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  • Server high memory usage at same time every day

    - by Sam Parmenter
    Right, we moved one of our main sites onto a new AWS box with plenty of grunt as it would allow us more control that we had before and future proof ourselves. About a month ago we started running into issues with high memory usage at the same time every day. In the morning an export is run to export data to a file which is the FTPed to a local machine for processing. The issues were co-inciding with the rough time of the export but when we didn't run the export one day, the server still ran into the same issues. The export has been run at other times in the day since to monitor memory usage to see if it spikes. The conclusion is that the export is fine and barely touches the sides memory wise. No noticeable change in memory usage. When the issue happens, its effect is to kill mysql and require us to restart the process. We think it might be a mysql memory issue, but might just be that mysql is just the first to feel it. Looking at the logs there is no particular query run before the memory usage hits 90%. When it strikes at about 9:20am, the memory usage spikes from a near constant 25% to 98% and very quickly kills mysql to save itself. It usually takes about 3-4 minutes to die. There are no cron jobs running at that time of the day and we haven't noticed a spike in traffic over the period of the issues. Any help would be massively appreciated! thanks.

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  • HTTP Upload Problems

    - by jfoster
    We are running a marketplace on ColdFusion8 and IIS with a widely geographically distributed user base and have been receiving complaints of issues with some HTTP uploads. Most of the complaints are coming from geographically distant locations from our main datacenter on the US east coast. I've attempted to upload the same 70MB file from a US West coast test server to both our main site and a backup running the same code on a different network route and I saw the same issues fairly consistently in both places, so I've ruled out the code, route, and internal network errors. I've also tested uploads using both the native cf upload tag and a third party tool called SaFileUp. I saw the same issues with both upload tools, so I also don't think this is necessarily a ColdFusion problem. I don't have any problems uploading the test file from the East coast to other east coast servers, so I'm beginning to think that the distance between our users and our equipment is a factor. I've also found that smaller files are more likely to succeed than large ones (< 10MB) I tried the test upload with both IE and FF and did notice a difference in the way that the browsers seemed to handle packet errors. IE seemed to have a tough time continuing an upload after dropped / bad packets, whereas FF seemed to have the ability to gracefully resume an upload after experiencing packet problems. Has anyone experienced similar issues? Is there anything we can do on our side to make uploads more forgiving to packet loss or resumable after an error? A different upload tool etc… Do we need upload servers in more than one location to shorten the network routes between clients and servers? Does anyone think that switching uploads to SSL will help (no layer7 packet sniffing may lead to a smoother upload). Thanks.

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  • Network access lags for Win7 when server network utilization is high

    - by Jeff Miles
    We have a Dell PE2950 file server running Windows 2008, hosting a DFS namespace of ~1.2 TB. This server has two Broadcom 1Gbps NICs teamed together. When there is high traffic going to the server across the network (greater than 200 Mbps), any Windows 7 client accessing a DFS share at the time experiences severe performance problems. For example: Computer A has an AutoCAD drawing opened directly from the DFS share. Performance is normal, not causing any issues. Computer B begins a file transfer, putting a 11GB file onto a different DFS namespace, on the same server Computer A immediately notices lag while using AutoCAD. The cursor momentarily freezes within AutoCAD every 10 seconds or so, and any browsing of the DFS share is extremely slow. Computer B completes file transfer, and performance resumes to normal for Computer A. This is only affecting Windows 7 clients, using a variety of hardware (desktop + laptop). All of our Windows XP clients see no performance impact during the file transfer. Things I have tried with no change: Had Computer A work from an entirely different RAID array from the file transfer destination Updated NIC drivers on clients and server Enabled TCP offload and receive side scaling on the server NIC (previously disabled when the issue began) Antivirus disabled during file transfer I am currently having a user test applications other than AutoCAD when the file transfer occurs, and will update the question with that result. Does anyone have any recommendations for resolution or additional troubleshooting steps?

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  • SSD for swap on Ubuntu server

    - by grs
    Currently I am reading SSD reviews and I wonder how much exactly I will benefit if I move the 24 GB swap from 7200rpm HDD to SSD. Does anyone implemented swap space on SSD? Is this generally good idea? On a side note: I read that ext4 has much better performance if the journal is on SSD. Anyone with such a setup? Thanks! Edit: Here I will answer the questions posted: Occasionally, relatively rare I am hitting the swap. I know what the swap is for and that is better to get more RAM. When the server begins to swap its performance degrades (not a surprise). The idea is if I have few memory hungry processes running, to improve the overall system performance at that time, using SSD for swap, instead of slower rotational media. At the end - I want to be able to login faster and check the server state during swapping, instead of waiting on the login prompt. And of what I see SSD is cheaper per GB than RAM. Would I have better server performance during swapping (as rare it is) using SSD compared to HDD? Where 10k or 15k rpm HDDs would rate in this scenario? Thank you all for your quick and prompt answers!

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  • MysqlTunner and query_cache_size dilemma

    - by wbad
    On a busy mysql server MySQLTuner 1.2.0 always recommends to add query_cache_size no matter how I increase the value (I tried up to 512MB). On the other hand it warns that : Increasing the query_cache size over 128M may reduce performance Here are the last results: >> MySQLTuner 1.2.0 - Major Hayden <[email protected]> >> Bug reports, feature requests, and downloads at http://mysqltuner.com/ >> Run with '--help' for additional options and output filtering -------- General Statistics -------------------------------------------------- [--] Skipped version check for MySQLTuner script [OK] Currently running supported MySQL version 5.5.25-1~dotdeb.0-log [OK] Operating on 64-bit architecture -------- Storage Engine Statistics ------------------------------------------- [--] Status: +Archive -BDB -Federated +InnoDB -ISAM -NDBCluster [--] Data in InnoDB tables: 6G (Tables: 195) [--] Data in PERFORMANCE_SCHEMA tables: 0B (Tables: 17) [!!] Total fragmented tables: 51 -------- Security Recommendations ------------------------------------------- [OK] All database users have passwords assigned -------- Performance Metrics ------------------------------------------------- [--] Up for: 1d 19h 17m 8s (254M q [1K qps], 5M conn, TX: 139B, RX: 32B) [--] Reads / Writes: 89% / 11% [--] Total buffers: 24.2G global + 92.2M per thread (1200 max threads) [!!] Maximum possible memory usage: 132.2G (139% of installed RAM) [OK] Slow queries: 0% (2K/254M) [OK] Highest usage of available connections: 32% (391/1200) [OK] Key buffer size / total MyISAM indexes: 128.0M/92.0K [OK] Key buffer hit rate: 100.0% (8B cached / 0 reads) [OK] Query cache efficiency: 79.9% (181M cached / 226M selects) [!!] Query cache prunes per day: 1033203 [OK] Sorts requiring temporary tables: 0% (341 temp sorts / 4M sorts) [OK] Temporary tables created on disk: 14% (760K on disk / 5M total) [OK] Thread cache hit rate: 99% (676 created / 5M connections) [OK] Table cache hit rate: 22% (1K open / 8K opened) [OK] Open file limit used: 0% (49/13K) [OK] Table locks acquired immediately: 99% (64M immediate / 64M locks) [OK] InnoDB data size / buffer pool: 6.1G/19.5G -------- Recommendations ----------------------------------------------------- General recommendations: Run OPTIMIZE TABLE to defragment tables for better performance Reduce your overall MySQL memory footprint for system stability Increasing the query_cache size over 128M may reduce performance Variables to adjust: *** MySQL's maximum memory usage is dangerously high *** *** Add RAM before increasing MySQL buffer variables *** query_cache_size (> 192M) [see warning above] The server has 76GB ram and dual E5-2650. The load is usually below 2. I appreciate your hints to interpret the recommendation and optimize the database configs.

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  • Replicated filesystem and EC2 MySQL

    - by El Yobo
    I'm currently investigating migrating our infrastructure over to run on Amazon's EC2 and am trying to figure out the best way to set up a MySQL service. I'm leaning towards running our own MySQL instances, rather than going with Amazon's RDS, but am still considering the best approach for performance and cost on the instance itself. In order to have persistent data, the MySQL data needs to be on an EBS volume (with some form of striped RAID, e.g. RAID0 or RAID10) to improve persistence. However, EBS IO is limited by the network interface (gigabit, so a theoretical maximum of 128 MB/s), while the ephemeral volumes have no such problem. I did see a suggestion for running two MySQL servers on an instance, with a master running on the ephemeral disk (which we would also RAID) and a slave storing changes to an EBS volume, but this has some additional overhead and complexity (two servers). What I was imagining is using some form of replicated file system such that I could have a filesystem on top of a RAID0 of ephemeral volumes to maximise performance all changes from the above immediately replicated to another RAID1 volume backed by multiple EBS volumes to ensure no data loss The advantages of this would be best possible IO performance for the DB server; no network delay in IO decreased IO on EBS volumes (as all read IO will be done on the ephemeral volumes) so decreased cost good data security, as it's backed onto redundant EBS volumes However, I haven't seen an appropriate system to replicate all changes from one volume to the other; is there a filesystem, or any other approach, which will do this? The distributed file systems, e.g. GlusterFS, DRBD etc seem to focus on replicating disks between servers, can they be set up to do what I'm interested in here? I also haven't seen anything about other's taking this approach. Do I have a solution in need of a problem here (i.e. is performance good enough, so this whole idea is redundant)? Is there some flaw in the plan?

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  • RAIDs with a lot of spindles - how to safely put to use the "wasted" space

    - by kubanczyk
    I have a fairly large number of RAID arrays (server controllers as well as midrange SAN storage) that all suffer from the same problem: barely enough spindles to keep the peak I/O performance, and tons of unused disk space. I guess it's a universal issue since vendors offer the smallest drives of 300 GB capacity but the random I/O performance hasn't really grown much since the time when the smallest drives were 36 GB. One example is a database that has 300 GB and needs random performance of 3200 IOPS, so it gets 16 disks (4800 GB minus 300 GB and we have 4.5 TB wasted space). Another common example are redo logs for a OLTP database that is sensitive in terms of response time. The redo logs get their own 300 GB mirror, but take 30 GB: 270 GB wasted. What I would like to see is a systematic approach for both Linux and Windows environment. How to set up the space so sysadmin team would be reminded about the risk of hindering the performance of the main db/app? Or, even better, to be protected from that risk? The typical situation that comes to my mind is "oh, I have this very large zip file, where do I uncompress it? Umm let's see the df -h and we figure something out in no time..." I don't put emphasis on strictness of the security (sysadmins are trusted to act in good faith), but on overall simplicity of the approach. For Linux, it would be great to have a filesystem customized to cap I/O rate to a very low level - is this possible?

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  • World Record Batch Rate on Oracle JD Edwards Consolidated Workload with SPARC T4-2

    - by Brian
    Oracle produced a World Record batch throughput for single system results on Oracle's JD Edwards EnterpriseOne Day-in-the-Life benchmark using Oracle's SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2. The workload includes both online and batch workload. The SPARC T4-2 server delivered a result of 8,000 online users while concurrently executing a mix of JD Edwards EnterpriseOne Long and Short batch processes at 95.5 UBEs/min (Universal Batch Engines per minute). In order to obtain this record benchmark result, the JD Edwards EnterpriseOne, Oracle WebLogic and Oracle Database 11g Release 2 servers were executed each in separate Oracle Solaris Containers which enabled optimal system resources distribution and performance together with scalable and manageable virtualization. One SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2 utilized only 55% of the available CPU power. The Oracle DB server in a Shared Server configuration allows for optimized CPU resource utilization and significant memory savings on the SPARC T4-2 server without sacrificing performance. This configuration with SPARC T4-2 server has achieved 33% more Users/core, 47% more UBEs/min and 78% more Users/rack unit than the IBM Power 770 server. The SPARC T4-2 server with 2 processors ran the JD Edwards "Day-in-the-Life" benchmark and supported 8,000 concurrent online users while concurrently executing mixed batch workloads at 95.5 UBEs per minute. The IBM Power 770 server with twice as many processors supported only 12,000 concurrent online users while concurrently executing mixed batch workloads at only 65 UBEs per minute. This benchmark demonstrates more than 2x cost savings by consolidating the complete solution in a single SPARC T4-2 server compared to earlier published results of 10,000 users and 67 UBEs per minute on two SPARC T4-2 and SPARC T4-1. The Oracle DB server used mirrored (RAID 1) volumes for the database providing high availability for the data without impacting performance. Performance Landscape JD Edwards EnterpriseOne Day in the Life (DIL) Benchmark Consolidated Online with Batch Workload System Rack Units BatchRate(UBEs/m) Online Users Users /Units Users /Core Version SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 3 95.5 8,000 2,667 500 9.0.2 IBM Power 770 (4 x POWER7, 3.3 GHz, 32 cores) 8 65 12,000 1,500 375 9.0.2 Batch Rate (UBEs/m) — Batch transaction rate in UBEs per minute Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 4 x 300 GB 10K RPM SAS internal disk 2 x 300 GB internal SSD 2 x Sun Storage F5100 Flash Arrays Software Configuration: Oracle Solaris 10 Oracle Solaris Containers JD Edwards EnterpriseOne 9.0.2 JD Edwards EnterpriseOne Tools (8.98.4.2) Oracle WebLogic Server 11g (10.3.4) Oracle HTTP Server 11g Oracle Database 11g Release 2 (11.2.0.1) Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE – Universal Business Engine workload of 61 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large and medium UBEs, and the QPROCESS queue for short UBEs run concurrently. Oracle's UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers, two Oracle WebLogic Servers 11g Release 1 coupled with two Oracle Web Tier HTTP server instances and one Oracle Database 11g Release 2 database on a single SPARC T4-2 server were hosted in separate Oracle Solaris Containers bound to four processor sets to demonstrate consolidation of multiple applications, web servers and the database with best resource utilizations. Interrupt fencing was configured on all Oracle Solaris Containers to channel the interrupts to processors other than the processor sets used for the JD Edwards Application server, Oracle WebLogic servers and the database server. A Oracle WebLogic vertical cluster was configured on each WebServer Container with twelve managed instances each to load balance users' requests and to provide the infrastructure that enables scaling to high number of users with ease of deployment and high availability. The database log writer was run in the real time RT class and bound to a processor set. The database redo logs were configured on the raw disk partitions. The Oracle Solaris Container running the Enterprise Application server completed 61 Short UBEs, 4 Long UBEs concurrently as the mixed size batch workload. The mixed size UBEs ran concurrently from the Enterprise Application server with the 8,000 online users driven by the LoadRunner. See Also SPARC T4-2 Server oracle.com OTN JD Edwards EnterpriseOne oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Oracle Fusion Middleware 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 09/30/2012.

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  • The way I think about Diagnostic tools

    - by Daniel Moth
    Every software has issues, or as we like to call them "bugs". That is not a discussion point, just a mere fact. It follows that an important skill for developers is to be able to diagnose issues in their code. Of course we need to advance our tools and techniques so we can prevent bugs getting into the code (e.g. unit testing), but beyond designing great software, diagnosing bugs is an equally important skill. To diagnose issues, the most important assets are good techniques, skill, experience, and maybe talent. What also helps is having good diagnostic tools and what helps further is knowing all the features that they offer and how to use them. The following classification is how I like to think of diagnostics. Note that like with any attempt to bucketize anything, you run into overlapping areas and blurry lines. Nevertheless, I will continue sharing my generalizations ;-) It is important to identify at the outset if you are dealing with a performance or a correctness issue. If you have a performance issue, use a profiler. I hear people saying "I am using the debugger to debug a performance issue", and that is fine, but do know that a dedicated profiler is the tool for that job. Just because you don't need them all the time and typically they cost more plus you are not as familiar with them as you are with the debugger, doesn't mean you shouldn't invest in one and instead try to exclusively use the wrong tool for the job. Visual Studio has a profiler and a concurrency visualizer (for profiling multi-threaded apps). If you have a correctness issue, then you have several options - that's next :-) This is how I think of identifying a correctness issue Do you want a tool to find the issue for you at design time? The compiler is such a tool - it gives you an exact list of errors. Compilers now also offer warnings, which is their way of saying "this may be an error, but I am not smart enough to know for sure". There are also static analysis tools, which go a step further than the compiler in identifying issues in your code, sometimes with the aid of code annotations and other times just by pointing them at your raw source. An example is FxCop and much more in Visual Studio 11 Code Analysis. Do you want a tool to find the issue for you with code execution? Just like static tools, there are also dynamic analysis tools that instead of statically analyzing your code, they analyze what your code does dynamically at runtime. Whether you have to setup some unit tests to invoke your code at runtime, or have to manually run your app (and interact with it) under the tool, or have to use a script to execute your binary under the tool… that varies. The result is still a list of issues for you to address after the analysis is complete or a pause of the execution when the first issue is encountered. If a code path was not taken, no analysis for it will exist, obviously. An example is the GPU Race detection tool that I'll be talking about on the C++ AMP team blog. Another example is the MSR concurrency CHESS tool. Do you want you to find the issue at design time using a tool? Perform a code walkthrough on your own or with colleagues. There are code review tools that go beyond just diffing sources, and they help you with that aspect too. For example, there is a new one in Visual Studio 11 and searching with my favorite search engine yielded this article based on the Developer Preview. Do you want you to find the issue with code execution? Use a debugger - let’s break this down further next. This is how I think of debugging: There is post mortem debugging. That means your code has executed and you did something in order to examine what happened during its execution. This can vary from manual printf and other tracing statements to trace events (e.g. ETW) to taking dumps. In all cases, you are left with some artifact that you examine after the fact (after code execution) to discern what took place hoping it will help you find the bug. Learn how to debug dump files in Visual Studio. There is live debugging. I will elaborate on this in a separate post, but this is where you inspect the state of your program during its execution, and try to find what the problem is. More from me in a separate post on live debugging. There is a hybrid of live plus post-mortem debugging. This is for example what tools like IntelliTrace offer. If you are a tools vendor interested in the diagnostics space, it helps to understand where in the above classification your tool excels, where its primary strength is, so you can market it as such. Then it helps to see which of the other areas above your tool touches on, and how you can make it even better there. Finally, see what areas your tool doesn't help at all with, and evaluate whether it should or continue to stay clear. Even though the classification helps us think about this space, the reality is that the best tools are either extremely excellent in only one of this areas, or more often very good across a number of them. Another approach is to offer a toolset covering all areas, with appropriate integration and hand off points from one to the other. Anyway, with that brain dump out of the way, in follow-up posts I will dive into live debugging, and specifically live debugging in Visual Studio - stay tuned if that interests you. Comments about this post by Daniel Moth welcome at the original blog.

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  • FireFox 6 Super Slow? Cache Settings Corruption

    - by Rick Strahl
    For those of you that follow me on Twitter, you've probably seen some of my tweets regarding major performance problems I've seen with the install of FireFox 6.0. FireFox 6.0 was released a couple of weeks ago and is treated as a 'force feed' update for FireFox 5.0. I'm not sure what the deal is with this braindead versioning that Mozilla is doing with major version releases coming out, what now every other month? Seriously that's retarded especially given the limited number of new features these releases bring, and the upgrade pain for plug-ins that the major version release causes. Anyway, after the FireFox updater bugged me long enough I finally gave in last week and updated to FireFox 6. Immediately after install I noticed terrible performance. Everything was running at a snail's pace with Web pages loading slowly and most content actually slowly 'painting' the page. A typical sign of content downloading slowly. However these are pages that should be mostly cached on my system and even repeated accesses ran just as slow. Just for a reality check I ran the same sites in Chrome (blazing fast) and IE (fast enough :-)) but FireFox - dog on a stick. Why so slow Boss? While complaining lots of people recommended to ditch FireFox - use Chrome, yada yada yada. Yeah, Chrome is fast and getting better but I have a number of plug-ins that I use in FF that I can't easily give up. So I suffered and started looking around more closely at what was happening. The first thing I noticed when accessing pages was that I continually saw accesses to the Google CDN downloading jQuery and jQuery UI. UI especially is pretty heavy in size and currently I'm in a location with a fairly slow IP connection where large files are a bit of an issue. However, seeing the CDN urls pop up repeatedly raised a flag with me. That stuff should be caching and it looked like each and every hit was reloading these scripts and various images over and over again. Fired up FireBug and sure enough I saw something like this on a repeated hit to my blog: Those two highlights are jquery and the main CSS file for the site and both are being loaded fully and taking a while to load. However, since this page had been loaded before, these items should be cached and show 304 requests instead of the full HTTP requests returning 200 result codes. In short it looked like FireFox was not caching ANY content at all and constantly reloading all page resources. No wonder things were running dog slow. Once I realized what the problem was I took a look in the about:config settings and lo and behold a bunch of the cache settings were set to not cache: In my case ALL the main cache flags were set to false for some reason that I can't figure out.  It appears that after the FireFox 6 update these flags somehow mysteriously changed and performance took a nose dive. Switching the .enable flags back to true and resetting all the cache settings tote default reverted performance back to the way it's supposed to be: reasonably fast and snappy as soon as content is cached and accessed again  from cache. I try not to muck with the about:config settings much (other than turning off the IPV6 option) but when there are problems access to these features can be really nice. However, I treat this as a last resort so it took me quite some time before I started looking through ALL the settings. This takes a while, not knowing what I was looking for exactly. If Web load performance is slow it's a good idea to check the cache settings. I have no idea what hosed these settings for me - I certainly didn't explicitly set them in about:config and while in FireFox's Options dialog I didn't see any option that would affect global caching like this, so this remains a mystery to me. Anyway, I hope that this is helpful to some, in case some of you end up running into a similar issue.© Rick Strahl, West Wind Technologies, 2005-2011Posted in FireFox   Tweet (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • Verizon Wireless Supports its Mission-Critical Employee Portal with MySQL

    - by Bertrand Matthelié
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Cambria","serif"; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin;} Verizon Wireless, the #1 mobile carrier in the United States, operates the nation’s largest 3G and 4G LTE network, with the most subscribers (109 millions) and the highest revenue ($70.2 Billion in 2011). Verizon Wireless built the first wide-area wireless broadband network and delivered the first wireless consumer 3G multimedia service in the US, and offers global voice and data services in more than 200 destinations around the world. To support 4.2 million daily wireless transactions and 493,000 calls and emails transactions produced by 94.2 million retail customers, Verizon Wireless employs over 78,000 employees with area headquarters across the United States. The Business Challenge Seeing the stupendous rise in social media, video streaming, live broadcasting…etc which redefined the scope of technology, Verizon Wireless, as a technology savvy company, wanted to provide a platform to its employees where they could network socially, view and host microsites, stream live videos, blog and provide the latest news. The IT team at Verizon Wireless had abundant experience with various technology platforms to support the huge number of applications in the company. However, open-source products weren’t yet widely used in the organization and the team had the ambition to adopt such technologies and see if the architecture could meet Verizon Wireless’ rigid requirements. After evaluating a few solutions, the IT team decided to use the LAMP stack for Vzweb, its mission-critical, 24x7 employee portal, with Drupal as the front end and MySQL on Linux as the backend, and for a few other internal websites also on MySQL. The MySQL Solution Verizon Wireless started to support its employee portal, Vzweb, its online streaming website, Vztube, and internal wiki pages, Vzwiki, with MySQL 5.1 in 2010. Vzweb is the main internal communication channel for Verizon Wireless, while Vztube hosts important company-wide webcasts regularly for executive-level announcements, so both channels have to be live and accessible all the time for its 78,000 employees across the United States. However during the initial deployment of the MySQL based Intranet, the application experienced performance issues. High connection spikes occurred causing slow user response time, and the IT team applied workarounds to continue the service. A number of key performance indexes (KPI) for the infrastructure were identified and the operational framework redesigned to support a more robust website and conform to the 99.985% uptime SLA (Service-Level Agreement). The MySQL DBA team made a series of upgrades in MySQL: Step 1: Moved from MyISAM to InnoDB storage engine in 2010 Step 2: Upgraded to the latest MySQL 5.1.54 release in 2010 Step 3: Upgraded from MySQL 5.1 to the latest GA release MySQL 5.5 in 2011, and leveraging MySQL Thread Pool as part of MySQL Enterprise Edition to scale better After making those changes, the team saw a much better response time during high concurrency use cases, and achieved an amazing performance improvement of 1400%! In January 2011, Verizon CEO, Ivan Seidenberg, announced the iPhone launch during the opening keynote at Consumer Electronic Show (CES) in Las Vegas, and that presentation was streamed live to its 78,000 employees. The event was broadcasted flawlessly with MySQL as the database. Later in 2011, Hurricane Irene attacked the East Coast of United States and caused major life and financial damages. During the hurricane, the team directed more traffic to its west coast data center to avoid potential infrastructure damage in the East Coast. Such transition was executed smoothly and even though the geographical distance became longer for the East Coast users, there was no impact in the performance of Vzweb and Vztube, and the SLA goal was achieved. “MySQL is the key component of Verizon Wireless’ mission-critical employee portal application,” said Shivinder Singh, senior DBA at Verizon Wireless. “We achieved 1400% performance improvement by moving from the MyISAM storage engine to InnoDB, upgrading to the latest GA release MySQL 5.5, and using the MySQL Thread Pool to support high concurrent user connections. MySQL has become part of our IT infrastructure, on which potentially more future applications will be built.” To learn more about MySQL Enterprise Edition, Get our Product Guide.

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  • SQL SERVER – SSMS: Top Object and Batch Execution Statistics Reports

    - by Pinal Dave
    The month of June till mid of July has been the fever of sports. First, it was Wimbledon Tennis and then the Soccer fever was all over. There is a huge number of fan followers and it is great to see the level at which people sometimes worship these sports. Being an Indian, I cannot forget to mention the India tour of England later part of July. Following these sports and as the events unfold to the finals, there are a number of ways the statisticians can slice and dice the numbers. Cue from soccer I can surely say there is a team performance against another team and then there is individual member fairs against a particular opponent. Such statistics give us a fair idea to how a team in the past or in the recent past has fared against each other, head-to-head stats during World cup and during other neutral venue games. All these statistics are just pointers. In reality, they don’t reflect the calibre of the current team because the individuals who performed in each of these games are totally different (Typical example being the Brazil Vs Germany semi-final match in FIFA 2014). So at times these numbers are misleading. It is worth investigating and get the next level information. Similar to these statistics, SQL Server Management studio is also equipped with a number of reports like a) Object Execution Statistics report and b) Batch Execution Statistics reports. As discussed in the example, the team scorecard is like the Batch Execution statistics and individual stats is like Object Level statistics. The analogy can be taken only this far, trust me there is no correlation between SQL Server functioning and playing sports – It is like I think about diet all the time except while I am eating. Performance – Batch Execution Statistics Let us view the first report which can be invoked from Server Node -> Reports -> Standard Reports -> Performance – Batch Execution Statistics. Most of the values that are displayed in this report come from the DMVs sys.dm_exec_query_stats and sys.dm_exec_sql_text(sql_handle). This report contains 3 distinctive sections as outline below.   Section 1: This is a graphical bar graph representation of Average CPU Time, Average Logical reads and Average Logical Writes for individual batches. The Batch numbers are indicative and the details of individual batch is available in section 3 (detailed below). Section 2: This represents a Pie chart of all the batches by Total CPU Time (%) and Total Logical IO (%) by batches. This graphical representation tells us which batch consumed the highest CPU and IO since the server started, provided plan is available in the cache. Section 3: This is the section where we can find the SQL statements associated with each of the batch Numbers. This also gives us the details of Average CPU / Average Logical Reads and Average Logical Writes in the system for the given batch with object details. Expanding the rows, I will also get the # Executions and # Plans Generated for each of the queries. Performance – Object Execution Statistics The second report worth a look is Object Execution statistics. This is a similar report as the previous but turned on its head by SQL Server Objects. The report has 3 areas to look as above. Section 1 gives the Average CPU, Average IO bar charts for specific objects. The section 2 is a graphical representation of Total CPU by objects and Total Logical IO by objects. The final section details the various objects in detail with the Avg. CPU, IO and other details which are self-explanatory. At a high-level both the reports are based on queries on two DMVs (sys.dm_exec_query_stats and sys.dm_exec_sql_text) and it builds values based on calculations using columns in them: SELECT * FROM    sys.dm_exec_query_stats s1 CROSS APPLY sys.dm_exec_sql_text(sql_handle) AS s2 WHERE   s2.objectid IS NOT NULL AND DB_NAME(s2.dbid) IS NOT NULL ORDER BY  s1.sql_handle; This is one of the simplest form of reports and in future blogs we will look at more complex reports. I truly hope that these reports can give DBAs and developers a hint about what is the possible performance tuning area. As a closing point I must emphasize that all above reports pick up data from the plan cache. If a particular query has consumed a lot of resources earlier, but plan is not available in the cache, none of the above reports would show that bad query. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

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  • How to Use Steam In-Home Streaming

    - by Chris Hoffman
    Steam’s In-Home Streaming is now available to everyone, allowing you to stream PC games from one PC to another PC on the same local network. Use your gaming PC to power your laptops and home theater system. This feature doesn’t allow you to stream games over the Internet, only the same local network. Even if you tricked Steam, you probably wouldn’t get good streaming performance over the Internet. Why Stream? When you use Steam In-Home streaming, one PC sends its video and audio to another PC. The other PC views the video and audio like it’s watching a movie, sending back mouse, keyboard, and controller input to the other PC. This allows you to have a fast gaming PC power your gaming experience on slower PCs. For example, you could play graphically demanding games on a laptop in another room of your house, even if that laptop has slower integrated graphics. You could connect a slower PC to your television and use your gaming PC without hauling it into a different room in your house. Streaming also enables cross-platform compatibility. You could have a Windows gaming PC and stream games to a Mac or Linux system. This will be Valve’s official solution for compatibility with old Windows-only games on the Linux (Steam OS) Steam Machines arriving later this year. NVIDIA offers their own game streaming solution, but it requires certain NVIDIA graphics hardware and can only stream to an NVIDIA Shield device. How to Get Started In-Home Streaming is simple to use and doesn’t require any complex configuration — or any configuration, really. First, log into the Steam program on a Windows PC. This should ideally be a powerful gaming PC with a powerful CPU and fast graphics hardware. Install the games you want to stream if you haven’t already — you’ll be streaming from your PC, not from Valve’s servers. (Valve will eventually allow you to stream games from Mac OS X, Linux, and Steam OS systems, but that feature isn’t yet available. You can still stream games to these other operating systems.) Next, log into Steam on another computer on the same network with the same Steam username. Both computers have to be on the same subnet of the same local network. You’ll see the games installed on your other PC in the Steam client’s library. Click the Stream button to start streaming a game from your other PC. The game will launch on your host PC, and it will send its audio and video to the PC in front of you. Your input on the client will be sent back to the server. Be sure to update Steam on both computers if you don’t see this feature. Use the Steam > Check for Updates option within Steam and install the latest update. Updating to the latest graphics drivers for your computer’s hardware is always a good idea, too. Improving Performance Here’s what Valve recommends for good streaming performance: Host PC: A quad-core CPU for the computer running the game, minimum. The computer needs enough processor power to run the game, compress the video and audio, and send it over the network with low latency. Streaming Client: A GPU that supports hardware-accelerated H.264 decoding on the client PC. This hardware is included on all recent laptops and PCs. Ifyou have an older PC or netbook, it may not be able to decode the video stream quickly enough. Network Hardware: A wired network connection is ideal. You may have success with wireless N or AC networks with good signals, but this isn’t guaranteed. Game Settings: While streaming a game, visit the game’s setting screen and lower the resolution or turn off VSync to speed things up. In-Home Steaming Settings: On the host PC, click Steam > Settings and select In-Home Streaming to view the In-Home Streaming settings. You can modify your streaming settings to improve performance and reduce latency. Feel free to experiment with the options here and see how they affect performance — they should be self-explanatory. Check Valve’s In-Home Streaming documentation for troubleshooting information. You can also try streaming non-Steam games. Click Games > Add a Non-Steam Game to My Library on your host PC and add a PC game you have installed elsewhere on your system. You can then try streaming it from your client PC. Valve says this “may work but is not officially supported.” Image Credit: Robert Couse-Baker on Flickr, Milestoned on Flickr

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  • Tuning Red Gate: #4 of Some

    - by Grant Fritchey
    First time connecting to these servers directly (keys to the kingdom, bwa-ha-ha-ha. oh, excuse me), so I'm going to take a look at the server properties, just to see if there are any issues there. Max memory is set, cool, first possible silly mistake clear. In fact, these look to be nicely set up. Oh, I'd like to see the ANSI Standards set by default, but it's not a big deal. The default location for database data is the F:\ drive, where I saw all the activity last time. Cool, the people maintaining the servers in our company listen, parallelism threshold is set to 35 and optimize for ad hoc is enabled. No shocks, no surprises. The basic setup is appropriate. On to the problem database. Nothing wrong in the properties. The database is in SIMPLE recovery, but I think it's a reporting system, so no worries there. Again, I'd prefer to see the ANSI settings for connections, but that's the worst thing I can see. Time to look at the queries, tables, indexes and statistics because all the information I've collected over the last several days suggests that we're not looking at a systemic problem (except possibly not enough memory), but at the traditional tuning issues. I just want to note that, I started looking at the system, not the queries. So should you when tuning your environment. I know, from the data collected through SQL Monitor, what my top poor performing queries are, and the most frequently called, etc. I'm starting with the most frequently called. I'm going to get the execution plan for this thing out of the cache (although, with the cache dumping constantly, I might not get it). And it's not there. Called 1.3 million times over the last 3 days, but it's not in cache. Wow. OK. I'll see what's in cache for this database: SELECT  deqs.creation_time,         deqs.execution_count,         deqs.max_logical_reads,         deqs.max_elapsed_time,         deqs.total_logical_reads,         deqs.total_elapsed_time,         deqp.query_plan,         SUBSTRING(dest.text, (deqs.statement_start_offset / 2) + 1,                   (deqs.statement_end_offset - deqs.statement_start_offset) / 2                   + 1) AS QueryStatement FROM    sys.dm_exec_query_stats AS deqs         CROSS APPLY sys.dm_exec_sql_text(deqs.sql_handle) AS dest         CROSS APPLY sys.dm_exec_query_plan(deqs.plan_handle) AS deqp WHERE   dest.dbid = DB_ID('Warehouse') AND deqs.statement_end_offset > 0 AND deqs.statement_start_offset > 0 ORDER BY deqs.max_logical_reads DESC ; And looking at the most expensive operation, we have our first bad boy: Multiple table scans against very large sets of data and a sort operation. a sort operation? It's an insert. Oh, I see, the table is a heap, so it's doing an insert, then sorting the data and then inserting into the primary key. First question, why isn't this a clustered index? Let's look at some more of the queries. The next one is deceiving. Here's the query plan: You're thinking to yourself, what's the big deal? Well, what if I told you that this thing had 8036318 reads? I know, you're looking at skinny little pipes. Know why? Table variable. Estimated number of rows = 1. Actual number of rows. well, I'm betting several more than one considering it's read 8 MILLION pages off the disk in a single execution. We have a serious and real tuning candidate. Oh, and I missed this, it's loading the table variable from a user defined function. Let me check, let me check. YES! A multi-statement table valued user defined function. And another tuning opportunity. This one's a beauty, seriously. Did I also mention that they're doing a hash against all the columns in the physical table. I'm sure that won't lead to scans of a 500,000 row table, no, not at all. OK. I lied. Of course it is. At least it's on the top part of the Loop which means the scan is only executed once. I just did a cursory check on the next several poor performers. all calling the UDF. I think I found a big tuning opportunity. At this point, I'm typing up internal emails for the company. Someone just had their baby called ugly. In addition to a series of suggested changes that we need to implement, I'm also apologizing for being such an unkind monster as to question whether that third eye & those flippers belong on such an otherwise lovely child.

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  • EPM and Business Analytics Talking-head Videos from Oracle OpenWorld 2013

    - by Mike.Hallett(at)Oracle-BI&EPM
    Normal 0 false false false EN-GB X-NONE X-NONE Here is a selection of 2 to 3 minute video interviews at this year’s Oracle OpenWorld: 1. George Somogyi, Solutions Architect, New Edge Group, talks about the importance of having their integrated Oracle Hyperion Platform consisting of Oracle Hyperion Financial Management, Oracle Hyperion Financial Data Quality Management, Oracle E-Business Suite R12 and Oracle Business Intelligence Extended Edition plus their use of Oracle Managed Cloud Services. Speaker: George Somogyi @ http://youtu.be/kWn0dQxCUy8 2. Gregg Thompson, Director of Financial Systems for ADT, talks about using Oracle Data Relationship Management prior to implementing an Enterprise Performance Management solution. Gregg confirmed that there are big benefits to bringing the full Oracle Hyperion Financial Close suite online with Oracle DRM as the metadata source. Reduced maintenance time and use of external consultants translates into significant time and cost savings and faster implementation times. Speaker: Gregg Thompson @ http://youtu.be/XnFrR9Uk4xk 3. Jeff Spangler, Director Financial Planning and Analysis for Speedy Cash Holdings Corp, talked to us about the benefits achieved through implementing Oracle Hyperion Planning and financial reporting solutions. He also describes how the use of Data Relationship Management will keep the process running smoothly now and in the future. Speaker: Jeff Spangler @ http://youtu.be/kkkuMkgJ22U 4. Marc Seewald, Senior Director of Product Management for Oracle Hyperion Tax Provision at Oracle, talks about Oracle Hyperion Tax Provision, how it is an integral part of the financial close process and that it provides better internal controls and automation of this task. Marc talks about Oracle Partners and customers alike who are seeing great value. Speaker: Marc Seewald @ http://youtu.be/lM_nfvACGuA 5. Matt Bradley, SVP of Product Development for Enterprise Performance Management (EPM) Applications at Oracle, talked to us about different deployment options for Oracle EPM. Cloud services (SaaS), managed services, on-premise, off-premise all have their merits, and organizations need flexibility to easily move between them as their companies evolve. Speaker: Matt Bradley @ http://youtu.be/ATO7Z9dbE-o 6. Neil Sellers, Partner, Qubix International talks about their experience with previewing Oracle’s new Planning and Budgeting Cloud Service. He describes the benefits of the step-by-step task lists, the speed of getting the application up and running, and the huge benefits of not having to manage the software and hardware side of the planning process. Speaker: Neil Sellers @ http://youtu.be/xmosO28e4_I 7. Praveen Pasupuleti, Senior Business Intelligence Development Manager of Citrix Systems Inc., talks about their Oracle Hyperion Planning upgrade and the huge performance improvement now experienced in forecasting. He also talked about the benefits of Oracle Hyperion Workforce Planning achieved by Citrix. Speaker: Praveen Pasupuleti @ http://youtu.be/d1e_4hLqw8c 8. CheckPoint Consulting, talked to us about how Enterprise Performance Management should be viewed as an entire solution, rather than as a bunch of applications in silos, to provide significant benefits; and how Data Relationship Management can tie it all together effectively. Speaker: Ron Dimon @ http://youtu.be/sRwbdbbXvUE 9. Sonal Kulkarni, Enterprise Performance Management Leader, Cummins Inc., talks about their use of Oracle Hyperion Financial Close Management (Account Reconciliation Manager), Oracle Hyperion Financial Management and Oracle Hyperion Financial Data Quality Management and how this is providing efficiency, visibility and compliance benefits. Speaker: Sonal Kulkarni @ http://youtu.be/OEgup5dKyVc 10. Todd Renard, Manager Financial Planning and Business Analytics for B/E Aerospace Inc., talks about the huge benefits that B/E Aerospace is experiencing from Oracle Financial Close Suite. He was extremely excited about Oracle Hyperion Financial Data Quality Management and how this helps them integrate a new business in as little as three weeks. Speaker: Todd Renard @ http://youtu.be/nIfqK46uVI8 11. Peter Smolianski, Chief Technology Officer for the District of Columbia Courts, talked to us about how D.C. Courts is using Oracle Scorecard and Strategy Management to push their 5 year plan forward, to report results to their constituents, and take accountability for process changes to become more efficient. Speaker: Peter Smolianski @ http://www.youtube.com/watch?v=T-DtB5pl-uk 12. Rich Wilkie, Senior Director of Product Management for Financial Close Suite at Oracle, talked to us about Oracle Financial Management Analytics. He told us how the prebuilt dashboards on top of Oracle Hyperion Financial Close Suite make it easy for everyone to see the numbers and understand where they are in the close process, and if there is an issue, they can see where it is. Executives are excited to get this information on mobile devices too. Speaker: Rich Wilkie @ http://www.youtube.com/watch?v=4UHuHgx74Yg 13. Dinesh Balebail, Senior Director of Software Development for Oracle Hyperion Profitability and Cost Management, talked to us about the power and speed of Oracle Hyperion Profitability and Cost Management and how it is being used to do deep costing for Telecoms, Hospitals, Banks and other high transaction volume organizations effectively. Speaker: Dinesh Balebail @ http://youtu.be/ivx5AZCXAfs /* 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:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}

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  • What's New In 11.1.2.1 (Talleyrand SP1)

    - by russ.bishop
    This release is primarily about bug fixes and that's what we spent the most time on, but we also addressed a number of other things: 1. Performance improvements We've done a lot of work to improve the performance of page load and execution times. For example, the View Compare page is about half the size it was previously! We've also done a lot of work on the server to improve performance of queries, exports, action scripts, etc. We implemented some finer-grained locking so fewer operations will block other users while they are in progress. We made some optimizations to improve performance when you have a lot of network or database latency as well. Just a few examples: An Import that previously took 8 GB of memory and hours to complete now runs in about 30 minutes and never takes more than 1 GB of RAM. Searching by exact Node Name now completes within 2 seconds even for a hierarchy with millions of nodes. Another search that was taking 30 seconds to run now completes in less than 5 seconds. 2. Upgrade support This release supports automatic upgrade from previous releases, built right into the console. 3. Console Improvements The Console has been reorganized and made easier to use. It is also much more multi-threaded so it responds quicker without freezing up when you save changes or when it needs to get status. 4. Property Namespaces Properties now have a concept called a Namespace. This is tied into the Application Templates to prevent conflicts with duplicate property names. Right now, if you have an AccountType and you pull in the HFM template, it also has AccountType so you end up creating properties with decorations on the name like "Account Type (HFM)". This is no longer necessary. In addition, properties within a namespace must have unique labels but they can be duplicated across namespaces. So in the Property Grid when you click on the HFM category, you just see "AccountType". When you click on MyCategory, you see "AccountType", but they are different properties with different values. Within formulas, the names are still unique (eg: Custom.AccountType vs HFM.AccountType). I'll write more about this one later. 5. Single Sign On DRM now supports Single Sign-On via HSS. For example, if you are using Oracle's OAM as your SSO solution then you configure HSS to use OAM just like you would before. You also configure DRM to use HSS, again just like before. Then you configure OAM to protect the DRM web app, like you would any other website. However once you do those things, users are no longer prompted to enter their username/password. They simply get redirected to OAM if they don't already have a login token, otherwise they pick their application and sail right into DRM. You can also avoid having to pick an application (see the next item) 6. URL-based navigation You can now specify the application you want to log into via the URL. Combined with SSO and your Intranet, it becomes easy to provide links on our intranet portal that take users directly into a specific DRM application. We also support specifying the Version, Hierarchy, and Node. Again, this can be used on your internal portal, but the scenarios get even more interesting when you are using workflow like Oracle BPEL you can automatically generate links within emails that will take users directly to a specific node in the UI. 7. Job status and cancellation A lot of the jobs now report their status and support true cancellation. Action Scripts also report a progress complete percentage since the amount of work is known ahead of time. 8. Action Script Options Action scripts support Option declarations at the top of the file so a script can self-describe (when specified in the file, the corresponding item in the file is ignored). For example: Option|DetectDelimiter Option|UsePropertyNames|true This will tell DRM to automatically detect the delimiter (a pipe symbol in this case) and that all references to properties are by Name, not by Label. Note that when you load a script in the UI, if you use Labels we automatically try to match them up if they are unique. Any duplicates are indicated and you are presented with a choice to pick which property you actually referred to. This is somewhat similar to Version substitution, but tailored for properties. There are other more minor changes and like I said earlier a lot of bug fixes and performance improvements. Hopefully I will get a chance to dig into some of these things in future blog posts.

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  • Some non-generic collections

    - by Simon Cooper
    Although the collections classes introduced in .NET 2, 3.5 and 4 cover most scenarios, there are still some .NET 1 collections that don't have generic counterparts. In this post, I'll be examining what they do, why you might use them, and some things you'll need to bear in mind when doing so. BitArray System.Collections.BitArray is conceptually the same as a List<bool>, but whereas List<bool> stores each boolean in a single byte (as that's what the backing bool[] does), BitArray uses a single bit to store each value, and uses various bitmasks to access each bit individually. This means that BitArray is eight times smaller than a List<bool>. Furthermore, BitArray has some useful functions for bitmasks, like And, Xor and Not, and it's not limited to 32 or 64 bits; a BitArray can hold as many bits as you need. However, it's not all roses and kittens. There are some fundamental limitations you have to bear in mind when using BitArray: It's a non-generic collection. The enumerator returns object (a boxed boolean), rather than an unboxed bool. This means that if you do this: foreach (bool b in bitArray) { ... } Every single boolean value will be boxed, then unboxed. And if you do this: foreach (var b in bitArray) { ... } you'll have to manually unbox b on every iteration, as it'll come out of the enumerator an object. Instead, you should manually iterate over the collection using a for loop: for (int i=0; i<bitArray.Length; i++) { bool b = bitArray[i]; ... } Following on from that, if you want to use BitArray in the context of an IEnumerable<bool>, ICollection<bool> or IList<bool>, you'll need to write a wrapper class, or use the Enumerable.Cast<bool> extension method (although Cast would box and unbox every value you get out of it). There is no Add or Remove method. You specify the number of bits you need in the constructor, and that's what you get. You can change the length yourself using the Length property setter though. It doesn't implement IList. Although not really important if you're writing a generic wrapper around it, it is something to bear in mind if you're using it with pre-generic code. However, if you use BitArray carefully, it can provide significant gains over a List<bool> for functionality and efficiency of space. OrderedDictionary System.Collections.Specialized.OrderedDictionary does exactly what you would expect - it's an IDictionary that maintains items in the order they are added. It does this by storing key/value pairs in a Hashtable (to get O(1) key lookup) and an ArrayList (to maintain the order). You can access values by key or index, and insert or remove items at a particular index. The enumerator returns items in index order. However, the Keys and Values properties return ICollection, not IList, as you might expect; CopyTo doesn't maintain the same ordering, as it copies from the backing Hashtable, not ArrayList; and any operations that insert or remove items from the middle of the collection are O(n), just like a normal list. In short; don't use this class. If you need some sort of ordered dictionary, it would be better to write your own generic dictionary combining a Dictionary<TKey, TValue> and List<KeyValuePair<TKey, TValue>> or List<TKey> for your specific situation. ListDictionary and HybridDictionary To look at why you might want to use ListDictionary or HybridDictionary, we need to examine the performance of these dictionaries compared to Hashtable and Dictionary<object, object>. For this test, I added n items to each collection, then randomly accessed n/2 items: So, what's going on here? Well, ListDictionary is implemented as a linked list of key/value pairs; all operations on the dictionary require an O(n) search through the list. However, for small n, the constant factor that big-o notation doesn't measure is much lower than the hashing overhead of Hashtable or Dictionary. HybridDictionary combines a Hashtable and ListDictionary; for small n, it uses a backing ListDictionary, but switches to a Hashtable when it gets to 9 items (you can see the point it switches from a ListDictionary to Hashtable in the graph). Apart from that, it's got very similar performance to Hashtable. So why would you want to use either of these? In short, you wouldn't. Any gain in performance by using ListDictionary over Dictionary<TKey, TValue> would be offset by the generic dictionary not having to cast or box the items you store, something the graphs above don't measure. Only if the performance of the dictionary is vital, the dictionary will hold less than 30 items, and you don't need type safety, would you use ListDictionary over the generic Dictionary. And even then, there's probably more useful performance gains you can make elsewhere.

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  • Part 1 - Load Testing In The Cloud

    - by Tarun Arora
    Azure is fascinating, but even more fascinating is the marriage of Azure and TFS! Introduction Recently a client I worked for had 2 major business critical applications being delivered, with very little time budgeted for Performance testing, we immediately hit a bottleneck when the performance testing phase started, the in house infrastructure team could not support the hardware requirements in the short notice. It was suggested that the performance testing be performed on one of the QA environments which was a fraction of the production environment. This didn’t seem right, the team decided to turn to the cloud. The team took advantage of the elasticity offered by Azure, starting with a single test agent which was provisioned and ready for use with in 30 minutes the team scaled up to 17 test agents to perform a very comprehensive performance testing cycle. Issues were identified and resolved but the highlight was that the cost of running the ‘test rig’ proved to be less than if hosted on premise by the infrastructure team. Thank you for taking the time out to read this blog post, in the series of posts, I’ll try and cover the start to end of everything you need to know to use Azure to build your Test Rig in the cloud. But Why Azure? I have my own Data Centre… If the environment is provisioned in your own datacentre, - No matter what level of service agreement you may have with your infrastructure team there will be down time when the environment is patched - How fast can you scale up or down the environments (keeping the enterprise processes in mind) Administration, Cost, Flexibility and Scalability are the areas you would want to think around when taking the decision between your own Data Centre and Azure! How is Microsoft's Public Cloud Offering different from Amazon’s Public Cloud Offering? Microsoft's offering of the Cloud is a hybrid of Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) which distinguishes Microsoft's offering from other providers such as Amazon (Amazon only offers IaaS). PaaS – Platform as a Service IaaS – Infrastructure as a Service Fills the needs of those who want to build and run custom applications as services. Similar to traditional hosting, where a business will use the hosted environment as a logical extension of the on-premises datacentre. A service provider offers a pre-configured, virtualized application server environment to which applications can be deployed by the development staff. Since the service providers manage the hardware (patching, upgrades and so forth), as well as application server uptime, the involvement of IT pros is minimized. On-demand scalability combined with hardware and application server management relieves developers from infrastructure concerns and allows them to focus on building applications. The servers (physical and virtual) are rented on an as-needed basis, and the IT professionals who manage the infrastructure have full control of the software configuration. This kind of flexibility increases the complexity of the IT environment, as customer IT professionals need to maintain the servers as though they are on-premises. The maintenance activities may include patching and upgrades of the OS and the application server, load balancing, failover clustering of database servers, backup and restoration, and any other activities that mitigate the risks of hardware and software failures.   The biggest advantage with PaaS is that you do not have to worry about maintaining the environment, you can focus all your time in solving the business problems with your solution rather than worrying about maintaining the environment. If you decide to use a VM Role on Azure, you are asking for IaaS, more on this later. A nice blog post here on the difference between Saas, PaaS and IaaS. Now that we are convinced why we should be turning to the cloud and why in specific Azure, let’s discuss about the Test Rig. The Load Test Rig – Topology Now the moment of truth, Of course a big part of getting value from cloud computing is identifying the most adequate workloads to take to the cloud, so I’ve decided to try to make a Load Testing rig where the Agents are running on Windows Azure.   I’ll talk you through the above Topology, - User: User kick starts the load test run from the developer workstation on premise. This passes the request to the Test Controller. - Test Controller: The Test Controller is on premise connected to the same domain as the developer workstation. As soon as the Test Controller receives the request it makes use of the Windows Azure Connect service to orchestrate the test responsibilities to all the Test Agents. The Windows Azure Connect endpoint software must be active on all Azure instances and on the Controller machine as well. This allows IP connectivity between them and, given that the firewall is properly configured, allows the Controller to send work loads to the agents. In parallel, the Controller will collect the performance data from the agents, using the traditional WMI mechanisms. - Test Agents: The Test Agents are on the Windows Azure Public Cloud, as soon as the test controller issues instructions to the test agents, the test agents start executing the load tests. The HTTP requests are issued against the web server on premise, the results are captured by the test agents. And finally the results are passed over to the controller. - Servers: The Web Server and DB Server are hosted on premise in the datacentre, this is usually the case with business critical applications, you probably want to manage them your self. Recap and What’s next? So, in the introduction in the series of blog posts on Load Testing in the cloud I highlighted why creating a test rig in the cloud is a good idea, what advantages does Windows Azure offer and the Test Rig topology that I will be using. I would also like to mention that i stumbled upon this [Video] on Azure in a nutshell, great watch if you are new to Windows Azure. In the next post I intend to start setting up the Load Test Environment and discuss pricing with respect to test agent machine types that will be used in the test rig. Hope you enjoyed this post, If you have any recommendations on things that I should consider or any questions or feedback, feel free to add to this blog post. Remember to subscribe to http://feeds.feedburner.com/TarunArora.  See you in Part II.   Share this post : CodeProject

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  • To My 24 Year Old Self, Wherever You Are&hellip;

    - by D'Arcy Lussier
    A decade is a milestone in one’s life, regardless of when it occurs. 2011 might seem like a weird year to mark a decade, but 2001 was a defining year for me. It marked my emergence into the technology industry, an unexpected loss of innocence, and triggered an ongoing struggle with faith and belief. Once you go through a valley, climbing the mountain and looking back over where you travelled, you can take in the entirety of the journey. Over the last 10 years I kept journals, and in this new year I took some time to review them. For those today that are me a decade ago, I share with you what I’ve gleamed from my experiences. Take it for what it’s worth, and safe travels on your own journeys through life. Life is a Performance-Based Sport Have confidence, believe you’re capable, but realize that life is a performance-based sport. Everything you get in life is based on whether you can show that you deserve it. Performance is also your best defense against personal attacks. Just make sure you know what standards you’re expected to hit and if people want to poke holes at you let them do the work of trying to find them. Sometimes performance won’t matter though. Good things will happen to bad people, and bad things to good people. What’s important is that you do the right things and ensure the good and bad even out in your own life. How you finish is just as important as how you start. Start strong, end strong. Respect is Your Most Prized Reward Respect is more important than status or ego. The formula is simple: Performing Well + Building Trust + Showing Dedication = Respect Focus on perfecting your craft and helping your team and respect will come. Life is a Team Sport Whatever aspect of your life, you can’t do it alone. You need to rely on the people around you and ensure you’re a positive aspect of their lives; even those that may be difficult or unpleasant. Avoid criticism and instead find ways to help colleagues and superiors better whatever environment you’re in (work, home, etc.). Don’t just highlight gaps and issues, but also come to the table with solutions. At the same time though, stand up for yourself and hold others accountable for the commitments they make to the team. A healthy team needs accountability. Give feedback early and often, and make it verbal. Issues should be dealt with immediately, and positives should be celebrated as they happen. Life is a Contact Sport Difficult moments will happen. Don’t run from them or shield yourself from experiencing them. Embrace them. They will further mold you and reveal who you will become. Find Your Tribe and Embrace Your Community We all need a tribe: a group of people that we gravitate to for support, guidance, wisdom, and friendship. Discover your tribe and immerse yourself in them. Don’t look for a non-existent tribe just to fill the need of belonging though that will leave you empty and bitter when they don’t meet your unrealistic expectations. Try to associate with people more experienced and more knowledgeable than you. You’ll always learn, and you’ll always remember you have much to learn. Put yourself out there, get involved with the community. Opportunities will present themselves. When we open ourselves up to be vulnerable, we also give others the chance to do the same. This helps us all to grow and help each other, it’s very important. And listen to your wife. (Easter *is* a romantic holiday btw, regardless of what you may think.) Don’t Believe Your Own Press Clippings (and by that I mean the ones you write) Until you have a track record of performance to refer to, any notions of grandeur are just that: notions. You lose your rookie status through trials and tribulations, not by the number of stamps in your passport. Be realistic about your own “experience and leadership” and be honest when you aren’t ready for something. And always remember: nobody really cares about you as much as you think they do. Don’t Let Assholes Get You Down The world isn’t evil, but there is evil in the world. Know the difference and don’t paint all people with the same brush. Do be wary of those that use personal beliefs to describe their business (i.e. “We’re a [religion] company”). What matters is the culture of the organization, and that will tell you the moral compass and what is truly valued. Don’t make someone or something a priority that only makes you an option. Life is unfair and enemies/opponents will succeed when you fail. Don’t waste your energy getting upset at this; the only one that will lose out is you. As mentioned earlier, nobody really cares about you as much as you think they do. Misc Ecclesiastes is bullshit. Everything is certainly *not* meaningless. Software development is about delivery, not the process. Having a great process means nothing if you don’t produce anything. Watch “The Weatherman” (“It’s not easy, but easy doesn’t enter into grownup life.”). Read Tony Dungee’s autobiography, even if you don’t like football, and even if you aren’t a Christian. Say no, don’t feel like you have to commit right away when someone asks you to.

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  • Plagued by multithreaded bugs

    - by koncurrency
    On my new team that I manage, the majority of our code is platform, TCP socket, and http networking code. All C++. Most of it originated from other developers that have left the team. The current developers on the team are very smart, but mostly junior in terms of experience. Our biggest problem: multi-threaded concurrency bugs. Most of our class libraries are written to be asynchronous by use of some thread pool classes. Methods on the class libraries often enqueue long running taks onto the thread pool from one thread and then the callback methods of that class get invoked on a different thread. As a result, we have a lot of edge case bugs involving incorrect threading assumptions. This results in subtle bugs that go beyond just having critical sections and locks to guard against concurrency issues. What makes these problems even harder is that the attempts to fix are often incorrect. Some mistakes I've observed the team attempting (or within the legacy code itself) includes something like the following: Common mistake #1 - Fixing concurrency issue by just put a lock around the shared data, but forgetting about what happens when methods don't get called in an expected order. Here's a very simple example: void Foo::OnHttpRequestComplete(statuscode status) { m_pBar->DoSomethingImportant(status); } void Foo::Shutdown() { m_pBar->Cleanup(); delete m_pBar; m_pBar=nullptr; } So now we have a bug in which Shutdown could get called while OnHttpNetworkRequestComplete is occuring on. A tester finds the bug, captures the crash dump, and assigns the bug to a developer. He in turn fixes the bug like this. void Foo::OnHttpRequestComplete(statuscode status) { AutoLock lock(m_cs); m_pBar->DoSomethingImportant(status); } void Foo::Shutdown() { AutoLock lock(m_cs); m_pBar->Cleanup(); delete m_pBar; m_pBar=nullptr; } The above fix looks good until you realize there's an even more subtle edge case. What happens if Shutdown gets called before OnHttpRequestComplete gets called back? The real world examples my team has are even more complex, and the edge cases are even harder to spot during the code review process. Common Mistake #2 - fixing deadlock issues by blindly exiting the lock, wait for the other thread to finish, then re-enter the lock - but without handling the case that the object just got updated by the other thread! Common Mistake #3 - Even though the objects are reference counted, the shutdown sequence "releases" it's pointer. But forgets to wait for the thread that is still running to release it's instance. As such, components are shutdown cleanly, then spurious or late callbacks are invoked on an object in an state not expecting any more calls. There are other edge cases, but the bottom line is this: Multithreaded programming is just plain hard, even for smart people. As I catch these mistakes, I spend time discussing the errors with each developer on developing a more appropriate fix. But I suspect they are often confused on how to solve each issue because of the enormous amount of legacy code that the "right" fix will involve touching. We're going to be shipping soon, and I'm sure the patches we're applying will hold for the upcoming release. Afterwards, we're going to have some time to improve the code base and refactor where needed. We won't have time to just re-write everything. And the majority of the code isn't all that bad. But I'm looking to refactor code such that threading issues can be avoided altogether. One approach I am considering is this. For each significant platform feature, have a dedicated single thread where all events and network callbacks get marshalled onto. Similar to COM apartment threading in Windows with use of a message loop. Long blocking operations could still get dispatched to a work pool thread, but the completion callback is invoked on on the component's thread. Components could possibly even share the same thread. Then all the class libraries running inside the thread can be written under the assumption of a single threaded world. Before I go down that path, I am also very interested if there are other standard techniques or design patterns for dealing with multithreaded issues. And I have to emphasize - something beyond a book that describes the basics of mutexes and semaphores. What do you think? I am also interested in any other approaches to take towards a refactoring process. Including any of the following: Literature or papers on design patterns around threads. Something beyond an introduction to mutexes and semaphores. We don't need massive parallelism either, just ways to design an object model so as to handle asynchronous events from other threads correctly. Ways to diagram the threading of various components, so that it will be easy to study and evolve solutions for. (That is, a UML equivalent for discussing threads across objects and classes) Educating your development team on the issues with multithreaded code. What would you do?

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • Is there a work around for slow performance of do.call(cbind.xts,...) in R 2.15.2?

    - by Petr Matousu
    I would expect cbind.xts and do.call(cbind.xts) to perform with similar elapsed time. That was true for R2.11, R2.14. For R2.15.2 and xts 0.8-8, the do.call(cbind.xts,...) variant performs drastically slower, which effectively breaks my previous codes. As Josh Ulrich notes in a comment below, the xts package maintainers are aware of this problem. In the meantime, is there a convenient work around?

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  • TRIM in centos 5.X?

    - by Frank Farmer
    I've got a bunch of centos 5 boxes with Intel X-25 drives (x25-m in dev, x25-e in prod, I think). We're seeing severely degraded disk performance on one of our dev boxes (which easily does 5+ gb of writes every day, meaning we write the full drive's worth of data several times a month). The box in question: Intel x25-m Ext3 (which doesn't support TRIM) centos 5 vmware ESXi Wikipedia mentions that newer versions of hdparm (which centos5 doesn't include) can bulk-TRIM free blocks. This utility also sounds potentially useful: http://blog.patshead.com/2009/12/a-quick-and-dirty-wipersh-fix-for-intel-x25-m.html Disk write performance has dropped to <1 MB/sec while copying a 300 meg directory on this system, as of a month or so ago -- it used to be able to perform the same copy operation at least 5 times faster. What can I do to recover performance on this system?

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  • What would happen in a Software Raid 1 of one HDD and one SSD?

    - by Adrian Grigore
    Hi, I'm running my Windows 7 installation and all of my apps from an SSD for performance reasons. Since SSD's can instantly die at any moment, I'm looking for some kind of data backup strategy. Right Now I regularly backing up the drive image on a hard disk, but that only happens once per day, which is not enough for my taste. So I got an idea: What if I created a software raid 1 of the SSD and partition on my Hard disk? All data would be mirrored on both drives, making this a lot safer. But what about performance? Will Windows 7 detect that the SSD is faster than the hard drive and always read from the SSD? Or will it randomly read from both, thus reducing read performance? Thanks, Adrian Edit: I just found this article which basically answers my question. Feel free to close this post.

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