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• SQL SERVER – IO_COMPLETION – Wait Type – Day 10 of 28

  - by pinaldave

  For any good system three things are vital: CPU, Memory and IO (disk). Among these three, IO is the most crucial factor of SQL Server. Looking at real-world cases, I do not see IT people upgrading CPU and Memory frequently. However, the disk is often upgraded for either improving the space, speed or throughput. Today we will look at an IO-related wait types. From Book On-Line: Occurs while waiting for I/O operations to complete. This wait type generally represents non-data page I/Os. Data page I/O completion waits appear as PAGEIOLATCH_* waits. IO_COMPLETION Explanation: Any tasks are waiting for I/O to finish. This is a good indication that IO needs to be looked over here. Reducing IO_COMPLETION wait: When it is an issue concerning the IO, one should look at the following things related to IO subsystem: Proper placing of the files is very important. We should check the file system for proper placement of files – LDF and MDF on a separate drive, TempDB on another separate drive, hot spot tables on separate filegroup (and on separate disk),etc. Check the File Statistics and see if there is higher IO Read and IO Write Stall SQL SERVER – Get File Statistics Using fn_virtualfilestats. Check event log and error log for any errors or warnings related to IO. If you are using SAN (Storage Area Network), check the throughput of the SAN system as well as the configuration of the HBA Queue Depth. In one of my recent projects, the SAN was performing really badly so the SAN administrator did not accept it. After some investigations, he agreed to change the HBA Queue Depth on development (test environment) set up and as soon as we changed the HBA Queue Depth to quite a higher value, there was a sudden big improvement in the performance. It is very possible that there are no proper indexes in the system and there are lots of table scans and heap scans. Creating proper index can reduce the IO bandwidth considerably. If SQL Server can use appropriate cover index instead of clustered index, it can effectively reduce lots of CPU, Memory and IO (considering cover index has lesser columns than cluster table and all other; it depends upon the situation). You can refer to the two articles that I wrote; they are about how to optimize indexes: Create Missing Indexes Drop Unused Indexes Checking Memory Related Perfmon Counters SQLServer: Memory Manager\Memory Grants Pending (Consistent higher value than 0-2) SQLServer: Memory Manager\Memory Grants Outstanding (Consistent higher value, Benchmark) SQLServer: Buffer Manager\Buffer Hit Cache Ratio (Higher is better, greater than 90% for usually smooth running system) SQLServer: Buffer Manager\Page Life Expectancy (Consistent lower value than 300 seconds) Memory: Available Mbytes (Information only) Memory: Page Faults/sec (Benchmark only) Memory: Pages/sec (Benchmark only) Checking Disk Related Perfmon Counters Average Disk sec/Read (Consistent higher value than 4-8 millisecond is not good) Average Disk sec/Write (Consistent higher value than 4-8 millisecond is not good) Average Disk Read/Write Queue Length (Consistent higher value than benchmark is not good) Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Types, SQL White Papers, T SQL, Technology

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• Validating Petabytes of Data with Regularity and Thoroughness

  - by rickramsey

  by Brian Zents When former Intel CEO Andy Grove said “only the paranoid survive,” he wasn’t necessarily talking about tape storage administrators, but it’s a lesson they’ve learned well. After all, tape storage is the last line of defense to prevent data loss, so tape administrators are extra cautious in making sure their data is secure. Not surprisingly, we are often asked for ways to validate tape media and the files on them. In the past, an administrator could validate the media, but doing so was often tedious or disruptive or both. The debut of the Data Integrity Validation (DIV) and Library Media Validation (LMV) features in the Oracle T10000C drive helped eliminate many of these pains. Also available with the Oracle T10000D drive, these features use hardware-assisted CRC checks that not only ensure the data is written correctly the first time, but also do so much more efficiently. Traditionally, a CRC check takes at least 25 seconds per 4GB file with a 2:1 compression ratio, but the T10000C/D drives can reduce the check to a maximum of nine seconds because the entire check is contained within the drive. No data needs to be sent to a host application. A time savings of at least 64 percent is extremely beneficial over the course of checking an entire 8.5TB T10000D tape. While the DIV and LMV features are better than anything else out there, what storage administrators really need is a way to check petabytes of data with regularity and thoroughness. With the launch of Oracle StorageTek Tape Analytics (STA) 2.0 in April, there is finally a solution that addresses this longstanding need. STA bundles these features into one interface to automate all media validation activities across all Oracle SL3000 and SL8500 tape libraries in an environment. And best of all, the validation process can be associated with the health checks an administrator would be doing already through STA. In fact, STA validates the media based on any of the following policies: Random Selection – Randomly selects media for validation whenever a validation drive in the standalone library or library complex is available. Media Health = Action – Selects media that have had a specified number of successive exchanges resulting in an Exchange Media Health of “Action.” You can specify from one to five exchanges. Media Health = Evaluate – Selects media that have had a specified number of successive exchanges resulting in an Exchange Media Health of “Evaluate.” You can specify from one to five exchanges. Media Health = Monitor – Selects media that have had a specified number of successive exchanges resulting in an Exchange Media Health of “Monitor.” You can specify from one to five exchanges. Extended Period of Non-Use – Selects media that have not had an exchange for a specified number of days. You can specify from 365 to 1,095 days (one to three years). Newly Entered – Selects media that have recently been entered into the library. Bad MIR Detected – Selects media with an exchange resulting in a “Bad MIR Detected” error. A bad media information record (MIR) indicates degraded high-speed access on the media. To avoid disrupting host operations, an administrator designates certain drives for media validation operations. If a host requests a file from media currently being validated, the host’s request takes priority. To ensure that the administrator really knows it is the media that is bad, as opposed to the drive, STA includes drive calibration and qualification features. In addition, validation requests can be re-prioritized or cancelled as needed. To ensure that a specific tape isn’t validated too often, STA prevents a tape from being validated twice within 24 hours via one of the policies described above. A tape can be validated more often if the administrator manually initiates the validation. When the validations are complete, STA reports the results. STA does not report simply a “good” or “bad” status. It also reports if media is even degraded so the administrator can migrate the data before there is a true failure. From that point, the administrators’ paranoia is relieved, as they have the necessary information to make a sound decision about the health of the tapes in their environment. About the Photograph Photograph taken by Rick Ramsey in Death Valley, California, May 2014 - Brian Follow OTN Garage on: Web | Facebook | Twitter | YouTube

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• PASS Summit 2011 &ndash; Part IV

  - by Tara Kizer

  This is the final blog for my PASS Summit 2011 series.  Well okay, a mini-series, I guess. On the last day of the conference, I attended Keith Elmore’ and Boris Baryshnikov’s (both from Microsoft) “Introducing the Microsoft SQL Server Code Named “Denali” Performance Dashboard Reports, Jeremiah Peschka’s (blog|twitter) “Rewrite your T-SQL for Great Good!”, and Kimberly Tripp’s (blog|twitter) “Isolated Disasters in VLDBs”. Keith and Boris talked about the lifecycle of a session, figuring out the running time and the waiting time.  They pointed out the transient nature of the reports.  You could be drilling into it to uncover a problem, but the session may have ended by the time you’ve drilled all of the way down.  Also, the reports are for troubleshooting live problems and not historical ones.  You can use Management Data Warehouse for historical troubleshooting.  The reports provide similar benefits to the Activity Monitor, however Activity Monitor doesn’t provide context sensitive drill through. One thing I learned in Keith’s and Boris’ session was that the buffer cache hit ratio should really never be below 87% due to the read-ahead mechanism in SQL Server.  When a page is read, it will read the entire extent.  So for every page read, you get 7 more read.  If you need any of those 7 extra pages, well they are already in cache.  I had a lot of fun in Jeremiah’s session about refactoring code plus I learned a lot.  His slides were visually presented in a fun way, which just made for a more upbeat presentation.  Jeremiah says that before you start refactoring, you should look at your system.  Investigate missing or too many indexes, out-of-date statistics, and other areas that could be leading to your code running slow.  He talked about code standards.  He suggested using common abbreviations for aliases instead of one-letter aliases.  I’m a big offender of one-letter aliases, but he makes a good point.  He said that join order does not matter to the optimizer, but it does matter to those who have to read your code.  Now let’s get into refactoring! Eliminate useless things – useless/unneeded joins and columns.  If you don’t need it, get rid of it! Instead of using DISTINCT/JOIN, replace with EXISTS Simplify your conditions; use UNION or better yet UNION ALL instead of OR to avoid a scan and use indexes for each union query Branching logic – instead of IF this, IF that, and on and on…use dynamic SQL (sp_executesql, please!) or use a parameterized query in the application Correlated subqueries – YUCK! Replace with a join Eliminate repeated patterns Last, but certainly not least, was Kimberly’s session.  Kimberly is my favorite speaker.  I attended her two-day pre-conference seminar at PASS Summit 2005 as well as a SQL Immersion Event last December.  Did I mention she’s my favorite speaker?  Okay, enough of that. Kimberly’s session was packed with demos.  I had seen some of it in the SQL Immersion Event, but it was very nice to get a refresher on these, especially since I’ve got a VLDB with some growing pains.  One key takeaway from her session is the idea to use a log shipping solution with a load delay, such as 6, 8, or 24 hours behind the primary.  In the case of say an accidentally dropped table in a VLDB, we could retrieve it from the secondary database rather than waiting an eternity for a restore to complete.  Kimberly let us know that in SQL Server 2012 (it finally has a name!), online rebuilds are supported even if there are LOB columns in your table.  This will simplify custom code that intelligently figures out if an online rebuild is possible. There was actually one last time slot for sessions that day, but I had an airplane to catch and my kids to see!

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• Indentify Codecs & Technical Information About Video Files

  - by DigitalGeekery

  Have you ever wanted to play an audio or video file but didn’t have the proper codec installed? Today we’ll show how to determine codecs, along with a host of other technical details about your media files with MediaInfo. Installation Download and install MediaInfo. You can find the download link at the bottom of the page. Note: When installing MediaInfo there is a recommended software bundle which you can opt out of by selecting Do not install option. Each recommended software choice may be different, like in this example it offers Spyware Terminator. The cool thing though is they use Open Candy which opts you out of the install. Just double check to make sure you’re not installing extra crapware. Using MediaInfo The first time you run MediaInfo it will display the Preferences window. There are various option such as language, output format, and whether or not you want MediaInfo to check for new versions. Click OK. Select a file or folder to analyze by clicking on the File or Folder icons on the left of the application window or by selecting File > Open from the menu. You can also drag and drop a file directly onto the application. MediaInfo will display details of your media file. In Basic view, you’ll see basic information. Notice in the example below the video and audio codecs, along with file size, running time of the media file, and even the application used to create the video file (Writing application).    You can switch to some of the other views by selecting View from the Menu and choosing form the dropdown list.   Sheet View will present the information a bit more clearly. You can see in the example below that the video and audio codec are listing in clearly identified columns. (AVC is often more commonly referred to H.264.)   Tree View is perhaps the most detailed. You can see from the example below the codec used for this AVI file is XviD.   Scrolling down even further you’ll see additional information like video and audio bit rates, frame rate, aspect ratio, and more.   In Basic View (and also in Sheet view) you can click to find a player for your file. In this instance with an MP4 file, it took me to the download page for Quicktime. This is by no means the only media player for this file, but if you are stuck for how to play a media file, this will forward you to a solution that works. You can do the same thing with Video codec. Click Go to the web site of this video codec to find a download.   MediaInfo is a simple but powerful tool that can be used to discover the details of a media file, or just to find a compatible codec. It works with most any video file type and is available for Windows, Mac, and Linux. Some Mac and Linux versions, however, are currently command line only. Download MediaInfo Similar Articles Productive Geek Tips How to Convert Videos to 3GP for Mobile PhonesFix for VLC Skipping and Lagging Playing High-Def Video FilesUsing VLC Player Under VistaUse Your Mac Mini as a Media Server Part 2How to Play .OGM Video Files in Windows Vista TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Xobni Plus for Outlook All My Movies 5.9 CloudBerry Online Backup 1.5 for Windows Home Server Snagit 10 2010 World Cup Schedule Boot Snooze – Reboot and then Standby or Hibernate Customize Everything Related to Dates, Times, Currency and Measurement in Windows 7 Google Earth replacement Icon (Icons we like) Build Great Charts in Excel with Chart Advisor tinysong gives a shortened URL for you to post on Twitter (or anywhere)

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• Columnstore Case Study #1: MSIT SONAR Aggregations

  - by aspiringgeek

  Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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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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• SQL Server Optimizer Malfunction?

  - by Tony Davis

  There was a sharp intake of breath from the audience when Adam Machanic declared the SQL Server optimizer to be essentially "stuck in 1997". It was during his fascinating "Query Tuning Mastery: Manhandling Parallelism" session at the recent PASS SQL Summit. Paraphrasing somewhat, Adam (blog | @AdamMachanic) offered a convincing argument that the optimizer often delivers flawed plans based on assumptions that are no longer valid with today’s hardware. In 1997, when Microsoft engineers re-designed the database engine for SQL Server 7.0, SQL Server got its initial implementation of a cost-based optimizer. Up to SQL Server 2000, the developer often had to deploy a steady stream of hints in SQL statements to combat the occasionally wilful plan choices made by the optimizer. However, with each successive release, the optimizer has evolved and improved in its decision-making. It is still prone to the occasional stumble when we tackle difficult problems, join large numbers of tables, perform complex aggregations, and so on, but for most of us, most of the time, the optimizer purrs along efficiently in the background. Adam, however, challenged further any assumption that the current optimizer is competent at providing the most efficient plans for our more complex analytical queries, and in particular of offering up correctly parallelized plans. He painted a picture of a present where complex analytical queries have become ever more prevalent; where disk IO is ever faster so that reads from disk come into buffer cache faster than ever; where the improving RAM-to-data ratio means that we have a better chance of finding our data in cache. Most importantly, we have more CPUs at our disposal than ever before. To get these queries to perform, we not only need to have the right indexes, but also to be able to split the data up into subsets and spread its processing evenly across all these available CPUs. Improvements such as support for ColumnStore indexes are taking things in the right direction, but, unfortunately, deficiencies in the current Optimizer mean that SQL Server is yet to be able to exploit properly all those extra CPUs. Adam’s contention was that the current optimizer uses essentially the same costing model for many of its core operations as it did back in the days of SQL Server 7, based on assumptions that are no longer valid. One example he gave was a "slow disk" bias that may have been valid back in 1997 but certainly is not on modern disk systems. Essentially, the optimizer assesses the relative cost of serial versus parallel plans based on the assumption that there is no IO cost benefit from parallelization, only CPU. It assumes that a single request will saturate the IO channel, and so a query would not run any faster if we parallelized IO because the disk system simply wouldn’t be able to handle the extra pressure. As such, the optimizer often decides that a serial plan is lower cost, often in cases where a parallel plan would improve performance dramatically. It was challenging and thought provoking stuff, as were his techniques for driving parallelism through query logic based on subsets of rows that define the "grain" of the query. I highly recommend you catch the session if you missed it. I’m interested to hear though, when and how often people feel the force of the optimizer’s shortcomings. Barring mistakes, such as stale statistics, how often do you feel the Optimizer fails to find the plan you think it should, and what are the most common causes? Is it fighting to induce it toward parallelism? Combating unexpected plans, arising from table partitioning? Something altogether more prosaic? Cheers, Tony.

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• Columnstore Case Study #1: MSIT SONAR Aggregations

  - by aspiringgeek

  Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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• Investigating on xVelocity (VertiPaq) column size

  - by Marco Russo (SQLBI)

    In January I published an article about how to optimize high cardinality columns in VertiPaq. In the meantime, VertiPaq has been rebranded to xVelocity: the official name is now “xVelocity in-memory analytics engine (VertiPaq)” but using xVelocity and VertiPaq when we talk about Analysis Services has the same meaning. In this post I’ll show how to investigate on columns size of an existing Tabular database so that you can find the most important columns to be optimized. A first approach can be looking in the DataDir of Analysis Services and look for the folder containing the database. Then, look for the biggest files in all subfolders and you will find the name of a file that contains the name of the most expensive column. However, this heuristic process is not very optimized. A better approach is using a DMV that provides the exact information. For example, by using the following query (open SSMS, open an MDX query on the database you are interested to and execute it) you will see all database objects sorted by used size in a descending way. SELECT * FROM $SYSTEM.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS ORDER BY used_size DESC You can look at the first rows in order to understand what are the most expensive columns in your tabular model. The interesting data provided are: TABLE_ID: it is the name of the object – it can be also a dictionary or an index COLUMN_ID: it is the column name the object belongs to – you can also see ID_TO_POS and POS_TO_ID in case they refer to internal indexes RECORDS_COUNT: it is the number of rows in the column USED_SIZE: it is the used memory for the object By looking at the ration between USED_SIZE and RECORDS_COUNT you can understand what you can do in order to optimize your tabular model. Your options are: Remove the column. Yes, if it contains data you will never use in a query, simply remove the column from the tabular model Change granularity. If you are tracking time and you included milliseconds but seconds would be enough, round the data source column to the nearest second. If you have a floating point number but two decimals are good enough (i.e. the temperature), round the number to the nearest decimal is relevant to you. Split the column. Create two or more columns that have to be combined together in order to produce the original value. This technique is described in VertiPaq optimization article. Sort the table by that column. When you read the data source, you might consider sorting data by this column, so that the compression will be more efficient. However, this technique works better on columns that don’t have too many distinct values and you will probably move the problem to another column. Sorting data starting from the lower density columns (those with a few number of distinct values) and going to higher density columns (those with high cardinality) is the technique that provides the best compression ratio. After the optimization you should be able to reduce the used size and improve the count/size ration you measured before. If you are interested in a longer discussion about internal storage in VertiPaq and you want understand why this approach can save you space (and time), you can attend my 24 Hours of PASS session “VertiPaq Under the Hood” on March 21 at 08:00 GMT.

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• Comparing Apples and Pairs

  - by Tony Davis

  A recent study, High Costs and Negative Value of Pair Programming, by Capers Jones, pulls no punches in its assessment of the costs-to- benefits ratio of pair programming, two programmers working together, at a single computer, rather than separately. He implies that pair programming is a method rushed into production on a wave of enthusiasm for Agile or Extreme Programming, without any real regard for its effectiveness. Despite admitting that his data represented a far from complete study of the economics of pair programming, his conclusions were stark: it was 2.5 times more expensive, resulted in a 15% drop in productivity, and offered no significant quality benefits. The author provides a more scientific analysis than Jon Evans’ Pair Programming Considered Harmful, but the theme is the same. In terms of upfront-coding costs, pair programming is surely more expensive. The claim of productivity loss is dubious and contested by other studies. The third claim, though, did surprise me. The author’s data suggests that if both the pair and the individual programmers employ static code analysis and testing, then there is no measurable difference in the resulting code quality, in terms of defects per function point. In other words, pair programming incurs a massive extra cost for no tangible return in investment. There were, inevitably, many criticisms of his data and his conclusions, a few of which are persuasive. Firstly, that the driver/observer model of pair programming, on which the study bases its findings, is far from the most effective. For example, many find Ping-Pong pairing, based on use of test-driven development, far more productive. Secondly, that it doesn’t distinguish between “expert” and “novice” pair programmers– that is, independently of other programming skills, how skilled was an individual at pair programming. Thirdly, that his measure of quality is too narrow. This point rings true, certainly at Red Gate, where developers don’t pair program all the time, but use the method in short bursts, while tackling a tricky problem and needing a fresh perspective on the best approach, or more in-depth knowledge in a particular domain. All of them argue that pair programming, and collective code ownership, offers significant rewards, if not in terms of immediate “bug reduction”, then in removing the likelihood of single points of failure, and improving the overall quality and longer-term adaptability/maintainability of the design. There is also a massive learning benefit for both participants. One developer told me how he once worked in the same team over consecutive summers, the first time with no pair programming and the second time pair-programming two-thirds of the time, and described the increased rate of learning the second time as “phenomenal”. There are a great many theories on how we should develop software (Scrum, XP, Lean, etc.), but woefully little scientific research in their effectiveness. For a group that spends so much time crunching other people’s data, I wonder if developers spend enough time crunching data about themselves. Capers Jones’ data may be incomplete, but should cause a pause for thought, especially for any large IT departments, supporting commerce and industry, who are considering pair programming. It certainly shouldn’t discourage teams from exploring new ways of developing software, as long as they also think about how to gather hard data to gauge their effectiveness.

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• Texture displays on Android emulator but not on device

  - by Rob

  I have written a simple UI which takes an image (256x256) and maps it to a rectangle. This works perfectly on the emulator however on the phone the texture does not show, I see only a white rectangle. This is my code: public void onSurfaceCreated(GL10 gl, EGLConfig config) { byteBuffer = ByteBuffer.allocateDirect(shape.length * 4); byteBuffer.order(ByteOrder.nativeOrder()); vertexBuffer = byteBuffer.asFloatBuffer(); vertexBuffer.put(cardshape); vertexBuffer.position(0); byteBuffer = ByteBuffer.allocateDirect(shape.length * 4); byteBuffer.order(ByteOrder.nativeOrder()); textureBuffer = byteBuffer.asFloatBuffer(); textureBuffer.put(textureshape); textureBuffer.position(0); // Set the background color to black ( rgba ). gl.glClearColor(0.0f, 0.0f, 0.0f, 0.5f); // Enable Smooth Shading, default not really needed. gl.glShadeModel(GL10.GL_SMOOTH); // Depth buffer setup. gl.glClearDepthf(1.0f); // Enables depth testing. gl.glEnable(GL10.GL_DEPTH_TEST); // The type of depth testing to do. gl.glDepthFunc(GL10.GL_LEQUAL); // Really nice perspective calculations. gl.glHint(GL10.GL_PERSPECTIVE_CORRECTION_HINT, GL10.GL_NICEST); gl.glEnable(GL10.GL_TEXTURE_2D); loadGLTexture(gl); } public void onDrawFrame(GL10 gl) { gl.glClear(GL10.GL_COLOR_BUFFER_BIT | GL10.GL_DEPTH_BUFFER_BIT); gl.glDisable(GL10.GL_DEPTH_TEST); gl.glMatrixMode(GL10.GL_PROJECTION); // Select Projection gl.glPushMatrix(); // Push The Matrix gl.glLoadIdentity(); // Reset The Matrix gl.glOrthof(0f, 480f, 0f, 800f, -1f, 1f); gl.glMatrixMode(GL10.GL_MODELVIEW); // Select Modelview Matrix gl.glPushMatrix(); // Push The Matrix gl.glLoadIdentity(); // Reset The Matrix gl.glEnableClientState(GL10.GL_VERTEX_ARRAY); gl.glEnableClientState(GL10.GL_TEXTURE_COORD_ARRAY); gl.glLoadIdentity(); gl.glTranslatef(card.x, card.y, 0.0f); gl.glBindTexture(GL10.GL_TEXTURE_2D, texture[0]); //activates texture to be used now gl.glVertexPointer(2, GL10.GL_FLOAT, 0, vertexBuffer); gl.glTexCoordPointer(2, GL10.GL_FLOAT, 0, textureBuffer); gl.glDrawArrays(GL10.GL_TRIANGLE_STRIP, 0, 4); gl.glDisableClientState(GL10.GL_VERTEX_ARRAY); gl.glDisableClientState(GL10.GL_TEXTURE_COORD_ARRAY); } public void onSurfaceChanged(GL10 gl, int width, int height) { // Sets the current view port to the new size. gl.glViewport(0, 0, width, height); // Select the projection matrix gl.glMatrixMode(GL10.GL_PROJECTION); // Reset the projection matrix gl.glLoadIdentity(); // Calculate the aspect ratio of the window GLU.gluPerspective(gl, 45.0f, (float) width / (float) height, 0.1f, 100.0f); // Select the modelview matrix gl.glMatrixMode(GL10.GL_MODELVIEW); // Reset the modelview matrix gl.glLoadIdentity(); } public int[] texture = new int[1]; public void loadGLTexture(GL10 gl) { // loading texture Bitmap bitmap; bitmap = BitmapFactory.decodeResource(context.getResources(), R.drawable.image); // generate one texture pointer gl.glGenTextures(0, texture, 0); //adds texture id to texture array // ...and bind it to our array gl.glBindTexture(GL10.GL_TEXTURE_2D, texture[0]); //activates texture to be used now // create nearest filtered texture gl.glTexParameterf(GL10.GL_TEXTURE_2D, GL10.GL_TEXTURE_MIN_FILTER, GL10.GL_NEAREST); gl.glTexParameterf(GL10.GL_TEXTURE_2D, GL10.GL_TEXTURE_MAG_FILTER, GL10.GL_LINEAR); // Use Android GLUtils to specify a two-dimensional texture image from our bitmap GLUtils.texImage2D(GL10.GL_TEXTURE_2D, 0, bitmap, 0); // Clean up bitmap.recycle(); } As per many other similar issues and resolutions on the web i have tried setting the minsdkversion is 3, loading the bitmap via an input stream bitmap = BitmapFactory.decodeStream(is), setting BitmapFactory.Options.inScaled to false, putting the images in the nodpi folder and putting them in the raw folder.. all of which didn't help. I'm not really sure what else to try..

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• Investigating on xVelocity (VertiPaq) column size

  - by Marco Russo (SQLBI)

    In January I published an article about how to optimize high cardinality columns in VertiPaq. In the meantime, VertiPaq has been rebranded to xVelocity: the official name is now “xVelocity in-memory analytics engine (VertiPaq)” but using xVelocity and VertiPaq when we talk about Analysis Services has the same meaning. In this post I’ll show how to investigate on columns size of an existing Tabular database so that you can find the most important columns to be optimized. A first approach can be looking in the DataDir of Analysis Services and look for the folder containing the database. Then, look for the biggest files in all subfolders and you will find the name of a file that contains the name of the most expensive column. However, this heuristic process is not very optimized. A better approach is using a DMV that provides the exact information. For example, by using the following query (open SSMS, open an MDX query on the database you are interested to and execute it) you will see all database objects sorted by used size in a descending way. SELECT * FROM $SYSTEM.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS ORDER BY used_size DESC You can look at the first rows in order to understand what are the most expensive columns in your tabular model. The interesting data provided are: TABLE_ID: it is the name of the object – it can be also a dictionary or an index COLUMN_ID: it is the column name the object belongs to – you can also see ID_TO_POS and POS_TO_ID in case they refer to internal indexes RECORDS_COUNT: it is the number of rows in the column USED_SIZE: it is the used memory for the object By looking at the ration between USED_SIZE and RECORDS_COUNT you can understand what you can do in order to optimize your tabular model. Your options are: Remove the column. Yes, if it contains data you will never use in a query, simply remove the column from the tabular model Change granularity. If you are tracking time and you included milliseconds but seconds would be enough, round the data source column to the nearest second. If you have a floating point number but two decimals are good enough (i.e. the temperature), round the number to the nearest decimal is relevant to you. Split the column. Create two or more columns that have to be combined together in order to produce the original value. This technique is described in VertiPaq optimization article. Sort the table by that column. When you read the data source, you might consider sorting data by this column, so that the compression will be more efficient. However, this technique works better on columns that don’t have too many distinct values and you will probably move the problem to another column. Sorting data starting from the lower density columns (those with a few number of distinct values) and going to higher density columns (those with high cardinality) is the technique that provides the best compression ratio. After the optimization you should be able to reduce the used size and improve the count/size ration you measured before. If you are interested in a longer discussion about internal storage in VertiPaq and you want understand why this approach can save you space (and time), you can attend my 24 Hours of PASS session “VertiPaq Under the Hood” on March 21 at 08:00 GMT.

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• Proving What You are Worth

  - by Ted Henson

  Here is a challenge for everyone. Just about everyone has been asked to provide or calculate the Return on Investment (ROI), so I will assume everyone has a method they use. The problem with stopping once you have an ROI is that those in the C-Suite probably do not care about the ROI as much as Return on Equity (ROE). Shareholders are mostly concerned with their return on the money the invested. Warren Buffett looks at ROE when deciding whether to make a deal or not. This article will outline how you can add more meaning to your ROI and show how you can potentially enhance the ROE of the company.   First I want to start with a base definition I am using for ROI and ROE. Return on investment (ROI) and return on equity (ROE) are ways to measure management effectiveness, parts of a system of measures that also includes profit margins for profitability, price-to-earnings ratio for valuation, and various debt-to-equity ratios for financial strength. Without a set of evaluation metrics, a company's financial performance cannot be fully examined by investors. ROI and ROE calculate the rate of return on a specific investment and the equity capital respectively, assessing how efficient financial resources have been used. Typically, the best way to improve financial efficiency is to reduce production cost, so that will be the focus. Now that the challenge has been made and items have been defined, let’s go deeper. Most research about implementation stops short at system start-up and seldom addresses post-implementation issues. However, we know implementation is a continuous improvement effort, and continued efforts after system start-up will influence the ultimate success of a system.   Most UPK ROI’s I have seen only include the cost savings in developing the training material. Some will also include savings based on reduced Help Desk calls. Using just those values you get a good ROI. To get an ROE you need to go a little deeper. Typically, the best way to improve financial efficiency is to reduce production cost, which is the purpose of implementing/upgrading an enterprise application. Let’s assume the new system is up and running and all users have been properly trained and are comfortable using the system. You provide senior management with your ROI that justifies the original cost. What you want to do now is develop a good base value to a measure the current efficiency. Using usage tracking you can look for various patterns. For example, you may find that users that are accessing UPK assistance are processing a procedure, such as entering an order, 5 minutes faster than those that don’t.  You do some research and discover each minute saved in processing a claim saves the company one dollar. That translates to the company saving five dollars on every transaction. Assuming 100,000 transactions are performed a year, and all users improve their performance, the company will be saving $500,000 a year. That $500,000 can be re-invested, used to reduce debt or paid to the shareholders.   With continued refinement during the life cycle, you should be able to find ways to reduce cost. These are the type of numbers and productivity gains that senior management and shareholders want to see. Being able to quantify savings and increase productivity may also help when seeking a raise or promotion.

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• Bios Memory settings and Virtualization + Ubuntu (Unofficial Answers Welcome) [closed]

  - by TardisGuy

  Attempting to optimize my (Main Windowless) Ubuntu system for my uses I will detail questions below, I understand this might be the wrong place to ask these questions. If so, my apologies and I thank you so much for your patience. Thanks to all the volenteers that have helped me learn ubuntu over the years (Since 5.10) This is a "short" list of questions I have been trying to figure out for some time. If you feel you can answer one but not another, that's already more than I could ask for. I have wrote this up in a format for easy navigation to important points Hopefully to less annoy your eyes. You're welcome :) or i'm sorry i annoy you. :( If you would be so kind, Please format answers as follows: question 1: _ _ _ _ _ or question 1-a: _ _ _ _ _ If you want to simply link me to relevant information, rather than type up something really detailed; that would be more than awesome! Memory Specific Questions Goal: Maximizing memory bandwith to better perform in Virtualization, and Large file compression. (Possible conflict?) Ganged vs Unganged "which is better?"** is relative, i know. But what about ganged vs unganged - With or without Bank/channel interleaving? a: Speculation - If i understand correctly, "channel interleaving has something to do with using both channels to read or write in a kind of "striping" pattern, as opposed to a standard half duplex operation.(probably wrong) but wouldn't ganged channels make this irrelevant? Memory Interleaving(bank). Does it have a down side? Does it require a ratio of clocks? (If I run 4x4gig ddr3) a. If im reading correctly(trying to learn), this is designed to spread operations between latency cycles to work around the higher latency of "normal" operation. b. However it seems to me that it has to be: divisible by fractions of a master clock? So if i run memory at 1333mhz, then the mean between 2 (physical) banks would operate every (roughly) 600Mhz? Warning! Possibly utter nonsense: (1333/2 interleaving to act like 1 memory module per 2 sticks of a total of 4 sticks, meaning 2x channels@4) c. which makes me wonder if there would be left over clock cycles the system would have to... "truncate/balance" or something? But I'm certain theres a feature somewhere i don't understand. Virtualization Questions AMD-V - Option of IOMMU Turned it on, why do i have extra option of "64MB"? If IOMMU is on, but "64MB" is "disabled", Is it on? (have scoured google, I still dont know) a. I think i understand that its supposed to (kind of) "set aside" a part of ram to act as a faster interactive zone for "stuff" (usb, Graphics, and... what?) b. I am using Nvidia graphics on AMD (Used kernel option "iommu=pt iommu=1, pt "passthrough"? No idea what they do, found it on google to solve boot up issue) c. Will this option help me use low latency sound hardware, like my midi keyboard? Can you recommend any additional tweaks? a. sysctl settings? b. swap settings? Grats, youve reached the end. Thanks for Reading.

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• How Does a 724% Return on Your Salesforce Automation Investment Sound?

  - by Brian Dayton

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Oracle Sales Cloud and Marketing Cloud customer Apex IT gained just that, a 724% return on investment (ROI) when they implemented these Oracle Cloud solutions in their fast-moving, rapidly-growing business. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";} Congratulations Apex IT! Apex IT was just announced as a winner of the Nucleus Research 11th annual Technology ROI Awards. The award, given by the analyst firm highlights organizations that have successfully leveraged IT deployments to maximize value per dollar spent. Fast Facts: Return on Investment - 724% Payback - 2 months Average annual benefit - $91,534 Cost: Benefit Ratio – 1:48 Business Benefits In addition to the ROI and cost metrics the award calls out improvements in Apex IT’s business operations—across both Sales and Marketing teams: Improved ability to identify new opportunities and focus sales resources on higher-probability deals Reduced administration and manual lead tracking—resulting in more time selling and a net new client increase of 46% Increased campaign productivity for both Marketing and Sales, including Oracle Marketing Cloud’s automation of campaign tracking and nurture programs Improved margins with more structured and disciplined sales processes—resulting in more effective deal negotiations Please join us in congratulating Apex IT on this award and their business achievements. Want More Details? Don’t take our word for it. Read the full Apex IT ROI Case Study and learn more about Apex IT’s business—including their work with Oracle Sales and Marketing Cloud on behalf of their clients in leading Sales organizations. Learn More About Oracle Sales Cloud www.oracle.com/salescloud www.facebook.com/oraclesalescloud www.youtube.com/oraclesalescloud Oracle Customer Experience and Complementary Sales Solutions Oracle Configure, Price and Quote (CPQ) Cloud Oracle Marketing Cloud Oracle Customer Experience /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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• How to get the height of an image and apply that height to a div? [migrated]

  - by Mick79

  I am building a mobile web app and I'm using jquerytools slider on it. i want te slider to show (in proper ratio) across all mobile devices so width of the images is 100% and height is auto in css. However as all the elements are floated and jquerytools slider requires the position be set to absolute, the containing div (#header) doesn't stretch to fit the content. I am trying to use jquery to get the height of the height of the img and apply that height to the header.... however I am having no luck. CSS: #header{ width:100%; position:relative; z-index: 20; /* box-shadow: 0 0 10px white; */ overflow: auto; } .scrollable { position:relative; overflow:hidden; width: 100%; height: 100%; /* box-shadow: 0 0 20px purple; */ /* height:198px; */ z-index: 20; overflow: auto; } .scrollable .items { /* this cannot be too large */ width:1000%; position:absolute; clear:both; /* box-shadow: 0 0 30px green; */ } .items div { float:left; width:10%; height:100%; } /* single scrollable item */ .scrollable img { /* float:left; */ width:100%; height: auto; /* height:198px; */ } /* active item */ .scrollable .active { border:2px solid #000; position:relative; cursor:default; } HTML <div id=header><!-- root element for scrollable --> <div class="scrollable" id="scrollable"> <!-- root element for the items --> <div class="items"> <div> <img src="img/img2.jpg" /> </div> <div> <img src="img/img1.jpg" /> </div> <div> <img src="img/img3.jpg" /> </div> <div> <img src="img/img4.jpg" /> </div> <div> <img src="img/img6.jpg" /> </div> </div><!-- items --> </div><!-- scrollable --> </div><!-- header -->

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• What are some of the best wireless routers for a price-conscious home power-user?

  - by Alain

  I'm extremely dissatisfied with the 'popular' choice for routers in homes and small offices. They are expensive (upwards of 60$), lack a great deal of useful configuration options, and seem to need to be restarted quite often. (Linksys comes to mind). I've been on the market for a good router lately, and slowly collecting a set of requirements I feel good routers should meet. Maximum number of TCP/IP connections. - This isn't something I see any routers advertise, but in terms of supporting torrent applications, I've been screwed by routers that support less than 20 here. From what I understand a fairly standard number is 200, but there are not so expensive routers that support thousands. Router configuration menu - Most have standard menu's that let you set up basic things like your wireless network encryption settings, uPnP, and maybe even DMZ (demilitarized zones). An absolute requirement for me, however, are routers with good enough firmware to support: Explicit Port forwarding Assigning static local ips to specific mac addresses, or at least Port forwarding by MAC address Port, IP and MAC filtering Dynamic DNS service for home users who want to set up a server but have a dynamic IP Traffic shaping (ideally) - giving priority to packets from certain machines or over certain ports. Strong wireless signal - If getting a reliable signal requires me to be so close to the router that I can connect an Ethernet cable, it's not good enough. As many Ethernet ports as possible. - Because I want to be able to switch from console gaming to PC gaming without visiting my router. So far, the best thing I've stumbled upon (in the bargain bin at staples) was a 20$ retail plus router. It was meant to be the cheapest alternative until I could find something better to purchase online, but I was actually blown away by the firmware capabilities. It supports defining reserved bandwidth for certain network traffic, dynamic DNS, reserving local IPs for specific MAC addresses, etc. At 2 am when my roommate is killing our Internet with their torrents, I can limit their bandwidth without outright blacklisting them. I have, however, met serious limitations when it comes to network traffic between local machines. It claims a 300Mbps connection, but I have trouble streaming videos from my PC to my console or other laptops wirelessly. It has a meltdown and needs to be reset once in a while (no more than a couple times a month), and it's got a 200 connection limit. There 4 Ethernet ports in the back but I'm pretty sure the first doesn't work. So some great answers to this question would be: Any metrics you use to compare routers, and requirements you have for new candidates. The best routers you've found for supporting home servers, file management systems, high volume torrent traffic, good price/feature ratio, etc. Good configuration advice (aside from 'use Ethernet whenever possible') Thanks for your feedback and experiences!

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• Network use of Gaming PC

  - by Matthew Patrick Cashatt

  Background After YEARS of waiting, I built the custom gaming PC of my dreams: Intel i7 - 975 Extreme Edition 3.3ghz (overclocked to 4.0) ATI Radeon 5970 2gb Corsair 256 gb SSD Drive 2 TB Sata II 3.0 7200rpm data drive 12 GB Kingston Hyper-X (1600mhz) DDR3 Windows 7 Ultra 64 bit And so on. . . Problem I hooked this beast up to our home theater and settled in for a great gaming season only to realize a couple of drawbacks: It's hard to accurately wax bad guys using a keyboard in your lap whilst reclined on your couch (and using a wireless keyboard). It's hard to read the text on the screen (i.e. menus, etc). I find that a 1:1 ratio (screen diagonal inch to inch away from screen) is optimum, but using the home theater, it's more like 1:3 which has me squinting unless I sit on the coffee table. The wife always seems to want the TV the same time I do and, unfortunately "Real Housewives of Beverly Hills" and Battlefield BC don't mix. I am losing the battle in the home theater room, but the PC has to stay there (long story). So, this leaves me with the option of playing in my home office which is about 30 feet away from the home theater. I am a software developer so I have a pretty decent set up in my office--multiple 1080p monitors, HP Envy 17 which can run games like Crysis in 720p with out stammering too much. Also, I can game very comfortably at my desk in the office. Still, even though the set up in my office can run games well enough, I don't want to regress to that when I have worked YEARS for an awesome gaming PC that can run everything on ultra high settings. My Question What are my options for running my games on the beastly desktop in the Home Theater, but physically playing in my office about 30 feet away? A really long HDMI cable? LAN/RDC? Details that May Help We have an open crawlspace so running cable from HT room to office is no problem. I already have networked the house with a LAN Any help is GREATLY appreciated. Thanks, Matt

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• HOW TO Convert DVD to iPad(Also converts to iPods)?

  - by goodm

  DVD to iPad Converter (Also converts to iPods) DVD to iPad Converter is the easiest-to-use and fastest DVD to iPad converter for Apple iPad movie and iPad video. It can convert almost any kind of DVD to iPad movie or iPad video format. It is also a powerful DVD to iPad converter with a conversion speed that is much faster than real-time. With this converter, you can use your iPad as a portable DVD player and enjoy your favorite DVDs on your iPad. http://www.softseeking.com/prodail.aspx?proid=83" Features of this DVD to iPad Converter Three Running Modes --In Direct Mode, you can directly click the DVD menu to select the movie you want to rip. This mode is very easy for ripping DVD movies. --In Batch Mode, you can select the DVD titles or chapters you want to rip via a checkbox list. This mode is very easy for batch ripping music DVDs, MTV DVDs and episodic DVDs. --In 1-Click Mode, you just need one click to open a DVD, after which the rest of the task will be done automatically. This is a “designed-for-dummies” mode. Input Types You can convert almost any kind of DVD format to iPad. Output Splitting You can split your output video by DVD chapters and titles. Fully supports MTV DVDs and episodic DVDs. File Size and Quality Adjustment You can customize the output file size and corresponding video quality. Flexible Output Profiles You can easily customize the various video settings such as brightness, bit rate, etc. Language Selection for Subtitles and Audio Track In Direct mode and in Batch mode, you can select the subtitle and audio track language. (In 1-Click mode, the default language is chosen automatically). Video Crop You can crop your video to 16:9, 4:3, full screen, etc. Video Resize You can resize your video. For example, you can set it to "Keep aspect ratio" or "Stretch to fit screen." Other Converts DVD to MP3 audio. Supports Dolby, DTS Surround audio track. Converts to the latest iPhone, 4th generation iPad nano, nano chromatic, 2nd generation iPad touch, and Apple TV.

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• Setting font size of Closed Captions on iPhone using ffmpeg or mencoder

  - by forthrin

  Does anyone know how to either: Make ffmpeg set subtitle font size in the output video file Make mencoder produce an iPhone-compatible video file (with subtitles) I finally found out how to get Closed Captions video on iPhone, with mkv and srt files as source material. The secret was using the mov_text subtitle codec in ffmpeg (and turning on Closed Captions in the iPhone settings of course): ffmpeg -y -i in.mkv -i in.srt -map 0:0 -map 0:1 -map 1:0 -vcodec copy -acodec aac -ab 256k -scodec mov_text -strict -2 -metadata title="Title" -metadata:s:s:0 language=eng out.mp4 However, the font size appears very small on the iPhone, and I can't find out how to set it with ffmpeg (the iPhone has no option for this). I found out that mencoder has a -subfont-text-scale option, but I don't have a lot of experience with this program. The following, my best attempt so far, produces an output file which is not playable on the iPhone. sudo port install mplayer +mencoder_extras +osd mencoder in.mkv -sub in.srt -o out.mp4 -ovc copy -oac faac -faacopts br=256:mpeg=4:object=2 -channels 2 -srate 48000 -subfont-text-scale 10 -of lavf -lavfopts format=mp4 PS! As requested, here is the output from mencoder: 192 audio & 400 video codecs success: format: 0 data: 0x0 - 0xb64b9d2f libavformat version 54.6.101 (internal) libavformat file format detected. [matroska,webm @ 0x1015c9a50]Unknown entry 0x80 [lavf] stream 0: video (h264), -vid 0 [lavf] stream 1: audio (ac3), -aid 0, -alang eng VIDEO: [H264] 1280x544 0bpp 49.894 fps 0.0 kbps ( 0.0 kbyte/s) [V] filefmt:44 fourcc:0x34363248 size:1280x544 fps:49.894 ftime:=0.0200 ========================================================================== Opening audio decoder: [ffmpeg] FFmpeg/libavcodec audio decoders libavcodec version 54.23.100 (internal) AUDIO: 48000 Hz, 2 ch, s16le, 448.0 kbit/29.17% (ratio: 56000->192000) Selected audio codec: [ffac3] afm: ffmpeg (FFmpeg AC-3) ========================================================================== ** MUXER_LAVF ***************************************************************** REMEMBER: MEncoder's libavformat muxing is presently broken and can generate INCORRECT files in the presence of B-frames. Moreover, due to bugs MPlayer will play these INCORRECT files as if nothing were wrong! ******************************************************************************* OK, exit. videocodec: framecopy (1280x544 0bpp fourcc=34363248) VIDEO CODEC ID: 28 AUDIO CODEC ID: 15002, TAG: 0 Writing header... [mp4 @ 0x1015c9a50]Codec for stream 0 does not use global headers but container format requires global headers [mp4 @ 0x1015c9a50]Codec for stream 1 does not use global headers but container format requires global headers Then the following repeats itself for every frame: Pos: 0.0s 1f ( 2%) 0.00fps Trem: 0min 0mb A-V:0.000 [0:0] [mp4 @ 0x1015c9a50]malformated aac bitstream, use -absf aac_adtstoasc Error while writing frame. I recognize -absf aac_adtstoasc as an ffmpeg option (does mencoder spawn ffmpeg?), but I don't know how to pass this option on (my hunch is this is not even the origin of the problem).

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• Airport Express chokes Wi-Fi for a few seconds, several times per hour. Any idea why?

  - by user13727

  I'm using a MacBookPro connected to an AiportExpress' Wi-FI network. Every once in a while, the Wi-Fi will choke up and either drop some packets, or lag horribly for several seconds. I'm losing hair over this because every time I chat on Skype, the call hangs randomly due to this problem. Any idea what's wrong? Some more details: two networks are set up: 2.4Ghz and 5Ghz, and the issue happens on both the network uses WPA2 Personal for security the Airport is in the same room with my computer the Airport is fairly new, bought this summer, model number off the back: A1392 tried connecting to a neighbours wifi to see if it's a problem with my computer, or interference. It's not, it doesn't happen on their network. tried resetting it several times tried changing channels manually Ping results are below, so you can see what I'm talking about. EDIT: 10.0.1.1 is the Airport's IP 64 bytes from 10.0.1.1: icmp_seq=1795 ttl=255 time=0.813 ms 64 bytes from 10.0.1.1: icmp_seq=1796 ttl=255 time=3.335 ms 64 bytes from 10.0.1.1: icmp_seq=1797 ttl=255 time=3.403 ms 64 bytes from 10.0.1.1: icmp_seq=1798 ttl=255 time=3.414 ms 64 bytes from 10.0.1.1: icmp_seq=1799 ttl=255 time=3.227 ms 64 bytes from 10.0.1.1: icmp_seq=1800 ttl=255 time=3.274 ms 64 bytes from 10.0.1.1: icmp_seq=1801 ttl=255 time=3.253 ms 64 bytes from 10.0.1.1: icmp_seq=1802 ttl=255 time=3.292 ms >>>> choke starts <<< 64 bytes from 10.0.1.1: icmp_seq=1803 ttl=255 time=53.977 ms 64 bytes from 10.0.1.1: icmp_seq=1804 ttl=255 time=35.049 ms 64 bytes from 10.0.1.1: icmp_seq=1805 ttl=255 time=19.820 ms >>>> choke ends <<< 64 bytes from 10.0.1.1: icmp_seq=1806 ttl=255 time=0.716 ms 64 bytes from 10.0.1.1: icmp_seq=1807 ttl=255 time=0.705 ms 64 bytes from 10.0.1.1: icmp_seq=1808 ttl=255 time=0.919 ms 64 bytes from 10.0.1.1: icmp_seq=1809 ttl=255 time=0.659 ms 64 bytes from 10.0.1.1: icmp_seq=1810 ttl=255 time=0.877 ms 64 bytes from 10.0.1.1: icmp_seq=1811 ttl=255 time=0.679 ms 64 bytes from 10.0.1.1: icmp_seq=1812 ttl=255 time=0.854 ms 64 bytes from 10.0.1.1: icmp_seq=1813 ttl=255 time=0.644 ms 64 bytes from 10.0.1.1: icmp_seq=1814 ttl=255 time=3.779 ms ... time passes .. 64 bytes from 10.0.1.1: icmp_seq=1599 ttl=255 time=0.674 ms 64 bytes from 10.0.1.1: icmp_seq=1600 ttl=255 time=0.930 ms 64 bytes from 10.0.1.1: icmp_seq=1601 ttl=255 time=0.665 ms 64 bytes from 10.0.1.1: icmp_seq=1602 ttl=255 time=1.085 ms Request timeout for icmp_seq 1603 Request timeout for icmp_seq 1604 64 bytes from 10.0.1.1: icmp_seq=1605 ttl=255 time=104.969 ms 64 bytes from 10.0.1.1: icmp_seq=1606 ttl=255 time=11.521 ms 64 bytes from 10.0.1.1: icmp_seq=1607 ttl=255 time=0.926 ms 64 bytes from 10.0.1.1: icmp_seq=1608 ttl=255 time=0.993 ms 64 bytes from 10.0.1.1: icmp_seq=1609 ttl=255 time=0.745 ms And the Signal-Noise ratio:

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• How can I fix Problems with interlaced video jerking/flicking when playedback on DVD players? (Mixin

  - by Simon P Stevens

  I'm trying to make a DVD and the final DVD jerks when played on standalone DVD players. It seems to play fine on PCs. I think the problem may be to do with interlacing settings when rendering the final output, but I'll outline the whole editing process I have followed in case I've made a mistake somewhere else. Most of the footage comes from a sony handy cam (one of those mini DVD ones) so isn't great quality. It was set to "high quality" (haha) and 16:9 aspect ratio when it was recorded. I copy the files directly from the mini DVDs onto the hard drive and import them into Cinelerra. In Cinelerra I set the format to 25fps, 720x576, RGBA-8bit, 16:9, interlaced bottom fields first. When I've finished the editing, I add a Fields to frames effect (set to bottom first) to each video track. I render to audio and video separately: Audio: AC3, 128kbps Video: YUV4MPEG steam, video pipe settings: ffmpeg -f yuv4mpegpipe -i - -y -target dvd -flags +ilme+ildct mpeg2video % Cinelerra often crashes during the rendering, so I set it to generate a new video file at each label, and combine them using cat when I've got a sucesful render of each one. Once I've combined them, I use mencoder to re-index them: mencoder -forceidx -oac copy -ovc copy merged.m2v -o mergedReIndexed.m2v I combine the audio and video files using ffmpeg: ffmpeg -i AudioFile.ac3 -i VideoFile.m2v -target dvd -flags +ilme+ildct FinalMovie.mpg Then I build the menus with spumux and I create the DVD file system with dvdauthor, and finally I write it do a dvd-r like this: nice -n -20 growisofs -dvd-compat -speed=2 -Z /dev/dvd -dvd-video -V VIDEO ./ && eject /dev/dvd Originally, when I did it the DVD flickered badly, so as suggested in a guide I added the fields to frames effect in cinelerra. Now it doesn't "flicker", but has become "jerky" when there is lots of motion, particularly when the camera is moving, so the whole background moves. This is what I've tried so far: Removed "mpeg2video" from cinelerra video render pipe. Removed +ilme from render pipe. Removed +ildct from render pipe. Removed +ilme from render audio/video rejoin command. Removed +ildct from render audio/video rejoin command. Added -alt to render pipe. Added -alt to render audio/video rejoin command. Tried with and without the frames to fields effect in Cinelerra. and various combinations of the above. I've also tried this: change the Cinelerra fps to 50, use fields to frames (instead of frames to fields), render to an intermediate QTforlinux jpeg video stream, re-importing that back into Cinelerra, adding a frames to fields effect and then rendering that output as normal (@25fps), and I still have the same problem. Has anyone experienced this "jerking" playback before? Can anyone give any suggestions on how to fix it? (Like I say, it plays back fine on a PC, but not on any of the standalone players I've tried)

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• Clamdscan scans file in 0 seconds

  - by SupaCoco

  I have to run clamav on large files. I was wondering which command was the fastest between clamscan and clamdscan. But it seems that clamdscan is not working properly: it scans file larger than 1 GB. Could you guys help me find why the heck clamdscan isn't working ? Between clamscan and clamdscan which one is less resource consuming ? I run ClamAV 0.97.8/18037 on Ubuntu 12.04.3 LTS. Please find below the execution result of both commands: clamscan myfile.zip ----------- SCAN SUMMARY ----------- Known viruses: 2864504 Engine version: 0.97.8 Scanned directories: 0 Scanned files: 1 Infected files: 0 Data scanned: 0.00 MB Data read: 1024.16 MB (ratio 0.00:1) Time: 9.145 sec (0 m 9 s) clamdscan myfile.zip /home/ubuntu/workspace/benchmark/myfile.zip: OK ----------- SCAN SUMMARY ----------- Infected files: 0 Time: 0.000 sec (0 m 0 s) And here are the clamav log file: Wed Oct 30 10:26:32 2013 -> Received POLLIN|POLLHUP on fd 4 Wed Oct 30 10:26:32 2013 -> Got new connection, FD 9 Wed Oct 30 10:26:32 2013 -> Received POLLIN|POLLHUP on fd 5 Wed Oct 30 10:26:32 2013 -> fds_poll_recv: timeout after 5 seconds Wed Oct 30 10:26:32 2013 -> Received POLLIN|POLLHUP on fd 9 Wed Oct 30 10:26:32 2013 -> got command CONTSCAN /home/ubuntu/workspace/benchmark/myfile.zip (51, 7), argument: /home/ubuntu/workspace/benchmark/myfile.zip Wed Oct 30 10:26:32 2013 -> mode -> MODE_WAITREPLY Wed Oct 30 10:26:32 2013 -> Breaking command loop, mode is no longer MODE_COMMAND Wed Oct 30 10:26:32 2013 -> Consumed entire command Wed Oct 30 10:26:32 2013 -> Number of file descriptors polled: 1 fds Wed Oct 30 10:26:32 2013 -> fds_poll_recv: timeout after 3600 seconds Wed Oct 30 10:26:32 2013 -> THRMGR: queue (single) crossed low threshold -> signaling Wed Oct 30 10:26:32 2013 -> THRMGR: queue (bulk) crossed low threshold -> signaling Wed Oct 30 10:26:32 2013 -> /home/ubuntu/workspace/benchmark/myfile.zip: OK Wed Oct 30 10:26:32 2013 -> Finished scanthread Wed Oct 30 10:26:32 2013 -> Scanthread: connection shut down (FD 9) Wed Oct 30 10:26:32 2013 -> THRMGR: queue (single) crossed low threshold -> signaling Wed Oct 30 10:26:32 2013 -> THRMGR: queue (bulk) crossed low threshold -> signaling

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• MongoDB and datasets that don't fit in RAM no matter how hard you shove

  - by sysadmin1138

  This is very system dependent, but chances are near certain we'll scale past some arbitrary cliff and get into Real Trouble. I'm curious what kind of rules-of-thumb exist for a good RAM to Disk-space ratio. We're planning our next round of systems, and need to make some choices regarding RAM, SSDs, and how much of each the new nodes will get. But now for some performance details! During normal workflow of a single project-run, MongoDB is hit with a very high percentage of writes (70-80%). Once the second stage of the processing pipeline hits, it's extremely high read as it needs to deduplicate records identified in the first half of processing. This is the workflow for which "keep your working set in RAM" is made for, and we're designing around that assumption. The entire dataset is continually hit with random queries from end-user derived sources; though the frequency is irregular, the size is usually pretty small (groups of 10 documents). Since this is user-facing, the replies need to be under the "bored-now" threshold of 3 seconds. This access pattern is much less likely to be in cache, so will be very likely to incur disk hits. A secondary processing workflow is high read of previous processing runs that may be days, weeks, or even months old, and is run infrequently but still needs to be zippy. Up to 100% of the documents in the previous processing run will be accessed. No amount of cache-warming can help with this, I suspect. Finished document sizes vary widely, but the median size is about 8K. The high-read portion of the normal project processing strongly suggests the use of Replicas to help distribute the Read traffic. I have read elsewhere that a 1:10 RAM-GB to HD-GB is a good rule-of-thumb for slow disks, As we are seriously considering using much faster SSDs, I'd like to know if there is a similar rule of thumb for fast disks. I know we're using Mongo in a way where cache-everything really isn't going to fly, which is why I'm looking at ways to engineer a system that can survive such usage. The entire dataset will likely be most of a TB within half a year and keep growing.

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• General guidelines / workflow to convert or transfer video "professionally"?

  - by cloneman

  I'm an IT "professional" who sometimes has to deal with small video conversion / video cutting projects, and I'd like to learn "the right way" to do this. Every time I search Google, there's always a disaster for weird, low-maturity trialware, or random forums threads from 3-4 years ago indicating various antiquated method to do it. The big question is the following: What are the "general" guidelines and tools to transcode video into some efficient (lossless?) intermediary, for editing purposes, for the purpose of eventually re-encoding it after? It seems to me like even the simplest of formats and tasks are a disaster of endless trial & error, or expertise only known by hardened experts who have a swiss army kife of weird conversion tools that they use, almost as if mounting an attack against the project. Here are a few cases in point: Simple VOB files extracted from DVD footage can't be imported into Adobe Premiere directly. Virtualdub is an old software people keep recommending but doesn't seem to support newer formats. I don't even know how to tell with certainty which codecs a video has, and weather the image is interlaced or not, and what resolution and codecs I'm dealing with. Problems: Choosing a wrong interlace option which diminishes quality Choosing a wrong pixel aspect ratio (stretches the image) Choosing a wrong "project type" in Premiere causing footage to require scaling Being forced to use some weird program that will have any number of negative effects What I'm looking for: Books or "Real knowledge" on format conversions, recognized tools, etc. that aren't some random forum guides on how to deal with video formats. Workflow guidelines on identifying a format going from one format to another without problems as mentioned above. Documentation on what programs like Adobe Premiere can and can't do with regards to formats, so that I don't use a wrench as a hammer. TL;DR How should you convert or "prepare" a video file to ensure it will be supported by Premiere for editing? Is premiere a suitable program to handle cropping, encoding, or should other tools be used for this, when making a video montage from a variety of source formats? What are some good books to read that specifically deal with converting videos that use any number of codecs?

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