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  • ActiveRecord and transactionsin between `before_save` and `save`

    - by JP
    I have some logic in before_save whereby (only) when some conditions are met I let the new row be created with special_number equal to the maximum special_number in the database + 1. (If the conditions aren't met then I do something different, so I can't use auto-increments) My worry is that two threads acting on this database at once might pick the same special_number if the second is executed while the first is saving. Is there way to lock the database between before_save and finishing the save, but only in some cases? I know all saves are sent in transactions, will this do the job for me? def before_save if things_are_just_right # -- Issue some kind of lock? # -- self.lock? I have no idea # Pick new special_number new_special = self.class.maximum('special_number') + 1 write_attribute('special_number',new_special) else # No need to lock in this case write_attribute('special_number',some_other_number) end end

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  • Proliant server will not accept new hard disks in RAID 1+0?

    - by Leigh
    I have a HP ProLiant DL380 G5, I have two logical drives configured with RAID. I have one logical drive RAID 1+0 with two 72 gb 10k sas 1 port spare no 376597-001. I had one hard disk fail and ordered a replacement. The configuration utility showed error and would not rebuild the RAID. I presumed a hard disk fault and ordered a replacement again. In the mean time I put the original failed disk back in the server and this started rebuilding. Currently shows ok status however in the log I can see hardware errors. The new disk has come and I again have the same problem of not accepting the hard disk. I have updated the P400 controller with the latest firmware 7.24 , but still no luck. The only difference I can see is the original drive has firmware 0103 (same as the RAID drive) and the new one has HPD2. Any advice would be appreciated. Thanks in advance Logs from server ctrl all show config Smart Array P400 in Slot 1 (sn: PAFGK0P9VWO0UQ) array A (SAS, Unused Space: 0 MB) logicaldrive 1 (68.5 GB, RAID 1, Interim Recovery Mode) physicaldrive 2I:1:1 (port 2I:box 1:bay 1, SAS, 73.5 GB, OK) physicaldrive 2I:1:2 (port 2I:box 1:bay 2, SAS, 72 GB, Failed array B (SAS, Unused Space: 0 MB) logicaldrive 2 (558.7 GB, RAID 5, OK) physicaldrive 1I:1:5 (port 1I:box 1:bay 5, SAS, 300 GB, OK) physicaldrive 2I:1:3 (port 2I:box 1:bay 3, SAS, 300 GB, OK) physicaldrive 2I:1:4 (port 2I:box 1:bay 4, SAS, 300 GB, OK) ctrl all show config detail Smart Array P400 in Slot 1 Bus Interface: PCI Slot: 1 Serial Number: PAFGK0P9VWO0UQ Cache Serial Number: PA82C0J9VWL8I7 RAID 6 (ADG) Status: Disabled Controller Status: OK Hardware Revision: E Firmware Version: 7.24 Rebuild Priority: Medium Expand Priority: Medium Surface Scan Delay: 15 secs Surface Scan Mode: Idle Wait for Cache Room: Disabled Surface Analysis Inconsistency Notification: Disabled Post Prompt Timeout: 0 secs Cache Board Present: True Cache Status: OK Cache Status Details: A cache error was detected. Run more information. Cache Ratio: 100% Read / 0% Write Drive Write Cache: Disabled Total Cache Size: 256 MB Total Cache Memory Available: 208 MB No-Battery Write Cache: Disabled Battery/Capacitor Count: 0 SATA NCQ Supported: True Array: A Interface Type: SAS Unused Space: 0 MB Status: Failed Physical Drive Array Type: Data One of the drives on this array have failed or has Logical Drive: 1 Size: 68.5 GB Fault Tolerance: RAID 1 Heads: 255 Sectors Per Track: 32 Cylinders: 17594 Strip Size: 128 KB Full Stripe Size: 128 KB Status: Interim Recovery Mode Caching: Enabled Unique Identifier: 600508B10010503956574F305551 Disk Name: \\.\PhysicalDrive0 Mount Points: C:\ 68.5 GB Logical Drive Label: A0100539PAFGK0P9VWO0UQ0E93 Mirror Group 0: physicaldrive 2I:1:2 (port 2I:box 1:bay 2, S Mirror Group 1: physicaldrive 2I:1:1 (port 2I:box 1:bay 1, S Drive Type: Data physicaldrive 2I:1:1 Port: 2I Box: 1 Bay: 1 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 73.5 GB Rotational Speed: 10000 Firmware Revision: 0103 Serial Number: B379P8C006RK Model: HP DG072A9B7 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 2I:1:2 Port: 2I Box: 1 Bay: 2 Status: Failed Drive Type: Data Drive Interface Type: SAS Size: 72 GB Rotational Speed: 15000 Firmware Revision: HPD9 Serial Number: D5A1PCA04SL01244 Model: HP EH0072FARUA PHY Count: 2 PHY Transfer Rate: Unknown, Unknown Array: B Interface Type: SAS Unused Space: 0 MB Status: OK Array Type: Data Logical Drive: 2 Size: 558.7 GB Fault Tolerance: RAID 5 Heads: 255 Sectors Per Track: 32 Cylinders: 65535 Strip Size: 64 KB Full Stripe Size: 128 KB Status: OK Caching: Enabled Parity Initialization Status: Initialization Co Unique Identifier: 600508B10010503956574F305551 Disk Name: \\.\PhysicalDrive1 Mount Points: E:\ 558.7 GB Logical Drive Label: AF14FD12PAFGK0P9VWO0UQD007 Drive Type: Data physicaldrive 1I:1:5 Port: 1I Box: 1 Bay: 5 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 300 GB Rotational Speed: 10000 Firmware Revision: HPD4 Serial Number: 3SE07QH300009923X1X3 Model: HP DG0300BALVP Current Temperature (C): 32 Maximum Temperature (C): 45 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 2I:1:3 Port: 2I Box: 1 Bay: 3 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 300 GB Rotational Speed: 10000 Firmware Revision: HPD4 Serial Number: 3SE0AHVH00009924P8F3 Model: HP DG0300BALVP Current Temperature (C): 34 Maximum Temperature (C): 47 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 2I:1:4 Port: 2I Box: 1 Bay: 4 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 300 GB Rotational Speed: 10000 Firmware Revision: HPD4 Serial Number: 3SE08NAK00009924KWD6 Model: HP DG0300BALVP Current Temperature (C): 35 Maximum Temperature (C): 47 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown

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  • Harmonizing Character Encoding Between Imported Data and MySQL

    MySQL's Latin-1 default encoding combined with MySQL 4.1.12's (or greater) UTF8 encoding allows the maximum number of characters codes, however incoming data with different character encoding can still present problems. Rob Gravelle shows you how to avoid problems before a lot of work is required to undo the damage.

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  • Harmonizing Character Encoding Between Imported Data and MySQL

    MySQL's Latin-1 default encoding combined with MySQL 4.1.12's (or greater) UTF8 encoding allows the maximum number of characters codes, however incoming data with different character encoding can still present problems. Rob Gravelle shows you how to avoid problems before a lot of work is required to undo the damage.

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  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • SQL SERVER – DMV – sys.dm_exec_query_optimizer_info – Statistics of Optimizer

    - by pinaldave
    Incredibly, SQL Server has so much information to share with us. Every single day, I am amazed with this SQL Server technology. Sometimes I find several interesting information by just querying few of the DMV. And when I present this info in front of my client during performance tuning consultancy, they are surprised with my findings. Today, I am going to share one of the hidden gems of DMV with you, the one which I frequently use to understand what’s going on under the hood of SQL Server. SQL Server keeps the record of most of the operations of the Query Optimizer. We can learn many interesting details about the optimizer which can be utilized to improve the performance of server. SELECT * FROM sys.dm_exec_query_optimizer_info WHERE counter IN ('optimizations', 'elapsed time','final cost', 'insert stmt','delete stmt','update stmt', 'merge stmt','contains subquery','tables', 'hints','order hint','join hint', 'view reference','remote query','maximum DOP', 'maximum recursion level','indexed views loaded', 'indexed views matched','indexed views used', 'indexed views updated','dynamic cursor request', 'fast forward cursor request') All occurrence values are cumulative and are set to 0 at system restart. All values for value fields are set to NULL at system restart. I have removed a few of the internal counters from the script above, and kept only documented details. Let us check the result of the above query. As you can see, there is so much vital information that is revealed in above query. I can easily say so many things about how many times Optimizer was triggered and what the average time taken by it to optimize my queries was. Additionally, I can also determine how many times update, insert or delete statements were optimized. I was able to quickly figure out that my client was overusing the Query Hints using this dynamic management view. If you have been reading my blog, I am sure you are aware of my series related to SQL Server Views SQL SERVER – The Limitations of the Views – Eleven and more…. With this, I can take a quick look and figure out how many times Views were used in various solutions within the query. Moreover, you can easily know what fraction of the optimizations has been involved in tuning server. For example, the following query would tell me, in total optimizations, what the fraction of time View was “reference“. As this View also includes system Views and DMVs, the number is a bit higher on my machine. SELECT (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'view reference') / (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'optimizations') AS ViewReferencedFraction Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • How To - Guide to Importing Data from a MySQL Database to Excel using MySQL for Excel

    - by Javier Treviño
    Fetching data from a database to then get it into an Excel spreadsheet to do analysis, reporting, transforming, sharing, etc. is a very common task among users. There are several ways to extract data from a MySQL database to then import it to Excel; for example you can use the MySQL Connector/ODBC to configure an ODBC connection to a MySQL database, then in Excel use the Data Connection Wizard to select the database and table from which you want to extract data from, then specify what worksheet you want to put the data into.  Another way is to somehow dump a comma delimited text file with the data from a MySQL table (using the MySQL Command Line Client, MySQL Workbench, etc.) to then in Excel open the file using the Text Import Wizard to attempt to correctly split the data in columns. These methods are fine, but involve some degree of technical knowledge to make the magic happen and involve repeating several steps each time data needs to be imported from a MySQL table to an Excel spreadsheet. So, can this be done in an easier and faster way? With MySQL for Excel you can. MySQL for Excel features an Import MySQL Data action where you can import data from a MySQL Table, View or Stored Procedure literally with a few clicks within Excel.  Following is a quick guide describing how to import data using MySQL for Excel. This guide assumes you already have a working MySQL Server instance, Microsoft Office Excel 2007 or 2010 and MySQL for Excel installed. 1. Opening MySQL for Excel Being an Excel Add-In, MySQL for Excel is opened from within Excel, so to use it open Excel, go to the Data tab located in the Ribbon and click MySQL for Excel at the far right of the Ribbon. 2. Creating a MySQL Connection (may be optional) If you have MySQL Workbench installed you will automatically see the same connections that you can see in MySQL Workbench, so you can use any of those and there may be no need to create a new connection. If you want to create a new connection (which normally you will do only once), in the Welcome Panel click New Connection, which opens the Setup New Connection dialog. Here you only need to give your new connection a distinctive Connection Name, specify the Hostname (or IP address) where the MySQL Server instance is running on (if different than localhost), the Port to connect to and the Username for the login. If you wish to test if your setup is good to go, click Test Connection and an information dialog will pop-up stating if the connection is successful or errors were found. 3.Opening a connection to a MySQL Server To open a pre-configured connection to a MySQL Server you just need to double-click it, so the Connection Password dialog is displayed where you enter the password for the login. 4. Selecting a MySQL Schema After opening a connection to a MySQL Server, the Schema Selection Panel is shown, where you can select the Schema that contains the Tables, Views and Stored Procedures you want to work with. To do so, you just need to either double-click the desired Schema or select it and click Next >. 5. Importing data… All previous steps were really the basic minimum needed to drill-down to the DB Object Selection Panel  where you can see the Database Objects (grouped by type: Tables, Views and Procedures in that order) that you want to perform actions against; in the case of this guide, the action of importing data from them. a. From a MySQL Table To import from a Table you just need to select it from the list of Database Objects’ Tables group, after selecting it you will note actions below the list become available; then click Import MySQL Data. The Import Data dialog is displayed; you can see some basic information here like the name of the Excel worksheet the data will be imported to (in the window title), the Table Name, the total Row Count and a 10 row preview of the data meant for the user to see the columns that the table contains and to provide a way to select which columns to import. The Import Data dialog is designed with defaults in place so all data is imported (all rows and all columns) by just clicking Import; this is important to minimize the number of clicks needed to get the job done. After the import is performed you will have the data in the Excel worksheet formatted automatically. If you need to override the defaults in the Import Data dialog to change the columns selected for import or to change the number of imported rows you can easily do so before clicking Import. In the screenshot below the defaults are overridden to import only the first 3 columns and rows 10 – 60 (Limit to 50 Rows and Start with Row 10). If the number of rows to be imported exceeds the maximum number of rows Excel can hold in its worksheet, a warning will be displayed in the dialog, meaning the imported number of rows will be limited by that maximum number (65,535 rows if the worksheet is in Compatibility Mode).  In the screenshot below you can see the Table contains 80,559 rows, but only 65,534 rows will be imported since the first row is used for the column names if the Include Column Names as Headers checkbox is checked. b. From a MySQL View Similar to the way of importing from a Table, to import from a View you just need to select it from the list of Database Objects’ Views group, then click Import MySQL Data. The Import Data dialog is displayed; identically to the way everything looks when importing from a table, the dialog displays the View Name, the total Row Count and the data preview grid. Since Views are really a filtered way to display data from Tables, it is actually as if we are extracting data from a Table; so the Import Data dialog is actually identical for those 2 Database Objects. After the import is performed, the data in the Excel spreadsheet looks like the following screenshot. Note that you can override the defaults in the Import Data dialog in the same way described above for importing data from Tables. Also the Compatibility Mode warning will be displayed if data exceeds the maximum number of rows explained before. c. From a MySQL Procedure Too import from a Procedure you just need to select it from the list of Database Objects’ Procedures group (note you can see Procedures here but not Functions since these return a single value, so by design they are filtered out). After the selection is made, click Import MySQL Data. The Import Data dialog is displayed, but this time you can see it looks different to the one used for Tables and Views.  Given the nature of Store Procedures, they require first that values are supplied for its Parameters and also Procedures can return multiple Result Sets; so the Import Data dialog shows the Procedure Name and the Procedure Parameters in a grid where their values are input. After you supply the Parameter Values click Call. After calling the Procedure, the Result Sets returned by it are displayed at the bottom of the dialog; output parameters and the return value of the Procedure are appended as the last Result Set of the group. You can see each Result Set is displayed as a tab so you can see a preview of the returned data.  You can specify if you want to import the Selected Result Set (default), All Result Sets – Arranged Horizontally or All Result Sets – Arranged Vertically using the Import drop-down list; then click Import. After the import is performed, the data in the Excel spreadsheet looks like the following screenshot.  Note in this example all Result Sets were imported and arranged vertically. As you can see using MySQL for Excel importing data from a MySQL database becomes an easy task that requires very little technical knowledge, so it can be done by any type of user. Hope you enjoyed this guide! Remember that your feedback is very important for us, so drop us a message: MySQL on Windows (this) Blog - https://blogs.oracle.com/MySqlOnWindows/ Forum - http://forums.mysql.com/list.php?172 Facebook - http://www.facebook.com/mysql Cheers!

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  • What scientific plotting software is available?

    - by Helix
    I am currently doing some experimental work and I have a lot of data to trawl though. I use Gnumeric, and it's very good, but often I feel there has to be something better. Ideally I would like the maximum number of features with a minimal learning curve, but really I'd just like to know if there is something better than Gnumeric that I can use for manipulating and plotting data. What would you recommend?

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  • Stairway to SQL Server Indexes: Step 1, Introduction to Indexes

    Indexes are the database objects that enable SQL Server to satisfy each data access request from a client application with the minimum amount of effort, resulting in the maximum performance of individual requests while also reducing the impact of one request upon another. Prerequisites: Familiarity with the following relational database concepts: Table, row, primary key, foreign key Join SQL Backup’s 35,000+ customers to compress and strengthen your backups "SQL Backup will be a REAL boost to any DBA lucky enough to use it." Jonathan Allen. Download a free trial now.

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  • Does hiding images on 404 error affect SEO?

    - by Question Overflow
    I have a dynamic website that allows registered users to upload and display images on the their profile page. As each user may upload less than the maximum limit of 20 images, there would be some "empty" images on the page. I am using javascript to hide these empty images. The loading of the profile page would generate a series of 404 errors depending on the number of empty images. Would these 404 errors affect the SEO of the page and the website?

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  • Don’t Miss The Top Exastack ISV Headlines – Week Of June 5

    - by Roxana Babiciu
    Smartsoft's OCEAN Payment Processing Solution achieves Oracle Exadata Optimized status. "Performance is the most important issue for our success in the market and running OCEAN on the Oracle Exadata Database Machine provides customers with extreme performance.” – Learn more Banking solution FORBIS Ltd’s FORPOST achieves Oracle Exadata, Exalogic and SuperCluster Ready Status. “We are glad to offer our current and future customers the newest features provided by Oracle Engineered Systems to achieve maximum reliability and speed operation.” – Learn more

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  • 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.)

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  • Slides and Links from SQL Azure session at BizSpark Azure Day in London

    - by Eric Nelson
    A big thanks to all who attended my two sessions on SQL Azure yesterday (29th March 2010). As promised, my slides and links from the session. SQL Azure Overview for Bizspark day View more presentations from Eric Nelson. Related Links: UK Azure Online Community – join today. UK Windows Azure Site Start working with Windows Azure SQL Azure maximum database size rises from 10GB to 50GB in June TCO and ROI calculator for Windows Azure SQL Azure Migration Wizard

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  • What's the best book for coding conventions?

    - by Joschua
    What's the best book about coding conventions (and perhaps design patterns), that you highly recommend (at best code samples in Python, C++ or Java)? It would be good, if the book (or just another) also covers the topics project management and agile software development if appropriate (for example how projects fail through spaghetti code). I will accept the answer with the book(s) (maximum two books per answer, please), that looks the most interesting, because the reading might take a while :)

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  • Notes from ODF Plugfest in Granada, Day One

    <b>Zona-M:</b> "The ODF Plugfest is a Conference whose goal is to to achieve the maximum interoperability between competing applications, platforms and technologies in the area of digital document sharing, and to promote the OpenDocument format (ODF)"

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  • SQL SERVER – Using expressor Composite Types to Enforce Business Rules

    - by pinaldave
    One of the features that distinguish the expressor Data Integration Platform from other products in the data integration space is its concept of composite types, which provide an effective and easily reusable way to clearly define the structure and characteristics of data within your application.  An important feature of the composite type approach is that it allows you to easily adjust the content of a record to its ultimate purpose.  For example, a record used to update a row in a database table is easily defined to include only the minimum set of columns, that is, a value for the key column and values for only those columns that need to be updated. Much like a class in higher level programming languages, you can also use the composite type as a way to enforce business rules onto your data by encapsulating a datum’s name, data type, and constraints (for example, maximum, minimum, or acceptable values) as a single entity, which ensures that your data can not assume an invalid value.  To what extent you use this functionality is a decision you make when designing your application; the expressor design paradigm does not force this approach on you. Let’s take a look at how these features are used.  Suppose you want to create a group of applications that maintain the employee table in your human resources database. Your table might have a structure similar to the HumanResources.Employee table in the AdventureWorks database.  This table includes two columns, EmployeID and rowguid, that are maintained by the relational database management system; you cannot provide values for these columns when inserting new rows into the table. Additionally, there are columns such as VacationHours and SickLeaveHours that you might choose to update for all employees on a monthly basis, which justifies creation of a dedicated application. By creating distinct composite types for the read, insert and update operations against this table, you can more easily manage this table’s content. When developing this application within expressor Studio, your first task is to create a schema artifact for the database table.  This process is completely driven by a wizard, only requiring that you select the desired database schema and table.  The resulting schema artifact defines the mapping of result set records to a record within the expressor data integration application.  The structure of the record within the expressor application is a composite type that is given the default name CompositeType1.  As you can see in the following figure, all columns from the table are included in the result set and mapped to an identically named attribute in the default composite type. If you are developing an application that needs to read this table, perhaps to prepare a year-end report of employees by department, you would probably not be interested in the data in the rowguid and ModifiedDate columns.  A typical approach would be to drop this unwanted data in a downstream operator.  But using an alternative composite type provides a better approach in which the unwanted data never enters your application. While working in expressor  Studio’s schema editor, simply create a second composite type within the same schema artifact, which you could name ReadTable, and remove the attributes corresponding to the unwanted columns. The value of an alternative composite type is even more apparent when you want to insert into or update the table.  In the composite type used to insert rows, remove the attributes corresponding to the EmployeeID primary key and rowguid uniqueidentifier columns since these values are provided by the relational database management system. And to update just the VacationHours and SickLeaveHours columns, use a composite type that includes only the attributes corresponding to the EmployeeID, VacationHours, SickLeaveHours and ModifiedDate columns. By specifying this schema artifact and composite type in a Write Table operator, your upstream application need only deal with the four required attributes and there is no risk of unintentionally overwriting a value in a column that does not need to be updated. Now, what about the option to use the composite type to enforce business rules?  If you review the composition of the default composite type CompositeType1, you will note that the constraints defined for many of the attributes mirror the table column specifications.  For example, the maximum number of characters in the NationaIDNumber, LoginID and Title attributes is equivalent to the maximum width of the target column, and the size of the MaritalStatus and Gender attributes is limited to a single character as required by the table column definition.  If your application code leads to a violation of these constraints, an error will be raised.  The expressor design paradigm then allows you to handle the error in a way suitable for your application.  For example, a string value could be truncated or a numeric value could be rounded. Moreover, you have the option of specifying additional constraints that support business rules unrelated to the table definition. Let’s assume that the only acceptable values for marital status are S, M, and D.  Within the schema editor, double-click on the MaritalStatus attribute to open the Edit Attribute window.  Then click the Allowed Values checkbox and enter the acceptable values into the Constraint Value text box. The schema editor is updated accordingly. There is one more option that the expressor semantic type paradigm supports.  Since the MaritalStatus attribute now clearly specifies how this type of information should be represented (a single character limited to S, M or D), you can convert this attribute definition into a shared type, which will allow you to quickly incorporate this definition into another composite type or into the description of an output record from a transform operator. Again, double-click on the MaritalStatus attribute and in the Edit Attribute window, click Convert, which opens the Share Local Semantic Type window that you use to name this shared type.  There’s no requirement that you give the shared type the same name as the attribute from which it was derived.  You should supply a name that makes it obvious what the shared type represents. In this posting, I’ve overviewed the expressor semantic type paradigm and shown how it can be used to make your application development process more productive.  The beauty of this feature is that you choose when and to what extent you utilize the functionality, but I’m certain that if you opt to follow this approach your efforts will become more efficient and your work will progress more quickly.  As always, I encourage you to download and evaluate expressor Studio for your current and future data integration needs. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, Pinal Dave, PostADay, SQL, SQL Authority, SQL Documentation, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • A Fully Featured Solution for Data Export

    For those who need to be able to automate the process of exporting databases with maximum speed, reliability and ease of use, FlySpeed Data Export offers the ultimate solution. The software is packed... [Author: William Potter - Computers and Internet - March 29, 2010]

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  • Windows 7 - traceroute hop with high latency! [closed]

    - by Mac
    I've been experiencing this problem for quite a while, and it's quite frustrating. I'll do a traceroute, to www.l.google.com, for example. This is the result (please note: I will replace some parts of personal information with text - i.e. ISP.IP is in reality an actual IP address, and ISPNAME replaces the actual ISP name): Tracing route to www.l.google.com [173.194.34.212] over a maximum of 30 hops: 1 1 ms 1 ms <1 ms 192.168.1.1 2 9 ms 8 ms 10 ms ISP.EXCHANGE.NAME [ISP.IP.172.205] 3 161 ms 171 ms 177 ms host-ISP.IP.215.246.ISPNAME.net [ISP.IP.215.246] 4 12 ms 9 ms 10 ms host-ISP.IP.215.246.ISPNAME.net [ISP.IP.215.246] 5 10 ms 9 ms 17 ms host-ISP.IP.224.165.ISPNAME.net [ISP.IP.224.165] 6 10 ms 9 ms 10 ms 10.42.0.3 7 9 ms 9 ms 10 ms host-ISP.IP.202.129.ISPNAME.net [ISP.IP.202.129] 8 10 ms 9 ms 9 ms host-ISP.IP.209.33.ISPNAME.net [ISP.IP.209.33] 9 77 ms 129 ms 164 ms host-ISP.IP.198.162.ISPNAME.net [ISP.IP.198.162] 10 43 ms 42 ms 43 ms 72.14.212.13 11 42 ms 42 ms 42 ms 209.85.252.36 12 59 ms 59 ms 59 ms 209.85.241.210 13 60 ms 76 ms 68 ms 72.14.237.124 14 59 ms 59 ms 58 ms mad01s08-in-f20.1e100.net [173.194.34.212] Trace complete. Notice that there is a spike on the 3rd hop, but also notice that the 3rd and 4th hop are to the exact same destination. Furthermore, when I ping the offended hop separately, I get the low latency I would expect to that server: Pinging ISP.IP.215.246 with 32 bytes of data: Reply from ISP.IP.215.246: bytes=32 time=10ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=9ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=12ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=9ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=10ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=9ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=10ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=9ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=10ms TTL=253 Reply from ISP.IP.215.246: bytes=32 time=10ms TTL=253 Ping statistics for ISP.IP.215.246: Packets: Sent = 10, Received = 10, Lost = 0 (0% loss), Approximate round trip times in milli-seconds: Minimum = 9ms, Maximum = 12ms, Average = 9ms I'm baffled as to why or how this is happening, and it seems to "fix itself" at random times. Here is an example of where it was working as expected: http://i.imgur.com/bysno.png Notice how many fewer hops were taken. Please note that all the posted results occurred within 10 minutes of testing. I've tried contacting my ISP, and they seem clueless; in their eyes, as long as "the download speed is not slow", then they're doing everything right. Any insight would be very much appreciated, and thanks in advanced!

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  • Das T5-4 TPC-H Ergebnis naeher betrachtet

    - by Stefan Hinker
    Inzwischen haben vermutlich viele das neue TPC-H Ergebnis der SPARC T5-4 gesehen, das am 7. Juni bei der TPC eingereicht wurde.  Die wesentlichen Punkte dieses Benchmarks wurden wie gewohnt bereits von unserer Benchmark-Truppe auf  "BestPerf" zusammengefasst.  Es gibt aber noch einiges mehr, das eine naehere Betrachtung lohnt. Skalierbarkeit Das TPC raet von einem Vergleich von TPC-H Ergebnissen in unterschiedlichen Groessenklassen ab.  Aber auch innerhalb der 3000GB-Klasse ist es interessant: SPARC T4-4 mit 4 CPUs (32 Cores mit 3.0 GHz) liefert 205,792 QphH. SPARC T5-4 mit 4 CPUs (64 Cores mit 3.6 GHz) liefert 409,721 QphH. Das heisst, es fehlen lediglich 1863 QphH oder 0.45% zu 100% Skalierbarkeit, wenn man davon ausgeht, dass die doppelte Anzahl Kerne das doppelte Ergebnis liefern sollte.  Etwas anspruchsvoller, koennte man natuerlich auch einen Faktor von 2.4 erwarten, wenn man die hoehere Taktrate mit beruecksichtigt.  Das wuerde die Latte auf 493901 QphH legen.  Dann waere die SPARC T5-4 bei 83%.  Damit stellt sich die Frage: Was hat hier nicht skaliert?  Vermutlich der Plattenspeicher!  Auch hier lohnt sich eine naehere Betrachtung: Plattenspeicher Im Bericht auf BestPerf und auch im Full Disclosure Report der TPC stehen einige interessante Details zum Plattenspeicher und der Konfiguration.   In der Konfiguration der SPARC T4-4 wurden 12 2540-M2 Arrays verwendet, die jeweils ca. 1.5 GB/s Durchsatz liefert, insgesamt also eta 18 GB/s.  Dabei waren die Arrays offensichtlich mit jeweils 2 Kabeln pro Array direkt an die 24 8GBit FC-Ports des Servers angeschlossen.  Mit den 2x 8GBit Ports pro Array koennte man so ein theoretisches Maximum von 2GB/s erreichen.  Tatsaechlich wurden 1.5GB/s geliefert, was so ziemlich dem realistischen Maximum entsprechen duerfte. Fuer den Lauf mit der SPARC T5-4 wurden doppelt so viele Platten verwendet.  Dafuer wurden die 2540-M2 Arrays mit je einem zusaetzlichen Plattentray erweitert.  Mit dieser Konfiguration wurde dann (laut BestPerf) ein Maximaldurchsatz von 33 GB/s erreicht - nicht ganz das doppelte des SPARC T4-4 Laufs.  Um tatsaechlich den doppelten Durchsatz (36 GB/s) zu liefern, haette jedes der 12 Arrays 3 GB/s ueber seine 4 8GBit Ports liefern muessen.  Im FDR stehen nur 12 dual-port FC HBAs, was die Verwendung der Brocade FC Switches erklaert: Es wurden alle 4 8GBit ports jedes Arrays an die Switches angeschlossen, die die Datenstroeme dann in die 24 16GBit HBA ports des Servers buendelten.  Das theoretische Maximum jedes Storage-Arrays waere nun 4 GB/s.  Wenn man jedoch den Protokoll- und "Realitaets"-Overhead mit einrechnet, sind die tatsaechlich gelieferten 2.75 GB/s gar nicht schlecht.  Mit diesen Zahlen im Hinterkopf ist die Verdopplung des SPARC T4-4 Ergebnisses eine gute Leistung - und gleichzeitig eine gute Erklaerung, warum nicht bis zum 2.4-fachen skaliert wurde. Nebenbei bemerkt: Weder die SPARC T4-4 noch die SPARC T5-4 hatten in der gemessenen Konfiguration irgendwelche Flash-Devices. Mitbewerb Seit die T4 Systeme auf dem Markt sind, bemuehen sich unsere Mitbewerber redlich darum, ueberall den Eindruck zu hinterlassen, die Leistung des SPARC CPU-Kerns waere weiterhin mangelhaft.  Auch scheinen sie ueberzeugt zu sein, dass (ueber)grosse Caches und hohe Taktraten die einzigen Schluessel zu echter Server Performance seien.  Wenn ich mir nun jedoch die oeffentlichen TPC-H Ergebnisse ansehe, sehe ich dies: TPC-H @3000GB, Non-Clustered Systems System QphH SPARC T5-4 3.6 GHz SPARC T5 4/64 – 2048 GB 409,721.8 SPARC T4-4 3.0 GHz SPARC T4 4/32 – 1024 GB 205,792.0 IBM Power 780 4.1 GHz POWER7 8/32 – 1024 GB 192,001.1 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64 – 512 GB 162,601.7 Kurz zusammengefasst: Mit 32 Kernen (mit 3 GHz und 4MB L3 Cache), liefert die SPARC T4-4 mehr QphH@3000GB ab als IBM mit ihrer 32 Kern Power7 (bei 4.1 GHz und 32MB L3 Cache) und auch mehr als HP mit einem 64 Kern Intel Xeon System (2.27 GHz und 24MB L3 Cache).  Ich frage mich, wo genau SPARC hier mangelhaft ist? Nun koennte man natuerlich argumentieren, dass beide Ergebnisse nicht gerade neu sind.  Nun, in Ermangelung neuerer Ergebnisse kann man ja mal ein wenig spekulieren: IBMs aktueller Performance Report listet die o.g. IBM Power 780 mit einem rPerf Wert von 425.5.  Ein passendes Nachfolgesystem mit Power7+ CPUs waere die Power 780+ mit 64 Kernen, verfuegbar mit 3.72 GHz.  Sie wird mit einem rPerf Wert von  690.1 angegeben, also 1.62x mehr.  Wenn man also annimmt, dass Plattenspeicher nicht der limitierende Faktor ist (IBM hat mit 177 SSDs getestet, sie duerfen das gerne auf 400 erhoehen) und IBMs eigene Leistungsabschaetzung zugrunde legt, darf man ein theoretisches Ergebnis von 311398 QphH@3000GB erwarten.  Das waere dann allerdings immer noch weit von dem Ergebnis der SPARC T5-4 entfernt, und gerade in der von IBM so geschaetzen "per core" Metric noch weniger vorteilhaft. In der x86-Welt sieht es nicht besser aus.  Leider gibt es von Intel keine so praktischen rPerf-Tabellen.  Daher muss ich hier fuer eine Schaetzung auf SPECint_rate2006 zurueckgreifen.  (Ich bin kein grosser Fan von solchen Kreuz- und Querschaetzungen.  Insb. SPECcpu ist nicht besonders geeignet, um Datenbank-Leistung abzuschaetzen, da fast kein IO im Spiel ist.)  Das o.g. HP System wird bei SPEC mit 1580 CINT2006_rate gelistet.  Das bis einschl. 2013-06-14 beste Resultat fuer den neuen Intel Xeon E7-4870 mit 8 CPUs ist 2180 CINT2006_rate.  Das ist immerhin 1.38x besser.  (Wenn man nur die Taktrate beruecksichtigen wuerde, waere man bei 1.32x.)  Hier weiter zu rechnen, ist muessig, aber fuer die ungeduldigen Leser hier eine kleine tabellarische Zusammenfassung: TPC-H @3000GB Performance Spekulationen System QphH* Verbesserung gegenueber der frueheren Generation SPARC T4-4 32 cores SPARC T4 205,792 2x SPARC T5-464 cores SPARC T5 409,721 IBM Power 780 32 cores Power7 192,001 1.62x IBM Power 780+ 64 cores Power7+  311,398* HP ProLiant DL980 G764 cores Intel Xeon X7560 162,601 1.38x HP ProLiant DL980 G780 cores Intel Xeon E7-4870    224,348* * Keine echten Resultate  - spekulative Werte auf der Grundlage von rPerf (Power7+) oder SPECint_rate2006 (HP) Natuerlich sind IBM oder HP herzlich eingeladen, diese Werte zu widerlegen.  Aber stand heute warte ich noch auf aktuelle Benchmark Veroffentlichungen in diesem Datensegment. Was koennen wir also zusammenfassen? Es gibt einige Hinweise, dass der Plattenspeicher der begrenzende Faktor war, der die SPARC T5-4 daran hinderte, auf jenseits von 2x zu skalieren Der Mythos, dass SPARC Kerne keine Leistung bringen, ist genau das - ein Mythos.  Wie sieht es umgekehrt eigentlich mit einem TPC-H Ergebnis fuer die Power7+ aus? Cache ist nicht der magische Performance-Schalter, fuer den ihn manche Leute offenbar halten. Ein System, eine CPU-Architektur und ein Betriebsystem jenseits einer gewissen Grenze zu skalieren ist schwer.  In der x86-Welt scheint es noch ein wenig schwerer zu sein. Was fehlt?  Nun, das Thema Preis/Leistung ueberlasse ich gerne den Verkaeufern ;-) Und zu guter Letzt: Nein, ich habe mich nicht ins Marketing versetzen lassen.  Aber manchmal kann ich mich einfach nicht zurueckhalten... Disclosure Statements The views expressed on this blog are my own and do not necessarily reflect the views of Oracle. TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org, results as of 6/7/13. Prices are in USD. SPARC T5-4 409,721.8 QphH@3000GB, $3.94/QphH@3000GB, available 9/24/13, 4 processors, 64 cores, 512 threads; SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads. SPEC and the benchmark names SPECfp and SPECint are registered trademarks of the Standard Performance Evaluation Corporation. Results as of June 18, 2013 from www.spec.org. HP ProLiant DL980 G7 (2.27 GHz, Intel Xeon X7560): 1580 SPECint_rate2006; HP ProLiant DL980 G7 (2.4 GHz, Intel Xeon E7-4870): 2180 SPECint_rate2006,

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  • installing ntop in ubuntu 12.4

    - by George Ninan
    When i try to start the ntop i get the following error - Secure Connection Failed An error occurred during a connection to 192.168.166.229:3000. SSL received a record that exceeded the maximum permissible length. (Error code: ssl_error_rx_record_too_long) The page you are trying to view cannot be shown because the authenticity of the received data could not be verified. Please contact the website owners to inform them of this problem. Alternatively, use the command found in the help menu to report this broken site. Please advice

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  • I can't get over a resolution of 640x480 after upgrading to 12.04, how can I fix it?

    - by Sandeep Srivastava
    Ever since I upgraded to 12.04 my screen resolution has gone down to 640 x 480, even though I had higher resolutions before. My xrand output looks as below : sandeep@sandeep-desktop:~$ xrandr xrandr: Failed to get size of gamma for output default Screen 0: minimum 640 x 480, current 640 x 480, maximum 640 x 480 default connected 640x480+0+0 0mm x 0mm 640x480 0.0* How can I get higher resolutions, I know that my monitor support higher resolutions.

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