. etc. get a lot of attention because they are well known (and of course because they are great)and thus get a lot of developers working on them. It would be nice to see some little known projects getting more attention and thus hopefully drawing some people to work on them. Currently there are many websites hosting os projects, such as github, sourceforge, google code etc. A website to centralise all of this into one place and categorise it would be awesome. Let me know your thoughts please. I'm not looking for an answer per se, so I will mark it is as a community wiki. Your thoughts would be great.

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• Reduce durability in MySQL for performance

  - by Paul Prescod

  My site occasionally has fairly predictable bursts of traffic that increase the throughput by 100 times more than normal. For example, we are going to be featured on a television show, and I expect in the hour after the show, I'll get more than 100 times more traffic than normal. My understanding is that MySQL (InnoDB) generally keeps my data in a bunch of different places: RAM Buffers commitlog binary log actual tables All of the above places on my DB slave This is too much "durability" given that I'm on an EC2 node and most of the stuff goes across the same network pipe (file systems are network attached). Plus the drives are just slow. The data is not high value and I'd rather take a small chance of a few minutes of data loss rather than have a high probability of an outage when the crowd arrives. During these traffic bursts I would like to do all of that I/O only if I can afford it. I'd like to just keep as much in RAM as possible (I have a fair chunk of RAM compared to the data size that would be touched over an hour). If buffers get scarce, or the I/O channel is not too overloaded, then sure, I'd like things to go to the commitlog or binary log to be sent to the slave. If, and only if, the I/O channel is not overloaded, I'd like to write back to the actual tables. In other words, I'd like MySQL/InnoDB to use a "write back" cache algorithm rather than a "write through" cache algorithm. Can I convince it to do that? If this is not possible, I am interested in general MySQL write-performance optimization tips. Most of the docs are about optimizing read performance, but when I get a crowd of users, I am creating accounts for all of them, so that's a write-heavy workload.

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• How many hours a day (of the standard 8) do you actually work? [closed]

  - by someone

  Possible Duplicate: How much do you [really] work a day When I started working (not so long ago), I was very conscientious about really working. If I didn't work for 10 minutes at a time, I felt like I was cheating. But as I started to look around me, I realized that I was the only one... and most of my coworkers were spending a big percentage of their time browsing the internet or playing solitaire. I started to slack off a little more than usual... while still basically getting all my work done. But while I do all that's required of me, and usually quickly, I no longer beg for work to fill up my spare time; I'm content to do what I'm told and play around when no one makes sure I'm busy enough. Which means that I'm often bored and underutilized. (Which I was even when I begged for work - people are pretty laid back about the workload and don't seem to realize how much I can get done if pushed to the fullest.) But I was just talking to a friend who graduated with me and also recently started working... and she came to me with the same concerns about slacking. She's working remotely, which means there are often gaps in communication when she can't really get anything done... And she's feeling guilty about it. Which made me rethink the whole thing... So, as workers, how many hours, out of the 8 standard average, are you actually working (honestly)? And, as bosses, how many hours do you expect your workers to work? And from an ethical standpoint, how much free time, or space out time, can workers have during the day without being considered to be "cheating" their office of labor and money?

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• TCP RST Reset Every 5 Minutes on Windows 2003 sp2

  - by Dan

  Hey, Recently I had a web developer come to me and ask why he was receiving connection errors in his app that was accessing a sql database. So, I went through my normal trouble shooting steps to isolate or reproduce the issue. I discovered that if I connected to the database using Query Analyzer and let the connection idle for 5 minutes it would disconnect. Meaning... I would no longer be able to refresh my tables or any other object/node within the object browser in Query Analyzer. I would have to right click on the instance and refresh for it to re-establish the connection. Next I went to wireshark and ran a capture on the client pc's nic card. Sure enough it was receiving a TCP RST reset every 5 min if the connection idled longer than 5 min. I also ran a capture on the SQL Server and noticed the TCP RST reset command as well. Attached below is the capture from the client Machine. If someone could please assist... That would be great. -I checked all settings within SQL Server 2000 against another server and they all seem to be the same. -Issue does not occur if I connect to any other SQL server 2000 server. -Issue does not occur if connecting to SQL on the server itself... so only over the network. -I consulted with network team and this is the response back: There are no firewalls or proxies in between SQL Server and your desktop. The traffic flows like this: Desktop-Access Switch-Distro Switch-Core Switch-Datacenter Switch-SQL Server None of the switches have security ACL’s configured on them. Also they stated that NAT was not turned on. -Issue does not occur with SQL server Enterprise Manager. -Ran SQL Profiler at the same time and did not see anything out of the ordinary during the RST I HAVE SEARCHED HIGH AND LOW ON GOOGLE FOR A RESOLUTION FOR THIS ISSUE. NO LUCK! My questions are: What could be causing this? Wrong Sequence number? setting in a router or switch the network team may have over looked? Setting within Windows? Setting within SQL Server 2000 that I have over looked? Better way to utilize Wireshark to find more answers? RST is about 10 from the bottom. No. Time Source Destination Protocol Info 258 24.390708 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [SYN] Seq=0 Len=0 MSS=1260 259 24.401679 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [SYN, ACK] Seq=0 Ack=1 Win=64240 Len=0 MSS=1460 260 24.401729 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [ACK] Seq=1 Ack=1 Win=65535 [TCP CHECKSUM INCORRECT] Len=0 261 24.402212 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [PSH, ACK] Seq=1 Ack=1 Win=65535 [TCP CHECKSUM INCORRECT] Len=42 262 24.413335 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [PSH, ACK] Seq=1 Ack=43 Win=64198 Len=37 285 24.466512 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [ACK] Seq=43 Ack=38 Win=65498 [TCP CHECKSUM INCORRECT] Len=1260 286 24.466536 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [PSH, ACK] Seq=1303 Ack=38 Win=65498 [TCP CHECKSUM INCORRECT] Len=437 289 24.478168 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [ACK] Seq=38 Ack=1740 Win=64240 Len=0 290 24.480078 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [PSH, ACK] Seq=38 Ack=1740 Win=64240 Len=385 293 24.493629 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [PSH, ACK] Seq=1740 Ack=423 Win=65113 [TCP CHECKSUM INCORRECT] Len=60 294 24.504637 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [PSH, ACK] Seq=423 Ack=1800 Win=64180 Len=17 295 24.533197 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [PSH, ACK] Seq=1800 Ack=440 Win=65096 [TCP CHECKSUM INCORRECT] Len=44 296 24.544098 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [PSH, ACK] Seq=440 Ack=1844 Win=64136 Len=17 297 24.544524 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [PSH, ACK] Seq=1844 Ack=457 Win=65079 [TCP CHECKSUM INCORRECT] Len=58 298 24.558033 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [PSH, ACK] Seq=457 Ack=1902 Win=64078 Len=31 299 24.558493 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [PSH, ACK] Seq=1902 Ack=488 Win=65048 [TCP CHECKSUM INCORRECT] Len=92 300 24.569984 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [PSH, ACK] Seq=488 Ack=1994 Win=63986 Len=70 301 24.577395 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [PSH, ACK] Seq=1994 Ack=558 Win=64978 [TCP CHECKSUM INCORRECT] Len=448 303 24.589834 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [PSH, ACK] Seq=558 Ack=2442 Win=63538 Len=64 304 24.590122 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [FIN, ACK] Seq=2442 Ack=622 Win=64914 [TCP CHECKSUM INCORRECT] Len=0 305 24.601094 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [ACK] Seq=622 Ack=2443 Win=63538 Len=0 306 24.601659 x.x.x.10 x.x.x.99 TCP 2226 > 14488 [FIN, ACK] Seq=622 Ack=2443 Win=63538 Len=0 307 24.601686 x.x.x.99 x.x.x.10 TCP 14488 > 2226 [ACK] Seq=2443 Ack=623 Win=64914 [TCP CHECKSUM INCORRECT] Len=0 321 25.839371 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [SYN] Seq=0 Len=0 MSS=1260 322 25.850291 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [SYN, ACK] Seq=0 Ack=1 Win=64240 Len=0 MSS=1460 323 25.850321 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [ACK] Seq=1 Ack=1 Win=65535 [TCP CHECKSUM INCORRECT] Len=0 324 25.850660 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=1 Ack=1 Win=65535 [TCP CHECKSUM INCORRECT] Len=42 325 25.861573 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=1 Ack=43 Win=64198 Len=37 326 25.863103 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [ACK] Seq=43 Ack=38 Win=65498 [TCP CHECKSUM INCORRECT] Len=1260 327 25.863130 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=1303 Ack=38 Win=65498 [TCP CHECKSUM INCORRECT] Len=463 328 25.874417 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [ACK] Seq=38 Ack=1766 Win=64240 Len=0 329 25.876315 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=38 Ack=1766 Win=64240 Len=385 330 25.876905 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=1766 Ack=423 Win=65113 [TCP CHECKSUM INCORRECT] Len=60 331 25.887773 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=423 Ack=1826 Win=64180 Len=17 332 25.888299 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=1826 Ack=440 Win=65096 [TCP CHECKSUM INCORRECT] Len=44 333 25.899169 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=440 Ack=1870 Win=64136 Len=17 334 25.899574 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=1870 Ack=457 Win=65079 [TCP CHECKSUM INCORRECT] Len=58 335 25.910618 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=457 Ack=1928 Win=64078 Len=31 336 25.911051 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=1928 Ack=488 Win=65048 [TCP CHECKSUM INCORRECT] Len=92 337 25.922068 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=488 Ack=2020 Win=63986 Len=70 338 25.922500 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2020 Ack=558 Win=64978 [TCP CHECKSUM INCORRECT] Len=34 339 25.933621 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=558 Ack=2054 Win=63952 Len=29 340 25.941165 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2054 Ack=587 Win=64949 [TCP CHECKSUM INCORRECT] Len=54 341 25.952164 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=587 Ack=2108 Win=63898 Len=17 342 25.952993 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2108 Ack=604 Win=64932 [TCP CHECKSUM INCORRECT] Len=72 343 25.963889 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=604 Ack=2180 Win=63826 Len=17 344 25.964366 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2180 Ack=621 Win=64915 [TCP CHECKSUM INCORRECT] Len=52 345 25.975253 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=621 Ack=2232 Win=63774 Len=17 346 25.975590 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2232 Ack=638 Win=64898 [TCP CHECKSUM INCORRECT] Len=32 347 25.986588 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=638 Ack=2264 Win=63742 Len=167 348 25.987262 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2264 Ack=805 Win=64731 [TCP CHECKSUM INCORRECT] Len=512 349 25.998464 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=805 Ack=2776 Win=63230 Len=89 350 25.998861 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2776 Ack=894 Win=64642 [TCP CHECKSUM INCORRECT] Len=46 351 26.009849 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=894 Ack=2822 Win=63184 Len=17 352 26.010175 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2822 Ack=911 Win=64625 [TCP CHECKSUM INCORRECT] Len=80 353 26.021220 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=911 Ack=2902 Win=63104 Len=33 354 26.022613 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [PSH, ACK] Seq=2902 Ack=944 Win=64592 [TCP CHECKSUM INCORRECT] Len=498 355 26.034018 x.x.x.10 x.x.x.99 TCP 2226 > 14492 [PSH, ACK] Seq=944 Ack=3400 Win=64240 Len=89 356 26.046501 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [SYN] Seq=0 Len=0 MSS=1260 357 26.057323 x.x.x.10 x.x.x.99 TCP 2226 > 14493 [SYN, ACK] Seq=0 Ack=1 Win=64240 Len=0 MSS=1460 358 26.057355 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [ACK] Seq=1 Ack=1 Win=65535 [TCP CHECKSUM INCORRECT] Len=0 359 26.057661 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [PSH, ACK] Seq=1 Ack=1 Win=65535 [TCP CHECKSUM INCORRECT] Len=42 361 26.068606 x.x.x.10 x.x.x.99 TCP 2226 > 14493 [PSH, ACK] Seq=1 Ack=43 Win=64198 Len=37 362 26.070087 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [ACK] Seq=43 Ack=38 Win=65498 [TCP CHECKSUM INCORRECT] Len=1260 363 26.070113 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [PSH, ACK] Seq=1303 Ack=38 Win=65498 [TCP CHECKSUM INCORRECT] Len=485 364 26.081336 x.x.x.10 x.x.x.99 TCP 2226 > 14493 [ACK] Seq=38 Ack=1788 Win=64240 Len=0 365 26.083330 x.x.x.10 x.x.x.99 TCP 2226 > 14493 [PSH, ACK] Seq=38 Ack=1788 Win=64240 Len=385 366 26.083943 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [PSH, ACK] Seq=1788 Ack=423 Win=65113 [TCP CHECKSUM INCORRECT] Len=46 368 26.094921 x.x.x.10 x.x.x.99 TCP 2226 > 14493 [PSH, ACK] Seq=423 Ack=1834 Win=64194 Len=17 369 26.095317 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [PSH, ACK] Seq=1834 Ack=440 Win=65096 [TCP CHECKSUM INCORRECT] Len=48 370 26.107553 x.x.x.10 x.x.x.99 TCP 2226 > 14493 [PSH, ACK] Seq=440 Ack=1882 Win=64146 Len=877 371 26.241285 x.x.x.99 x.x.x.10 TCP 14492 > 2226 [ACK] Seq=3400 Ack=1033 Win=64503 [TCP CHECKSUM INCORRECT] Len=0 372 26.241307 x.x.x.99 x.x.x.10 TCP 14493 > 2226 [ACK] Seq=1882 Ack=1317 Win=65535 [TCP CHECKSUM INCORRECT] Len=0 653 55.913838 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 > 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 654 55.924547 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 > 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 910 85.887176 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 > 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 911 85.898010 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 > 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 1155 115.859520 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 1156 115.870285 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 1395 145.934403 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 1396 145.945938 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 1649 175.906767 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 1650 175.917741 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 1887 205.881080 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 1888 205.891818 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 2112 235.854408 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 2113 235.865482 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 2398 265.928342 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 2399 265.939242 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 2671 295.900714 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 2672 295.911590 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 2880 315.705029 x.x.x.10 x.x.x.99 TCP 2226 14493 [RST] Seq=1317 Len=0 2973 325.975607 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive] 14492 2226 [ACK] Seq=3399 Ack=1033 Win=64503 Len=1 2974 325.986337 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive ACK] 2226 14492 [ACK] Seq=1033 Ack=3400 Win=64240 Len=0 2975 326.154327 x.x.x.10 x.x.x.99 TCP [TCP Keep-Alive] 2226 14492 [ACK] Seq=1032 Ack=3400 Win=64240 Len=1 2976 326.154350 x.x.x.99 x.x.x.10 TCP [TCP Keep-Alive ACK] 14492 2226 [ACK] Seq=3400 Ack=1033 Win=64503 [TCP CHECKSUM INCORRECT] Len=0

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• 2 Servers setup for redundency, backup

  - by minal

  I presently have 1 dedicated virtual server running my website/blog/mail, etc. This is on Hyper-V with 512MB RAM. Windows Web2008. With the VM, I have these running within it: SmarterMail – for emails MS DNS – I have my own nameservers on this server SQL Express IIS7 2 IP Address I have now leased 2 physical servers : P4 2.6Ghz 1GB RAM 80GB HDD. With these new servers, I get 2 IPs per server as well. These are running Windows 2008 Standard. With the VM the HDD was obviously on a RAID setup so I was not worried about hardware issues as it fell on the provider to manage. However, with the new servers the HDD is not RAID’d, hence my concern is that if it fails I need a backup position. What would be the most ideal setup to go for? I am thinking: Server 1: (Web/PrimaryDNS) DNS – NS1 SQL Express – OFF turn on when required, ie. Server2 is down SmarterMail – OFF turn on when required, ie. Server2 is down IIS 7 Server2:(SQL/Backup) DNS – NS2 SQL Web Edition SmarterMail IIS 7 How can I set it up so that if 1 goes down I can have everything on 2 instantly or by manual switching over. I am confused as other DNS servers will cache the web servers IP address for requests, and if that server goes down, the backup server will have a different IP. How do I make this work? I will be doing routine backups, in which case I will keep copies of backups on both servers. If I am copying the same stuff on both servers like a mirror then I am losing on using the true performance out of it. It's like 1 server is always on standby. Ideally I want SQL and web on 2 diff machines for best performance. If Server1 goes down, I should be able to switch to Server2 fairly easily. I don't have a problem with manual intervention to start the sql/mail services, etc. In terms of scalabilty, the VM has coped pretty well to date. Moving forward the SQL and IIS workload is going to double pretty quickly. Some ideas would be great.

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• How do I get Lucene (.NET) to highlight correctly with wildcards?

  - by Scott Stafford

  I am using the Lucene.NET API directly in my ASP.NET/C# web application. When I search using a wildcard, like "fuc*", the highlighter doesn't highlight anything, but when I search for the whole word, like "fuchsia", it highlights fine. Does Lucene have the ability to highlight using the same logic it used to match with? Various maybe-relevant code-snippets below: var formatter = new Lucene.Net.Highlight.SimpleHTMLFormatter( "<span class='srhilite'>", "</span>"); var fragmenter = new Lucene.Net.Highlight.SimpleFragmenter(100); var scorer = new Lucene.Net.Highlight.QueryScorer(query); var highlighter = new Lucene.Net.Highlight.Highlighter(formatter, scorer); highlighter.SetTextFragmenter(fragmenter); and then on each hit... string description = Server.HtmlEncode(doc.Get("Description")); var stream = analyzer.TokenStream("Description", new System.IO.StringReader(description)); string highlighted_text = highlighter.GetBestFragments( stream, description, 1, "..."); And I'm using the QueryParser and the StandardAnalyzer.

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