Search Results

Search found 453 results on 19 pages for 'throughput'.

Page 17/19 | < Previous Page | 13 14 15 16 17 18 19  | Next Page >

• On ESXi, guest machines hang for significant intervals compared to real machines. How can I fix this?

  - by Tarbox

  This is ESXi version 5.0.0. We plan on upgrading to 5.5 eventually. I have four code profiles, two taken on a real, unvirtualized machine, two taken on a virtual machine. Ordering the list of subroutines by time spent in each one, the two real profiles are practically identical. The two virtual profiles are different from each other and from the real profiles: a subset of subroutines are taking a lot more time on the virtual machines, and the subset is different for each run. The two virtual profiles take a similar amount of time, which is 3 times the amount of time the real profiles take. This gross "how long does it take?" result is consistent after hundreds of tests across three different virtual machines on two different host machines -- the virtual machine is just slower. I've only the code profiling on the four, however. Here's the most guilty set of lines: This is the real machine: 8µs $text = '' unless defined $text; 1.48ms foreach ( split( "\n", $text ) ) { This is the first run on the virtual machine: 20.1ms $text = '' unless defined $text; 1.49ms foreach ( split( "\n", $text ) ) { This is the second run on the virtual machine: 6µs $text = '' unless defined $text; 21.9ms foreach ( split( "\n", $text ) ) { My WAG is that the VM is swapping out the thread and then swapping it back in, destroying some level of cache in the process, but these code profiles were taken when the vm in question was the only active vm on the host, so... what? What does that mean? The guest itself is under light load, this is a latency problem for my users rather than throughput. The host is also under a light load, if I knew what resources to assign where, I could do it without worrying about the cost. I've attempted to lock memory, reserve cpu, assign a restrictive affinity, and disable hyperthread sharing. They don't help, it still takes the VM 2-4x the amount of time to do the same thing as the real machine. The host the tests were run on is 6x2.50GHz, Intel Xeon E5-26400 w/ 16gigs of ram. The guest exhibits the same performance under a wide combination of settings. The real machine is 4x2.13GHz, Xeon E5506 w/ 2 gigs of ram. Thank you for all advice.

  Read the article

• How to store data on a machine whose power gets cut at random

  - by Sevas

  I have a virtual machine (Debian) running on a physical machine host. The virtual machine acts as a buffer for data that it frequently receives over the local network (the period for this data is 0.5s, so a fairly high throughput). Any data received is stored on the virtual machine and repeatedly forwarded to an external server over UDP. Once the external server acknowledges (over UDP) that it has received a data packet, the original data is deleted from the virtual machine and not sent to the external server again. The internet connection that connects the VM and the external server is unreliable, meaning it could be down for days at a time. The physical machine that hosts the VM gets its power cut several times per day at random. There is no way to tell when this is about to happen and it is not possible to add a UPS, a battery, or a similar solution to the system. Originally, the data was stored on a file-based HSQLDB database on the virtual machine. However, the frequent power cuts eventually cause the database script file to become corrupted (not at the file system level, i.e. it is readable, but HSQLDB can't make sense of it), which leads to my question: How should data be stored in an environment where power cuts can and do happen frequently? One option I can think of is using flat files, saving each packet of data as a file on the file system. This way if a file is corrupted due to loss of power, it can be ignored and the rest of the data remains intact. This poses a few issues however, mainly related to the amount of data likely being stored on the virtual machine. At 0.5s between each piece of data, 1,728,000 files will be generated in 10 days. This at least means using a file system with an increased number of inodes to store this data (the current file system setup ran out of inodes at ~250,000 messages and 30% disk space used). Also, it is hard (not impossible) to manage. Are there any other options? Are there database engines that run on Debian that would not get corrupted by power cuts? Also, what file system should be used for this? ext3 is what is used at the moment. The software that runs on the virtual machine is written using Java 6, so hopefully the solution would not be incompatible.

  Read the article

• Linux-Containers — Part 1: Overview

  - by Lenz Grimmer

  "Containers" by Jean-Pierre Martineau (CC BY-NC-SA 2.0). Linux Containers (LXC) provide a means to isolate individual services or applications as well as of a complete Linux operating system from other services running on the same host. To accomplish this, each container gets its own directory structure, network devices, IP addresses and process table. The processes running in other containers or the host system are not visible from inside a container. Additionally, Linux Containers allow for fine granular control of resources like RAM, CPU or disk I/O. Generally speaking, Linux Containers use a completely different approach than "classicial" virtualization technologies like KVM or Xen (on which Oracle VM Server for x86 is based on). An application running inside a container will be executed directly on the operating system kernel of the host system, shielded from all other running processes in a sandbox-like environment. This allows a very direct and fair distribution of CPU and I/O-resources. Linux containers can offer the best possible performance and several possibilities for managing and sharing the resources available. Similar to Containers (or Zones) on Oracle Solaris or FreeBSD jails, the same kernel version runs on the host as well as in the containers; it is not possible to run different Linux kernel versions or other operating systems like Microsoft Windows or Oracle Solaris for x86 inside a container. However, it is possible to run different Linux distribution versions (e.g. Fedora Linux in a container on top of an Oracle Linux host), provided it supports the version of the Linux kernel that runs on the host. This approach has one caveat, though - if any of the containers causes a kernel crash, it will bring down all other containers (and the host system) as well. For example, Oracle's Unbreakable Enterprise Kernel Release 2 (2.6.39) is supported for both Oracle Linux 5 and 6. This makes it possible to run Oracle Linux 5 and 6 container instances on top of an Oracle Linux 6 system. Since Linux Containers are fully implemented on the OS level (the Linux kernel), they can be easily combined with other virtualization technologies. It's certainly possible to set up Linux containers within a virtualized Linux instance that runs inside Oracle VM Server for Oracle VM Virtualbox. Some use cases for Linux Containers include: Consolidation of multiple separate Linux systems on one server: instances of Linux systems that are not performance-critical or only see sporadic use (e.g. a fax or print server or intranet services) do not necessarily need a dedicated server for their operations. These can easily be consolidated to run inside containers on a single server, to preserve energy and rack space. Running multiple instances of an application in parallel, e.g. for different users or customers. Each user receives his "own" application instance, with a defined level of service/performance. This prevents that one user's application could hog the entire system and ensures, that each user only has access to his own data set. It also helps to save main memory — if multiple instances of a same process are running, the Linux kernel can share memory pages that are identical and unchanged across all application instances. This also applies to shared libraries that applications may use, they are generally held in memory once and mapped to multiple processes. Quickly creating sandbox environments for development and testing purposes: containers that have been created and configured once can be archived as templates and can be duplicated (cloned) instantly on demand. After finishing the activity, the clone can safely be discarded. This allows to provide repeatable software builds and test environments, because the system will always be reset to its initial state for each run. Linux Containers also boot significantly faster than "classic" virtual machines, which can save a lot of time when running frequent build or test runs on applications. Safe execution of an individual application: if an application running inside a container has been compromised because of a security vulnerability, the host system and other containers remain unaffected. The potential damage can be minimized, analyzed and resolved directly from the host system. Note: Linux Containers on Oracle Linux 6 with the Unbreakable Enterprise Kernel Release 2 (2.6.39) are still marked as Technology Preview - their use is only recommended for testing and evaluation purposes. The Open-Source project "Linux Containers" (LXC) is driving the development of the technology behind this, which is based on the "Control Groups" (CGroups) and "Name Spaces" functionality of the Linux kernel. Oracle is actively involved in the Linux Containers development and contributes patches to the upstream LXC code base. Control Groups provide means to manage and monitor the allocation of resources for individual processes or process groups. Among other things, you can restrict the maximum amount of memory, CPU cycles as well as the disk and network throughput (in MB/s or IOP/s) that are available for an application. Name Spaces help to isolate process groups from each other, e.g. the visibility of other running processes or the exclusive access to a network device. It's also possible to restrict a process group's access and visibility of the entire file system hierarchy (similar to a classic "chroot" environment). CGroups and Name Spaces provide the foundation on which Linux containers are based on, but they can actually be used independently as well. A more detailed description of how Linux Containers can be created and managed on Oracle Linux will be explained in the second part of this article. Additional links related to Linux Containers: OTN Article: The Role of Oracle Solaris Zones and Linux Containers in a Virtualization Strategy Linux Containers on Wikipedia - Lenz Grimmer Follow me on: Personal Blog | Facebook | Twitter | Linux Blog |

  Read the article

• Interview with Tomas Ulin at the MySQL Innovation Day

  - by Monica Kumar

  MySQL Innovation Day held on June 5, 2012 was a great event for the MySQL engineers, users and customers to gather, share and network. I was able to get a few minutes with Tomas Ulin, Vice President of MySQL Engineering at Oracle, to ask him some questions. Here are the highlights of my interview with Tomas. Monica: This was the first MySQL Innovation Day, correct?  Why now, what was the strategy behind hosting this kind of event? Tomas: In the last year, we have rolled out an incredible number of MySQL events worldwide – some targeted at developers that are new to MySQL and others for the MySQL savvy. At the MySQL Innovation Day, our first event of this kind,, we had a number of our key engineers presenting lightning talks delivering previews of key new features as well as discussing roadmap. Our goal is to keep an open dialogue with the MySQL community. In fact, we are hosting a two-day conference, another first, for the MySQL community called MySQL Connect on Sept. 29-30 in San Francisco. If you attended the MySQL Innovation Day and liked what we did, you are going to love MySQL Connect. We’ll have a lot more of our engineers and many users and community members presenting hour long sessions and hands on labs. Our engineers will be presenting new MySQL features as well offer previews of upcoming enhancements. Monica: What's the big take-away from today's MySQL Innovation Day? Tomas: I hope the most important takeaway for attendees was to see that Oracle has been driving, and continues to drive MySQL innovation with a steady stream of new great GA and Development Milestone releases. Monica: What were attendees most interested in? What feedback did they have? Tomas: Feedback from attendees was incredibly positive and encouraging. In particular, they liked the interaction with the MySQL engineers and were also excited about the new early access features in MySQL 5.6 and MySQL Cluster 7.3. In addition, sessions delivered by MySQL users like Facebook, Pinterest and Twitter were very well received. For example, Pinterest talked about using MySQL to scale from 0 to billions of page views/month, Twitter talked about “Scaling twitter with MySQL” and Facebook discussed the many options to implement MySQL master failover solutions. The presentations are already available for download while some of the session videos will be made available on the MySQL Innovation Day web page shortly. Monica: How would you distinguish the use of MySQL vs. Oracle Database? What key factors should customers consider? Tomas: MySQL and Oracle Database complement each other. They are very different products, best suited to different use cases. Customers can choose world-class solutions from Oracle to fulfill a variety of needs. MySQL is a great choice for enterprise web-based, custom and embedded apps. Oracle Database is the leading choice for enterprise packaged applications such as ERP, CRM as well as high-end data warehousing and business intelligence applications. Monica: What are the highlights of the current MySQL 5.6 Development Milestone Release and early access features for MySQL Cluster 7.3? Tomas: MySQL 5.6 development milestone release builds on MySQL 5.5 by improving: Optimizer for better Performance, Scalability Performance Schema for better instrumentation InnoDB for better transactional throughput Replication for higher availability, data integrity NoSQL options for more flexibility We announced some new early access features in MySQL 5.6, including binary log group commit. We also announced early access features in MySQL Cluster 7.3 including support for foreign key constraints. Monica: How do people get these releases? Tomas: You can access development milestone releases by going to: http://dev.mysql.com/downloads/mysqlThen select the “Development Release” tab. The MySQL Cluster 7.3 and other early access features can be downloaded at: http://labs.mysql.com Monica: What's coming up next for MySQL? Tomas: Our development team is working in overdrive, cranking out new features with community feedback. Don’t miss the MySQL Connect conference being held in San Francisco on Sept. 29 and 30th. My team and I will be there. I hope you can join us! Monica: Thank you for your time, Tomas. I look forward to seeing you at the MySQL Connect conference. To our followers, I hope you found this interview informative. I welcome your comments. Please stay tuned here for more updates on MySQL. Note: Monica Kumar is Senior Director of product marketing for Linux, Virtualization and MySQL at Oracle.

  Read the article

• BizTalk Server 2009 - Architecture Options

  - by StuartBrierley

  I recently needed to put forward a proposal for a BizTalk 2009 implementation and as a part of this needed to describe some of the basic architecture options available for consideration.  While I already had an idea of the type of environment that I would be looking to recommend, I felt that presenting a range of options while trying to explain some of the strengths and weaknesses of those options was a good place to start.  These outline architecture options should be equally valid for any version of BizTalk Server from 2004, through 2006 and R2, up to 2009.   The following diagram shows a crude representation of the common implementation options to consider when designing a BizTalk environment.         Each of these options provides differing levels of resilience in the case of failure or disaster, with the later options also providing more scope for performance tuning and scalability.   Some of the options presented above make use of clustering. Clustering may best be described as a technology that automatically allows one physical server to take over the tasks and responsibilities of another physical server that has failed. Given that all computer hardware and software will eventually fail, the goal of clustering is to ensure that mission-critical applications will have little or no downtime when such a failure occurs. Clustering can also be configured to provide load balancing, which should generally lead to performance gains and increased capacity and throughput.   (A) Single Servers   This option is the most basic BizTalk implementation that should be considered. It involves the deployment of a single BizTalk server in conjunction with a single SQL server. This configuration does not provide for any resilience in the case of the failure of either server. It is however the cheapest and easiest to implement option of those available.   Using a single BizTalk server does not provide for the level of performance tuning that is otherwise available when using more than one BizTalk server in a cluster.   The common edition of BizTalk used in single server implementations is the standard edition. It should be noted however that if future demand requires increased capacity for a solution, this BizTalk edition is limited to scaling up the implementation and not scaling out the number of servers in use. Any need to scale out the solution would require an upgrade to the enterprise edition of BizTalk.   (B) Single BizTalk Server with Clustered SQL Servers   This option uses a single BizTalk server with a cluster of SQL servers. By utilising clustered SQL servers we can ensure that there is some resilience to the implementation in respect of the databases that BizTalk relies on to operate. The clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition. While this option offers improved resilience over option (A) it does still present a potential single point of failure at the BizTalk server.   Using a single BizTalk server does not provide for the level of performance tuning that is otherwise available when using more than one BizTalk server in a cluster.   The common edition of BizTalk used in single server implementations is the standard edition. It should be noted however that if future demand requires increased capacity for a solution, this BizTalk edition is limited to scaling up the implementation and not scaling out the number of servers in use. You are also unable to take advantage of multiple message boxes, which would allow us to balance the SQL load in the event of any bottlenecks in this area of the implementation. Any need to scale out the solution would require an upgrade to the enterprise edition of BizTalk.   (C) Clustered BizTalk Servers with Clustered SQL Servers   This option makes use of a cluster of BizTalk servers with a cluster of SQL servers to offer high availability and resilience in the case of failure of either of the server types involved. Clustering of BizTalk is only available with the enterprise edition of the product. Clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition.    The use of a BizTalk cluster also provides for the ability to balance load across the servers and gives more scope for performance tuning any implemented solutions. It is also possible to add more BizTalk servers to an existing cluster, giving scope for scaling out the solution as future demand requires.   This might be seen as the middle cost option, providing a good level of protection in the case of failure, a decent level of future proofing, but at a higher cost than the single BizTalk server implementations.   (D) Clustered BizTalk Servers with Clustered SQL Servers – with disaster recovery/service continuity   This option is similar to that offered by (C) and makes use of a cluster of BizTalk servers with a cluster of SQL servers to offer high availability and resilience in case of failure of either of the server types involved. Clustering of BizTalk is only available with the enterprise edition of the product. Clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition.    As with (C) the use of a BizTalk cluster also provides for the ability to balance load across the servers and gives more scope for performance tuning the implemented solution. It is also possible to add more BizTalk servers to an existing cluster, giving scope for scaling the solution out as future demand requires.   In this scenario however, we would be including some form of disaster recovery or service continuity. An example of this would be making use of multiple sites, with the BizTalk server cluster operating across sites to offer resilience in case of the loss of one or more sites. In this scenario there are options available for the SQL implementation depending on the network implementation; making use of either one cluster per site or a single SQL cluster across the network. A multi-site SQL implementation would require some form of data replication across the sites involved.   This is obviously an expensive and complex option, but does provide an extraordinary amount of protection in the case of failure.

  Read the article

• 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.

  Read the article

• IBM "per core" comparisons for SPECjEnterprise2010

  - by jhenning

  I recently stumbled upon a blog entry from Roman Kharkovski (an IBM employee) comparing some SPECjEnterprise2010 results for IBM vs. Oracle. Mr. Kharkovski's blog claims that SPARC delivers half the transactions per core vs. POWER7. Prior to any argument, I should say that my predisposition is to like Mr. Kharkovski, because he says that his blog is intended to be factual; that the intent is to try to avoid marketing hype and FUD tactic; and mostly because he features a picture of himself wearing a bike helmet (me too). Therefore, in a spirit of technical argument, rather than FUD fight, there are a few areas in his comparison that should be discussed. Scaling is not free For any benchmark, if a small system scores 13k using quantity R1 of some resource, and a big system scores 57k using quantity R2 of that resource, then, sure, it's tempting to divide: is  13k/R1 > 57k/R2 ? It is tempting, but not necessarily educational. The problem is that scaling is not free. Building big systems is harder than building small systems. Scoring  13k/R1  on a little system provides no guarantee whatsoever that one can sustain that ratio when attempting to handle more than 4 times as many users. Choosing the denominator radically changes the picture When ratios are used, one can vastly manipulate appearances by the choice of denominator. In this case, lots of choices are available for the resource to be compared (R1 and R2 above). IBM chooses to put cores in the denominator. Mr. Kharkovski provides some reasons for that choice in his blog entry. And yet, it should be noted that the very concept of a core is: arbitrary: not necessarily comparable across vendors; fluid: modern chips shift chip resources in response to load; and invisible: unless you have a microscope, you can't see it. By contrast, one can actually see processor chips with the naked eye, and they are a bit easier to count. If we put chips in the denominator instead of cores, we get: 13161.07 EjOPS / 4 chips = 3290 EjOPS per chip for IBM vs 57422.17 EjOPS / 16 chips = 3588 EjOPS per chip for Oracle The choice of denominator makes all the difference in the appearance. Speaking for myself, dividing by chips just seems to make more sense, because: I can see chips and count them; and I can accurately compare the number of chips in my system to the count in some other vendor's system; and Tthe probability of being able to continue to accurately count them over the next 10 years of microprocessor development seems higher than the probability of being able to accurately and comparably count "cores". SPEC Fair use requirements Speaking as an individual, not speaking for SPEC and not speaking for my employer, I wonder whether Mr. Kharkovski's blog article, taken as a whole, meets the requirements of the SPEC Fair Use rule www.spec.org/fairuse.html section I.D.2. For example, Mr. Kharkovski's footnote (1) begins Results from http://www.spec.org as of 04/04/2013 Oracle SUN SPARC T5-8 449 EjOPS/core SPECjEnterprise2010 (Oracle's WLS best SPECjEnterprise2010 EjOPS/core result on SPARC). IBM Power730 823 EjOPS/core (World Record SPECjEnterprise2010 EJOPS/core result) The questionable tactic, from a Fair Use point of view, is that there is no such metric at the designated location. At www.spec.org, You can find the SPEC metric 57422.17 SPECjEnterprise2010 EjOPS for Oracle and You can also find the SPEC metric 13161.07 SPECjEnterprise2010 EjOPS for IBM. Despite the implication of the footnote, you will not find any mention of 449 nor anything that says 823. SPEC says that you can, under its fair use rule, derive your own values; but it emphasizes: "The context must not give the appearance that SPEC has created or endorsed the derived value." Substantiation and transparency Although SPEC disclaims responsibility for non-SPEC information (section I.E), it says that non-SPEC data and methods should be accurate, should be explained, should be substantiated. Unfortunately, it is difficult or impossible for the reader to independently verify the pricing: Were like units compared to like (e.g. list price to list price)? Were all components (hw, sw, support) included? Were all fees included? Note that when tpc.org shows IBM pricing, there are often items such as "PROCESSOR ACTIVATION" and "MEMORY ACTIVATION". Without the transparency of a detailed breakdown, the pricing claims are questionable. T5 claim for "Fastest Processor" Mr. Kharkovski several times questions Oracle's claim for fastest processor, writing You see, when you publish industry benchmarks, people may actually compare your results to other vendor's results. Well, as we performance people always say, "it depends". If you believe in performance-per-core as the primary way of looking at the world, then yes, the POWER7+ is impressive, spending its chip resources to support up to 32 threads (8 cores x 4 threads). Or, it just might be useful to consider performance-per-chip. Each SPARC T5 chip allows 128 hardware threads to be simultaneously executing (16 cores x 8 threads). The Industry Standard Benchmark that focuses specifically on processor chip performance is SPEC CPU2006. For this very well known and popular benchmark, SPARC T5: provides better performance than both POWER7 and POWER7+, for 1 chip vs. 1 chip, for 8 chip vs. 8 chip, for integer (SPECint_rate2006) and floating point (SPECfp_rate2006), for Peak tuning and for Base tuning. For example, at the 8-chip level, integer throughput (SPECint_rate2006) is: 3750 for SPARC 2170 for POWER7+. You can find the details at the March 2013 BestPerf CPU2006 page SPEC is a trademark of the Standard Performance Evaluation Corporation, www.spec.org. The two specific results quoted for SPECjEnterprise2010 are posted at the URLs linked from the discussion. Results for SPEC CPU2006 were verified at spec.org 1 July 2013, and can be rechecked here.

  Read the article

• Windows Azure Recipe: Software as a Service (SaaS)

  - by Clint Edmonson

  The cloud was tailor built for aspiring companies to create innovative internet based applications and solutions. Whether you’re a garage startup with very little capital or a Fortune 1000 company, the ability to quickly setup, deliver, and iterate on new products is key to capturing market and mind share. And if you can capture that share and go viral, having resiliency and infinite scale at your finger tips is great peace of mind. Drivers Cost avoidance Time to market Scalability Solution Here’s a sketch of how a basic Software as a Service solution might be built out: Ingredients Web Role – this hosts the core web application. Each web role will host an instance of the software and as the user base grows, additional roles can be spun up to meet demand. Access Control – this service is essential to managing user identity. It’s backed by a full blown implementation of Active Directory and allows the definition and management of users, groups, and roles. A pre-built ASP.NET membership provider is included in the training kit to leverage this capability but it’s also flexible enough to be combined with external Identity providers including Windows LiveID, Google, Yahoo!, and Facebook. The provider model provides extensibility to hook into other industry specific identity providers as well. Databases – nearly every modern SaaS application is backed by a relational database for its core operational data. If the solution is sold to organizations, there’s a good chance multi-tenancy will be needed. An emerging best practice for SaaS applications is to stand up separate SQL Azure database instances for each tenant’s proprietary data to ensure isolation from other tenants. Worker Role – this is the best place to handle autonomous background processing such as data aggregation, billing through external services, and other specialized tasks that can be performed asynchronously. Placing these tasks in a worker role frees the web roles to focus completely on user interaction and data input and provides finer grained control over the system’s scalability and throughput. Caching (optional) – as a web site traffic grows caching can be leveraged to keep frequently used read-only, user specific, and application resource data in a high-speed distributed in-memory for faster response times and ultimately higher scalability without spinning up more web and worker roles. It includes a token based security model that works alongside the Access Control service. Blobs (optional) – depending on the nature of the software, users may be creating or uploading large volumes of heterogeneous data such as documents or rich media. Blob storage provides a scalable, resilient way to store terabytes of user data. The storage facilities can also integrate with the Access Control service to ensure users’ data is delivered securely. Training & Examples These links point to online Windows Azure training labs and examples where you can learn more about the individual ingredients described above. (Note: The entire Windows Azure Training Kit can also be downloaded for offline use.) Windows Azure (16 labs) Windows Azure is an internet-scale cloud computing and services platform hosted in Microsoft data centers, which provides an operating system and a set of developer services which can be used individually or together. It gives developers the choice to build web applications; applications running on connected devices, PCs, or servers; or hybrid solutions offering the best of both worlds. New or enhanced applications can be built using existing skills with the Visual Studio development environment and the .NET Framework. With its standards-based and interoperable approach, the services platform supports multiple internet protocols, including HTTP, REST, SOAP, and plain XML SQL Azure (7 labs) Microsoft SQL Azure delivers on the Microsoft Data Platform vision of extending the SQL Server capabilities to the cloud as web-based services, enabling you to store structured, semi-structured, and unstructured data. Windows Azure Services (9 labs) As applications collaborate across organizational boundaries, ensuring secure transactions across disparate security domains is crucial but difficult to implement. Windows Azure Services provides hosted authentication and access control using powerful, secure, standards-based infrastructure. Developing Applications for the Cloud, 2nd Edition (eBook) This book demonstrates how you can create from scratch a multi-tenant, Software as a Service (SaaS) application to run in the cloud using the latest versions of the Windows Azure Platform and tools. The book is intended for any architect, developer, or information technology (IT) professional who designs, builds, or operates applications and services that run on or interact with the cloud. Fabrikam Shipping (SaaS reference application) This is a full end to end sample scenario which demonstrates how to use the Windows Azure platform for exposing an application as a service. We developed this demo just as you would: we had an existing on-premises sample, Fabrikam Shipping, and we wanted to see what it would take to transform it in a full subscription based solution. The demo you find here is the result of that investigation See my Windows Azure Resource Guide for more guidance on how to get started, including more links web portals, training kits, samples, and blogs related to Windows Azure.

  Read the article

• ASP.NET: Using pickup directory for outgoing e-mails

  - by DigiMortal

  Sending e-mails out from web applications is very common task. When we are working on or test our systems with real e-mail addresses we don’t want recipients to receive e-mails (specially if we are using some subset of real data9. In this posting I will show you how to make ASP.NET SMTP client to write e-mails to disc instead of sending them out. SMTP settings for web application I have seen many times the code where all SMTP information is kept in app settings just to read them in code and give to SMTP client. It is not necessary because we can define all these settings under system.web => mailsettings node. If you are using web.config to keep SMTP settings then all you have to do in your code is just to create SmtpClient with empty constructor. var smtpClient = new SmtpClient(); Empty constructor means that all settings are read from web.config file. What is pickup directory? If you want drastically raise e-mail throughput of your SMTP server then it is not very wise plan to communicate with it using SMTP protocol. it adds only additional overhead to your network and SMTP server. Okay, clients make connections, send messages out and it is also overhead we can avoid. If clients write their e-mails to some folder that SMTP server can access then SMTP server has e-mail forwarding as only resource-eager task to do. File operations are way faster than communication over SMTP protocol. The directory where clients write their e-mails as files is called pickup directory. By example, Exchange server has support for pickup directories. And as there are applications with a lot of users who want e-mail notifications then .NET SMTP client supports writing e-mails to pickup directory instead of sending them out. How to configure ASP.NET SMTP to use pickup directory? Let’s say, it is more than easy. It is very easy. This is all you need. <system.net>   <mailSettings>     <smtp deliveryMethod="SpecifiedPickupDirectory">       <specifiedPickupDirectory pickupDirectoryLocation="c:\temp\maildrop\"/>     </smtp>   </mailSettings> </system.net> Now make sure you don’t miss come points: Pickup directory must physically exist because it is not created automatically. IIS (or Cassini) must have write permissions to pickup directory. Go through your code and look for hardcoded SMTP settings. Also take a look at all places in your code where you send out e-mails that there are not some custom settings used for SMTP! Also don’t forget that your mails will be written now to pickup directory and they are not sent out to recipients anymore. Advanced scenario: configuring SMTP client in code In some advanced scenarios you may need to support multiple SMTP servers. If configuration is dynamic or it is not kept in web.config you need to initialize your SmtpClient in code. This is all you need to do. var smtpClient = new SmtpClient(); smtpClient.DeliveryMethod = SmtpDeliveryMethod.SpecifiedPickupDirectory; smtpClient.PickupDirectoryLocation = pickupFolder; Easy, isn’t it? i like when advanced scenarios end up with simple and elegant solutions but not with rocket science. Note for IIS SMTP service SMTP service of IIS is also able to use pickup directory. If you have set up IIS with SMTP service you can configure your ASP.NET application to use IIS pickup folder. In this case you have to use the following setting for delivery method. SmtpDeliveryMethod.PickupDirectoryFromIis You can set this setting also in web.config file. <system.net>   <mailSettings>     <smtp deliveryMethod="PickupDirectoryFromIis" />   </mailSettings> </system.net> Conclusion Who was still using different methods to avoid sending e-mails out in development or testing environment can now remove all the bad code from application and live on mail settings of ASP.NET. It is easy to configure and you have less code to support e-mails when you use built-in e-mail features wisely.

  Read the article

• ROracle support for TimesTen In-Memory Database

  - by Sam Drake

  Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

  Read the article

• Data management in unexpected places

  - by Ashok_Ora

  Normal 0 false false false EN-US X-NONE X-NONE Data management in unexpected places When you think of network switches, routers, firewall appliances, etc., it may not be obvious that at the heart of these kinds of solutions is an engine that can manage huge amounts of data at very high throughput with low latencies and high availability. Consider a network router that is processing tens (or hundreds) of thousands of network packets per second. So what really happens inside a router? Packets are streaming in at the rate of tens of thousands per second. Each packet has multiple attributes, for example, a destination, associated SLAs etc. For each packet, the router has to determine the address of the next “hop” to the destination; it has to determine how to prioritize this packet. If it’s a high priority packet, then it has to be sent on its way before lower priority packets. As a consequence of prioritizing high priority packets, lower priority data packets may need to be temporarily stored (held back), but addressed fairly. If there are security or privacy requirements associated with the data packet, those have to be enforced. You probably need to keep track of statistics related to the packets processed (someone’s sure to ask). You have to do all this (and more) while preserving high availability i.e. if one of the processors in the router goes down, you have to have a way to continue processing without interruption (the customer won’t be happy with a “choppy” VoIP conversation, right?). And all this has to be achieved without ANY intervention from a human operator – the router is most likely to be in a remote location – it must JUST CONTINUE TO WORK CORRECTLY, even when bad things happen. How is this implemented? As soon as a packet arrives, it is interpreted by the receiving software. The software decodes the packet headers in order to determine the destination, kind of packet (e.g. voice vs. data), SLAs associated with the “owner” of the packet etc. It looks up the internal database of “rules” of how to process this packet and handles the packet accordingly. The software might choose to hold on to the packet safely for some period of time, if it’s a low priority packet. Ah – this sounds very much like a database problem. For each packet, you have to minimally · Look up the most efficient next “hop” towards the destination. The “most efficient” next hop can change, depending on latency, availability etc. · Look up the SLA and determine the priority of this packet (e.g. voice calls get priority over data ftp) · Look up security information associated with this data packet. It may be necessary to retrieve the context for this network packet since a network packet is a small “slice” of a session. The context for the “header” packet needs to be stored in the router, in order to make this work. · If the priority of the packet is low, then “store” the packet temporarily in the router until it is time to forward the packet to the next hop. · Update various statistics about the packet. In most cases, you have to do all this in the context of a single transaction. For example, you want to look up the forwarding address and perform the “send” in a single transaction so that the forwarding address doesn’t change while you’re sending the packet. So, how do you do all this? Berkeley DB is a proven, reliable, high performance, highly available embeddable database, designed for exactly these kinds of usage scenarios. Berkeley DB is a robust, reliable, proven solution that is currently being used in these scenarios. First and foremost, Berkeley DB (or BDB for short) is very very fast. It can process tens or hundreds of thousands of transactions per second. It can be used as a pure in-memory database, or as a disk-persistent database. BDB provides high availability – if one board in the router fails, the system can automatically failover to another board – no manual intervention required. BDB is self-administering – there’s no need for manual intervention in order to maintain a BDB application. No need to send a technician to a remote site in the middle of nowhere on a freezing winter day to perform maintenance operations. BDB is used in over 200 million deployments worldwide for the past two decades for mission-critical applications such as the one described here. You have a choice of spending valuable resources to implement similar functionality, or, you could simply embed BDB in your application and off you go! I know what I’d do – choose BDB, so I can focus on my business problem. What will you do? /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

  Read the article

• ROracle support for TimesTen In-Memory Database

  - by Sherry LaMonica

  Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

  Read the article

• A Patent for Workload Management Based on Service Level Objectives

  - by jsavit

  I'm very pleased to announce that after a tiny :-) wait of about 5 years, my patent application for a workload manager was finally approved. Background Many operating systems have a resource manager which lets you control machine resources. For example, Solaris provides controls for CPU with several options: shares for proportional CPU allocation. If you have twice as many shares as me, and we are competing for CPU, you'll get about twice as many CPU cycles), dedicated CPU allocation in which a number of CPUs are exclusively dedicated to an application's use. You can say that a zone or project "owns" 8 CPUs on a 32 CPU machine, for example. And, capped CPU in which you specify the upper bound, or cap, of how much CPU an application gets. For example, you can throttle an application to 0.125 of a CPU. (This isn't meant to be an exhaustive list of Solaris RM controls.) Workload management Useful as that is (and tragic that some other operating systems have little resource management and isolation, and frighten people into running only 1 app per OS instance - and wastefully size every server for the peak workload it might experience) that's not really workload management. With resource management one controls the resources, and hope that's enough to meet application service objectives. In fact, we hold resource distribution constant, see if that was good enough, and adjust resource distribution if that didn't meet service level objectives. Here's an example of what happens today: Let's try 30% dedicated CPU. Not enough? Let's try 80% Oh, that's too much, and we're achieving much better response time than the objective, but other workloads are starving. Let's back that off and try again. It's not the process I object to - it's that we to often do this manually. Worse, we sometimes identify and adjust the wrong resource and fiddle with that to no useful result. Back in my days as a customer managing large systems, one of my users would call me up to beg for a "CPU boost": Me: "it won't make any difference - there's plenty of spare CPU to be had, and your application is completely I/O bound." User: "Please do it anyway." Me: "oh, all right, but it won't do you any good." (I did, because he was a friend, but it didn't help.) Prior art There are some operating environments that take a stab about workload management (rather than resource management) but I find them lacking. I know of one that uses synthetic "service units" composed of the sum of CPU, I/O and memory allocations multiplied by weighting factors. A workload is set to make a target rate of service units consumed per second. But this seems to be missing a key point: what is the relationship between artificial 'service units' and actually meeting a throughput or response time objective? What if I get plenty of one of the components (so am getting enough service units), but not enough of the resource whose needed to remove the bottleneck? Actual workload management That's not really the answer either. What is needed is to specify a workload's service levels in terms of externally visible metrics that are meaningful to a business, such as response times or transactions per second, and have the workload manager figure out which resources are not being adequately provided, and then adjust it as needed. If an application is not meeting its service level objectives and the reason is that it's not getting enough CPU cycles, adjust its CPU resource accordingly. If the reason is that the application isn't getting enough RAM to keep its working set in memory, then adjust its RAM assignment appropriately so it stops swapping. Simple idea, but that's a task we keep dumping on system administrators. In other words - don't hold the number of CPU shares constant and watch the achievement of service level vary. Instead, hold the service level constant, and dynamically adjust the number of CPU shares (or amount of other resources like RAM or I/O bandwidth) in order to meet the objective. Instrumenting non-instrumented applications There's one little problem here: how do I measure application performance in a way relating to a service level. I don't want to do it based on internal resources like number of CPU seconds it received per minute - We need to make resource decisions based on externally visible and meaningful measures of performance, not synthetic items or internal resource counters. If I have a way of marking the beginning and end of a transaction, I can then measure whether or not the application is meeting an objective based on it. If I can observe the delay factors for an application, I can see which resource shortages are slowing an application enough to keep it from meeting its objectives. I can then adjust resource allocations to relieve those shortages. Fortunately, Solaris provides facilities for both marking application progress and determining what factors cause application latency. The Solaris DTrace facility let's me introspect on application behavior: in particular I can see events like "receive a web hit" and "respond to that web hit" so I can get transaction rate and response time. DTrace (and tools like prstat) let me see where latency is being added to an application, so I know which resource to adjust. Summary After a delay of a mere few years, I am the proud creator of a patent (advice to anyone interested in going through the process: don't hold your breath!). The fundamental idea is fairly simple: instead of holding resource constant and suffering variable levels of success meeting service level objectives, properly characterise the service level objective in meaningful terms, instrument the application to see if it's meeting the objective, and then have a workload manager change resource allocations to remove delays preventing service level attainment. I've done it by hand for a long time - I think that's what a computer should do for me.

  Read the article

• What might cause the big overhead of making a HttpWebRequest call?

  - by Dimitri C.

  When I send/receive data using HttpWebRequest (on Silverlight, using the HTTP POST method) in small blocks, I measure the very small throughput of 500 bytes/s over a "localhost" connection. When sending the data in large blocks, I get 2 MB/s, which is some 5000 times faster. Does anyone know what could cause this incredibly big overhead? Update: I did the performance measurement on both Firefox 3.6 and Internet Explorer 7. Both showed similar results. Update: The Silverlight client-side code I use is essentially my own implementation of the WebClient class. The reason I wrote it is because I noticed the same performance problem with WebClient, and I thought that the HttpWebRequest would allow to tweak the performance issue. Regrettably, this did not work. The implementation is as follows: public class HttpCommChannel { public delegate void ResponseArrivedCallback(object requestContext, BinaryDataBuffer response); public HttpCommChannel(ResponseArrivedCallback responseArrivedCallback) { this.responseArrivedCallback = responseArrivedCallback; this.requestSentEvent = new ManualResetEvent(false); this.responseArrivedEvent = new ManualResetEvent(true); } public void MakeRequest(object requestContext, string url, BinaryDataBuffer requestPacket) { responseArrivedEvent.WaitOne(); responseArrivedEvent.Reset(); this.requestMsg = requestPacket; this.requestContext = requestContext; this.webRequest = WebRequest.Create(url) as HttpWebRequest; this.webRequest.AllowReadStreamBuffering = true; this.webRequest.ContentType = "text/plain"; this.webRequest.Method = "POST"; this.webRequest.BeginGetRequestStream(new AsyncCallback(this.GetRequestStreamCallback), null); this.requestSentEvent.WaitOne(); } void GetRequestStreamCallback(IAsyncResult asynchronousResult) { System.IO.Stream postStream = webRequest.EndGetRequestStream(asynchronousResult); postStream.Write(requestMsg.Data, 0, (int)requestMsg.Size); postStream.Close(); requestSentEvent.Set(); webRequest.BeginGetResponse(new AsyncCallback(this.GetResponseCallback), null); } void GetResponseCallback(IAsyncResult asynchronousResult) { HttpWebResponse response = (HttpWebResponse)webRequest.EndGetResponse(asynchronousResult); Stream streamResponse = response.GetResponseStream(); Dim.Ensure(streamResponse.CanRead); byte[] readData = new byte[streamResponse.Length]; Dim.Ensure(streamResponse.Read(readData, 0, (int)streamResponse.Length) == streamResponse.Length); streamResponse.Close(); response.Close(); webRequest = null; responseArrivedEvent.Set(); responseArrivedCallback(requestContext, new BinaryDataBuffer(readData)); } HttpWebRequest webRequest; ManualResetEvent requestSentEvent; BinaryDataBuffer requestMsg; object requestContext; ManualResetEvent responseArrivedEvent; ResponseArrivedCallback responseArrivedCallback; } I use this code to send data back and forth to an HTTP server.

  Read the article

• Good Email Notification Sending Service

  - by Philibert Perusse

  I need to send a few but important email notifications to individual users. For instance, when they register their software I send them a confirmation email. Right now, I am using 'sendmail' from my Perl CGI script to do the job. Most of my automated email are lost or marked as junk. Unfortunately, I am using shared hosting services and not a very good control over the SPF and SenderID DNS records. Even more bad, some other user of that shared server has been infected with some kind of SPAM-BOT and the IP is now blacklisted until further notice! Anyway I just don't want to deal with this kind of headache. I am looking for an online service that I will be able to subscribe to and pay something like 0.10$ per email I send with no monthly fees. I just need and API to be able to send the email from PHP or Perl code I will have to write. I have been looking around at all those "Email Sending Services" and they are all wrapped around creating campains and managing lists for bulk email marketing distribution and newsletters. But remember, I want to send an email notification to a "single" recipient. So far, I have look at MailChimp, SocketLabs, iContact, ConstantContact, StreamSend and so many others to no avail. I have seen one comment at Hackers News saying that MailChimp have an API for transactional e-mails (i.e. ad-hoc ones to welcome a user for example). So you're not just restricted to using them for bulk emails But I cannot find this in the API documentation supplied, maybe this was removed. Any suggestions out there. Here is a summary of my requirements: Allows ad hoc sending of email to a single recipient. Throughput may well be throttle I don't care, i am sending like 2-5 emails a day. API available in PHP or Perl to connect to that web service. Ideally I can send HTML formatted emails, otherwise I will live with text only. Solution not too expensive, between 0.01$ and 0.25$ per email would be acceptable. No recurring monthly fees.

  Read the article

• Improving TCP performance over a gigabit network with lots of connections and high traffic of small packets

  - by MinimeDJ

  I’m trying to improve my TCP throughput over a “gigabit network with lots of connections and high traffic of small packets”. My server OS is Ubuntu 11.10 Server 64bit. There are about 50.000 (and growing) clients connected to my server through TCP Sockets (all on the same port). 95% of of my packets have size of 1-150 bytes (TCP header and payload). The rest 5% vary from 150 up to 4096+ bytes. With the config below my server can handle traffic up to 30 Mbps (full duplex). Can you please advice best practice to tune OS for my needs? My /etc/sysctl.cong looks like this: kernel.pid_max = 1000000 net.ipv4.ip_local_port_range = 2500 65000 fs.file-max = 1000000 # net.core.netdev_max_backlog=3000 net.ipv4.tcp_sack=0 # net.core.rmem_max = 16777216 net.core.wmem_max = 16777216 net.core.somaxconn = 2048 # net.ipv4.tcp_rmem = 4096 87380 16777216 net.ipv4.tcp_wmem = 4096 65536 16777216 # net.ipv4.tcp_synack_retries = 2 net.ipv4.tcp_syncookies = 1 net.ipv4.tcp_mem = 50576 64768 98152 # net.core.wmem_default = 65536 net.core.rmem_default = 65536 net.ipv4.tcp_window_scaling=1 # net.ipv4.tcp_mem= 98304 131072 196608 # net.ipv4.tcp_timestamps = 0 net.ipv4.tcp_rfc1337 = 1 net.ipv4.ip_forward = 0 net.ipv4.tcp_congestion_control=cubic net.ipv4.tcp_tw_recycle = 0 net.ipv4.tcp_tw_reuse = 0 # net.ipv4.tcp_orphan_retries = 1 net.ipv4.tcp_fin_timeout = 25 net.ipv4.tcp_max_orphans = 8192 Here are my limits: $ ulimit -a core file size (blocks, -c) 0 data seg size (kbytes, -d) unlimited scheduling priority (-e) 0 file size (blocks, -f) unlimited pending signals (-i) 193045 max locked memory (kbytes, -l) 64 max memory size (kbytes, -m) unlimited open files (-n) 1000000 pipe size (512 bytes, -p) 8 POSIX message queues (bytes, -q) 819200 real-time priority (-r) 0 stack size (kbytes, -s) 8192 cpu time (seconds, -t) unlimited max user processes (-u) 1000000 [ADDED] My NICs are the following: $ dmesg | grep Broad [ 2.473081] Broadcom NetXtreme II 5771x 10Gigabit Ethernet Driver bnx2x 1.62.12-0 (2011/03/20) [ 2.477808] bnx2x 0000:02:00.0: eth0: Broadcom NetXtreme II BCM57711E XGb (A0) PCI-E x4 5GHz (Gen2) found at mem fb000000, IRQ 28, node addr d8:d3:85:bd:23:08 [ 2.482556] bnx2x 0000:02:00.1: eth1: Broadcom NetXtreme II BCM57711E XGb (A0) PCI-E x4 5GHz (Gen2) found at mem fa000000, IRQ 40, node addr d8:d3:85:bd:23:0c [ADDED 2] ethtool -k eth0 Offload parameters for eth0: rx-checksumming: on tx-checksumming: on scatter-gather: on tcp-segmentation-offload: on udp-fragmentation-offload: off generic-segmentation-offload: on generic-receive-offload: on large-receive-offload: on rx-vlan-offload: on tx-vlan-offload: on ntuple-filters: off receive-hashing: off [ADDED 3] sudo ethtool -S eth0|grep -vw 0 NIC statistics: [1]: rx_bytes: 17521104292 [1]: rx_ucast_packets: 118326392 [1]: tx_bytes: 35351475694 [1]: tx_ucast_packets: 191723897 [2]: rx_bytes: 16569945203 [2]: rx_ucast_packets: 114055437 [2]: tx_bytes: 36748975961 [2]: tx_ucast_packets: 194800859 [3]: rx_bytes: 16222309010 [3]: rx_ucast_packets: 109397802 [3]: tx_bytes: 36034786682 [3]: tx_ucast_packets: 198238209 [4]: rx_bytes: 14884911384 [4]: rx_ucast_packets: 104081414 [4]: rx_discards: 5828 [4]: rx_csum_offload_errors: 1 [4]: tx_bytes: 35663361789 [4]: tx_ucast_packets: 194024824 [5]: rx_bytes: 16465075461 [5]: rx_ucast_packets: 110637200 [5]: tx_bytes: 43720432434 [5]: tx_ucast_packets: 202041894 [6]: rx_bytes: 16788706505 [6]: rx_ucast_packets: 113123182 [6]: tx_bytes: 38443961940 [6]: tx_ucast_packets: 202415075 [7]: rx_bytes: 16287423304 [7]: rx_ucast_packets: 110369475 [7]: rx_csum_offload_errors: 1 [7]: tx_bytes: 35104168638 [7]: tx_ucast_packets: 184905201 [8]: rx_bytes: 12689721791 [8]: rx_ucast_packets: 87616037 [8]: rx_discards: 2638 [8]: tx_bytes: 36133395431 [8]: tx_ucast_packets: 196547264 [9]: rx_bytes: 15007548011 [9]: rx_ucast_packets: 98183525 [9]: rx_csum_offload_errors: 1 [9]: tx_bytes: 34871314517 [9]: tx_ucast_packets: 188532637 [9]: tx_mcast_packets: 12 [10]: rx_bytes: 12112044826 [10]: rx_ucast_packets: 84335465 [10]: rx_discards: 2494 [10]: tx_bytes: 36562151913 [10]: tx_ucast_packets: 195658548 [11]: rx_bytes: 12873153712 [11]: rx_ucast_packets: 89305791 [11]: rx_discards: 2990 [11]: tx_bytes: 36348541675 [11]: tx_ucast_packets: 194155226 [12]: rx_bytes: 12768100958 [12]: rx_ucast_packets: 89350917 [12]: rx_discards: 2667 [12]: tx_bytes: 35730240389 [12]: tx_ucast_packets: 192254480 [13]: rx_bytes: 14533227468 [13]: rx_ucast_packets: 98139795 [13]: tx_bytes: 35954232494 [13]: tx_ucast_packets: 194573612 [13]: tx_bcast_packets: 2 [14]: rx_bytes: 13258647069 [14]: rx_ucast_packets: 92856762 [14]: rx_discards: 3509 [14]: rx_csum_offload_errors: 1 [14]: tx_bytes: 35663586641 [14]: tx_ucast_packets: 189661305 rx_bytes: 226125043936 rx_ucast_packets: 1536428109 rx_bcast_packets: 351 rx_discards: 20126 rx_filtered_packets: 8694 rx_csum_offload_errors: 11 tx_bytes: 548442367057 tx_ucast_packets: 2915571846 tx_mcast_packets: 12 tx_bcast_packets: 2 tx_64_byte_packets: 35417154 tx_65_to_127_byte_packets: 2006984660 tx_128_to_255_byte_packets: 373733514 tx_256_to_511_byte_packets: 378121090 tx_512_to_1023_byte_packets: 77643490 tx_1024_to_1522_byte_packets: 43669214 tx_pause_frames: 228 Some info about SACK: When to turn TCP SACK off?

  Read the article

• What's up with LDoms: Part 4 - Virtual Networking Explained

  - by Stefan Hinker

  I'm back from my summer break (and some pressing business that kept me away from this), ready to continue with Oracle VM Server for SPARC ;-) In this article, we'll have a closer look at virtual networking.  Basic connectivity as we've seen it in the first, simple example, is easy enough.  But there are numerous options for the virtual switches and virtual network ports, which we will discuss in more detail now.   In this section, we will concentrate on virtual networking - the capabilities of virtual switches and virtual network ports - only.  Other options involving hardware assignment or redundancy will be covered in separate sections later on. There are two basic components involved in virtual networking for LDoms: Virtual switches and virtual network devices.  The virtual switch should be seen just like a real ethernet switch.  It "runs" in the service domain and moves ethernet packets back and forth.  A virtual network device is plumbed in the guest domain.  It corresponds to a physical network device in the real world.  There, you'd be plugging a cable into the network port, and plug the other end of that cable into a switch.  In the virtual world, you do the same:  You create a virtual network device for your guest and connect it to a virtual switch in a service domain.  The result works just like in the physical world, the network device sends and receives ethernet packets, and the switch does all those things ethernet switches tend to do. If you look at the reference manual of Oracle VM Server for SPARC, there are numerous options for virtual switches and network devices.  Don't be confused, it's rather straight forward, really.  Let's start with the simple case, and work our way to some more sophisticated options later on.  In many cases, you'll want to have several guests that communicate with the outside world on the same ethernet segment.  In the real world, you'd connect each of these systems to the same ethernet switch.  So, let's do the same thing in the virtual world: root@sun # ldm add-vsw net-dev=nxge2 admin-vsw primary root@sun # ldm add-vnet admin-net admin-vsw mars root@sun # ldm add-vnet admin-net admin-vsw venus We've just created a virtual switch called "admin-vsw" and connected it to the physical device nxge2.  In the physical world, we'd have powered up our ethernet switch and installed a cable between it and our big enterprise datacenter switch.  We then created a virtual network interface for each one of the two guest systems "mars" and "venus" and connected both to that virtual switch.  They can now communicate with each other and with any system reachable via nxge2.  If primary were running Solaris 10, communication with the guests would not be possible.  This is different with Solaris 11, please see the Admin Guide for details.  Note that I've given both the vswitch and the vnet devices some sensible names, something I always recommend. Unless told otherwise, the LDoms Manager software will automatically assign MAC addresses to all network elements that need one.  It will also make sure that these MAC addresses are unique and reuse MAC addresses to play nice with all those friendly DHCP servers out there.  However, if we want to do this manually, we can also do that.  (One reason might be firewall rules that work on MAC addresses.)  So let's give mars a manually assigned MAC address: root@sun # ldm set-vnet mac-addr=0:14:4f:f9:c4:13 admin-net mars Within the guest, these virtual network devices have their own device driver.  In Solaris 10, they'd appear as "vnet0".  Solaris 11 would apply it's usual vanity naming scheme.  We can configure these interfaces just like any normal interface, give it an IP-address and configure sophisticated routing rules, just like on bare metal.  In many cases, using Jumbo Frames helps increase throughput performance.  By default, these interfaces will run with the standard ethernet MTU of 1500 bytes.  To change this,  it is usually sufficient to set the desired MTU for the virtual switch.  This will automatically set the same MTU for all vnet devices attached to that switch.  Let's change the MTU size of our admin-vsw from the example above: root@sun # ldm set-vsw mtu=9000 admin-vsw primary Note that that you can set the MTU to any value between 1500 and 16000.  Of course, whatever you set needs to be supported by the physical network, too. Another very common area of network configuration is VLAN tagging. This can be a little confusing - my advise here is to be very clear on what you want, and perhaps draw a little diagram the first few times.  As always, keeping a configuration simple will help avoid errors of all kind.  Nevertheless, VLAN tagging is very usefull to consolidate different networks onto one physical cable.  And as such, this concept needs to be carried over into the virtual world.  Enough of the introduction, here's a little diagram to help in explaining how VLANs work in LDoms: Let's remember that any VLANs not explicitly tagged have the default VLAN ID of 1. In this example, we have a vswitch connected to a physical network that carries untagged traffic (VLAN ID 1) as well as VLANs 11, 22, 33 and 44.  There might also be other VLANs on the wire, but the vswitch will ignore all those packets.  We also have two vnet devices, one for mars and one for venus.  Venus will see traffic from VLANs 33 and 44 only.  For VLAN 44, venus will need to configure a tagged interface "vnet44000".  For VLAN 33, the vswitch will untag all incoming traffic for venus, so that venus will see this as "normal" or untagged ethernet traffic.  This is very useful to simplify guest configuration and also allows venus to perform Jumpstart or AI installations over this network even if the Jumpstart or AI server is connected via VLAN 33.  Mars, on the other hand, has full access to untagged traffic from the outside world, and also to VLANs 11,22 and 33, but not 44.  On the command line, we'd do this like this: root@sun # ldm add-vsw net-dev=nxge2 pvid=1 vid=11,22,33,44 admin-vsw primary root@sun # ldm add-vnet admin-net pvid=1 vid=11,22,33 admin-vsw mars root@sun # ldm add-vnet admin-net pvid=33 vid=44 admin-vsw venus Finally, I'd like to point to a neat little option that will make your live easier in all those cases where configurations tend to change over the live of a guest system.  It's the "id=<somenumber>" option available for both vswitches and vnet devices.  Normally, Solaris in the guest would enumerate network devices sequentially.  However, it has ways of remembering this initial numbering.  This is good in the physical world.  In the virtual world, whenever you unbind (aka power off and disassemble) a guest system, remove and/or add network devices and bind the system again, chances are this numbering will change.  Configuration confusion will follow suit.  To avoid this, nail down the initial numbering by assigning each vnet device it's device-id explicitly: root@sun # ldm add-vnet admin-net id=1 admin-vsw venus Please consult the Admin Guide for details on this, and how to decipher these network ids from Solaris running in the guest. Thanks for reading this far.  Links for further reading are essentially only the Admin Guide and Reference Manual and can be found above.  I hope this is useful and, as always, I welcome any comments.

  Read the article

• Pre-rentrée Oracle Open World 2012 : à vos agendas

  - by Eric Bezille

  A maintenant moins d'un mois de l’événement majeur d'Oracle, qui se tient comme chaque année à San Francisco, fin septembre, début octobre, les spéculations vont bon train sur les annonces qui vont y être dévoilées... Et sans lever le voile, je vous engage à prendre connaissance des sujets des "Key Notes" qui seront tenues par Larry Ellison, Mark Hurd, Thomas Kurian (responsable des développements logiciels) et John Fowler (responsable des développements systèmes) afin de vous donner un avant goût. Stratégie et Roadmaps Oracle Bien entendu, au-delà des séances plénières qui vous donnerons  une vision précise de la stratégie, et pour ceux qui seront sur place, je vous engage à ne pas manquer les séances d'approfondissement qui auront lieu dans la semaine, dont voici quelques morceaux choisis : "Accelerate your Business with the Oracle Hardware Advantage" avec John Fowler, le lundi 1er Octobre, 3:15pm-4:15pm "Why Oracle Softwares Runs Best on Oracle Hardware" , avec Bradley Carlile, le responsable des Benchmarks, le lundi 1er Octobre, 12:15pm-13:15pm "Engineered Systems - from Vision to Game-changing Results", avec Robert Shimp, le lundi 1er Octobre 1:45pm-2:45pm "Database and Application Consolidation on SPARC Supercluster", avec Hugo Rivero, responsable dans les équipes d'intégration matériels et logiciels, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle’s SPARC Server Strategy Update", avec Masood Heydari, responsable des développements serveurs SPARC, le mardi 2 Octobre, 10:15am - 11:15am "Oracle Solaris 11 Strategy, Engineering Insights, and Roadmap", avec Markus Flier, responsable des développements Solaris, le mercredi 3 Octobre, 10:15am - 11:15am "Oracle Virtualization Strategy and Roadmap", avec Wim Coekaerts, responsable des développement Oracle VM et Oracle Linux, le lundi 1er Octobre, 12:15pm-1:15pm "Big Data: The Big Story", avec Jean-Pierre Dijcks, responsable du développement produits Big Data, le lundi 1er Octobre, 3:15pm-4:15pm "Scaling with the Cloud: Strategies for Storage in Cloud Deployments", avec Christine Rogers,  Principal Product Manager, et Chris Wood, Senior Product Specialist, Stockage , le lundi 1er Octobre, 10:45am-11:45am Retours d'expériences et témoignages Si Oracle Open World est l'occasion de partager avec les équipes de développement d'Oracle en direct, c'est aussi l'occasion d'échanger avec des clients et experts qui ont mis en oeuvre  nos technologies pour bénéficier de leurs retours d'expériences, comme par exemple : "Oracle Optimized Solution for Siebel CRM at ACCOR", avec les témoignages d'Eric Wyttynck, directeur IT Multichannel & CRM  et Pascal Massenet, VP Loyalty & CRM systems, sur les bénéfices non seulement métiers, mais également projet et IT, le mercredi 3 Octobre, 1:15pm-2:15pm "Tips from AT&T: Oracle E-Business Suite, Oracle Database, and SPARC Enterprise", avec le retour d'expérience des experts Oracle, le mardi 2 Octobre, 11:45am-12:45pm "Creating a Maximum Availability Architecture with SPARC SuperCluster", avec le témoignage de Carte Wright, Database Engineer à CKI, le mercredi 3 Octobre, 11:45am-12:45pm "Multitenancy: Everybody Talks It, Oracle Walks It with Pillar Axiom Storage", avec le témoignage de Stephen Schleiger, Manager Systems Engineering de Navis, le lundi 1er Octobre, 1:45pm-2:45pm "Oracle Exadata for Database Consolidation: Best Practices", avec le retour d'expérience des experts Oracle ayant participé à la mise en oeuvre d'un grand client du monde bancaire, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle Exadata Customer Panel: Packaged Applications with Oracle Exadata", animé par Tim Shetler, VP Product Management, mardi 2 Octobre, 1:15pm-2:15pm "Big Data: Improving Nearline Data Throughput with the StorageTek SL8500 Modular Library System", avec le témoignage du CTO de CSC, Alan Powers, le jeudi 4 Octobre, 12:45pm-1:45pm "Building an IaaS Platform with SPARC, Oracle Solaris 11, and Oracle VM Server for SPARC", avec le témoignage de Syed Qadri, Lead DBA et Michael Arnold, System Architect d'US Cellular, le mardi 2 Octobre, 10:15am-11:15am "Transform Data Center TCO with Oracle Optimized Servers: A Customer Panel", avec les témoignages notamment d'AT&T et Liberty Global, le mardi 2 Octobre, 11:45am-12:45pm "Data Warehouse and Big Data Customers’ View of the Future", avec The Nielsen Company US, Turkcell, GE Retail Finance, Allianz Managed Operations and Services SE, le lundi 1er Octobre, 4:45pm-5:45pm "Extreme Storage Scale and Efficiency: Lessons from a 100,000-Person Organization", le témoignage de l'IT interne d'Oracle sur la transformation et la migration de l'ensemble de notre infrastructure de stockage, mardi 2 Octobre, 1:15pm-2:15pm Echanges avec les groupes d'utilisateurs et les équipes de développement Oracle Si vous avez prévu d'arriver suffisamment tôt, vous pourrez également échanger dès le dimanche avec les groupes d'utilisateurs, ou tous les soirs avec les équipes de développement Oracle sur des sujets comme : "To Exalogic or Not to Exalogic: An Architectural Journey", avec Todd Sheetz - Manager of DBA and Enterprise Architecture, Veolia Environmental Services, le dimanche 30 Septembre, 2:30pm-3:30pm "Oracle Exalytics and Oracle TimesTen for Exalytics Best Practices", avec Mark Rittman, de Rittman Mead Consulting Ltd, le dimanche 30 Septembre, 10:30am-11:30am "Introduction of Oracle Exadata at Telenet: Bringing BI to Warp Speed", avec Rudy Verlinden & Eric Bartholomeus - Managers IT infrastructure à Telenet, le dimanche 30 Septembre, 1:15pm-2:00pm "The Perfect Marriage: Sun ZFS Storage Appliance with Oracle Exadata", avec Melanie Polston, directeur, Data Management, de Novation et Charles Kim, Managing Director de Viscosity, le dimanche 30 Septembre, 9:00am-10am "Oracle’s Big Data Solutions: NoSQL, Connectors, R, and Appliance Technologies", avec Jean-Pierre Dijcks et les équipes de développement Oracle, le lundi 1er Octobre, 6:15pm-7:00pm Testez et évaluez les solutions Et pour finir, vous pouvez même tester les technologies au travers du Oracle DemoGrounds, (1133 Moscone South pour la partie Systèmes Oracle, OS, et Virtualisation) et des "Hands-on-Labs", comme : "Deploying an IaaS Environment with Oracle VM", le mardi 2 Octobre, 10:15am-11:15am "Virtualize and Deploy Oracle Applications in Minutes with Oracle VM: Hands-on Lab", le mardi 2 Octobre, 11:45am-12:45pm (il est fortement conseillé d'avoir suivi le "Hands-on-Labs" précédent avant d'effectuer ce Lab. "x86 Enterprise Cloud Infrastructure with Oracle VM 3.x and Sun ZFS Storage Appliance", le mercredi 3 Octobre, 5:00pm-6:00pm "StorageTek Tape Analytics: Managing Tape Has Never Been So Simple", le mercredi 3 Octobre, 1:15pm-2:15pm "Oracle’s Pillar Axiom 600 Storage System: Power and Ease", le lundi 1er Octobre, 12:15pm-1:15pm "Enterprise Cloud Infrastructure for SPARC with Oracle Enterprise Manager Ops Center 12c", le lundi 1er Octobre, 1:45pm-2:45pm "Managing Storage in the Cloud", le mardi 2 Octobre, 5:00pm-6:00pm "Learn How to Write MapReduce on Oracle’s Big Data Platform", le lundi 1er Octobre, 12:15pm-1:15pm "Oracle Big Data Analytics and R", le mardi 2 Octobre, 1:15pm-2:15pm "Reduce Risk with Oracle Solaris Access Control to Restrain Users and Isolate Applications", le lundi 1er Octobre, 10:45am-11:45am "Managing Your Data with Built-In Oracle Solaris ZFS Data Services in Release 11", le lundi 1er Octobre, 4:45pm-5:45pm "Virtualizing Your Oracle Solaris 11 Environment", le mardi 2 Octobre, 1:15pm-2:15pm "Large-Scale Installation and Deployment of Oracle Solaris 11", le mercredi 3 Octobre, 3:30pm-4:30pm En conclusion, une semaine très riche en perspective, et qui vous permettra de balayer l'ensemble des sujets au coeur de vos préoccupations, de la stratégie à l'implémentation... Cette semaine doit se préparer, pour tailler votre agenda sur mesure, à travers les plus de 2000 sessions dont je ne vous ai fait qu'un extrait, et dont vous pouvez retrouver l'ensemble en ligne.

  Read the article

• MySQL Cluster 7.2: Over 8x Higher Performance than Cluster 7.1

  - by Mat Keep

  0 0 1 893 5092 Homework 42 11 5974 14.0 Normal 0 false false false EN-US JA 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-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:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Summary The scalability enhancements delivered by extensions to multi-threaded data nodes enables MySQL Cluster 7.2 to deliver over 8x higher performance than the previous MySQL Cluster 7.1 release on a recent benchmark What’s New in MySQL Cluster 7.2 MySQL Cluster 7.2 was released as GA (Generally Available) in February 2012, delivering many enhancements to performance on complex queries, new NoSQL Key / Value API, cross-data center replication and ease-of-use. These enhancements are summarized in the Figure below, and detailed in the MySQL Cluster New Features whitepaper Figure 1: Next Generation Web Services, Cross Data Center Replication and Ease-of-Use Once of the key enhancements delivered in MySQL Cluster 7.2 is extensions made to the multi-threading processes of the data nodes. Multi-Threaded Data Node Extensions The MySQL Cluster 7.2 data node is now functionally divided into seven thread types: 1) Local Data Manager threads (ldm). Note – these are sometimes also called LQH threads. 2) Transaction Coordinator threads (tc) 3) Asynchronous Replication threads (rep) 4) Schema Management threads (main) 5) Network receiver threads (recv) 6) Network send threads (send) 7) IO threads Each of these thread types are discussed in more detail below. MySQL Cluster 7.2 increases the maximum number of LDM threads from 4 to 16. The LDM contains the actual data, which means that when using 16 threads the data is more heavily partitioned (this is automatic in MySQL Cluster). Each LDM thread maintains its own set of data partitions, index partitions and REDO log. The number of LDM partitions per data node is not dynamically configurable, but it is possible, however, to map more than one partition onto each LDM thread, providing flexibility in modifying the number of LDM threads. The TC domain stores the state of in-flight transactions. This means that every new transaction can easily be assigned to a new TC thread. Testing has shown that in most cases 1 TC thread per 2 LDM threads is sufficient, and in many cases even 1 TC thread per 4 LDM threads is also acceptable. Testing also demonstrated that in some instances where the workload needed to sustain very high update loads it is necessary to configure 3 to 4 TC threads per 4 LDM threads. In the previous MySQL Cluster 7.1 release, only one TC thread was available. This limit has been increased to 16 TC threads in MySQL Cluster 7.2. The TC domain also manages the Adaptive Query Localization functionality introduced in MySQL Cluster 7.2 that significantly enhanced complex query performance by pushing JOIN operations down to the data nodes. Asynchronous Replication was separated into its own thread with the release of MySQL Cluster 7.1, and has not been modified in the latest 7.2 release. To scale the number of TC threads, it was necessary to separate the Schema Management domain from the TC domain. The schema management thread has little load, so is implemented with a single thread. The Network receiver domain was bound to 1 thread in MySQL Cluster 7.1. With the increase of threads in MySQL Cluster 7.2 it is also necessary to increase the number of recv threads to 8. This enables each receive thread to service one or more sockets used to communicate with other nodes the Cluster. The Network send thread is a new thread type introduced in MySQL Cluster 7.2. Previously other threads handled the sending operations themselves, which can provide for lower latency. To achieve highest throughput however, it has been necessary to create dedicated send threads, of which 8 can be configured. It is still possible to configure MySQL Cluster 7.2 to a legacy mode that does not use any of the send threads – useful for those workloads that are most sensitive to latency. The IO Thread is the final thread type and there have been no changes to this domain in MySQL Cluster 7.2. Multiple IO threads were already available, which could be configured to either one thread per open file, or to a fixed number of IO threads that handle the IO traffic. Except when using compression on disk, the IO threads typically have a very light load. Benchmarking the Scalability Enhancements The scalability enhancements discussed above have made it possible to scale CPU usage of each data node to more than 5x of that possible in MySQL Cluster 7.1. In addition, a number of bottlenecks have been removed, making it possible to scale data node performance by even more than 5x. Figure 2: MySQL Cluster 7.2 Delivers 8.4x Higher Performance than 7.1 The flexAsynch benchmark was used to compare MySQL Cluster 7.2 performance to 7.1 across an 8-node Intel Xeon x5670-based cluster of dual socket commodity servers (6 cores each). As the results demonstrate, MySQL Cluster 7.2 delivers over 8x higher performance per data nodes than MySQL Cluster 7.1. More details of this and other benchmarks will be published in a new whitepaper – coming soon, so stay tuned! In a following blog post, I’ll provide recommendations on optimum thread configurations for different types of server processor. You can also learn more from the Best Practices Guide to Optimizing Performance of MySQL Cluster Conclusion MySQL Cluster has achieved a range of impressive benchmark results, and set in context with the previous 7.1 release, is able to deliver over 8x higher performance per node. As a result, the multi-threaded data node extensions not only serve to increase performance of MySQL Cluster, they also enable users to achieve significantly improved levels of utilization from current and future generations of massively multi-core, multi-thread processor designs.

  Read the article

• Thread placement policies on NUMA systems - update

  - by Dave

  In a prior blog entry I noted that Solaris used a "maximum dispersal" placement policy to assign nascent threads to their initial processors. The general idea is that threads should be placed as far away from each other as possible in the resource topology in order to reduce resource contention between concurrently running threads. This policy assumes that resource contention -- pipelines, memory channel contention, destructive interference in the shared caches, etc -- will likely outweigh (a) any potential communication benefits we might achieve by packing our threads more densely onto a subset of the NUMA nodes, and (b) benefits of NUMA affinity between memory allocated by one thread and accessed by other threads. We want our threads spread widely over the system and not packed together. Conceptually, when placing a new thread, the kernel picks the least loaded node NUMA node (the node with lowest aggregate load average), and then the least loaded core on that node, etc. Furthermore, the kernel places threads onto resources -- sockets, cores, pipelines, etc -- without regard to the thread's process membership. That is, initial placement is process-agnostic. Keep reading, though. This description is incorrect. On Solaris 10 on a SPARC T5440 with 4 x T2+ NUMA nodes, if the system is otherwise unloaded and we launch a process that creates 20 compute-bound concurrent threads, then typically we'll see a perfect balance with 5 threads on each node. We see similar behavior on an 8-node x86 x4800 system, where each node has 8 cores and each core is 2-way hyperthreaded. So far so good; this behavior seems in agreement with the policy I described in the 1st paragraph. I recently tried the same experiment on a 4-node T4-4 running Solaris 11. Both the T5440 and T4-4 are 4-node systems that expose 256 logical thread contexts. To my surprise, all 20 threads were placed onto just one NUMA node while the other 3 nodes remained completely idle. I checked the usual suspects such as processor sets inadvertently left around by colleagues, processors left offline, and power management policies, but the system was configured normally. I then launched multiple concurrent instances of the process, and, interestingly, all the threads from the 1st process landed on one node, all the threads from the 2nd process landed on another node, and so on. This happened even if I interleaved thread creating between the processes, so I was relatively sure the effect didn't related to thread creation time, but rather that placement was a function of process membership. I this point I consulted the Solaris sources and talked with folks in the Solaris group. The new Solaris 11 behavior is intentional. The kernel is no longer using a simple maximum dispersal policy, and thread placement is process membership-aware. Now, even if other nodes are completely unloaded, the kernel will still try to pack new threads onto the home lgroup (socket) of the primordial thread until the load average of that node reaches 50%, after which it will pick the next least loaded node as the process's new favorite node for placement. On the T4-4 we have 64 logical thread contexts (strands) per socket (lgroup), so if we launch 48 concurrent threads we will find 32 placed on one node and 16 on some other node. If we launch 64 threads we'll find 32 and 32. That means we can end up with our threads clustered on a small subset of the nodes in a way that's quite different that what we've seen on Solaris 10. So we have a policy that allows process-aware packing but reverts to spreading threads onto other nodes if a node becomes too saturated. It turns out this policy was enabled in Solaris 10, but certain bugs suppressed the mixed packing/spreading behavior. There are configuration variables in /etc/system that allow us to dial the affinity between nascent threads and their primordial thread up and down: see lgrp_expand_proc_thresh, specifically. In the OpenSolaris source code the key routine is mpo_update_tunables(). This method reads the /etc/system variables and sets up some global variables that will subsequently be used by the dispatcher, which calls lgrp_choose() in lgrp.c to place nascent threads. Lgrp_expand_proc_thresh controls how loaded an lgroup must be before we'll consider homing a process's threads to another lgroup. Tune this value lower to have it spread your process's threads out more. To recap, the 'new' policy is as follows. Threads from the same process are packed onto a subset of the strands of a socket (50% for T-series). Once that socket reaches the 50% threshold the kernel then picks another preferred socket for that process. Threads from unrelated processes are spread across sockets. More precisely, different processes may have different preferred sockets (lgroups). Beware that I've simplified and elided details for the purposes of explication. The truth is in the code. Remarks: It's worth noting that initial thread placement is just that. If there's a gross imbalance between the load on different nodes then the kernel will migrate threads to achieve a better and more even distribution over the set of available nodes. Once a thread runs and gains some affinity for a node, however, it becomes "stickier" under the assumption that the thread has residual cache residency on that node, and that memory allocated by that thread resides on that node given the default "first-touch" page-level NUMA allocation policy. Exactly how the various policies interact and which have precedence under what circumstances could the topic of a future blog entry. The scheduler is work-conserving. The x4800 mentioned above is an interesting system. Each of the 8 sockets houses an Intel 7500-series processor. Each processor has 3 coherent QPI links and the system is arranged as a glueless 8-socket twisted ladder "mobius" topology. Nodes are either 1 or 2 hops distant over the QPI links. As an aside the mapping of logical CPUIDs to physical resources is rather interesting on Solaris/x4800. On SPARC/Solaris the CPUID layout is strictly geographic, with the highest order bits identifying the socket, the next lower bits identifying the core within that socket, following by the pipeline (if present) and finally the logical thread context ("strand") on the core. But on Solaris on the x4800 the CPUID layout is as follows. [6:6] identifies the hyperthread on a core; bits [5:3] identify the socket, or package in Intel terminology; bits [2:0] identify the core within a socket. Such low-level details should be of interest only if you're binding threads -- a bad idea, the kernel typically handles placement best -- or if you're writing NUMA-aware code that's aware of the ambient placement and makes decisions accordingly. Solaris introduced the so-called critical-threads mechanism, which is expressed by putting a thread into the FX scheduling class at priority 60. The critical-threads mechanism applies to placement on cores, not on sockets, however. That is, it's an intra-socket policy, not an inter-socket policy. Solaris 11 introduces the Power Aware Dispatcher (PAD) which packs threads instead of spreading them out in an attempt to be able to keep sockets or cores at lower power levels. Maximum dispersal may be good for performance but is anathema to power management. PAD is off by default, but power management polices constitute yet another confounding factor with respect to scheduling and dispatching. If your threads communicate heavily -- one thread reads cache lines last written by some other thread -- then the new dense packing policy may improve performance by reducing traffic on the coherent interconnect. On the other hand if your threads in your process communicate rarely, then it's possible the new packing policy might result on contention on shared computing resources. Unfortunately there's no simple litmus test that says whether packing or spreading is optimal in a given situation. The answer varies by system load, application, number of threads, and platform hardware characteristics. Currently we don't have the necessary tools and sensoria to decide at runtime, so we're reduced to an empirical approach where we run trials and try to decide on a placement policy. The situation is quite frustrating. Relatedly, it's often hard to determine just the right level of concurrency to optimize throughput. (Understanding constructive vs destructive interference in the shared caches would be a good start. We could augment the lines with a small tag field indicating which strand last installed or accessed a line. Given that, we could augment the CPU with performance counters for misses where a thread evicts a line it installed vs misses where a thread displaces a line installed by some other thread.)

  Read the article

• Oracle Expands Sun Blade Portfolio for Cloud and Highly Virtualized Environments

  - by Ferhat Hatay

  Oracle announced the expansion of Sun Blade Portfolio for cloud and highly virtualized environments that deliver powerful performance and simplified management as tightly integrated systems.  Along with the SPARC T3-1B blade server, Oracle VM blade cluster reference configuration and Oracle's optimized solution for Oracle WebLogic Suite, Oracle introduced the dual-node Sun Blade X6275 M2 server module with some impressive benchmark results.   Benchmarks on the Sun Blade X6275 M2 server module demonstrate the outstanding performance characteristics critical for running varied commercial applications used in cloud and highly virtualized environments.  These include best-in-class SPEC CPU2006 results with the Intel Xeon processor 5600 series, six Fluent world records and 1.8 times the price-performance of the IBM Power 755 running NAMD, a prominent bio-informatics workload.   Benchmarks for Sun Blade X6275 M2 server module  SPEC CPU2006  The Sun Blade X6275 M2 server module demonstrated best in class SPECint_rate2006 results for all published results using the Intel Xeon processor 5600 series, with a result of 679.  This result is 97% better than the HP BL460c G7 blade, 80% better than the IBM HS22V blade, and 79% better than the Dell M710 blade.  This result demonstrates the density advantage of the new Oracle's server module for space-constrained data centers.     Sun Blade X6275M2 (2 Nodes, Intel Xeon X5670 2.93GHz) - 679 SPECint_rate2006; HP ProLiant BL460c G7 (2.93 GHz, Intel Xeon X5670) - 347 SPECint_rate2006; IBM BladeCenter HS22V (Intel Xeon X5680)  - 377 SPECint_rate2006; Dell PowerEdge M710 (Intel Xeon X5680, 3.33 GHz) - 380 SPECint_rate2006.  SPEC, SPECint, SPECfp reg tm of Standard Performance Evaluation Corporation. Results from www.spec.org as of 11/24/2010 and this report.    For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Fluent The Sun Fire X6275 M2 server module produced world-record results on each of the six standard cases in the current "FLUENT 12" benchmark test suite at 8-, 12-, 24-, 32-, 64- and 96-core configurations. These results beat the most recent QLogic score with IBM DX 360 M series platforms and QLogic "Truescale" interconnects.  Results on sedan_4m test case on the Sun Blade X6275 M2 server module are 23% better than the HP C7000 system, and 20% better than the IBM DX 360 M2; Dell has not posted a result for this test case.  Results can be found at the FLUENT website.   ANSYS's FLUENT software solves fluid flow problems, and is based on a numerical technique called computational fluid dynamics (CFD), which is used in the automotive, aerospace, and consumer products industries. The FLUENT 12 benchmark test suite consists of seven models that are well suited for multi-node clustered environments and representative of modern engineering CFD clusters. Vendors benchmark their systems with the principal objective of providing comparative performance information for FLUENT software that, among other things, depends on compilers, optimization, interconnect, and the performance characteristics of the hardware.   FLUENT application performance is representative of other commercial applications that require memory and CPU resources to be available in a scalable cluster-ready format.  FLUENT benchmark has six conventional test cases (eddy_417k, turbo_500k, aircraft_2m, sedan_4m, truck_14m, truck_poly_14m) at various core counts.   All information on the FLUENT website (http://www.fluent.com) is Copyrighted1995-2010 by ANSYS Inc. Results as of November 24, 2010. For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   NAMD Results on the Sun Blade X6275 M2 server module running NAMD (a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems) show up to a 1.8X better price/performance than IBM's Power 7-based system.  For space-constrained environments, the ultra-dense Sun Blade X6275 M2 server module provides a 1.7X better price/performance per rack unit than IBM's system.     IBM Power 755 4-way Cluster (16U). Total price for cluster: $324,212. See IBM United States Hardware Announcement 110-008, dated February 9, 2010, pp. 4, 21 and 39-46.  Sun Blade X6275 M2 8-Blade Cluster (10U). Total price for cluster:  $193,939. Price/performance and performance/RU comparisons based on f1ATPase molecule test results. Sun Blade X6275 M2 cluster: $3,568/step/sec, 5.435 step/sec/RU. IBM Power 755 cluster: $6,355/step/sec, 3.189 step/sec/U. See http://www-03.ibm.com/systems/power/hardware/reports/system_perf.html. See http://www.ks.uiuc.edu/Research/namd/performance.html for more information, results as of 11/24/10.   For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Reverse Time Migration The Reverse Time Migration is heavily used in geophysical imaging and modeling for Oil & Gas Exploration.  The Sun Blade X6275 M2 server module showed up to a 40% performance improvement over the previous generation server module with super-linear scalability to 16 nodes for the 9-Point Stencil used in this Reverse Time Migration computational kernel.  The balanced combination of Oracle's Sun Storage 7410 system with the Sun Blade X6275 M2 server module cluster showed linear scalability for the total application throughput, including the I/O and MPI communication, to produce a final 3-D seismic depth imaged cube for interpretation. The final image write time from the Sun Blade X6275 M2 server module nodes to Oracle's Sun Storage 7410 system achieved 10GbE line speed of 1.25 GBytes/second or better performance. Between subsequent runs, the effects of I/O buffer caching on the Sun Blade X6275 M2 server module nodes and write optimized caching on the Sun Storage 7410 system gave up to 1.8 GBytes/second effective write performance. The performance results and characterization of this Reverse Time Migration benchmark could serve as a useful measure for many other I/O intensive commercial applications. 3D VTI Reverse Time Migration Seismic Depth Imaging, see http://blogs.sun.com/BestPerf/entry/3d_vti_reverse_time_migration for more information, results as of 11/14/2010.                            

  Read the article

• T-SQL Tuesday #31 - Logging Tricks with CONTEXT_INFO

  - by Most Valuable Yak (Rob Volk)

  This month's T-SQL Tuesday is being hosted by Aaron Nelson [b | t], fellow Atlantan (the city in Georgia, not the famous sunken city, or the resort in the Bahamas) and covers the topic of logging (the recording of information, not the harvesting of trees) and maintains the fine T-SQL Tuesday tradition begun by Adam Machanic [b | t] (the SQL Server guru, not the guy who fixes cars, check the spelling again, there will be a quiz later). This is a trick I learned from Fernando Guerrero [b | t] waaaaaay back during the PASS Summit 2004 in sunny, hurricane-infested Orlando, during his session on Secret SQL Server (not sure if that's the correct title, and I haven't used parentheses in this paragraph yet).  CONTEXT_INFO is a neat little feature that's existed since SQL Server 2000 and perhaps even earlier.  It lets you assign data to the current session/connection, and maintains that data until you disconnect or change it.  In addition to the CONTEXT_INFO() function, you can also query the context_info column in sys.dm_exec_sessions, or even sysprocesses if you're still running SQL Server 2000, if you need to see it for another session. While you're limited to 128 bytes, one big advantage that CONTEXT_INFO has is that it's independent of any transactions.  If you've ever logged to a table in a transaction and then lost messages when it rolled back, you can understand how aggravating it can be.  CONTEXT_INFO also survives across multiple SQL batches (GO separators) in the same connection, so for those of you who were going to suggest "just log to a table variable, they don't get rolled back":  HA-HA, I GOT YOU!  Since GO starts a new batch all variable declarations are lost. Here's a simple example I recently used at work.  I had to test database mirroring configurations for disaster recovery scenarios and measure the network throughput.  I also needed to log how long it took for the script to run and include the mirror settings for the database in question.  I decided to use AdventureWorks as my database model, and Adam Machanic's Big Adventure script to provide a fairly large workload that's repeatable and easily scalable.  My test would consist of several copies of AdventureWorks running the Big Adventure script while I mirrored the databases (or not). Since Adam's script contains several batches, I decided CONTEXT_INFO would have to be used.  As it turns out, I only needed to grab the start time at the beginning, I could get the rest of the data at the end of the process.   The code is pretty small: declare @time binary(128)=cast(getdate() as binary(8)) set context_info @time   ... rest of Big Adventure code ...   go use master; insert mirror_test(server,role,partner,db,state,safety,start,duration) select @@servername, mirroring_role_desc, mirroring_partner_instance, db_name(database_id), mirroring_state_desc, mirroring_safety_level_desc, cast(cast(context_info() as binary(8)) as datetime), datediff(s,cast(cast(context_info() as binary(8)) as datetime),getdate()) from sys.database_mirroring where db_name(database_id) like 'Adv%';   I declared @time as a binary(128) since CONTEXT_INFO is defined that way.  I couldn't convert GETDATE() to binary(128) as it would pad the first 120 bytes as 0x00.  To keep the CAST functions simple and avoid using SUBSTRING, I decided to CAST GETDATE() as binary(8) and let SQL Server do the implicit conversion.  It's not the safest way perhaps, but it works on my machine. :) As I mentioned earlier, you can query system views for sessions and get their CONTEXT_INFO.  With a little boilerplate code this can be used to monitor long-running procedures, in case you need to kill a process, or are just curious  how long certain parts take.  In this example, I added code to Adam's Big Adventure script to set CONTEXT_INFO messages at strategic places I want to monitor.  (His code is in UPPERCASE as it was in the original, mine is all lowercase): declare @msg binary(128) set @msg=cast('Altering bigProduct.ProductID' as binary(128)) set context_info @msg go ALTER TABLE bigProduct ALTER COLUMN ProductID INT NOT NULL GO set context_info 0x0 go declare @msg1 binary(128) set @msg1=cast('Adding pk_bigProduct Constraint' as binary(128)) set context_info @msg1 go ALTER TABLE bigProduct ADD CONSTRAINT pk_bigProduct PRIMARY KEY (ProductID) GO set context_info 0x0 go declare @msg2 binary(128) set @msg2=cast('Altering bigTransactionHistory.TransactionID' as binary(128)) set context_info @msg2 go ALTER TABLE bigTransactionHistory ALTER COLUMN TransactionID INT NOT NULL GO set context_info 0x0 go declare @msg3 binary(128) set @msg3=cast('Adding pk_bigTransactionHistory Constraint' as binary(128)) set context_info @msg3 go ALTER TABLE bigTransactionHistory ADD CONSTRAINT pk_bigTransactionHistory PRIMARY KEY NONCLUSTERED(TransactionID) GO set context_info 0x0 go declare @msg4 binary(128) set @msg4=cast('Creating IX_ProductId_TransactionDate Index' as binary(128)) set context_info @msg4 go CREATE NONCLUSTERED INDEX IX_ProductId_TransactionDate ON bigTransactionHistory(ProductId,TransactionDate) INCLUDE(Quantity,ActualCost) GO set context_info 0x0   This doesn't include the entire script, only those portions that altered a table or created an index.  One annoyance is that SET CONTEXT_INFO requires a literal or variable, you can't use an expression.  And since GO starts a new batch I need to declare a variable in each one.  And of course I have to use CAST because it won't implicitly convert varchar to binary.  And even though context_info is a nullable column, you can't SET CONTEXT_INFO NULL, so I have to use SET CONTEXT_INFO 0x0 to clear the message after the statement completes.  And if you're thinking of turning this into a UDF, you can't, although a stored procedure would work. So what does all this aggravation get you?  As the code runs, if I want to see which stage the session is at, I can run the following (assuming SPID 51 is the one I want): select CAST(context_info as varchar(128)) from sys.dm_exec_sessions where session_id=51   Since SQL Server 2005 introduced the new system and dynamic management views (DMVs) there's not as much need for tagging a session with these kinds of messages.  You can get the session start time and currently executing statement from them, and neatly presented if you use Adam's sp_whoisactive utility (and you absolutely should be using it).  Of course you can always use xp_cmdshell, a CLR function, or some other tricks to log information outside of a SQL transaction.  All the same, I've used this trick to monitor long-running reports at a previous job, and I still think CONTEXT_INFO is a great feature, especially if you're still using SQL Server 2000 or want to supplement your instrumentation.  If you'd like an exercise, consider adding the system time to the messages in the last example, and an automated job to query and parse it from the system tables.  That would let you track how long each statement ran without having to run Profiler. #TSQL2sDay

  Read the article

• Using Node.js as an accelerator for WCF REST services

  - by Elton Stoneman

  Node.js is a server-side JavaScript platform "for easily building fast, scalable network applications". It's built on Google's V8 JavaScript engine and uses an (almost) entirely async event-driven processing model, running in a single thread. If you're new to Node and your reaction is "why would I want to run JavaScript on the server side?", this is the headline answer: in 150 lines of JavaScript you can build a Node.js app which works as an accelerator for WCF REST services*. It can double your messages-per-second throughput, halve your CPU workload and use one-fifth of the memory footprint, compared to the WCF services direct.   Well, it can if: 1) your WCF services are first-class HTTP citizens, honouring client cache ETag headers in request and response; 2) your services do a reasonable amount of work to build a response; 3) your data is read more often than it's written. In one of my projects I have a set of REST services in WCF which deal with data that only gets updated weekly, but which can be read hundreds of times an hour. The services issue ETags and will return a 304 if the client sends a request with the current ETag, which means in the most common scenario the client uses its local cached copy. But when the weekly update happens, then all the client caches are invalidated and they all need the same new data. Then the service will get hundreds of requests with old ETags, and they go through the full service stack to build the same response for each, taking up threads and processing time. Part of that processing means going off to a database on a separate cloud, which introduces more latency and downtime potential.   We can use ASP.NET output caching with WCF to solve the repeated processing problem, but the server will still be thread-bound on incoming requests, and to get the current ETags reliably needs a database call per request. The accelerator solves that by running as a proxy - all client calls come into the proxy, and the proxy routes calls to the underlying REST service. We could use Node as a straight passthrough proxy and expect some benefit, as the server would be less thread-bound, but we would still have one WCF and one database call per proxy call. But add some smart caching logic to the proxy, and share ETags between Node and WCF (so the proxy doesn't even need to call the servcie to get the current ETag), and the underlying service will only be invoked when data has changed, and then only once - all subsequent client requests will be served from the proxy cache.   I've built this as a sample up on GitHub: NodeWcfAccelerator on sixeyed.codegallery. Here's how the architecture looks:     The code is very simple. The Node proxy runs on port 8010 and all client requests target the proxy. If the client request has an ETag header then the proxy looks up the ETag in the tag cache to see if it is current - the sample uses memcached to share ETags between .NET and Node. If the ETag from the client matches the current server tag, the proxy sends a 304 response with an empty body to the client, telling it to use its own cached version of the data. If the ETag from the client is stale, the proxy looks for a local cached version of the response, checking for a file named after the current ETag. If that file exists, its contents are returned to the client as the body in a 200 response, which includes the current ETag in the header. If the proxy does not have a local cached file for the service response, it calls the service, and writes the WCF response to the local cache file, and to the body of a 200 response for the client. So the WCF service is only troubled if both client and proxy have stale (or no) caches.   The only (vaguely) clever bit in the sample is using the ETag cache, so the proxy can serve cached requests without any communication with the underlying service, which it does completely generically, so the proxy has no notion of what it is serving or what the services it proxies are doing. The relative path from the URL is used as the lookup key, so there's no shared key-generation logic between .NET and Node, and when WCF stores a tag it also stores the "read" URL against the ETag so it can be used for a reverse lookup, e.g:   Key Value /WcfSampleService/PersonService.svc/rest/fetch/3 "28cd4796-76b8-451b-adfd-75cb50a50fa6" "28cd4796-76b8-451b-adfd-75cb50a50fa6" /WcfSampleService/PersonService.svc/rest/fetch/3    In Node we read the cache using the incoming URL path as the key and we know that "28cd4796-76b8-451b-adfd-75cb50a50fa6" is the current ETag; we look for a local cached response in /caches/28cd4796-76b8-451b-adfd-75cb50a50fa6.body (and the corresponding .header file which contains the original service response headers, so the proxy response is exactly the same as the underlying service). When the data is updated, we need to invalidate the ETag cache – which is why we need the reverse lookup in the cache. In the WCF update service, we don't need to know the URL of the related read service - we fetch the entity from the database, do a reverse lookup on the tag cache using the old ETag to get the read URL, update the new ETag against the URL, store the new reverse lookup and delete the old one.   Running Apache Bench against the two endpoints gives the headline performance comparison. Making 1000 requests with concurrency of 100, and not sending any ETag headers in the requests, with the Node proxy I get 102 requests handled per second, average response time of 975 milliseconds with 90% of responses served within 850 milliseconds; going direct to WCF with the same parameters, I get 53 requests handled per second, mean response time of 1853 milliseconds, with 90% of response served within 3260 milliseconds. Informally monitoring server usage during the tests, Node maxed at 20% CPU and 20Mb memory; IIS maxed at 60% CPU and 100Mb memory.   Note that the sample WCF service does a database read and sleeps for 250 milliseconds to simulate a moderate processing load, so this is *not* a baseline Node-vs-WCF comparison, but for similar scenarios where the  service call is expensive but applicable to numerous clients for a long timespan, the performance boost from the accelerator is considerable.     * - actually, the accelerator will work nicely for any HTTP request, where the URL (path + querystring) uniquely identifies a resource. In the sample, there is an assumption that the ETag is a GUID wrapped in double-quotes (e.g. "28cd4796-76b8-451b-adfd-75cb50a50fa6") – which is the default for WCF services. I use that assumption to name the cache files uniquely, but it is a trivial change to adapt to other ETag formats.

  Read the article

• Optimizing AES modes on Solaris for Intel Westmere

  - by danx

  Optimizing AES modes on Solaris for Intel Westmere Review AES is a strong method of symmetric (secret-key) encryption. It is a U.S. FIPS-approved cryptographic algorithm (FIPS 197) that operates on 16-byte blocks. AES has been available since 2001 and is widely used. However, AES by itself has a weakness. AES encryption isn't usually used by itself because identical blocks of plaintext are always encrypted into identical blocks of ciphertext. This encryption can be easily attacked with "dictionaries" of common blocks of text and allows one to more-easily discern the content of the unknown cryptotext. This mode of encryption is called "Electronic Code Book" (ECB), because one in theory can keep a "code book" of all known cryptotext and plaintext results to cipher and decipher AES. In practice, a complete "code book" is not practical, even in electronic form, but large dictionaries of common plaintext blocks is still possible. Here's a diagram of encrypting input data using AES ECB mode: Block 1 Block 2 PlainTextInput PlainTextInput | | | | \/ \/ AESKey-->(AES Encryption) AESKey-->(AES Encryption) | | | | \/ \/ CipherTextOutput CipherTextOutput Block 1 Block 2 What's the solution to the same cleartext input producing the same ciphertext output? The solution is to further process the encrypted or decrypted text in such a way that the same text produces different output. This usually involves an Initialization Vector (IV) and XORing the decrypted or encrypted text. As an example, I'll illustrate CBC mode encryption: Block 1 Block 2 PlainTextInput PlainTextInput | | | | \/ \/ IV >----->(XOR) +------------->(XOR) +---> . . . . | | | | | | | | \/ | \/ | AESKey-->(AES Encryption) | AESKey-->(AES Encryption) | | | | | | | | | \/ | \/ | CipherTextOutput ------+ CipherTextOutput -------+ Block 1 Block 2 The steps for CBC encryption are: Start with a 16-byte Initialization Vector (IV), choosen randomly. XOR the IV with the first block of input plaintext Encrypt the result with AES using a user-provided key. The result is the first 16-bytes of output cryptotext. Use the cryptotext (instead of the IV) of the previous block to XOR with the next input block of plaintext Another mode besides CBC is Counter Mode (CTR). As with CBC mode, it also starts with a 16-byte IV. However, for subsequent blocks, the IV is just incremented by one. Also, the IV ix XORed with the AES encryption result (not the plain text input). Here's an illustration: Block 1 Block 2 PlainTextInput PlainTextInput | | | | \/ \/ AESKey-->(AES Encryption) AESKey-->(AES Encryption) | | | | \/ \/ IV >----->(XOR) IV + 1 >---->(XOR) IV + 2 ---> . . . . | | | | \/ \/ CipherTextOutput CipherTextOutput Block 1 Block 2 Optimization Which of these modes can be parallelized? ECB encryption/decryption can be parallelized because it does more than plain AES encryption and decryption, as mentioned above. CBC encryption can't be parallelized because it depends on the output of the previous block. However, CBC decryption can be parallelized because all the encrypted blocks are known at the beginning. CTR encryption and decryption can be parallelized because the input to each block is known--it's just the IV incremented by one for each subsequent block. So, in summary, for ECB, CBC, and CTR modes, encryption and decryption can be parallelized with the exception of CBC encryption. How do we parallelize encryption? By interleaving. Usually when reading and writing data there are pipeline "stalls" (idle processor cycles) that result from waiting for memory to be loaded or stored to or from CPU registers. Since the software is written to encrypt/decrypt the next data block where pipeline stalls usually occurs, we can avoid stalls and crypt with fewer cycles. This software processes 4 blocks at a time, which ensures virtually no waiting ("stalling") for reading or writing data in memory. Other Optimizations Besides interleaving, other optimizations performed are Loading the entire key schedule into the 128-bit %xmm registers. This is done once for per 4-block of data (since 4 blocks of data is processed, when present). The following is loaded: the entire "key schedule" (user input key preprocessed for encryption and decryption). This takes 11, 13, or 15 registers, for AES-128, AES-192, and AES-256, respectively The input data is loaded into another %xmm register The same register contains the output result after encrypting/decrypting Using SSSE 4 instructions (AESNI). Besides the aesenc, aesenclast, aesdec, aesdeclast, aeskeygenassist, and aesimc AESNI instructions, Intel has several other instructions that operate on the 128-bit %xmm registers. Some common instructions for encryption are: pxor exclusive or (very useful), movdqu load/store a %xmm register from/to memory, pshufb shuffle bytes for byte swapping, pclmulqdq carry-less multiply for GCM mode Combining AES encryption/decryption with CBC or CTR modes processing. Instead of loading input data twice (once for AES encryption/decryption, and again for modes (CTR or CBC, for example) processing, the input data is loaded once as both AES and modes operations occur at in the same function Performance Everyone likes pretty color charts, so here they are. I ran these on Solaris 11 running on a Piketon Platform system with a 4-core Intel Clarkdale processor @3.20GHz. Clarkdale which is part of the Westmere processor architecture family. The "before" case is Solaris 11, unmodified. Keep in mind that the "before" case already has been optimized with hand-coded Intel AESNI assembly. The "after" case has combined AES-NI and mode instructions, interleaved 4 blocks at-a-time. « For the first table, lower is better (milliseconds). The first table shows the performance improvement using the Solaris encrypt(1) and decrypt(1) CLI commands. I encrypted and decrypted a 1/2 GByte file on /tmp (swap tmpfs). Encryption improved by about 40% and decryption improved by about 80%. AES-128 is slighty faster than AES-256, as expected. The second table shows more detail timings for CBC, CTR, and ECB modes for the 3 AES key sizes and different data lengths. » The results shown are the percentage improvement as shown by an internal PKCS#11 microbenchmark. And keep in mind the previous baseline code already had optimized AESNI assembly! The keysize (AES-128, 192, or 256) makes little difference in relative percentage improvement (although, of course, AES-128 is faster than AES-256). Larger data sizes show better improvement than 128-byte data. Availability This software is in Solaris 11 FCS. It is available in the 64-bit libcrypto library and the "aes" Solaris kernel module. You must be running hardware that supports AESNI (for example, Intel Westmere and Sandy Bridge, microprocessor architectures). The easiest way to determine if AES-NI is available is with the isainfo(1) command. For example, $ isainfo -v 64-bit amd64 applications pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu No special configuration or setup is needed to take advantage of this software. Solaris libraries and kernel automatically determine if it's running on AESNI-capable machines and execute the correctly-tuned software for the current microprocessor. Summary Maximum throughput of AES cipher modes can be achieved by combining AES encryption with modes processing, interleaving encryption of 4 blocks at a time, and using Intel's wide 128-bit %xmm registers and instructions. References "Block cipher modes of operation", Wikipedia Good overview of AES modes (ECB, CBC, CTR, etc.) "Advanced Encryption Standard", Wikipedia "Current Modes" describes NIST-approved block cipher modes (ECB,CBC, CFB, OFB, CCM, GCM)

  Read the article

• SQL SERVER – Weekly Series – Memory Lane – #034

  - by Pinal Dave

  Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 UDF – User Defined Function to Strip HTML – Parse HTML – No Regular Expression The UDF used in the blog does fantastic task – it scans entire HTML text and removes all the HTML tags. It keeps only valid text data without HTML task. This is one of the quite commonly requested tasks many developers have to face everyday. De-fragmentation of Database at Operating System to Improve Performance Operating system skips MDF file while defragging the entire filesystem of the operating system. It is absolutely fine and there is no impact of the same on performance. Read the entire blog post for my conversation with our network engineers. Delay Function – WAITFOR clause – Delay Execution of Commands How do you delay execution of the commands in SQL Server – ofcourse by using WAITFOR keyword. In this blog post, I explain the same with the help of T-SQL script. Find Length of Text Field To measure the length of TEXT fields the function is DATALENGTH(textfield). Len will not work for text field. As of SQL Server 2005, developers should migrate all the text fields to VARCHAR(MAX) as that is the way forward. Retrieve Current Date Time in SQL Server CURRENT_TIMESTAMP, GETDATE(), {fn NOW()} There are three ways to retrieve the current datetime in SQL SERVER. CURRENT_TIMESTAMP, GETDATE(), {fn NOW()} Explanation and Comparison of NULLIF and ISNULL An interesting observation is NULLIF returns null if it comparison is successful, whereas ISNULL returns not null if its comparison is successful. In one way they are opposite to each other. Here is my question to you - How to create infinite loop using NULLIF and ISNULL? If this is even possible? 2008 Introduction to SERVERPROPERTY and example SERVERPROPERTY is a very interesting system function. It returns many of the system values. I use it very frequently to get different server values like Server Collation, Server Name etc. SQL Server Start Time We can use DMV to find out what is the start time of SQL Server in 2008 and later version. In this blog you can see how you can do the same. Find Current Identity of Table Many times we need to know what is the current identity of the column. I have found one of my developers using aggregated function MAX () to find the current identity. However, I prefer following DBCC command to figure out current identity. Create Check Constraint on Column Some time we just need to create a simple constraint over the table but I have noticed that developers do many different things to make table column follow rules than just creating constraint. I suggest constraint is a very useful concept and every SQL Developer should pay good attention to this subject. 2009 List Schema Name and Table Name for Database This is one of the blog post where I straight forward display script. One of the kind of blog posts, which I still love to read and write. Clustered Index on Separate Drive From Table Location A table devoid of primary key index is called heap, and here data is not arranged in a particular order, which gives rise to issues that adversely affect performance. Data must be stored in some kind of order. If we put clustered index on it then the order will be forced by that index and the data will be stored in that particular order. Understanding Table Hints with Examples Hints are options and strong suggestions specified for enforcement by the SQL Server query processor on DML statements. The hints override any execution plan the query optimizer might select for a query. 2010 Data Pages in Buffer Pool – Data Stored in Memory Cache One of my earlier year article, which I still read it many times and point developers to read it again. It is clear from the Resultset that when more than one index is used, datapages related to both or all of the indexes are stored in Memory Cache separately. TRANSACTION, DML and Schema Locks Can you create a situation where you can see Schema Lock? Well, this is a very simple question, however during the interview I notice over 50 candidates failed to come up with the scenario. In this blog post, I have demonstrated the situation where we can see the schema lock in database. 2011 Solution – Puzzle – Statistics are not updated but are Created Once In this example I have created following situation: Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated Auto Update Statistics and Auto Create Statistics for database is TRUE Now I have requested two things in the example 1) Why this is happening? 2) How to fix this issue? Selecting Domain from Email Address This is a straight to script blog post where I explain how to select only domain name from entire email address. Solution – Generating Zero Without using Any Numbers in T-SQL How to get zero digit without using any digit? This is indeed a very interesting question and the answer is even interesting. Try to come up with answer in next 10 minutes and if you can’t come up with the answer the blog post read this post for solution. 2012 Simple Explanation and Puzzle with SOUNDEX Function and DIFFERENCE Function In simple words - SOUNDEX converts an alphanumeric string to a four-character code to find similar-sounding words or names. DIFFERENCE function returns an integer value. The  integer returned is the number of characters in the SOUNDEX values that are the same. Read Only Files and SQL Server Management Studio (SSMS) I have come across a very interesting feature in SSMS related to “Read Only” files. I believe it is a little unknown feature as well so decided to write a blog about the same. Identifying Column Data Type of uniqueidentifier without Querying System Tables How do I know if any table has a uniqueidentifier column and what is its value without using any DMV or System Catalogues? Only information you know is the table name and you are allowed to return any kind of error if the table does not have uniqueidentifier column. Read the blog post to find the answer. Solution – User Not Able to See Any User Created Object in Tables – Security and Permissions Issue Interesting question – “When I try to connect to SQL Server, it lets me connect just fine as well let me open and explore the database. I noticed that I do not see any user created instances but when my colleague attempts to connect to the server, he is able to explore the database as well see all the user created tables and other objects. Can you help me fix it?” Importing CSV File Into Database – SQL in Sixty Seconds #018 – Video Here is interesting small 60 second video on how to import CSV file into Database. ColumnStore Index – Batch Mode vs Row Mode Here is the logic behind when Columnstore Index uses Batch Mode and when it uses Row Mode. A batch typically represents about 1000 rows of data. Batch mode processing also uses algorithms that are optimized for the multicore CPUs and increased memory throughput. Follow up – Usage of $rowguid and $IDENTITY This is an excellent follow up blog post of my earlier blog post where I explain where to use $rowguid and $identity.  If you do not know the difference between them, this is a blog with a script example. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

  Read the article

< Previous Page | 13 14 15 16 17 18 19  | Next Page >