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  • SQL server recursive query error.The maximum recursion 100 has been exhausted before statement completion

    - by ienax_ridens
    I have a recursive query that returns an error when I run it; in other databases (with more data) I have not the problem. In my case this query returns 2 colums (ID_PARENT and ID_CHILD) doing a recursion because my tree can have more than one level, bit I wanna have only "direct" parent. NOTE: I tried to put OPTION (MAXRECURSION 0) at the end of the query, but with no luck. The following query is only a part of the entire query, I tried to put OPTION only at the end of the "big query" having a continous running query, but no errors displayed. Error have in SQL Server: "The statement terminated.The maximum recursion 100 has been exhausted before statement completion" The query is the following: WITH q AS (SELECT ID_ITEM, ID_ITEM AS ID_ITEM_ANCESTOR FROM ITEMS_TABLE i JOIN ITEMS_TYPES_TABLE itt ON itt.ID_ITEM_TYPE = i.ID_ITEM_TYPE UNION ALL SELECT i.ID_ITEM, q.ID_ITEM_ANCESTOR FROM q JOIN ITEMS_TABLE i ON i.ID_ITEM_PADRE = q.ID_ITEM JOIN ITEMS_TYPES_TABLE itt ON itt.ID_ITEM_TYPE = i.ID_ITEM_TYPE) SELECT ID_ITEM AS ID_CHILD, ID_ITEM_ANCESTOR AS ID_PARENT FROM q I need a suggestion to re-write this query to avoid the error of recursion and see the data, that are few.

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  • Search Engine Optimization (SEO) Tips - Code Optimization

    Code optimization is a very important in making your website Search Engine Friendly. A webpage is called Search Engine Friendly when it is coded in such a way that search engines can read and understand it to the maximum. For making your Webpage Search Engine Friendly you have to keep the following factors in mind and code accordingly.

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  • SQL SERVER – Fundamentals of Columnstore Index

    - by pinaldave
    There are two kind of storage in database. Row Store and Column Store. Row store does exactly as the name suggests – stores rows of data on a page – and column store stores all the data in a column on the same page. These columns are much easier to search – instead of a query searching all the data in an entire row whether the data is relevant or not, column store queries need only to search much lesser number of the columns. This means major increases in search speed and hard drive use. Additionally, the column store indexes are heavily compressed, which translates to even greater memory and faster searches. I am sure this looks very exciting and it does not mean that you convert every single index from row store to column store index. One has to understand the proper places where to use row store or column store indexes. Let us understand in this article what is the difference in Columnstore type of index. Column store indexes are run by Microsoft’s VertiPaq technology. However, all you really need to know is that this method of storing data is columns on a single page is much faster and more efficient. Creating a column store index is very easy, and you don’t have to learn new syntax to create them. You just need to specify the keyword “COLUMNSTORE” and enter the data as you normally would. Keep in mind that once you add a column store to a table, though, you cannot delete, insert or update the data – it is READ ONLY. However, since column store will be mainly used for data warehousing, this should not be a big problem. You can always use partitioning to avoid rebuilding the index. A columnstore index stores each column in a separate set of disk pages, rather than storing multiple rows per page as data traditionally has been stored. The difference between column store and row store approaches is illustrated below: In case of the row store indexes multiple pages will contain multiple rows of the columns spanning across multiple pages. In case of column store indexes multiple pages will contain multiple single columns. This will lead only the columns needed to solve a query will be fetched from disk. Additionally there is good chance that there will be redundant data in a single column which will further help to compress the data, this will have positive effect on buffer hit rate as most of the data will be in memory and due to same it will not need to be retrieved. Let us see small example of how columnstore index improves the performance of the query on a large table. As a first step let us create databaseset which is large enough to show performance impact of columnstore index. The time taken to create sample database may vary on different computer based on the resources. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 Now let us do quick performance test. I have kept STATISTICS IO ON for measuring how much IO following queries take. In my test first I will run query which will use regular index. We will note the IO usage of the query. After that we will create columnstore index and will measure the IO of the same. -- Performance Test -- Comparing Regular Index with ColumnStore Index USE AdventureWorks GO SET STATISTICS IO ON GO -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO -- Table 'MySalesOrderDetail'. Scan count 1, logical reads 342261, physical reads 0, read-ahead reads 0. -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO -- Select Table with Columnstore Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO It is very clear from the results that query is performance extremely fast after creating ColumnStore Index. The amount of the pages it has to read to run query is drastically reduced as the column which are needed in the query are stored in the same page and query does not have to go through every single page to read those columns. If we enable execution plan and compare we can see that column store index performance way better than regular index in this case. Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO In future posts we will see cases where Columnstore index is not appropriate solution as well few other tricks and tips of the columnstore index. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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

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

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  • The Expert Secret to Search Engine Optimization - Effective Website Optimization

    Throwing keywords into a program that shows you how popular they are and then using those keywords without doing a little bit of preliminary research and answering some very important questions can just spell disaster. There are three questions that are extremely important to ask yourself before just doing random search engine optimization. And believe it or not those three questions are not, "What are the most popular keywords for my particular website?" Those questions are much more fundamental and strategic and they can be much more important to your overall efforts in getting your site ranked on the search engines.

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  • Search Engine Optimization - How Search Engine Optimization Works

    There are many ways to direct extra traffic to your web site, but search engine optimization may be the best. Unlike many traditional types of advertising, such as banner ads, this technique does not cast a wide net and hope for the best. Instead, it takes the opposite approach, simply making your site easier to find for people who are looking for a site that is similar to it. This is a much more reliable method of gaining additional viewers for your site, especially because it only targets people who are interested in you in the first place.

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  • SQL SERVER – Four Posts on Removing the Bookmark Lookup – Key Lookup

    - by pinaldave
    In recent times I have observed that not many people have proper understanding of what is bookmark lookup or key lookup. Increasing numbers of the questions tells me that this is something developers are encountering every single day but have no idea how to deal with it. I have previously written three articles on this subject. I want to point all of you looking for further information on the same post. SQL SERVER – Query Optimization – Remove Bookmark Lookup – Remove RID Lookup – Remove Key Lookup SQL SERVER – Query Optimization – Remove Bookmark Lookup – Remove RID Lookup – Remove Key Lookup – Part 2 SQL SERVER – Query Optimization – Remove Bookmark Lookup – Remove RID Lookup – Remove Key Lookup – Part 3 SQL SERVER – Interesting Observation – Execution Plan and Results of Aggregate Concatenation Queries In one of my recent class we had in depth conversation about what are the alternative of creating covering indexes to remove the bookmark lookup. I really want to this question open to all of you and see what community thinks about the same. Is there any other way then creating covering index or included index to remove his expensive keylookup? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Backup and Restore, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLAuthority News, SQLServer, T SQL, Technology

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  • SQL SERVER – Video – Beginning Performance Tuning with SQL Server Execution Plan

    - by pinaldave
    Traveling can be most interesting or most exhausting experience. However, traveling is always the most enlightening experience one can have. While going to long journey one has to prepare a lot of things. Pack necessary travel gears, clothes and medicines. However, the most essential part of travel is the journey to the destination. There are many variations one prefer but the ultimate goal is to have a delightful experience during the journey. Here is the video available which explains how to begin with SQL Server Execution plans. Performance Tuning is a Journey Performance tuning is just like a long journey. The goal of performance tuning is efficient and least resources consuming query execution with accurate results. Just as maps are the most essential aspect of performance tuning the same way, execution plans are essentially maps for SQL Server to reach to the resultset. The goal of the execution plan is to find the most efficient path which translates the least usage of the resources (CPU, memory, IO etc). Execution Plans are like Maps When online maps were invented (e.g. Bing, Google, Mapquests etc) initially it was not possible to customize them. They were given a single route to reach to the destination. As time evolved now it is possible to give various hints to the maps, for example ‘via public transport’, ‘walking’, ‘fastest route’, ‘shortest route’, ‘avoid highway’. There are places where we manually drag the route and make it appropriate to our needs. The same situation is with SQL Server Execution Plans, if we want to tune the queries, we need to understand the execution plans and execution plans internals. We need to understand the smallest details which relate to execution plan when we our destination is optimal queries. Understanding Execution Plans The biggest challenge with maps are figuring out the optimal path. The same way the  most common challenge with execution plans is where to start from and which precise route to take. Here is a quick list of the frequently asked questions related to execution plans: Should I read the execution plans from bottoms up or top down? Is execution plans are left to right or right to left? What is the relational between actual execution plan and estimated execution plan? When I mouse over operator I see CPU and IO but not memory, why? Sometime I ran the query multiple times and I get different execution plan, why? How to cache the query execution plan and data? I created an optimal index but the query is not using it. What should I change – query, index or provide hints? What are the tools available which helps quickly to debug performance problems? Etc… Honestly the list is quite a big and humanly impossible to write everything in the words. SQL Server Performance:  Introduction to Query Tuning My friend Vinod Kumar and I have created for the same a video learning course for beginning performance tuning. We have covered plethora of the subject in the course. Here is the quick list of the same: Execution Plan Basics Essential Indexing Techniques Query Design for Performance Performance Tuning Tools Tips and Tricks Checklist: Performance Tuning We believe we have covered a lot in this four hour course and we encourage you to go over the video course if you are interested in Beginning SQL Server Performance Tuning and Query Tuning. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Execution Plan

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  • SQL SERVER – Subquery or Join – Various Options – SQL Server Engine knows the Best

    - by pinaldave
    This is followup post of my earlier article SQL SERVER – Convert IN to EXISTS – Performance Talk, after reading all the comments I have received I felt that I could write more on the same subject to clear few things out. First let us run following four queries, all of them are giving exactly same resultset. USE AdventureWorks GO -- use of = SELECT * FROM HumanResources.Employee E WHERE E.EmployeeID = ( SELECT EA.EmployeeID FROM HumanResources.EmployeeAddress EA WHERE EA.EmployeeID = E.EmployeeID) GO -- use of in SELECT * FROM HumanResources.Employee E WHERE E.EmployeeID IN ( SELECT EA.EmployeeID FROM HumanResources.EmployeeAddress EA WHERE EA.EmployeeID = E.EmployeeID) GO -- use of exists SELECT * FROM HumanResources.Employee E WHERE EXISTS ( SELECT EA.EmployeeID FROM HumanResources.EmployeeAddress EA WHERE EA.EmployeeID = E.EmployeeID) GO -- Use of Join SELECT * FROM HumanResources.Employee E INNER JOIN HumanResources.EmployeeAddress EA ON E.EmployeeID = EA.EmployeeID GO Let us compare the execution plan of the queries listed above. Click on image to see larger image. It is quite clear from the execution plan that in case of IN, EXISTS and JOIN SQL Server Engines is smart enough to figure out what is the best optimal plan of Merge Join for the same query and execute the same. However, in the case of use of Equal (=) Operator, SQL Server is forced to use Nested Loop and test each result of the inner query and compare to outer query, leading to cut the performance. Please note that here I no mean suggesting that Nested Loop is bad or Merge Join is better. This can very well vary on your machine and amount of resources available on your computer. When I see Equal (=) operator used in query like above, I usually recommend to see if user can use IN or EXISTS or JOIN. As I said, this can very much vary on different system. What is your take in above query? I believe SQL Server Engines is usually pretty smart to figure out what is ideal execution plan and use it. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Joins, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – Subquery or Join – Various Options – SQL Server Engine Knows the Best – Part 2

    - by pinaldave
    This blog post is part 2 of the earlier written article SQL SERVER – Subquery or Join – Various Options – SQL Server Engine knows the Best by Paulo R. Pereira. Paulo has left excellent comment to earlier article once again proving the point that SQL Server Engine is smart enough to figure out the best plan itself and uses the same for the query. Let us go over his comment as he has posted. “I think IN or EXISTS is the best choice, because there is a little difference between ‘Merge Join’ of query with JOIN (Inner Join) and the others options (Left Semi Join), and JOIN can give more results than IN or EXISTS if the relationship is 1:0..N and not 1:0..1. And if I try use NOT IN and NOT EXISTS the query plan is different from LEFT JOIN too (Left Anti Semi Join vs. Left Outer Join + Filter). So, I found a case where EXISTS has a different query plan than IN or ANY/SOME:” USE AdventureWorks GO -- use of SOME SELECT * FROM HumanResources.Employee E WHERE E.EmployeeID = SOME ( SELECT EA.EmployeeID FROM HumanResources.EmployeeAddress EA UNION ALL SELECT EA.EmployeeID FROM HumanResources.EmployeeDepartmentHistory EA ) -- use of IN SELECT * FROM HumanResources.Employee E WHERE E.EmployeeID IN ( SELECT EA.EmployeeID FROM HumanResources.EmployeeAddress EA UNION ALL SELECT EA.EmployeeID FROM HumanResources.EmployeeDepartmentHistory EA ) -- use of EXISTS SELECT * FROM HumanResources.Employee E WHERE EXISTS ( SELECT EA.EmployeeID FROM HumanResources.EmployeeAddress EA UNION ALL SELECT EA.EmployeeID FROM HumanResources.EmployeeDepartmentHistory EA ) When looked into execution plan of the queries listed above indeed we do get different plans for queries and SQL Server Engines creates the best (least cost) plan for each query. Click on image to see larger images. Thanks Paulo for your wonderful contribution. Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, Readers Contribution, SQL, SQL Authority, SQL Joins, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • query optimization

    - by Gaurav
    I have a query of the form SELECT uid1,uid2 FROM friend WHERE uid1 IN (SELECT uid2 FROM friend WHERE uid1='.$user_id.') and uid2 IN (SELECT uid2 FROM friend WHERE uid1='.$user_id.') The problem now is that the nested query SELECT uid2 FROM friend WHERE uid1='.$user_id.' returns a very large number of ids(approx. 5000). The table structure of the friend table is uid1(int), uid2(int). This table is used to determine whether two users are linked together as friends. Any workaround? Can I write the query in a different way? Or is there some other way to solve this issue. I'm sure I am not the first person to face such a problem. Any help would be greatly appreciated.

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  • MySQL query optimization.

    - by PiKey
    I'm so bad in making good MySQL queries. I've created this one: http://pastebin.com/GtDfgky8 products Table have about 17k rows, allegro Table have about 3k of rows. The query Idea is select all products, where stock_quanity 3, where is photo, and where is no product id in allegro table. Now query takes about 10 seconds... I have no idea how I can optimize this query. Please help my, I'll be thankfully! :) & Sorry for my bad English also

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  • What is the best retort to "premature optimization is the root of all evil"

    - by waffles
    Often I hear the sentiment ... "Why worry about performance, write slow code, get your product to market ... don't worry about performance. You can sort that out later" The culmination of this sentiment is: "... premature optimization is the root of all evil ... #winning" I was wondering, does anybody have a good retort to this one liner. Ideally an equally strong one liner that encompasses the reverse of this sentiment?

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  • mysql query optimization

    - by vamsivanka
    I would need some help on how to optimize the query. select * from transaction where id < 7500001 order by id desc limit 16 when i do an explain plan on this - the type is "range" and rows is "7500000" According to the some online reference's this is explained as, it took the query 7,500,000 rows to scan and get the data. Is there any way i can optimize so it uses less rows to scan and get the data. Also, id is the primary key column.

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  • Web page database query optimization

    - by morpheous
    I am putting together a web page which is quite 'expensive' in terms of database hits. I don't want to start optimizing at this stage - though with me trying to hit a deadline, I may end up not optimizing at all. Currently the page requires 18 (that's right eighteen) hits to the db. I am already using joins, and some of the queries are UNIONed to minimize the trips to the db. My local dev machine can handle this (page is not slow) however, I feel if I release this into the wild, the number of queries will quickly overwhelm my database (MySQL). I could always use memcache or something similar, but I would much rather continue with my other dev work that needs to be completed before the deadline - at least retrieving the page works - its simply a matter of optimization now (if required). My question therefore is - is 18 db queries for a single page retrieval completely outrageous - (i.e. I should put everything on hold and optimize the hell of the retrieval logic), or shall I continue as normal, meet the deadline and release on schedule and see what happens? [Edit] Just to clarify, I have already done the 'obvious' things like using (single and composite) indexes for fields used in the queries. What I haven't yet done is to run a query analyzer to see if my indexes etc are optimal.

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  • Why would this Lua optimization hack help?

    - by Ian Boyd
    i'm looking over a document that describes various techniques to improve performance of Lua script code, and i'm shocked that such tricks would be required. (Although i'm quoting Lua, i've seen similar hacks in Javascript). Why would this optimization be required: For instance, the code for i = 1, 1000000 do local x = math.sin(i) end runs 30% slower than this one: local sin = math.sin for i = 1, 1000000 do local x = sin(i) end They're re-declaring sin function locally. Why would this be helpful? It's the job of the compiler to do that anyway. Why is the programmer having to do the compiler's job? i've seen similar things in Javascript; and so obviously there must be a very good reason why the interpreting compiler isn't doing its job. What is it? i see it repeatedly in the Lua environment i'm fiddling in; people redeclaring variables as local: local strfind = strfind local strlen = strlen local gsub = gsub local pairs = pairs local ipairs = ipairs local type = type local tinsert = tinsert local tremove = tremove local unpack = unpack local max = max local min = min local floor = floor local ceil = ceil local loadstring = loadstring local tostring = tostring local setmetatable = setmetatable local getmetatable = getmetatable local format = format local sin = math.sin What is going on here that people have to do the work of the compiler? Is the compiler confused by how to find format? Why is this an issue that a programmer has to deal with? Why would this not have been taken care of in 1993? i also seem to have hit a logical paradox: Optimizatin should not be done without profiling Lua has no ability to be profiled Lua should not be optimized

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  • Optimizing MySQL update query

    - by Jernej Jerin
    This is currently my MySQL UPDATE query, which is called from program written in Java: String query = "UPDATE maxday SET DatePressureREL = (SELECT Date FROM ws3600 WHERE PressureREL = (SELECT MAX" + "(PressureREL) FROM ws3600 WHERE Date >= '" + Date + "') AND Date >= '" + Date + "' ORDER BY Date DESC LIMIT 1), " + "PressureREL = (SELECT PressureREL FROM ws3600 WHERE PressureREL = (SELECT MAX(PressureREL) FROM ws3600 " + "WHERE Date >= '" + Date + "') AND Date >= '" + Date + "' ORDER BY Date DESC LIMIT 1), ..."; try { s.execute(query); } catch (SQLException e) { System.out.println("SQL error"); } catch(Exception e) { e.printStackTrace(); } Let me explain first, what does it do. I have two tables, first is ws3600, which holds columns (Date, PressureREL, TemperatureOUT, Dewpoint, ...). Then I have second table, called maxday, which holds columns like DatePressureREL, PressureREL, DateTemperatureOUT, TemperatureOUT,... Now as you can see from an example, I update each column, the question is, is there a faster way? I am asking this, because I am calling MAX twice, first to find the Date for that value and secondly to find the actual value. Now I know that I could write like that: SELECT Date, PressureREL FROM ws3600 WHERE PressureREL = (SELECT MAX(PressureREL) FROM ws3600 WHERE Date >= '" + Date + "') AND Date >= '" + Date + "' ORDER BY Date DESC LIMIT 1 That way I get the Date of the max and the max value at the same time and then update with those values the data in maxday table. But the problem of this solution is, that I have to execute many queries, which as I understand takes alot more time compared to executing one long mysql query because of overhead in sending each query to the server. If there is no better way, which solution beetwen this two should I choose. The first, which only takes one query but is very unoptimized or the second which is beter in terms of optimization, but needs alot more queries which probably means that the preformance gain is lost because of overhead in sending each query to the server?

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  • SQL SERVER – Introduction to Extended Events – Finding Long Running Queries

    - by pinaldave
    The job of an SQL Consultant is very interesting as always. The month before, I was busy doing query optimization and performance tuning projects for our clients, and this month, I am busy delivering my performance in Microsoft SQL Server 2005/2008 Query Optimization and & Performance Tuning Course. I recently read white paper about Extended Event by SQL Server MVP Jonathan Kehayias. You can read the white paper here: Using SQL Server 2008 Extended Events. I also read another appealing chapter by Jonathan in the book, SQLAuthority Book Review – Professional SQL Server 2008 Internals and Troubleshooting. After reading these excellent notes by Jonathan, I decided to upgrade my course and include Extended Event as one of the modules. This week, I have delivered Extended Events session two times and attendees really liked the said course. They really think Extended Events is one of the most powerful tools available. Extended Events can do many things. I suggest that you read the white paper I mentioned to learn more about this tool. Instead of writing a long theory, I am going to write a very quick script for Extended Events. This event session captures all the longest running queries ever since the event session was started. One of the many advantages of the Extended Events is that it can be configured very easily and it is a robust method to collect necessary information in terms of troubleshooting. There are many targets where you can store the information, which include XML file target, which I really like. In the following Events, we are writing the details of the event at two locations: 1) Ringer Buffer; and 2) XML file. It is not necessary to write at both places, either of the two will do. -- Extended Event for finding *long running query* IF EXISTS(SELECT * FROM sys.server_event_sessions WHERE name='LongRunningQuery') DROP EVENT SESSION LongRunningQuery ON SERVER GO -- Create Event CREATE EVENT SESSION LongRunningQuery ON SERVER -- Add event to capture event ADD EVENT sqlserver.sql_statement_completed ( -- Add action - event property ACTION (sqlserver.sql_text, sqlserver.tsql_stack) -- Predicate - time 1000 milisecond WHERE sqlserver.sql_statement_completed.duration > 1000 ) -- Add target for capturing the data - XML File ADD TARGET package0.asynchronous_file_target( SET filename='c:\LongRunningQuery.xet', metadatafile='c:\LongRunningQuery.xem'), -- Add target for capturing the data - Ring Bugger ADD TARGET package0.ring_buffer (SET max_memory = 4096) WITH (max_dispatch_latency = 1 seconds) GO -- Enable Event ALTER EVENT SESSION LongRunningQuery ON SERVER STATE=START GO -- Run long query (longer than 1000 ms) SELECT * FROM AdventureWorks.Sales.SalesOrderDetail ORDER BY UnitPriceDiscount DESC GO -- Stop the event ALTER EVENT SESSION LongRunningQuery ON SERVER STATE=STOP GO -- Read the data from Ring Buffer SELECT CAST(dt.target_data AS XML) AS xmlLockData FROM sys.dm_xe_session_targets dt JOIN sys.dm_xe_sessions ds ON ds.Address = dt.event_session_address JOIN sys.server_event_sessions ss ON ds.Name = ss.Name WHERE dt.target_name = 'ring_buffer' AND ds.Name = 'LongRunningQuery' GO -- Read the data from XML File SELECT event_data_XML.value('(event/data[1])[1]','VARCHAR(100)') AS Database_ID, event_data_XML.value('(event/data[2])[1]','INT') AS OBJECT_ID, event_data_XML.value('(event/data[3])[1]','INT') AS object_type, event_data_XML.value('(event/data[4])[1]','INT') AS cpu, event_data_XML.value('(event/data[5])[1]','INT') AS duration, event_data_XML.value('(event/data[6])[1]','INT') AS reads, event_data_XML.value('(event/data[7])[1]','INT') AS writes, event_data_XML.value('(event/action[1])[1]','VARCHAR(512)') AS sql_text, event_data_XML.value('(event/action[2])[1]','VARCHAR(512)') AS tsql_stack, CAST(event_data_XML.value('(event/action[2])[1]','VARCHAR(512)') AS XML).value('(frame/@handle)[1]','VARCHAR(50)') AS handle FROM ( SELECT CAST(event_data AS XML) event_data_XML, * FROM sys.fn_xe_file_target_read_file ('c:\LongRunningQuery*.xet', 'c:\LongRunningQuery*.xem', NULL, NULL)) T GO -- Clean up. Drop the event DROP EVENT SESSION LongRunningQuery ON SERVER GO Just run the above query, afterwards you will find following result set. This result set contains the query that was running over 1000 ms. In our example, I used the XML file, and it does not reset when SQL services or computers restarts (if you are using DMV, it will reset when SQL services restarts). This event session can be very helpful for troubleshooting. Let me know if you want me to write more about Extended Events. I am totally fascinated with this feature, so I’m planning to acquire more knowledge about it so I can determine its other usages. Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Training, SQLServer, T SQL, Technology Tagged: SQL Extended Events

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  • SQL – Quick Start with Explorer Sections of NuoDB – Query NuoDB Database

    - by Pinal Dave
    This is the third post in the series of the blog posts I am writing about NuoDB. NuoDB is very innovative and easy-to-use product. I can clearly see how one can scale-out NuoDB with so much ease and confidence. In my very first blog post we discussed how we can install NuoDB (link), and in my second post I discussed how we can manage the NuoDB database transaction engines and storage managers with a few clicks (link). Note: You can Download NuoDB from here. In this post, we will learn how we can use the Explorer feature of NuoDB to do various SQL operations. NuoDB has a browser-based Explorer, which is very powerful and has many of the features any IDE would normally have. Let us see how it works in the following step-by-step tutorial. Let us go to the NuoDBNuoDB Console by typing the following URL in your browser: http://localhost:8080/ It will bring you to the QuickStart screen. Make sure that you have created the sample database. If you have not created sample database, click on Create Database and create it successfully. Now go to the NuoDB Explorer by clicking on the main tab, and it will ask you for your domain username and password. Enter the username as a domain and password as a bird. Alternatively you can also enter username as a quickstart and password as a quickstart. Once you enter the password you will be able to see the databases. In our example we have installed the Sample Database hence you will see the Test database in our Database Hierarchy screen. When you click on database it will ask for the database login. Note that Database Login is different from Domain login and you will have to enter your database login over here. In our case the database username is dba and password is goalie. Once you enter a valid username and password it will display your database. Further expand your database and you will notice various objects in your database. Once you explore various objects, select any database and click on Open. When you click on execute, it will display the SQL script to select the data from the table. The autogenerated script displays entire result set from the database. The NuoDB Explorer is very powerful and makes the life of developers very easy. If you click on List SQL Statements it will list all the available SQL statements right away in Query Editor. You can see the popup window in following image. Here is the cool thing for geeks. You can even click on Query Plan and it will display the text based query plan as well. In case of a SELECT, the query plan will be much simpler, however, when we write complex queries it will be very interesting. We can use the query plan tab for performance tuning of the database. Here is another feature, when we click on List Tables in NuoDB Explorer.  It lists all the available tables in the query editor. This is very helpful when we are writing a long complex query. Here is a relatively complex example I have built using Inner Join syntax. Right below I have displayed the Query Plan. The query plan displays all the little details related to the query. Well, we just wrote multi-table query and executed it against the NuoDB database. You can use the NuoDB Admin section and do various analyses of the query and its performance. NuoDB is a distributed database built on a patented emergent architecture with full support for SQL and ACID guarantees.  It allows you to add Transaction Engine processes to a running system to improve the performance of your system.  You can also add a second Storage Engine to your running system for redundancy purposes.  Conversely, you can shut down processes when you don’t need the extra database resources. NuoDB also provides developers and administrators with a single intuitive interface for centrally monitoring deployments. If you have read my blog posts and have not tried out NuoDB, I strongly suggest that you download it today and catch up with the learnings with me. Trust me though the product is very powerful, it is extremely easy to learn and use. Reference: Pinal Dave (http://blog.sqlauthority.com)   Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

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  • Better way to search for text in two columns

    - by David
    Here is the scenario. I am making a custom blogging software for my site. I am implementing a search feature. It's not very sophisticated - basically it just takes the search phrase entered and runs this query: $query="SELECT * FROM `blog` WHERE `title` LIKE '%$q%' OR `post` LIKE '%$q%'"; Which is meant to simply search the title and post body for the phrase entered. Is there a better way to do that, keeping in mind how long it would take to run the query on up to 100 rows, each with a post length of up to 1500 characters? I have considered using a LIMIT statement to (sometimes) restrict the number of rows that the query would examine. Good idea?

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  • Optimization of time-varying parameters

    - by brama
    I need to find an optimal set of "n" parameter values that minimize an objective function (a 2-hr simulation of a system). I have looked at genetic algorithm and simulated annealing methods, but was wondering if there are any better algorithms and guidance on their merits and limitations. With the above optimization methods I can find the optimal parameter values that hold true for the entire simulation duration. Incase, I want to find the optimal "time varying" parameter values (parameter values change with time during the 2-hr simulation), are there any methods/ideas other than making each time varying parameter value a variable to optimize? Any thoughts?

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  • PostgreSQL - Why are some queries on large datasets so incredibly slow

    - by Brad Mathews
    Hello, I have two types of queries I run often on two large datasets. They run much slower than I would expect them to. The first type is a sequential scan updating all records: Update rcra_sites Set street = regexp_replace(street,'/','','i') rcra_sites has 700,000 records. It takes 22 minutes from pgAdmin! I wrote a vb.net function that loops through each record and sends an update query for each record (yes, 700,000 update queries!) and it runs in less than half the time. Hmmm.... The second type is a simple update with a relation and then a sequential scan: Update rcra_sites as sites Set violations='No' From narcra_monitoring as v Where sites.agencyid=v.agencyid and v.found_violation_flag='N' narcra_monitoring has 1,700,000 records. This takes 8 minutes. The query planner refuses to use my indexes. The query runs much faster if I start with a set enable_seqscan = false;. I would prefer if the query planner would do its job. I have appropriate indexes, I have vacuumed and analyzed. I optimized my shared_buffers and effective_cache_size best I know to use more memory since I have 4GB. My hardware is pretty darn good. I am running v8.4 on Windows 7. Is PostgreSQL just this slow? Or am I still missing something? Thanks! Brad

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  • MySQL query optimization JOIN

    - by Pierre
    Hi, I need your help to optimize those mysql query, both are in my slow query logs. SELECT a.nom, c.id_apps, c.id_commentaire, c.id_utilisateur, c.note_commentaire, u.nom_utilisateur FROM comments AS c LEFT JOIN apps AS a ON c.id_apps = a.id_apps LEFT JOIN users AS u ON c.id_utilisateur = u.id_utilisateur ORDER BY c.date_commentaire DESC LIMIT 5; There is a MySQL INDEX on c.id_apps, a.id_apps, c.id_utilisateur, u.id_utilisateur and c.date_commentaire. SELECT a.id_apps, a.id_itunes, a.nom, a.prix, a.resume, c.nom_fr_cat, e.nom_edit FROM apps AS a LEFT JOIN cat AS c ON a.categorie = c.id_cat LEFT JOIN edit AS e ON a.editeur = e.id_edit ORDER BY a.id_apps DESC LIMIT 20; There is a MySQL INDEX on a.categorie, c.id_cat, a.editeur, e.id_edit and a.id_apps Thanks

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