Search Results

Search found 105847 results on 4234 pages for 'sql server performance'.

Page 221/4234 | < Previous Page | 217 218 219 220 221 222 223 224 225 226 227 228  | Next Page >

  • DBCC CHECKDB fails and quits job, ambiguous error message.

    - by ddono25
    I received a notice that one of our servers' DBCC CHECKDB for all databases has been failing the past four times it has been run. We don't have any data prior to that, but it doesn't look like it has been succeeding for awhile. There are no errors in the log file only: DBCC results for 'sys.sysxmlfacet'. [SQLSTATE 01000] Msg 0, Sev 0, State 1: Unspecified error occurred on SQL Server. Connection may have been terminated by the server. [SQLSTATE HY000] There are 112 rows in 1 pages for object "sys.sysxmlfacet". [SQLSTATE 01000] I ran a DBCC CHECKDB using sp_MSForEachDB to get more accurate results and had the same error on the same DB but at a separate point: DBCC results for 'NameValuePair_Greek_CI_AS'. [SQLSTATE 01000] Msg 0, Sev 0, State 1: Unspecified error occurred on SQL Server. Connection may have been terminated by the server. [SQLSTATE HY000] There are 0 rows in 0 pages for object "NameValuePair_Greek_CI_AS". [SQLSTATE 01000] Also, the error-log states that the DBCC completed without errors for this database. I can't figure out how to track down this ambiguous issue that only happens on this database out of the dozens on this server. Any help is appreciated!

    Read the article

  • sql user not associated with a trusted connection

    - by homestead
    just setup a new server with sql server express 2005. (want to connect remotely) I set logins both windows and sql I modified so both local and remote connections are allowed I restarted the server windows firewall is not connected. (have an external firewall with a hole at port 1443) user account is active, no password policy or forcing to change on next login etc. If I even try to connect using this username locally on the server using 'file | connect to object explorer' I get the same message that the user is not associated to a trusted connection.

    Read the article

  • High Steal Time utilization on Apache Linux Server

    - by JMC
    I have a CentOS "development / testing" server that runs extremely slowly. It's running Apache and Mysql using PHP. Top reports that 98% of the CPU utilization is frequently spent on "st" - Steal Time. What could cause a server to spend so much CPU on steal time, and how can I diagnose the problem? I didn't notice the problem until after I granted a third party developer root access (for all I know it has a root kit running, though unlikely).

    Read the article

  • Recommendations for remote server management software, similar to Puppet or Canonical Landscape?

    - by rmh
    We currently have five Ubuntu 10.04 LTS servers, and keeping them all up-to-date is starting to be a pain. I've been looking into solutions like Puppet and Canonical Landscape. Out of the two I prefer Puppet -- it would be useful to be able to ensure the permissions of various directories on the machines, and define groups and users on the server which are then propagated to clients. Is there any other software in this vein that I should be taking a look at?

    Read the article

  • SharePoint Session Management - which SQL Server option?

    - by frumious
    We're developing some custom web parts for our WSS 3 intranet, and have just run into something we'd like to use ASP.NET sessions for. This isn't currently enabled on the development server. We'd like to use SQL Server as the storage mechanism, because the production environment is a web farm with very simple load-balancing. There are 3 options you can choose from to set up the SQL Server session storage, tempdb, default separate DB, named DB. Both tempdb and default separate DB create a new DB to store certain information in; tempdb stores the actual session info in tempdb, which doesn't survive a reboot, and default separate DB stores everything in the new DB. Since you've got to create the new DB either way, my question is this: why would you ever choose to store the session info in tempdb? The only thing I can think of is if you'd like to have the ability to wipe the session by rebooting the server, but that seems quite apocalyptic!

    Read the article

  • SQL Server: Network pauses after installing cheap SATA card: Is there a solution?

    - by samsmith
    At the risk of being assigned to the "bad DBA" club... I did something desperate, and may have to undo it. Problem: After installing a low cost eSATA board, my SQL Server is intermittently unresponsive (seemingly when there is a lot of IO to the eSATA drive). Questions: 1) Is there a solution to the intermittent unresponsiveness that allows me to keep the eSATA in place? 2) Whether or not (1==true): What is a decent, low cost way to add 1-3 TB storage to SQL for non-critical SQL DBs? Detail: Our SAN is full, and expanding it is costly and will take a month. I have a pressing need to add 1-3 TB for some development DBs (e.g. not mission critical; data loss is OK). As a bandaid, I threw a $20 eSATA PCI board in the Dell 1950 server, and attached an external 2TB eSATA drive. This seemed to work fine, but I notice that our production SQL DBs, and even remote desktop, now experience network "pauses" that they never did before (with both SQL client apps and remote desktop throwing "networking problem" errors). This SQL Server has lots of memory, and runs an instance of SQL 2005 (where all line of business apps reside) and an instance SQL 2008 (for development db's). SQL Server RAM has been appropriately configured, and this setup has run great for years. The server is: Dell 1950 Win2003 x64 14GB RAM PERC controller, 2 mirrored hd's internal Dell SAN over gbit ethernet, dual homed 2 PCIx slots (1 used by NIC for SAN, 1 now in use for eSATA board) Thank you for suggestions!

    Read the article

  • MS SQL server 2005 replication

    - by hubertus
    Hi. I have a problem with replication between 3 servers. I made something like this: server A replicate (transactional replication) to server B (to 'mydb' database), then server B replicate 'mydb' (using transactional replication) to server C. On the beginning it looks and works fine, but something wrong is going on (about 2-3 month later) and replication break up. SQL say that hi can replicate db because db is allready use to replicate. Any one had similar broblem? Mayby someone knows hot can I make alternative configuration to have similar funcionality?

    Read the article

  • BizTalk Server 2009 R2 = BizTalk Server 2010

    - by Rajesh Charagandla
    Microsoft has renamed BizTalk Server 2009 R2 as BizTalk Server 2010, and is now telling customers that the evolution of the product recommends it as a major version versus a minor update. BizTalk Server 2009 R2 was designed mainly to bring to the table support for the company’s latest technologies, including Windows Server 2008 R2, SQL Server 2008 R2 and Visual Studio 2010. Following is list of key capabilities added to the release 1.       Enhanced trading partner management that will enable our customers to manage complex B2B relationships with ease 2.       Increase productivity through enhanced BizTalk Mapper. These enhancements are critical in increasing productivity in both EAI and B2B solutions; and a favorite feature of our customers. 3.       Enable secure data transfer across business partners with FTPS adapter 4.       Updated adapters for SAP 7, Oracle eBusiness Suite 12.1, SharePoint 2010 and SQL Server 2008 R2 5.       Improved and simplified management with updated System Center management pack 6.       Simplified management through single dashboard which enables IT Pros to backup and restore BizTalk configuration 7.       Enhanced performance tuning capabilities at Host and Host Instance level 8.       Continued innovation in RFID Space with out of box event filtering and delivery of RFID events

    Read the article

  • Configuring Team Foundation Server Basic on Home Server.

    - by Enrique Lima
    For the installation I selected only the Team Foundation Server role. Then, I opened the Team Foundation Server Administration Console (which I think is a great addition and improvement over the way TFS was configured in the past) to proceed with the configuration of the pieces. Once I selected the Configure Installed Features, the Configuration Center opened up. Now, the choices … In my implementation here I just want to take advantage of Source Control primarily.  I want to be able to store my code and projects.  So, Basic it is! So, the Basic Configuration Wizard opens up.  Now the options to configure are very limited, but we have to provide details for the SQL Server Instance. And now, to select Install SQL Server express.  If you want to take advantage of another system in your environment to host your database, well you could Use an existing SQL Server Instance. Once it has the details it needs, you get a Summary view to confirm your choices. Once, you click next or verify, it runs readiness checks on your system to make sure the installation will have a successful pass.  And we love GREEN! Now, since got the green flag, our next stop is to let the wizard do its magic, click on Configure.  And once again, we love GREEN! We click Next, and … We like a big Green Success sign … We close the Configuration Center … First results … Web Access …  Nothing to show … but we are there! And all this running from a Microsoft Home Server installation.

    Read the article

  • Creating a DNS Server

    - by c.adhityaa
    OK, I am a complete newbie to all this, so please bear with me. I want to create a DNS Server (like Google does - 8.8.8.8). I understand that a DNS Server is a Server that gives a IP on being given a hostname, ie. when I ask it what is the IP of google.com, it says "64.233.160.0". So, what I want to do is create a similar one that holds records of what translates to what. I thought of this since it looks to be similar to a webserver - ask for a page and it gives back the page. That is, when my machine has the IP xxx.xxx.xxx.xxx and people chose xxx.xxx.xxx.xxx as their Primary DNS Server, then when they ask "www.google.com", I sould be able to tell "64.233.160.0". So, how do I create this DNS Server that is accessible to everyone in the world ? It would be easier if we have something like EasyPHP which is the analogue to a webserver here. I am sorry if I have caused any trauma because this might seem rubbish to experts ;) Adhityaa

    Read the article

  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

    Read the article

  • push email / email server tutorial

    - by David A
    Does anyone happen to know the current status of push email in the linux world? From my searching at the moment I have seen Z-push http://www.ifusio.com/blog/setup-your-own-push-mail-server-with-z-push-on-debian-linux and https://peterkieser.com/2011/03/25/androids-k-9-mail-battery-life-and-dovecots-push-imap/ Are there other solutions? Does anyone have any experiences with these? They're somewhat different in that Z-push seems to work in conjunction with an existing imap server? Some time ago I did manage to compile and build Dovecot 2 (since only Dovecot 1 was available in the Ubuntu repos at the time), it would have been a real fluke because I had no idea what I was doing but it seemed to work well with my mobile phone, that said, I can't say for sure that it was pushing, but it seemed like it. Anyway, I'm here again and looking to set up a mail server. I'm hoping to do a better of a job this time around with virtual users and such. Without installing ispconfig3 (or something similar), does anyone have any recent email server tutorials (that cover all aspects MTA, MDA...) that can supply push email on a Ubuntu 12.04 server? (I'm probably of slightly above newb status, but not far) Thanks a bunch

    Read the article

  • "Virtual Machine Manager" and "Virtual Machine Server" setup manual

    - by urtihu
    Is there a manual available that covers the proper setup of a "Virtual Machine Server" with no GUI with an Ubuntu Workstation with a GUI and "Virtual Machine Manager" installed? Both are 12.04 version. I get the following error message: unable to connect to libvirt Verify that -The libvirt-bin package is installed -The libvirt daemon has been started -you are a member of the libvirtd group the package is installed for some reason starting the daemon seems to crash libvirtd start info: libvirt version 0.9.8 error: virExecWithHook:328 : cannot find 'pm-is-supported' in path: No such file or directory also qemucapsInit:856: Failed to get host power management capabilities So I guess I did not set the server up correctly. All manuals I found do not mention "Virtual Machine Manager". I only chose the packages to connect with SSH remotely and the "Virtual Machine Server" for the server installation. So I would like to find a manual that covers this combo or then covered only GUI machines that have both on the same machine, which will not really help with system performance as a hypervisor.

    Read the article

  • problem installing mysql on ubuntu server 10.10 machine

    - by badperson
    Hi, I tried installing mysql a couple of times and I'm having problems. First of all, when I install it gives me a message that it's setting up and it just hangs. I can't ctl + c out of it, so I reboot the server and try to log into the db with sudo mysql -u root -p I enter my password and then get ERROR 2002 (HY000): Can't connect to local MySQL server through socket '/var/run/mysqld/mysqld.sock' (2) I restart the server: sudo /etc/init.d/mysql start Rather than invoking init scripts through /etc/init.d, use the service(8) utility, e.g. service mysql start Since the script you are attempting to invoke has been converted to an Upstart job, you may also use the start(8) utility, e.g. start mysql ~$ I try this: aptitude search mysql | grep ^i i A libdbd-mysql-perl - Perl5 database interface to the MySQL data i libmysql-java - Java database (JDBC) driver for MySQL i A libmysqlclient16 - MySQL database client library i mysql-client-5.1 - MySQL database client binaries i A mysql-client-core-5.1 - MySQL database core client binaries i mysql-common - MySQL database common files, e.g. /etc/mys i mysql-embedded - MySQL - embedded library i mysql-server-core-5.1 - MySQL database server binaries When I navigate to the folder to see if the *.sock file exists: '/var/run/mysqld/mysqld.sock' it does not. I also try this: service mysql status status: Unable to connect to system bus: Failed to connect to socket /var/run/dbus/system_bus_socket: No such file or directory Any ideas? On my other machines installing mysql has been a snap, not sure what the problem is here. bp

    Read the article

  • Multiple Homed Windows 2008 Server / Windows 7 Client

    - by Daniel Scott
    I have a small Windows 2008 network, with some Windows 7 clients. The clients are both laptops with docking stations and I would like them to communicate with the Windows 2008 server (for filesharing) through the wired network whilst they're docked. Internet connectivity for all machines (clients and server) is via a Wireless LAN, so the wireless adapter in the Windows 7 clients stays active while they're docked. When the laptops are un-docked, it would be nice to still be able to contact the windows 2008 server for print sharing (and slower file sharing) - hence the server also being on the wireless LAN. The windows 2008 server is running Active Directory, DHCP and DNS. It controls DHCP leases on the wired network and holds the DNS records for "myserver.mycompany.local", which is what the filesharing clients connect to. Ideally I'd like the DNS records to return the wired IP first so that this is the address that the laptops will attempt initially - but there doesn't seem to be a way to do that? At present the server's IP on the wireless LAN comes out of an nslookup above the wired Lan IP. The multi-homing works perfectly - but in the wrong order! Switch on the wireless lan and ping myserver and it goes to the wireless IP. Disable the wireless on the client and do the same ping again and after a couple of seconds it starts pinging the wired address. Does anyone have any suggestions on how to make this work in a predictable order? - or even if it can work. Alternative 1? If it can't work, then would this work: Remove the wireless adapter from the server, put a wireless router/bridge on the wired network (set up to route to/from the wireless LAN's subnet), then configure the clients with two routes to the (now) single IP of the server with metrics favouring direct communication over the wired LAN first? Alternative 2? Should I instead single-home the laptops so all of their connectivity is via the wired-LAN while they're docked? (and route via the windows 2008 server - or a dedicated wireless bridge/router)? My concern here is that I'd like undocking to be seamless - and if the clients are in the middle of downloading something from the internet I wouldn't want whatever they're doing interupted as they switch IP addresses onto the Wireless network. Perhaps this isn't the case and I'm concerned over nothing? Any thoughts? :) UPDATE I seem to have cracked it (at least DNS entries come out in the order I hope for - and pinging the server with various combinations of wired, wireless and both interfaces enabled uses the IP I want) ... I set the binding order of the NICs on the Server (which is acting as Domain Controller, DHCP and DNS server) so that the Wired NIC is before the Wireless adapter. (Start -- type "Network Interfaces" -- Select "View Network Connections" -- Press Alt to show classic dropdown menus -- Advanced -- Advanced Settings) Now, an nslookup (from the client) of the server's hostname returns the Wired IP first, followed by the Wireless IP. The wired IP now seems to be used whenever it's contactable. Incidentally, the metrics on the wired and wireless routes (on the client) also favour the wired LAN (based on Windows' automatically assigned metrics) - but this was always the case, even when I was having trouble getting the wired IP to be "favoured". I'm not entirely sure if this is coincidence - or if a DNS server running on Windows, handing back IP addresses for itself does actually take the binding order of it's own network interfaces into account? It would be interesting to hear from someone who can confirm or deny that (or confirm that the binding order on the server plays a role for some other reason?)

    Read the article

  • Drupal and FTP server

    - by burak
    When I install a new module on Drupal, I get this error: Warning: ftp_login(): Login authentication failed in FileTransferFTPExtension-connect() (line 59 of /home/burak/ public_html/beytepe/includes/filetransfer/ftp.inc). Failed to connect to the server. The server reports the following message: Cannot log in to FTP server. Check username and password What can I do? How can I solve this?

    Read the article

  • [MINI HOW-TO] Remove a Network Computer from Windows Home Server

    - by Mysticgeek
    One of the cool features of Windows Home Server is the ability to backup and monitor the computers on your network. If you no longer need a machine on to be monitored or backed up, here we show you how to remove it. Remove Computer from WHS The process if straight-forward and basic –Open Windows Home Server Console and click on Computers & Backup. Right-click on the computer that you no longer need and click Remove. You’ll be prompted to verify that you want to remove the machine and delete all of its backup data. Check the box I am sure I want to remove this computer then click the Remove button. That’s all there is to it! The computer and all of its backup data is removed. Remember that if you remove a computer, all of its backup data will be deleted as well. If you no longer have the computer, you probably don’t need the backed up data anyway, but you’ll want to be sure you no longer need it before removing it. Similar Articles Productive Geek Tips GMedia Blog: Setting Up a Windows Home ServerRestore Files from Backups on Windows Home ServerCreate A Windows Home Server Home Computer Restore DiscInstalling Windows Home ServerChange Ubuntu Server from DHCP to a Static IP Address TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Beware Hover Kitties Test Drive Mobile Phones Online With TryPhone Ben & Jerry’s Free Cone Day, 3/23/10 New Stinger from McAfee Helps Remove ‘FakeAlert’ Threats Google Apps Marketplace: Tools & Services For Google Apps Users Get News Quick and Precise With Newser

    Read the article

  • Backup Your Windows Home Server Off-Site with Asus Webstorage

    - by Mysticgeek
    Windows Home Server lets you backup machines on your network easily. But what about backing up the server data? Today we take a look at ASUS WebStorage for Windows Home Server, which provides you with secure off-site backup for WHS. To use the ASUS WebStorage service you’ll need to sign up for a free account. It offers 1GB of free storage, then you can purchase an unlimited backup package for $39.99 for a year subscription. Note: They also offer online storage for individual PCs as well. Install ASUS WebStorage for WHS Browse to your shared folders on the server and open the Add-Ins folder and copy over the WHSConnectorSetup2.2.4.088.msi file (link below) then close out of the folder. Now launch Windows Home Server Console from one of the computers on your network, click Settings, then Add-ins. Under Available Add-ins click the Available tab and you’ll see the Asus WebStorage installer file we just copied over. Click the Install button. Installation kicks off and when it’s complete, you’ll need to close out of the console and reconnect. Using ASUS WebStorage WHS Connector  When you reconnect to WHS Console, scroll over to the ASUS WebStorage icon and click on Settings. Now log into your ASUS account… Now select the folders you want to backup to the WebStorage service. Select the radio button next to Enable to initialize the backup process… The backup process begins. You can change which folders are backed up simply by disabling the backup process, uncheck the folder(s), then enable the backup again. ASUS WebStorage Site After you have files backed up to the ASUS site, log into your account, and your presented with an overview of the amount of storage you’re using. It also shows what type of files are taking certain amounts of space.   You can browse through your backed up files and folders. It allows you to share and sync backed up data as well. Navigate to the file you want and you can easily download it by clicking on it, or share it out by clicking the share link below it. If you choose to share it, you’re provided with a link to the file to send out to other users.   Conclusion Users of Windows Home Server have been looking for an inexpensive cloud backup solution for quite some time. There are services such as JungleDisk, KeepVault, Wuala…etc. These services probably do a better job, but can start getting expensive once you start uploading a GBs of data. Another disappointment of ASUS WebStorage is you can only backup your WHS shares (from what we’ve been able to determine), it’s an “all or nothing” type of thing. You cannot go in and select individual files and folders. The initial upload speeds can be a bit slow as well, although that might have something to do with limited upload speeds on the DSL connection we used to test it. Retrieving your data from the ASUS site is a breeze though, and all the data files are organized quite well. The WHS Addin is very easy to install and use. If you’re looking for an off-site solution to backup your WHS data, you can test out ASUS WebStorage for free with a 1GB limit. This is good for testing the service and it might be exactly what you’re looking for. Other users may want a more advanced solution like KeepVault or CloudBerry…which is a front end for Amazon S3 storage. Download ASUS WebStorage WHS Addin Other WHS Offsite Backup Solutions CloudBerry, JungleDisk, KeepVault, Wuala Similar Articles Productive Geek Tips Restore Files from Backups on Windows Home ServerGMedia Blog: Setting Up a Windows Home ServerCreate A Windows Home Server Home Computer Restore DiscRemove a Network Computer from Windows Home ServerShare Ubuntu Home Directories using Samba TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Gadfly is a cool Twitter/Silverlight app Enable DreamScene in Windows 7 Microsoft’s “How Do I ?” Videos Home Networks – How do they look like & the problems they cause Check Your IMAP Mail Offline In Thunderbird Follow Finder Finds You Twitter Users To Follow

    Read the article

  • Building small Ubuntu server - What hardware is recommended?

    - by 10robinho
    There are many of us who need to build small Ubuntu server. Problem is that in some countries it is hard to find and quite expensive to buy server motherboards and processors. And when one is building small server with limited budget, buying some Xenons is not really an option. So, are there any general recommendations for hardware (I think that motherboards are the main issue) that is stable and fast under Linux? I read that Intel should be the best choice for cpu + mbo combo. So, I was looking around for some Intel motherboards + i7 Ivy Bridge (like Intel DZ77BH-55K with Z77 chipset and Intel i7 3770K) but I've read that they have some issues with kernel, booting and USB ports. That is why I ask community if you have any experience with this. Maybe Intel is not the best choice here? Maybe ASUS or Gigabyte or _other company_ are more stable with Linux? I hope that this Q&As can help people in building stable Ubuntu server.

    Read the article

  • What's the format of Real World Performance Day?

    - by william.hardie
    A question that has cropped a lot of late is "what's the format of Real World Performance Day?" Not an unreasonable question you might think. Sure enough, a quick check of the Independent Oracle User Group's website tells us a bit about the Real World Performance Day event, but no formal agenda? This was one of the questions I posed to Tom Kyte (one of the main presenters) in our recent podcast. Tom tells us that this isn't your traditional event where one speaker follows another with loads of slides. In fact, the Real World Performance Day features Tom and fellow Oracle performance experts - Andrew Holdsworth and Graham Wood - continuously on stage throughout the day. All three will be discussing database performance challenges and solutions from development, architectural design and management perspectives. There's going to be multi-terabyte demos on show, less of the traditional slides, and more interactive debate and discussion going on. Tune-in and hear what else Tom has to say about this fairly unique event!

    Read the article

  • Java EE and GlassFish Server Roadmap Update

    - by John Clingan
    2013 has been a stellar year for both the Java EE and GlassFish Server communities. On June 12, Oracle and its partners announced the release of Java EE 7, which delivers on three major themes – HTML5, developer productivity, and meeting enterprise demands. The online event attracted over 10,000 views in the first two days! During the online event, Oracle also announced the availability of GlassFish Server Open Source Edition 4, the world's first Java EE 7 compatible application server. The primary role of GlassFish Server Open Source Edition has been, and continues to be, driving adoption of the latest release of the Java Platform, Enterprise Edition. Oracle also announced the Java EE 7 SDK, which bundles GlassFish Server Open Source Edition 4, as a Java EE 7 learning aid. Last, Oracle publicly announced the Java EE 7 reference implementation based on GlassFish Server Open Source Edition 4. Java EE is a popular platform, as evidenced by the 20+ Java EE 6 compatible implementations available to choose from. After the launch of Java EE 7 and GlassFish Server Open Source Edition 4, we began planning the Java EE 8 roadmap, which was covered during the JavaOne Strategy Keynote. To summarize, there is a lot of interest in improving on HTML5 support, Cloud, and investigating NoSQL support. We received a lot of great feedback from the community and customers on what they would like to see in Java EE 8. As we approached JavaOne 2013, we started planning the GlassFish Server roadmap. What we announced at JavaOne was that GlassFish Server Open Source Edition 4.1 is scheduled for 2014. Here is an update to that roadmap. GlassFish Server Open Source Edition 4.1 is scheduled for 2014 We are planning updates as needed to GlassFish Server Open Source Edition, which is commercially unsupported As we head towards Java EE 8: The trunk will eventually transition to GlassFish Server Open Source Edition 5 as a Java EE 8 implementation The Java EE 8 Reference Implementation will be derived from GlassFish Server Open Source Edition 5. This replicates what has been done in past Java EE and GlassFish Server releases. Oracle will no longer release future major releases of Oracle GlassFish Server with commercial support – specifically Oracle GlassFish Server 4.x with commercial Java EE 7 support will not be released. Commercial Java EE 7 support will be provided from WebLogic Server. Oracle GlassFish Server will not be releasing a 4.x commercial version Expanding on that last bullet, new and existing Oracle GlassFish Server 2.1.x and 3.1.x commercial customers will continue to be supported according to the Oracle Lifetime Support Policy. Oracle recommends that existing commercial Oracle GlassFish Server customers begin planning to move to Oracle WebLogic Server, which is a natural technical and license migration path forward: Applications developed to Java EE standards can be deployed to both GlassFish Server and Oracle WebLogic Server GlassFish Server and Oracle WebLogic Server have implementation-specific deployment descriptor interoperability (here and here). GlassFish Server 3.x and Oracle WebLogic Server share quite a bit of code, so there are quite a bit of configuration and (extended) feature similarities. Shared code includes JPA, JAX-RS, WebSockets (pre JSR 356 in both cases), CDI, Bean Validation, JAX-WS, JAXB, and WS-AT. Both Oracle GlassFish Server 3.x and Oracle WebLogic Server 12c support Oracle Access Manager, Oracle Coherence, Oracle Directory Server, Oracle Virtual Directory, Oracle Database, Oracle Enterprise Manager and are entitled to support for the underlying Oracle JDK. To summarize, Oracle is committed to the future of Java EE.  Java EE 7 has been released and planning for Java EE 8 has begun. GlassFish Server Open Source Edition continues to be the strategic foundation for Java EE reference implementation going forward. And for developers, updates will be delivered as needed to continue to deliver a great developer experience for GlassFish Server Open Source Edition. We are planning for GlassFish Server Open Source Edition 5 as the foundation for the Java EE 8 reference implementation, as well as bundling GlassFish Server Open Source Edition 5 in a Java EE 8 SDK, which is the most popular distribution of GlassFish. This will allow GlassFish releases to be more focused on the Java EE platform and community-driven requirements. We continue to encourage community contributions, bug reports, participation on the GlassFish forum, etc. Going forward, Oracle WebLogic Server will be the single strategic commercially supported application server from Oracle. Disclaimer: The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract.It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

    Read the article

  • Design pattern for client/server sessions?

    - by nonot1
    Are there any common patterns or general guidance I can learn from for how to design a client/server system where the both the client and server must maintain some kind per-client session state? I've found any number of libraries that can help with some of the plumbing, but it's the overall design I'm wondering about. Open issues in my mind: How to structure the client/server communication so that bidirectional synchronous and asynchronous requests are possible? The server side needs to spawn a couple of per-connected-client session-long helper process. How to manage that? How to manage the mapping from a given client (and any of it's requests) to server state and helper process instances in the face of multiple clients and intermittent network connectivity. Most communication can be simple blocking request/reply, but some will be long running processing tasks that the client will want to keep tabs on. To the extent that it matters, the platform is Linux/C/C++. Not web based. Just an existing thick-client software app being modified to talk to backend servers for some tasks.

    Read the article

  • Analysing Group & Individual Member Performance -RUP

    - by user23871
    I am writing a report which requires the analysis of performance of each individual team member. This is for a software development project developed using the Unified Process (UP). I was just wondering if there are any existing group & individual appraisal metrics used so I don't have to reinvent the wheel... EDIT This is by no means correct but something like: Individual Contribution (IC) = time spent (individual) / time spent (total) = Performance = ? (should use individual contribution (IC) combined with something to gain a measure of overall performance).... Maybe I am talking complete hash and I know generally its really difficult to analyse performance with numbers but any mathematicians out there that can lend a hand or know a somewhat more accurate method of analysing performance than arbitrary marking (e.g. 8 out 10)

    Read the article

< Previous Page | 217 218 219 220 221 222 223 224 225 226 227 228  | Next Page >