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  • ?!Solaris ??20??? ????Solaris 11.1????????!

    - by OTN-J Master
    Solaris 11.1 ??????????? US OTN???????????????????????????????Solaris 11.1??????????? (OTN Japan???????????????????????????????) ???????Oracle Solaris ?????????????????????????????????????????????????????????????????????????????Solaris 11??????????????300???????????????10?4??Oracle OpenWorld?????Solaris 11.1?????????(??????????)?????????????Solaris?????20????????20??????????????????? ???????????????????????OS??????????????????????????Solaris 11.1????? ?????????????2012?11?7???8:00????Oracle Solaris 11.1?????Oracle Solaris Cluster???????·????·???????????????????????Solaris??20??????????????Solaris??????????????Oracle Solaris 11.1??Oracle Solarus Cluster???????????????????????11?8???1?????????????????????????????????Solaris?????????????????·???????????????????

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  • ???·20??!?? ??????????!??????????????????

    - by OTN-J Master
    2011??1??????????????????????????????????????????????????20?????????????????????1????????????????????????20?????????????????????????????????????????????????????????????????!??????????????????????????????????????????????? 20????????????????????????????--------------------------------------------------------------------------???1?????????????????20?????????????? ????????????????????????????? ????????????????????????????????????? ??????(?????????????????????????? ???????????????????????)? ???????????????Oracle???????????????????? ?????????????????????????????????????? ??????????????????????????????????????? ??????????????????????????????????? ?????????????????? ????--------------------------------------------------------------------------??????????????????????????????????????????????????(1??????????)???????????????????????? ?1? ????????????????? ?2? RAC(Real Application Clusters)???????????? ?3? Statspack????????????????????? ?4? ???????FAQ:???????????????? ?5? ???????????????????????? ?6? ??????????????????? ?7? ????????? ?8? ??????? ?9? ??SQL???? ?10? ??????????? ?11? ??SQL????(2????? ?12? I/O?????? ?13? ??????????? ?14? ???·?????????? ?15? ????????? ?16? ?????????? ?17? ?????????? ?18? ??????? ?19??UNDO????REDO??????? ?20????????????? ??????????OTN Newsletter?OTN Twitter???????????????????????????????????????????????????????????????????????????????????????????????OTN??????????????????????????????????????????????????”???????????”?????????????????URL?????????? (????????????????????????????????????)???????????????????

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  • SQL SERVER – Database Dynamic Caching by Automatic SQL Server Performance Acceleration

    - by pinaldave
    My second look at SafePeak’s new version (2.1) revealed to me few additional interesting features. For those of you who hadn’t read my previous reviews SafePeak and not familiar with it, here is a quick brief: SafePeak is in business of accelerating performance of SQL Server applications, as well as their scalability, without making code changes to the applications or to the databases. SafePeak performs database dynamic caching, by caching in memory result sets of queries and stored procedures while keeping all those cache correct and up to date. Cached queries are retrieved from the SafePeak RAM in microsecond speed and not send to the SQL Server. The application gets much faster results (100-500 micro seconds), the load on the SQL Server is reduced (less CPU and IO) and the application or the infrastructure gets better scalability. SafePeak solution is hosted either within your cloud servers, hosted servers or your enterprise servers, as part of the application architecture. Connection of the application is done via change of connection strings or adding reroute line in the c:\windows\system32\drivers\etc\hosts file on all application servers. For those who would like to learn more on SafePeak architecture and how it works, I suggest to read this vendor’s webpage: SafePeak Architecture. More interesting new features in SafePeak 2.1 In my previous review of SafePeak new I covered the first 4 things I noticed in the new SafePeak (check out my article “SQLAuthority News – SafePeak Releases a Major Update: SafePeak version 2.1 for SQL Server Performance Acceleration”): Cache setup and fine-tuning – a critical part for getting good caching results Database templates Choosing which database to cache Monitoring and analysis options by SafePeak Since then I had a chance to play with SafePeak some more and here is what I found. 5. Analysis of SQL Performance (present and history): In SafePeak v.2.1 the tools for understanding of performance became more comprehensive. Every 15 minutes SafePeak creates and updates various performance statistics. Each query (or a procedure execute) that arrives to SafePeak gets a SQL pattern, and after it is used again there are statistics for such pattern. An important part of this product is that it understands the dependencies of every pattern (list of tables, views, user defined functions and procs). From this understanding SafePeak creates important analysis information on performance of every object: response time from the database, response time from SafePeak cache, average response time, percent of traffic and break down of behavior. One of the interesting things this behavior column shows is how often the object is actually pdated. The break down analysis allows knowing the above information for: queries and procedures, tables, views, databases and even instances level. The data is show now on all arriving queries, both read queries (that can be cached), but also any types of updates like DMLs, DDLs, DCLs, and even session settings queries. The stats are being updated every 15 minutes and SafePeak dashboard allows going back in time and investigating what happened within any time frame. 6. Logon trigger, for making sure nothing corrupts SafePeak cache data If you have an application with many parts, many servers many possible locations that can actually update the database, or the SQL Server is accessible to many DBAs or software engineers, each can access some database directly and do some changes without going thru SafePeak – this can create a potential corruption of the data stored in SafePeak cache. To make sure SafePeak cache is correct it needs to get all updates to arrive to SafePeak, and if a DBA will access the database directly and do some changes, for example, then SafePeak will simply not know about it and will not clean SafePeak cache. In the new version, SafePeak brought a new feature called “Logon Trigger” to solve the above challenge. By special click of a button SafePeak can deploy a special server logon trigger (with a CLR object) on your SQL Server that actually monitors all connections and informs SafePeak on any connection that is coming not from SafePeak. In SafePeak dashboard there is an interface that allows to control which logins can be ignored based on login names and IPs, while the rest will invoke cache cleanup of SafePeak and actually locks SafePeak cache until this connection will not be closed. Important to note, that this does not interrupt any logins, only informs SafePeak on such connection. On the Dashboard screen in SafePeak you will be able to see those connections and then decide what to do with them. Configuration of this feature in SafePeak dashboard can be done here: Settings -> SQL instances management -> click on instance -> Logon Trigger tab. Other features: 7. User management ability to grant permissions to someone without changing its configuration and only use SafePeak as performance analysis tool. 8. Better reports for analysis of performance using 15 minute resolution charts. 9. Caching of client cursors 10. Support for IPv6 Summary SafePeak is a great SQL Server performance acceleration solution for users who want immediate results for sites with performance, scalability and peak spikes challenges. Especially if your apps are packaged or 3rd party, since no code changes are done. SafePeak can significantly increase response times, by reducing network roundtrip to the database, decreasing CPU resource usage, eliminating I/O and storage access. SafePeak team provides a free fully functional trial www.safepeak.com/download and actually provides a one-on-one assistance during such trial. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology

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  • SQL SERVER – Solution to Puzzle – Simulate LEAD() and LAG() without Using SQL Server 2012 Analytic Function

    - by pinaldave
    Earlier I wrote a series on SQL Server Analytic Functions of SQL Server 2012. During the series to keep the learning maximum and having fun, we had few puzzles. One of the puzzle was simulating LEAD() and LAG() without using SQL Server 2012 Analytic Function. Please read the puzzle here first before reading the solution : Write T-SQL Self Join Without Using LEAD and LAG. When I was originally wrote the puzzle I had done small blunder and the question was a bit confusing which I corrected later on but wrote a follow up blog post on over here where I describe the give-away. Quick Recap: Generate following results without using SQL Server 2012 analytic functions. I had received so many valid answers. Some answers were similar to other and some were very innovative. Some answers were very adaptive and some did not work when I changed where condition. After selecting all the valid answer, I put them in table and ran RANDOM function on the same and selected winners. Here are the valid answers. No Joins and No Analytic Functions Excellent Solution by Geri Reshef – Winner of SQL Server Interview Questions and Answers (India | USA) WITH T1 AS (SELECT Row_Number() OVER(ORDER BY SalesOrderDetailID) N, s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663)) SELECT SalesOrderID,SalesOrderDetailID,OrderQty, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY N/2) END LeadVal, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY N/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) END LagVal FROM T1 ORDER BY SalesOrderID, SalesOrderDetailID, OrderQty; GO No Analytic Function and Early Bird Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription -- a query to emulate LEAD() and LAG() ;WITH s AS ( SELECT 1 AS ldOffset, -- equiv to 2nd param of LEAD 1 AS lgOffset, -- equiv to 2nd param of LAG NULL AS ldDefVal, -- equiv to 3rd param of LEAD NULL AS lgDefVal, -- equiv to 3rd param of LAG ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLd.SalesOrderDetailID, s.ldDefVal) AS LeadValue, ISNULL( sLg.SalesOrderDetailID, s.lgDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLd ON s.row = sLd.row - s.ldOffset LEFT OUTER JOIN s AS sLg ON s.row = sLg.row + s.lgOffset ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and Partition By Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription /* a query to emulate LEAD() and LAG() */ ;WITH s AS ( SELECT 1 AS LeadOffset, /* equiv to 2nd param of LEAD */ 1 AS LagOffset, /* equiv to 2nd param of LAG */ NULL AS LeadDefVal, /* equiv to 3rd param of LEAD */ NULL AS LagDefVal, /* equiv to 3rd param of LAG */ /* Try changing the values of the 4 integer values above to see their effect on the results */ /* The values given above of 0, 0, null and null behave the same as the default 2nd and 3rd parameters to LEAD() and LAG() */ ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLead.SalesOrderDetailID, s.LeadDefVal) AS LeadValue, ISNULL( sLag.SalesOrderDetailID, s.LagDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLead ON s.row = sLead.row - s.LeadOffset /* Try commenting out this next line when LeadOffset != 0 */ AND s.SalesOrderID = sLead.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LEAD() function */ LEFT OUTER JOIN s AS sLag ON s.row = sLag.row + s.LagOffset /* Try commenting out this next line when LagOffset != 0 */ AND s.SalesOrderID = sLag.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LAG() function */ ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and CTE Usage Excellent Solution by Pravin Patel - Winner of SQL Server Interview Questions and Answers (India | USA) --CTE based solution ; WITH cteMain AS ( SELECT SalesOrderID, SalesOrderDetailID, OrderQty, ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS sn FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, sLead.SalesOrderDetailID AS leadvalue, sLeg.SalesOrderDetailID AS leagvalue FROM cteMain AS m LEFT OUTER JOIN cteMain AS sLead ON sLead.sn = m.sn+1 LEFT OUTER JOIN cteMain AS sLeg ON sLeg.sn = m.sn-1 ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty No Analytic Function and Co-Related Subquery Usage Excellent Solution by Pravin Patel – Winner of SQL Server Interview Questions and Answers (India | USA) -- Co-Related subquery SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, ( SELECT MIN(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID >= m.SalesOrderID AND l.SalesOrderDetailID > m.SalesOrderDetailID ) AS lead, ( SELECT MAX(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID <= m.SalesOrderID AND l.SalesOrderDetailID < m.SalesOrderDetailID ) AS leag FROM Sales.SalesOrderDetail AS m WHERE m.SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty This was one of the most interesting Puzzle on this blog. Giveaway Winners will get following giveaways. Geri Reshef and Pravin Patel SQL Server Interview Questions and Answers (India | USA) DHall Pluralsight 30 days Subscription Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Function, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Is Tax Localization a good use for Workflow Foundation?

    - by JustinDoesWork
    Scenario: We have both Winforms and MVC code that is being used to work on a nation wide multi-user platform that does lots of logistics for lots of users. Tax rules change per state and even per city or county. These tax rules make a huge difference for our industry. The other issue is that rules can change based on legislation. The system will have to handle cases where before a date it works one way and then different after that date. This changeover will need to be entered into the system and tested before that date comes. Proposed Solution: Use Workflow Foundation to create a time based system where our users can change and add rules that change the way taxes are calculated. Question: I have not used Workflow Foundation and searching has returned books to look at but not a lot of examples of people using this technology successfully. Is my scenario a good use of Workflow Foundation?(I think so.) If you have any experience with Workflow Foundation, any tips on making this work well?

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  • SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

    - by Pinal Dave
    In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Koenig

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  • SQL SERVER – SSMS: Disk Usage Report

    - by Pinal Dave
    Let us start with humor!  I think we the series on various reports, we come to a logical point. We covered all the reports at server level. This means the reports we saw were targeted towards activities that are related to instance level operations. These are mostly like how a doctor diagnoses a patient. At this point I am reminded of a dialog which I read somewhere: Patient: Doc, It hurts when I touch my head. Doc: Ok, go on. What else have you experienced? Patient: It hurts even when I touch my eye, it hurts when I touch my arms, it even hurts when I touch my feet, etc. Doc: Hmmm … Patient: I feel it hurts when I touch anywhere in my body. Doc: Ahh … now I get it. You need a plaster to your finger John. Sometimes the server level gives an indicator to what is happening in the system, but we need to get to the root cause for a specific database. So, this is the first blog in series where we would start discussing about database level reports. To launch database level reports, expand selected server in Object Explorer, expand the Databases folder, and then right-click any database for which we want to look at reports. From the menu, select Reports, then Standard Reports, and then any of database level reports. In this blog, we would talk about four “disk” reports because they are similar: Disk Usage Disk Usage by Top Tables Disk Usage by Table Disk Usage by Partition Disk Usage This report shows multiple information about the database. Let us discuss them one by one.  We have divided the output into 5 different sections. Section 1 shows the high level summary of the database. It shows the space used by database files (mdf and ldf). Under the hood, the report uses, various DMVs and DBCC Commands, it is using sys.data_spaces and DBCC SHOWFILESTATS. Section 2 and 3 are pie charts. One for data file allocation and another for the transaction log file. Pie chart for “Data Files Space Usage (%)” shows space consumed data, indexes, allocated to the SQL Server database, and unallocated space which is allocated to the SQL Server database but not yet filled with anything. “Transaction Log Space Usage (%)” used DBCC SQLPERF (LOGSPACE) and shows how much empty space we have in the physical transaction log file. Section 4 shows the data from Default Trace and looks at Event IDs 92, 93, 94, 95 which are for “Data File Auto Grow”, “Log File Auto Grow”, “Data File Auto Shrink” and “Log File Auto Shrink” respectively. Here is an expanded view for that section. If default trace is not enabled, then this section would be replaced by the message “Trace Log is disabled” as highlighted below. Section 5 of the report uses DBCC SHOWFILESTATS to get information. Here is the enhanced version of that section. This shows the physical layout of the file. In case you have In-Memory Objects in the database (from SQL Server 2014), then report would show information about those as well. Here is the screenshot taken for a different database, which has In-Memory table. I have highlighted new things which are only shown for in-memory database. The new sections which are highlighted above are using sys.dm_db_xtp_checkpoint_files, sys.database_files and sys.data_spaces. The new type for in-memory OLTP is ‘FX’ in sys.data_space. The next set of reports is targeted to get information about a table and its storage. These reports can answer questions like: Which is the biggest table in the database? How many rows we have in table? Is there any table which has a lot of reserved space but its unused? Which partition of the table is having more data? Disk Usage by Top Tables This report provides detailed data on the utilization of disk space by top 1000 tables within the Database. The report does not provide data for memory optimized tables. Disk Usage by Table This report is same as earlier report with few difference. First Report shows only 1000 rows First Report does order by values in DMV sys.dm_db_partition_stats whereas second one does it based on name of the table. Both of the reports have interactive sort facility. We can click on any column header and change the sorting order of data. Disk Usage by Partition This report shows the distribution of the data in table based on partition in the table. This is so similar to previous output with the partition details now. Here is the query taken from profiler. SELECT row_number() OVER (ORDER BY a1.used_page_count DESC, a1.index_id) AS row_number ,      (dense_rank() OVER (ORDER BY a5.name, a2.name))%2 AS l1 ,      a1.OBJECT_ID ,      a5.name AS [schema] ,       a2.name ,       a1.index_id ,       a3.name AS index_name ,       a3.type_desc ,       a1.partition_number ,       a1.used_page_count * 8 AS total_used_pages ,       a1.reserved_page_count * 8 AS total_reserved_pages ,       a1.row_count FROM sys.dm_db_partition_stats a1 INNER JOIN sys.all_objects a2  ON ( a1.OBJECT_ID = a2.OBJECT_ID) AND a1.OBJECT_ID NOT IN (SELECT OBJECT_ID FROM sys.tables WHERE is_memory_optimized = 1) INNER JOIN sys.schemas a5 ON (a5.schema_id = a2.schema_id) LEFT OUTER JOIN  sys.indexes a3  ON ( (a1.OBJECT_ID = a3.OBJECT_ID) AND (a1.index_id = a3.index_id) ) WHERE (SELECT MAX(DISTINCT partition_number) FROM sys.dm_db_partition_stats a4 WHERE (a4.OBJECT_ID = a1.OBJECT_ID)) >= 1 AND a2.TYPE <> N'S' AND  a2.TYPE <> N'IT' ORDER BY a5.name ASC, a2.name ASC, a1.index_id, a1.used_page_count DESC, a1.partition_number Using all of the above reports, you should be able to get the usage of database files and also space used by tables. I think this is too much disk information for a single blog and I hope you have used them in the past to get data. Do let me know if you found anything interesting using these reports in your environments. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

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  • Android: OutOfMemoryError while uploading video...

    - by AP257
    Hi all, I have the same problem as described here, but I will supply a few more details. While trying to upload a video in Android, I'm reading it into memory, and if the video is large I get an OutOfMemoryError. Here's my code: // get bytestream to upload videoByteArray = getBytesFromFile(cR, fileUriString); public static byte[] getBytesFromFile(ContentResolver cR, String fileUriString) throws IOException { Uri tempuri = Uri.parse(fileUriString); InputStream is = cR.openInputStream(tempuri); byte[] b3 = readBytes(is); is.close(); return b3; } public static byte[] readBytes(InputStream inputStream) throws IOException { ByteArrayOutputStream byteBuffer = new ByteArrayOutputStream(); // this is storage overwritten on each iteration with bytes int bufferSize = 1024; byte[] buffer = new byte[bufferSize]; int len = 0; while ((len = inputStream.read(buffer)) != -1) { byteBuffer.write(buffer, 0, len); } return byteBuffer.toByteArray(); } And here's the traceback (the error is thrown on the byteBuffer.write(buffer, 0, len) line): 04-08 11:56:20.456: ERROR/dalvikvm-heap(6088): Out of memory on a 16775184-byte allocation. 04-08 11:56:20.456: INFO/dalvikvm(6088): "IntentService[UploadService]" prio=5 tid=17 RUNNABLE 04-08 11:56:20.456: INFO/dalvikvm(6088): | group="main" sCount=0 dsCount=0 s=N obj=0x449a3cf0 self=0x38d410 04-08 11:56:20.456: INFO/dalvikvm(6088): | sysTid=6119 nice=0 sched=0/0 cgrp=default handle=4010416 04-08 11:56:20.456: INFO/dalvikvm(6088): at java.io.ByteArrayOutputStream.expand(ByteArrayOutputStream.java:~93) 04-08 11:56:20.456: INFO/dalvikvm(6088): at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:218) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.readBytes(UploadService.java:199) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.getBytesFromFile(UploadService.java:182) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.doUploadinBackground(UploadService.java:118) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.onHandleIntent(UploadService.java:85) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.app.IntentService$ServiceHandler.handleMessage(IntentService.java:30) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.Handler.dispatchMessage(Handler.java:99) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.Looper.loop(Looper.java:123) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.HandlerThread.run(HandlerThread.java:60) 04-08 11:56:20.467: WARN/dalvikvm(6088): threadid=17: thread exiting with uncaught exception (group=0x4001b180) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): Uncaught handler: thread IntentService[UploadService] exiting due to uncaught exception 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): java.lang.OutOfMemoryError 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at java.io.ByteArrayOutputStream.expand(ByteArrayOutputStream.java:93) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:218) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.readBytes(UploadService.java:199) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.getBytesFromFile(UploadService.java:182) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.doUploadinBackground(UploadService.java:118) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.onHandleIntent(UploadService.java:85) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.app.IntentService$ServiceHandler.handleMessage(IntentService.java:30) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.Handler.dispatchMessage(Handler.java:99) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.Looper.loop(Looper.java:123) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.HandlerThread.run(HandlerThread.java:60) 04-08 11:56:20.496: INFO/Process(4657): Sending signal. PID: 6088 SIG: 3 I guess that as @DroidIn suggests, I need to upload it in chunks. But (newbie question alert) does that mean that I should make multiple PostMethod requests, and glue the file together at the server end? Or can I load the bytestream into memory in chunks, and glue it together in the Android code? If anyone could give me a clue as to the best approach, I would be very grateful.

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  • Android: OutOfMemoryError while uploading video - how best to chunk?

    - by AP257
    Hi all, I have the same problem as described here, but I will supply a few more details. While trying to upload a video in Android, I'm reading it into memory, and if the video is large I get an OutOfMemoryError. Here's my code: // get bytestream to upload videoByteArray = getBytesFromFile(cR, fileUriString); public static byte[] getBytesFromFile(ContentResolver cR, String fileUriString) throws IOException { Uri tempuri = Uri.parse(fileUriString); InputStream is = cR.openInputStream(tempuri); byte[] b3 = readBytes(is); is.close(); return b3; } public static byte[] readBytes(InputStream inputStream) throws IOException { ByteArrayOutputStream byteBuffer = new ByteArrayOutputStream(); // this is storage overwritten on each iteration with bytes int bufferSize = 1024; byte[] buffer = new byte[bufferSize]; int len = 0; while ((len = inputStream.read(buffer)) != -1) { byteBuffer.write(buffer, 0, len); } return byteBuffer.toByteArray(); } And here's the traceback (the error is thrown on the byteBuffer.write(buffer, 0, len) line): 04-08 11:56:20.456: ERROR/dalvikvm-heap(6088): Out of memory on a 16775184-byte allocation. 04-08 11:56:20.456: INFO/dalvikvm(6088): "IntentService[UploadService]" prio=5 tid=17 RUNNABLE 04-08 11:56:20.456: INFO/dalvikvm(6088): | group="main" sCount=0 dsCount=0 s=N obj=0x449a3cf0 self=0x38d410 04-08 11:56:20.456: INFO/dalvikvm(6088): | sysTid=6119 nice=0 sched=0/0 cgrp=default handle=4010416 04-08 11:56:20.456: INFO/dalvikvm(6088): at java.io.ByteArrayOutputStream.expand(ByteArrayOutputStream.java:~93) 04-08 11:56:20.456: INFO/dalvikvm(6088): at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:218) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.readBytes(UploadService.java:199) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.getBytesFromFile(UploadService.java:182) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.doUploadinBackground(UploadService.java:118) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.onHandleIntent(UploadService.java:85) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.app.IntentService$ServiceHandler.handleMessage(IntentService.java:30) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.Handler.dispatchMessage(Handler.java:99) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.Looper.loop(Looper.java:123) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.HandlerThread.run(HandlerThread.java:60) 04-08 11:56:20.467: WARN/dalvikvm(6088): threadid=17: thread exiting with uncaught exception (group=0x4001b180) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): Uncaught handler: thread IntentService[UploadService] exiting due to uncaught exception 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): java.lang.OutOfMemoryError 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at java.io.ByteArrayOutputStream.expand(ByteArrayOutputStream.java:93) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:218) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.readBytes(UploadService.java:199) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.getBytesFromFile(UploadService.java:182) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.doUploadinBackground(UploadService.java:118) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.onHandleIntent(UploadService.java:85) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.app.IntentService$ServiceHandler.handleMessage(IntentService.java:30) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.Handler.dispatchMessage(Handler.java:99) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.Looper.loop(Looper.java:123) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.HandlerThread.run(HandlerThread.java:60) 04-08 11:56:20.496: INFO/Process(4657): Sending signal. PID: 6088 SIG: 3 I guess that as @DroidIn suggests, I need to upload it in chunks. But (newbie question alert) does that mean that I should make multiple PostMethod requests, and glue the file together at the server end? Or can I load the bytestream into memory in chunks, and glue it together in the Android code? If anyone could give me a clue as to the best approach, I would be very grateful.

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  • How to configure SSL on an instance of SQL Server to allow dedicated users to remotely access it?

    - by The Good Boy
    I have configured the instance of SQL Server to allow dedicated users to access it remotely. Connection string Data Source = 192.168.1.2,1433\sqlexpress;etc... has been tested and works. However, I have not configured the SSL to secure the communication. How to configure SSL on an instance of SQL Server to allow dedicated users to remotely access it? edit 1 The dedicated user will administer its database using Sql Server Management Studio. What I want to do is to secure the communication when he/she administers the database using Sql Server Management Studio.

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  • "Hello World" in less than 20 bytes

    - by xelurg
    We have had an interesting competition once, where everyone would write their implementation of hello world program. One requirement was that is should be less than 20 bytes in compiled form. the winner would be the one whose version is the smallest... What would be your solution? :) Platform: 32bit, x86 OS: DOS, Win, GNU/Linux, *BSD Language: Asm, C, or anything else that compiles into binary executable (i.e. no bash scripts and stuff ;)

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  • flash game lags every 20 seg

    - by ZoserLock
    My game has delta time for frame independent movement, at 250 fps run perfectly smooth, but if i limit the fps to 60, the game slow down for a 2-4 seg every 20 seg aprox, even in small programs i have this same problem. no memory is created or released i comment everything i can and the problem persist thanks and sorry for my english

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  • Why limit WCF ServiceContracts to 10-20 OperationContracts?

    - by Gary B
    I've seen recommendations (Juval Lowy, et al) that a service contract should have "no more than 20 members...twelve is probably the practical limit". Why? It seems that if you wish to provide a service as the interface to a relatively large db (50-100 tables) you're going to go way past that in just CRUD alone. I've worked with plenty of other services that provided hundreds of 'OperationContracts'...is there something peculiar about WCF? Is there something I'm missing here?

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  • MySQL Optimization 20 gig table

    - by user169743
    I have a 20 gig table that has a large amount of inserts and updates daily. This table is also frequently searched. I'd like to know if the MySQL indices can become fragmented and perhaps need to be rebuilt or something similar. I'm finding it difficult to figure out which of the CHECK TABLE, REPAIR TABLE or something similar? Any guidance appreciated, I'm a db newb.

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  • c source code to remove subset transactions form text file

    - by user324887
    I have a file containing data as follows 10 20 30 40 70 20 30 70 30 40 10 20 29 70 80 90 20 30 40 40 45 65 10 20 80 45 65 20 I want to remove all subset transaction from this file. output file should be like follows 10 20 30 40 70 29 70 80 90 20 30 40 40 45 65 10 20 80 Where records like 20 30 70 30 40 10 20 45 65 20 are removed because of they are subset of other records.

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  • c source code to remove subset transactions from text file

    - by user324887
    I have a file containing data as follows 10 20 30 40 70 20 30 70 30 40 10 20 29 70 80 90 20 30 40 40 45 65 10 20 80 45 65 20 I want to remove all subset transaction from this file. output file should be like follows 10 20 30 40 70 29 70 80 90 20 30 40 40 45 65 10 20 80 Where records like 20 30 70 30 40 10 20 45 65 20 are removed because of they are subset of other records.

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  • removing subset transactions form file

    - by user324887
    I have a file containing data as follows 10 20 30 40 70 20 30 70 30 40 10 20 29 70 80 90 20 30 40 40 45 65 10 20 80 45 65 20 I want to remove all subset transaction from this file. output file should be like follows 10 20 30 40 70 29 70 80 90 20 30 40 40 45 65 10 20 80 Where records like 20 30 70 30 40 10 20 45 65 20 are removed because of they are subset of other records.

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  • toURI method of File transform space character into %20

    - by piero
    toURI method of File transform space character into %20 On windows XP with Java 6 public static void main(String[] args) { File f = new File("C:\\My dir\\test.txt"); URI uri = f.toURI(); System.out.println(f.getAbsolutePath()); System.out.println(uri); } C:\My dir\test.txt file:/C:/My%20dir/test.txt

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