So we will focus on the "Relational Engine" part of the execution plan because from here we will get our Answer to the Question "Why my query is getting to much time to execute? When we generate the Estimated plan and when we run the query actually and get the actual execution plan then there may be some difference between them.
This difference may be due to following scenarios. Happy learning! Rate this:.
Share this: Twitter Facebook Reddit. Like this: Like Loading Roulette Craig permalink. Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:. Email required Address never made public. Name required. Subscribe in a reader. Based on a work at dbalink. Microsoft SQL Server. Create your Fan Badge. Twitter Updates follow me on Twitter. Depending on how you define minimal, that could be ok, but it is critical to have indexes on all foreign keys and I wouldn't want to push a database that didn't have indexes on a few fields that are most often in the where clauses.
If your users are outside clients and not internal, they won't complain about how slow your site is, they will go elsewhere. It only makes busness sense to plan for efficient database access from the start. One of my main concerns about failing to consider efficiency from the beginning is that the first couple of times that things are too slow companies tend to just throw more equipment at the issue rather than performance tune. By the time people start performacne tuning you have a several gigabyte or more database with many unhappy customers who are getting timeouts more than results.
At this point, often almost everything in the database has to be re-written and in the meantime you are losing customers.
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I remember providing support at one company with a commercial application that it literally took ten minutes for the customer service reps to move from one screen to another while they were trying to help already disgruntled customers on the phone. You can imagine how many customers the company lost due to poorly designed database queries in the commercial product that we could not change. After you profile, put the queries you see as troublesome into SQL Query Analyzer and display the execution plan. Identify portions of the queries that are performing costly table scans and re-index those tables to minimize this cost.
Of course you have to profile your queries and look at the execution plan. But the two main things that come up over and over again are filter out as much as you can as soon as you can and try to avoid cursors. I saw an application where someone downloaded an entire database table of events to a client and then went through each row one by one filtering based on some criteria.
SQL Server Execution Plan Recompile and Clean
There was a HUGE performance increase in passing the filter criteria to the database and having the query apply the criteria in a where clause. This is obvious to people who work with databases, but I have seen similar things crop up. Also some people have queries that store a bunch of temp tables full of rows that they don't need which are then eliminated in a final join of the temp tables.
Basically if you eliminate from the queries that populate the temp tables then there is less data for the rest of the query and the whole query runs faster. Cursors are obvious.
Dissecting SQL Server Execution Plans by Grant Fritchey - PDF Drive
If you have a million rows and go row by row then it will take forever. Doing some tests, if you connect to a database even with a "slow" dynamic language like Perl and perform some row by row operation on a dataset, the speed will still be much greater than a cursor in the database. If you must use a cursor, rewriting that part in any programming language and getting it out of the database will probably yield huge performance increases. Basically this is a cursor as well.
If you can change the operation to be set based it will be much faster That being said, cursors have a place for some things Also beware the execution plan. Sometimes it estimates operations that take seconds to be very expensive and operations that take minutes to be very cheap. My advice is that "premature optimization is the root of all evil" in this context is absoulte nonsense. In my view its all about design - you need to think about concurrency, hotspots, indexing, scaling and usage patterns when you are designing your data schema.
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If you don't know what indexes you need and how they need to be configured right off the bat without doing profiling you have already failed. There are millions of ways to optimize query execution that are all well and good but at the end of the day the data lands where you tell it to. This will ensure that every table has a clustered index created and hence, the corresponding pages of the table are physically sorted in the disk according to the primary key field.
Unnecessary columns may get fetched that will add expense to the data retrieval time. The database engine cannot utilize the benefit of "Covered Index" and hence the query performs slowly. Sometimes we may have more than one sub query in our main query. We should try to minimize the number of sub query block in our query.
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Selecting unnecessary columns in a Select query adds overhead to the actual query, especially if the unnecessary columns are of LOB types. Including unnecessary tables in join conditions forces the database engine to retrieve and fetch unnecessary data and increases the query execution time.
It counts all matching values, either by doing a table scan or by scanning the smallest non-clustered index. When joining between two columns of different data types, one of the columns must be converted to the type of the other. The column whose type is lower is the one that is converted. If you are joining tables with incompatible types, one of them can use an index, but the query optimizer cannot choose an index on the column that it converts.
This query will perform a full table scan to get the row count. The following query would not require a full table scan. Unless really required, try to avoid the use of temporary tables. Rather use table variables. Temporary tables reside in the TempDb database. So operating on temporary tables require inter database communication and hence will be slower. Full text searches always outperform LIKE searches. Full text searches will enable you to implement complex search criteria that can't be implemented using a LIKE search, such as searching on a single word or phrase and optionally, ranking the result set , searching on a word or phrase close to another word or phrase, or searching on synonymous forms of a specific word.
Implementing full text search is easier to implement than LIKE search especially in the case of complex search requirements. Try not to use "OR" in a query.