I have a lot of records in table. When I execute the following query it takes a lot of time. How can I improve the performance?
SET ROWCOUNT 10
SELECT StxnID
,Sprovider.description as SProvider
,txnID
,Request
,Raw
,Status
,txnBal
,Stxn.CreatedBy
,Stxn.CreatedOn
,Stxn.ModifiedBy
,Stxn.ModifiedOn
,Stxn.isDeleted
FROM Stxn,Sprovider
WHERE Stxn.SproviderID = SProvider.Sproviderid
AND Stxn.SProviderid = ISNULL(#pSProviderID,Stxn.SProviderid)
AND Stxn.status = ISNULL(#pStatus,Stxn.status)
AND Stxn.CreatedOn BETWEEN ISNULL(#pStartDate,getdate()-1) and ISNULL(#pEndDate,getdate())
AND Stxn.CreatedBy = ISNULL(#pSellerId,Stxn.CreatedBy)
ORDER BY StxnID DESC
The stxn table has more than 100,000 records.
The query is run from a report viewer in asp.net c#.
This is my go-to article when I'm trying to do a search query that has several search conditions which might be optional.
http://www.sommarskog.se/dyn-search-2008.html
The biggest problem with your query is the column=ISNULL(#column, column) syntax. MSSQL won't use an index for that. Consider changing it to (column = #column AND #column IS NOT NULL)
You should consider using the execution plan and look for missing indexes. Also, how long it takes to execute? What is slow for you?
Maybe you could also not return so many rows, but that is just a guess. Actually we need to see your table and indexes plus the execution plan.
Check sql-tuning-tutorial
For one, use SELECT TOP () instead of SET ROWCOUNT - the optimizer will have a much better chance that way. Another suggestion is to use a proper inner join instead of potentially ending up with a cartesian product using the old style table,table join syntax (this is not the case here but it can happen much easier with the old syntax). Should be:
...
FROM Stxn INNER JOIN Sprovider
ON Stxn.SproviderID = SProvider.Sproviderid
...
And if you think 100K rows is a lot, or that this volume is a reason for slowness, you're sorely mistaken. Most likely you have really poor indexing strategies in place, possibly some parameter sniffing, possibly some implicit conversions... hard to tell without understanding the data types, indexes and seeing the plan.
There are a lot of things that could impact the performance of query. Although 100k records really isn't all that many.
Items to consider (in no particular order)
Hardware:
Is SQL Server memory constrained? In other words, does it have enough RAM to do its job? If it is swapping memory to disk, then this is a sure sign that you need an upgrade.
Is the machine disk constrained. In other words, are the drives fast enough to keep up with the queries you need to run? If it's memory constrained, then disk speed becomes a larger factor.
Is the machine processor constrained? For example, when you execute the query does the processor spike for long periods of time? Or, are there already lots of other queries running that are taking resources away from yours...
Database Structure:
Do you have indexes on the columns used in your where clause? If the tables do not have indexes then it will have to do a full scan of both tables to determine which records match.
Eliminate the ISNULL function calls. If this is a direct query, have the calling code validate the parameters and set default values before executing. If it is in a stored procedure, do the checks at the top of the s'proc. Unless you are executing this with RECOMPILE that does parameter sniffing, those functions will have to be evaluated for each row..
Network:
Is the network slow between you and the server? Depending on the amount of data pulled you could be pulling GB's of data across the wire. I'm not sure what is stored in the "raw" column. The first question you need to ask here is "how much data is going back to the client?" For example, if each record is 1MB+ in size, then you'll probably have disk and network constraints at play.
General:
I'm not sure what "slow" means in your question. Does it mean that the query is taking around 1 second to process or does it mean it's taking 5 minutes? Everything is relative here.
Basically, it is going to be impossible to give a hard answer without a lot of questions asked by you. All of these will bear out if you profile the queries, understand what and how much is going back to the client and watch the interactions amongst the various parts.
Finally depending on the amount of data going back to the client there might not be a way to improve performance short of hardware changes.
Make sure Stxn.SproviderID, Stxn.status, Stxn.CreatedOn, Stxn.CreatedBy, Stxn.StxnID and SProvider.Sproviderid all have indexes defined.
(NB -- you might not need all, but it can't hurt.)
I don't see much that can be done on the query itself, but I can see things being done on the schema :
Create an index / PK on Stxn.SproviderID
Create an index / PK on SProvider.Sproviderid
Create indexes on status, CreatedOn, CreatedBy, StxnID
Something to consider: When ROWCOUNT or TOP are used with an ORDER BY clause, the entire result set is created and sorted first and then the top 10 results are returned.
How does this run without the Order By clause?
Related
I am learning SQL following "SQL in 10 minutes",
Reference to use wildcards to retrieve all the records, it states that:
As a rule, you are better off not using the * wildcard unless you really do need every column in the table. Even though use of wildcards may save you the time and effort needed to list the desired columns explicitly, retrieving unnecessary columns usually slows down the performance of your retrieval and your application.
However, It consume less time to retrieve all the records than to retrieve multiple fields:
As the result indicate, wildcards for 0.02 seconds V.S. 0.1 seconds
I tested several times, wildcards faster than multiple specified columns constantly, even though time consumed varied every times.
Kudos to you for attempting to validate advice you get in a book! A single test neither invalidates the advice nor invalidates the test. It is worthwhile to dive further.
The advice provided in SQL In 10 Minutes is sound advice -- and it explicitly states that the purpose related to performance. (Another consideration is that that it makes the code unstable when the database changes.) As a note: I regularly use select t.* for ad-hoc queries.
Why are the results different? There can be multiple reasons for this:
Databases do not have deterministic performance, so other considerations -- such as other processes running on the machine or resource contention -- can affect the performance.
As mentioned in a comment, caching can be the reason. Specifically, running the first query may require loading the data from disk, and it is already in memory for the first.
Another form of caching is for the execution plan, so perhaps the first execution plan is cached but not the second.
You don't mention the database, but perhaps your database has a really, really slow compiler and compiling the first takes longer than the second.
Fundamentally, the advice is sound from a common-sense perspective. Moving less data around should be more efficient. That is really what the advice is saying.
In any case, the difference between 10 milliseconds and 2 milliseconds is very short. I would not generalize this performance to larger data and say that the second is 5 times faster than the first in general. For whatever reason, it is 8 milliseconds shorter on a very small data set, one so small that performance would not be a consideration anyway.
For manual testing the data that's in a table or tables?
Then it doesn't matter much whether you used a * or the column names.
Sure, if the table has like 100 columns and you only are interested in a few? Then explicitly adding the columnnames will give you a less convulted result.
Plus, you can choose the order they appear in the result.
And using a * in a sub-query would drag all the fields into the resultset.
While if you only selected the columns you need could improve performance.
For manual testing, that normally doesn't matter much.
Whether a test SQL runs 1 seconds or 2 seconds, if it's a test or an ad-hoc query then it wouldn't bother you.
What the suggestion is more intended for, is about coding SQL's that are to be used in a production environment.
When using * in a SQL, that means that when something changes in the tables that are used in the query, that it can affect the output of that query.
Possibly leading to errors. Your boss would frown upon that!
For example, a SQL with a select * from tableA union select * from tableB that you coded a year ago suddenly starts crashing because a column was added to tableB. Ouch.
But by explicitly putting the column names, adding a column to 1 of the tables wouldn't make any difference to that SQL.
In other words.
In production, stability and performance matter much more than golf-coding.
Another thing to keep in mind is the effect of caching.
Some databases can temporarly store metadata or even data in memory.
Which can speed up the retrieval of a query that gets the same results of a query that just run before it.
So try running the following SQL's.
Which are in a different order than in the question.
And check if there's still a speed difference.
select * from products;
select prod_id, prod_name, prod_price from products;
i used to write sql statments like
select * from teacher where (TeacherID = #TeacherID) OR (#TeacherID = -1)
read more
and pass #TeacherID value = -1 to select all teachers
now i'm worry about the performance
can you tell me is that a good practice or bad one?
many thanks
If TeacherID is indexed and you are passing a value other than -1 as TeacherID to search for details of a specific teacher then this query will end up doing a full table scan rather than the potentially far more efficient option of seeking into the index to retrieve the details of the specific teacher...
... Unless you are on SQL 2008 SP1 CU5 and later and use the OPTION (RECOMPILE) hint. See Dynamic Search Conditions in T-SQL for the definitive article on the topic.
We use this in a very limited fashion in stored procedures.
The problem is that the database engine isn't able to keep a good query plan for it. When dealing with a lot of data this can have a serious negative performance impact.
However, for smaller data sets (I'd say less than 1000 records, but that's a guess) it should be fine. You'll have to test in your particular environment.
If it's in a stored procedure, you might want to include something like a WITH RECOMPILE option so that the plan is regenerated on each execution. This adds (slightly) to the time for each run, but over several runs can actually reduce the average execution time. Also, this allows the database to inspect the actual query and "short circuit" the parts that aren't necessary on each call.
If you are directly creating your SQL and passing it through, then I'd suggest you make the part that builds your sql a little smarter so that it only includes the part of the where clause you actually need.
Another path you might consider is using UNION ALL queries as opposed to optional parameters. For example:
SELECT * FROM Teacher WHERE (TeacherId = #TeacherID)
UNION ALL
SELECT * FROM Teacher WHERE (#TeacherId = -1)
This actually accomplishes the exact same thing; however, the query plan is cacheable. We've used this method in a few places as well and saw performance improvements over using WITH RECOMPILE. We don't do this everywhere because some of our queries are extremely complicated and I'd rather have a performance hit than to complicate them further.
Ultimately though, you need to do a lot of testing.
There is a second part here that you should reconsider. SELECT *. It is ALWAYS preferable to actually name the columns you want returned and to make sure that you are only returning the ones you will actually need. Moving data across network boundaries is very expensive and you can generally get a fair amount of performance boost simply by specifying exactly what you want. In addition if what you need is very limited you can sometimes do covering indexes so that the database engine doesn't even have to touch the underlying tables to get the data you want.
If you're really worried about performance, you could break up your procedure to call on two different procs: one for all records, and one based on the parameter.
If #TeacherID = -1
exec proc_Get_All_Teachers
else
exec proc_Get_Teacher_By_TeacherID #TeacherID
Each one can be optimized individually.
It's your system, compare the performance. Consider optimizing on the most popular choice. If most users are going to select a single record, why hider their preformance just to accomodate the few that selct all teachers (And should have a reasonable expectation of performance.).
I know a single select query is easier to maintain, but at some point ease of maintenance eventually gives way to performance.
I am working on someone else's PHP code and seeing this pattern over and over:
(pseudocode)
result = SELECT blah1, blah2, foreign_key FROM foo WHERE key=bar
if foreign_key > 0
other_result = SELECT something FROM foo2 WHERE key=foreign_key
end
The code needs to branch if there is no related row in the other table, but couldn't this be done better by doing a LEFT JOIN in a single SELECT statement? Am I missing some performance benefit? Portability issue? Or am I just nitpicking?
This is definitely wrong. You are going over the wire a second time for no reason. DBs are very fast at their problem space. Joining tables is one of those and you'll see more of a performance degradation from the second query then the join. Unless your tablespace is hundreds of millions of records, this is not a good idea.
There is not enough information to really answer the question. I've worked on applications where decreasing the query count for one reason and increasing the query count for another reason both gave performance improvements. In the same application!
For certain combinations of table size, database configuration and how often the foreign table would be queried, doing the two queries can be much faster than a LEFT JOIN. But experience and testing is the only thing that will tell you that. MySQL with moderately large tables seems to be susceptable to this, IME. Performing three queries on one table can often be much faster than one query JOINing the three. I've seen speedups of an order of magnitude.
I'm with you - a single SQL would be better
There's a danger of treating your SQL DBMS as if it was a ISAM file system, selecting from a single table at a time. It might be cleaner to use a single SELECT with the outer join. On the other hand, detecting null in the application code and deciding what to do based on null vs non-null is also not completely clean.
One advantage of a single statement - you have fewer round trips to the server - especially if the SQL is prepared dynamically each time the other result is needed.
On average, then, a single SELECT statement is better. It gives the optimizer something to do and saves it getting too bored as well.
It seems to me that what you're saying is fairly valid - why fire off two calls to the database when one will do - unless both records are needed independently as objects(?)
Of course while it might not be as simple code wise to pull it all back in one call from the database and separate out the fields into the two separate objects, it does mean that you're only dependent on the database for one call rather than two...
This would be nicer to read as a query:
Select a.blah1, a.blah2, b.something From foo a Left Join foo2 b On a.foreign_key = b.key Where a.Key = bar;
And this way you can check you got a result in one go and have the database do all the heavy lifting in one query rather than two...
Yeah, I think it seems like what you're saying is correct.
The most likely explanation is that the developer simply doesn't know how outer joins work. This is very common, even among developers who are quite experienced in their own specialty.
There's also a widespread myth that "queries with joins are slow." So many developers blindly avoid joins at all costs, even to the extreme of running multiple queries where one would be better.
The myth of avoiding joins is like saying we should avoid writing loops in our application code, because running a line of code multiple times is obviously slower than running it once. To say nothing of the "overhead" of ++i and testing i<20 during every iteration!
You are completely correct that the single query is the way to go. To add some value to the other answers offered let me add this axiom: "Use the right tool for the job, the Database server should handle the querying work, the code should handle the procedural work."
The key idea behind this concept is that the compiler/query optimizers can do a better job if they know the entire problem domain instead of half of it.
Considering that in one database hit you have all the data you need having one single SQL statement would be better performance 99% of the time. Not sure if the connections is being creating dynamically in this case or not but if so doing so is expensive. Even if the process if reusing existing connections the DBMS is not getting optimize the queries be best way and not really making use of the relationships.
The only way I could ever see doing the calls like this for performance reasons is if the data being retrieved by the foreign key is a large amount and it is only needed in some cases. But in the sample you describe it just grabs it if it exists so this is not the case and therefore not gaining any performance.
The only "gotcha" to all of this is if the result set to work with contains a lot of joins, or even nested joins.
I've had two or three instances now where the original query I was inheriting consisted of a single query that had so a lot of joins in it and it would take the SQL a good minute to prepare the statement.
I went back into the procedure, leveraged some table variables (or temporary tables) and broke the query down into a lot of the smaller single select type statements and constructed the final result set in this manner.
This update dramatically fixed the response time, down to a few seconds, because it was easier to do a lot of simple "one shots" to retrieve the necessary data.
I'm not trying to object for objections sake here, but just to point out that the code may have been broken down to such a granular level to address a similar issue.
A single SQL query would lead in more performance as the SQL server (Which sometimes doesn't share the same location) just needs to handle one request, if you would use multiple SQL queries then you introduce a lot of overhead:
Executing more CPU instructions,
sending a second query to the server,
create a second thread on the server,
execute possible more CPU instructions
on the sever, destroy a second thread
on the server, send the second results
back.
There might be exceptional cases where the performance could be better, but for simple things you can't reach better performance by doing a bit more work.
Doing a simple two table join is usually the best way to go after this problem domain, however depending on the state of the tables and indexing, there are certain cases where it may be better to do the two select statements, but typically I haven't run into this problem until I started approaching 3-5 joined tables, not just 2.
Just make sure you have covering indexes on both tables to ensure you aren't scanning the disk for all records, that is the biggest performance hit a database gets (in my limited experience)
You should always try to minimize the number of query to the database when you can. Your example is perfect for only 1 query. This way you will be able later to cache more easily or to handle more request in same time because instead of always using 2-3 query that require a connexion, you will have only 1 each time.
There are many cases that will require different solutions and it isn't possible to explain all together.
Join scans both the tables and loops to match the first table record in second table. Simple select query will work faster in many cases as It only take cares for the primary/unique key(if exists) to search the data internally.
What are the patterns you use to determine the frequent queries?
How do you select the optimization factors?
What are the types of changes one can make?
This is a nice question, if rather broad (and none the worse for that).
If I understand you, then you're asking how to attack the problem of optimisation starting from scratch.
The first question to ask is: "is there a performance problem?"
If there is no problem, then you're done. This is often the case. Nice.
On the other hand...
Determine Frequent Queries
Logging will get you your frequent queries.
If you're using some kind of data access layer, then it might be simple to add code to log all queries.
It is also a good idea to log when the query was executed and how long each query takes. This can give you an idea of where the problems are.
Also, ask the users which bits annoy them. If a slow response doesn't annoy the user, then it doesn't matter.
Select the optimization factors?
(I may be misunderstanding this part of the question)
You're looking for any patterns in the queries / response times.
These will typically be queries over large tables or queries which join many tables in a single query. ... but if you log response times, you can be guided by those.
Types of changes one can make?
You're specifically asking about optimising tables.
Here are some of the things you can look for:
Denormalisation. This brings several tables together into one wider table, so in stead of your query joining several tables together, you can just read one table. This is a very common and powerful technique. NB. I advise keeping the original normalised tables and building the denormalised table in addition - this way, you're not throwing anything away. How you keep it up to date is another question. You might use triggers on the underlying tables, or run a refresh process periodically.
Normalisation. This is not often considered to be an optimisation process, but it is in 2 cases:
updates. Normalisation makes updates much faster because each update is the smallest it can be (you are updating the smallest - in terms of columns and rows - possible table. This is almost the very definition of normalisation.
Querying a denormalised table to get information which exists on a much smaller (fewer rows) table may be causing a problem. In this case, store the normalised table as well as the denormalised one (see above).
Horizontal partitionning. This means making tables smaller by putting some rows in another, identical table. A common use case is to have all of this month's rows in table ThisMonthSales, and all older rows in table OldSales, where both tables have an identical schema. If most queries are for recent data, this strategy can mean that 99% of all queries are only looking at 1% of the data - a huge performance win.
Vertical partitionning. This is Chopping fields off a table and putting them in a new table which is joinned back to the main table by the primary key. This can be useful for very wide tables (e.g. with dozens of fields), and may possibly help if tables are sparsely populated.
Indeces. I'm not sure if your quesion covers these, but there are plenty of other answers on SO concerning the use of indeces. A good way to find a case for an index is: find a slow query. look at the query plan and find a table scan. Index fields on that table so as to remove the table scan. I can write more on this if required - leave a comment.
You might also like my post on this.
That's difficult to answer without knowing which system you're talking about.
In Oracle, for example, the Enterprise Manager lets you see which queries took up the most time, lets you compare different execution profiles, and lets you analyze queries over a block of time so that you don't add an index that's going to help one query at the expense of every other one you run.
Your question is a bit vague. Which DB platform?
If we are talking about SQL Server:
Use the Dynamic Management Views. Use SQL Profiler. Install the SP2 and the performance dashboard reports.
After determining the most costly queries (i.e. number of times run x cost one one query), examine their execution plans, and look at the sizes of the tables involved, and whether they are predominately Read or Write, or a mixture of both.
If the system is under your full control (apps. and DB) you can often re-write queries that are badly formed (quite a common occurrance), such as deep correlated sub-queries which can often be re-written as derived table joins with a little thought. Otherwise, you options are to create covering non-clustered indexes and ensure that statistics are kept up to date.
For MySQL there is a feature called log slow queries
The rest is based on what kind of data you have and how it is setup.
In SQL server you can use trace to find out how your query is performing. Use ctrl + k or l
For example if u see full table scan happening in a table with large number of records then it probably is not a good query.
A more specific question will definitely fetch you better answers.
If your table is predominantly read, place a clustered index on the table.
My experience is with mainly DB2 and a smattering of Oracle in the early days.
If your DBMS is any good, it will have the ability to collect stats on specific queries and explain the plan it used for extracting the data.
For example, if you have a table (x) with two columns (date and diskusage) and only have an index on date, the query:
select diskusage from x where date = '2008-01-01'
will be very efficient since it can use the index. On the other hand, the query
select date from x where diskusage > 90
would not be so efficient. In the former case, the "explain plan" would tell you that it could use the index. In the latter, it would have said that it had to do a table scan to get the rows (that's basically looking at every row to see if it matches).
Really intelligent DBMS' may also explain what you should do to improve the performance (add an index on diskusage in this case).
As to how to see what queries are being run, you can either collect that from the DBMS (if it allows it) or force everyone to do their queries through stored procedures so that the DBA control what the queries are - that's their job, keeping the DB running efficiently.
indices on PKs and FKs and one thing that always helps PARTITIONING...
1. What are the patterns you use to determine the frequent queries?
Depends on what level you are dealing with the database. If you're a DBA or a have access to the tools, db's like Oracle allow you to run jobs and generate stats/reports over a specified period of time. If you're a developer writing an application against a db, you can just do performance profiling within your app.
2. How do you select the optimization factors?
I try and get a general feel for how the table is being used and the data it contains. I go about with the following questions.
Is it going to be updated a ton and on what fields do updates occur?
Does it have columns with low cardinality?
Is it worth indexing? (tables that are very small can be slowed down if accessed by an index)
How much maintenance/headache is it worth to have it run faster?
Ratio of updates/inserts vs queries?
etc.
3. What are the types of changes one can make?
-- If using Oracle, keep statistics up to date! =)
-- Normalization/De-Normalization either one can improve performance depending on the usage of the table. I almost always normalize and then only if I can in no other practical way make the query faster will de-normalize. A nice way to denormalize for queries and when your situation allows it is to keep the real tables normalized and create a denormalized "table" with a materialized view.
-- Index judiciously. Too many can be bad on many levels. BitMap indexes are great in Oracle as long as you're not updating the column frequently and that column has a low cardinality.
-- Using Index organized tables.
-- Partitioned and sub-partitioned tables and indexes
-- Use stored procedures to reduce round trips by applications, increase security, and enable query optimization without affecting users.
-- Pin tables in memory if appropriate (accessed a lot and fairly small)
-- Device partitioning between index and table database files.
..... the list goes on. =)
Hope this is helpful for you.
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Closed 11 years ago.
When you have a query or stored procedure that needs performance tuning, what are some of the first things you try?
Here is the handy-dandy list of things I always give to someone asking me about optimisation.
We mainly use Sybase, but most of the advice will apply across the board.
SQL Server, for example, comes with a host of performance monitoring / tuning bits, but if you don't have anything like that (and maybe even if you do) then I would consider the following...
99% of problems I have seen are caused by putting too many tables in a join. The fix for this is to do half the join (with some of the tables) and cache the results in a temporary table. Then do the rest of the query joining on that temporary table.
Query Optimisation Checklist
Run UPDATE STATISTICS on the underlying tables
Many systems run this as a scheduled weekly job
Delete records from underlying tables (possibly archive the deleted records)
Consider doing this automatically once a day or once a week.
Rebuild Indexes
Rebuild Tables (bcp data out/in)
Dump / Reload the database (drastic, but might fix corruption)
Build new, more appropriate index
Run DBCC to see if there is possible corruption in the database
Locks / Deadlocks
Ensure no other processes running in database
Especially DBCC
Are you using row or page level locking?
Lock the tables exclusively before starting the query
Check that all processes are accessing tables in the same order
Are indices being used appropriately?
Joins will only use index if both expressions are exactly the same data type
Index will only be used if the first field(s) on the index are matched in the query
Are clustered indices used where appropriate?
range data
WHERE field between value1 and value2
Small Joins are Nice Joins
By default the optimiser will only consider the tables 4 at a time.
This means that in joins with more than 4 tables, it has a good chance of choosing a non-optimal query plan
Break up the Join
Can you break up the join?
Pre-select foreign keys into a temporary table
Do half the join and put results in a temporary table
Are you using the right kind of temporary table?
#temp tables may perform much better than #table variables with large volumes (thousands of rows).
Maintain Summary Tables
Build with triggers on the underlying tables
Build daily / hourly / etc.
Build ad-hoc
Build incrementally or teardown / rebuild
See what the query plan is with SET SHOWPLAN ON
See what’s actually happenning with SET STATS IO ON
Force an index using the pragma: (index: myindex)
Force the table order using SET FORCEPLAN ON
Parameter Sniffing:
Break Stored Procedure into 2
call proc2 from proc1
allows optimiser to choose index in proc2 if #parameter has been changed by proc1
Can you improve your hardware?
What time are you running? Is there a quieter time?
Is Replication Server (or other non-stop process) running? Can you suspend it? Run it eg. hourly?
Have a pretty good idea of the optimal path of running the query in your head.
Check the query plan - always.
Turn on STATS, so that you can examine both IO and CPU performance. Focus on driving those numbers down, not necessarily the query time (as that can be influenced by other activity, cache, etc.).
Look for large numbers of rows coming into an operator, but small numbers coming out. Usually, an index would help by limiting the number of rows coming in (which saves disk reads).
Focus on the largest cost subtree first. Changing that subtree can often change the entire query plan.
Common problems I've seen are:
If there's a lot of joins, sometimes Sql Server will choose to expand the joins, and then apply WHERE clauses. You can usually fix this by moving the WHERE conditions into the JOIN clause, or a derived table with the conditions inlined. Views can cause the same problems.
Suboptimal joins (LOOP vs HASH vs MERGE). My rule of thumb is to use a LOOP join when the top row has very few rows compared to the bottom, a MERGE when the sets are roughly equal and ordered, and a HASH for everything else. Adding a join hint will let you test your theory.
Parameter sniffing. If you ran the stored proc with unrealistic values at first (say, for testing), then the cached query plan may be suboptimal for your production values. Running again WITH RECOMPILE should verify this. For some stored procs, especially those that deal with varying sized ranges (say, all dates between today and yesterday - which would entail an INDEX SEEK - or, all dates between last year and this year - which would be better off with an INDEX SCAN) you may have to run it WITH RECOMPILE every time.
Bad indentation...Okay, so Sql Server doesn't have an issue with this - but I sure find it impossible to understand a query until I've fixed up the formatting.
Slightly off topic but if you have control over these issues...
High level and High Impact.
For high IO environments make sure your disks are for either RAID 10 or RAID 0+1 or some nested implementation of raid 1 and raid 0.
Don't use drives less than 1500K.
Make sure your disks are only used for your Database. IE no logging no OS.
Turn off auto grow or similar feature. Let the database use all storage that is anticipated. Not necessarily what is currently being used.
design your schema and indexes for the type queries.
if it's a log type table (insert only) and must be in the DB don't index it.
if your doing allot of reporting (complex selects with many joins) then you should look at creating a data warehouse with a star or snowflake schema.
Don't be afraid of replicating data in exchange for performance!
CREATE INDEX
Assure there are indexes available for your WHERE and JOIN clauses. This will speed data access greatly.
If your environment is a data mart or warehouse, indexes should abound for almost any conceivable query.
In a transactional environment, the number of indexes should be lower and their definitions more strategic so that index maintenance doesn't drag down resources. (Index maintenance is when the leaves of an index must be changed to reflect a change in the underlying table, as with INSERT, UPDATE, and DELETE operations.)
Also, be mindful of the order of fields in the index - the more selective (higher cardinality) a field, the earlier in the index it should appear. For example, say you're querying for used automobiles:
SELECT i.make, i.model, i.price
FROM dbo.inventory i
WHERE i.color = 'red'
AND i.price BETWEEN 15000 AND 18000
Price generally has higher cardinality. There may be only a few dozen colors available, but quite possibly thousands of different asking prices.
Of these index choices, idx01 provides the faster path to satisfy the query:
CREATE INDEX idx01 ON dbo.inventory (price, color)
CREATE INDEX idx02 ON dbo.inventory (color, price)
This is because fewer cars will satisfy the price point than the color choice, giving the query engine far less data to analyze.
I've been known to have two very similar indexes differing only in the field order to speed queries (firstname, lastname) in one and (lastname, firstname) in the other.
Assuming MySQL here, use EXPLAIN to find out what is going on with the query, make sure that the indexes are being used as efficiently as possible and try to eliminate file sorts. High Performance MySQL: Optimization, Backups, Replication, and More is a great book on this topic as is MySQL Performance Blog.
A trick I recently learned is that SQL Server can update local variables as well as fields, in an update statement.
UPDATE table
SET #variable = column = #variable + otherColumn
Or the more readable version:
UPDATE table
SET
#variable = #variable + otherColumn,
column = #variable
I've used this to replace complicated cursors/joins when implementing recursive calculations, and also gained a lot in performance.
Here's details and example code that made fantastic improvements in performance:
Link
#Terrapin there are a few other differences between isnull and coalesce that are worth mentioning (besides ANSI compliance, which is a big one for me).
Coalesce vs. IsNull
Sometimes in SQL Server if you use an OR in a where clause it will really jack with performance. Instead of using the OR just do two selects and union them together. You get the same results at 1000x the speed.
Look at the where clause - verify use of indexes / verify nothing silly is being done
where SomeComplicatedFunctionOf(table.Column) = #param --silly
I'll generally start with the joins - I'll knock each one of them out of the query one at a time and re-run the query to get an idea if there's a particular join I'm having a problem with.
On all of my temp tables, I like to add unique constraints (where appropriate) to make indexes, and primary keys (almost always).
declare #temp table(
RowID int not null identity(1,1) primary key,
SomeUniqueColumn varchar(25) not null,
SomeNotUniqueColumn varchar(50) null,
unique(SomeUniqueColumn)
)
#DavidM
Assuming MySQL here, use EXPLAIN to find out what is going on with the query, make sure that the indexes are being used as efficiently as possible...
In SQL Server, execution plan gets you the same thing - it tells you what indexes are being hit, etc.
Not necessarily a SQL performance trick per se but definately related:
A good idea would be to use memcached where possible as it would be much faster just fetching the precompiled data directly from memory rather than getting it from the database. There's also a flavour of MySQL that got memcached built in (third party).
Make sure your index lengths are as small as possible. This allows the DB to read more keys at a time from the file system, thus speeding up your joins. I assume this works with all DB's, but I know it's a specific recommendation for MySQL.
I've made it a habit to always use bind variables. It's possible bind variables won't help if the RDBMS doesn't cache SQL statements. But if you don't use bind variables the RDBMS doesn't have a chance to reuse query execution plans and parsed SQL statements. The savings can be enormous: http://www.akadia.com/services/ora_bind_variables.html. I work mostly with Oracle, but Microsoft SQL Server works pretty much the same way.
In my experience, if you don't know whether or not you are using bind variables, you probably aren't. If your application language doesn't support them, find one that does. Sometimes you can fix query A by using bind variables for query B.
After that, I talk to our DBA to find out what's causing the RDBMS the most pain. Note that you shouldn't ask "Why is this query slow?" That's like asking your doctor to take out you appendix. Sure your query might be the problem, but it's just as likely that something else is going wrong. As developers, we we tend to think in terms of lines of code. If a line is slow, fix that line. But a RDBMS is a really complicated system and your slow query might be the symptom of a much larger problem.
Way too many SQL tuning tips are cargo cult idols. Most of the time the problem is unrelated or minimally related to the syntax you use, so it's normally best to use the cleanest syntax you can. Then you can start looking at ways to tune the database (not the query). Only tweak the syntax when that fails.
Like any performance tuning, always collect meaningful statistics. Don't use wallclock time unless it's the user experience you are tuning. Instead look at things like CPU time, rows fetched and blocks read off of disk. Too often people optimize for the wrong thing.
First step:
Look at the Query Execution Plan!
TableScan -> bad
NestedLoop -> meh warning
TableScan behind a NestedLoop -> DOOM!
SET STATISTICS IO ON
SET STATISTICS TIME ON
Running the query using WITH (NoLock) is pretty much standard operation in my place. Anyone caught running queries on the tens-of-gigabytes tables without it is taken out and shot.
Convert NOT IN queries to LEFT OUTER JOINS if possible. For example if you want to find all rows in Table1 that are unused by a foreign key in Table2 you could do this:
SELECT *
FROM Table1
WHERE Table1.ID NOT IN (
SELECT Table1ID
FROM Table2)
But you get much better performance with this:
SELECT Table1.*
FROM Table1
LEFT OUTER JOIN Table2 ON Table1.ID = Table2.Table1ID
WHERE Table2.ID is null
Index the table(s) by the clm(s) you filter by
Prefix all tables with dbo. to prevent recompilations.
View query plans and hunt for table/index scans.
In 2005, scour the management views for missing indexes.
I like to use
isnull(SomeColThatMayBeNull, '')
Over
coalesce(SomeColThatMayBeNull, '')
When I don't need the multiple argument support that coalesce gives you.
http://blog.falafel.com/2006/04/05/SQLServerArcanaISNULLVsCOALESCE.aspx
I look out for:
Unroll any CURSOR loops and convert into set based UPDATE / INSERT statements.
Look out for any application code that:
Calls an SP that returns a large set of records,
Then in the application, goes through each record and calls an SP with parameters to update records.
Convert this into a SP that does all the work in one transaction.
Any SP that does lots of string manipulation. It's evidence that the data is not structured correctly / normalised.
Any SP's that re-invent the wheel.
Any SP's that I can't understand what it's trying to do within a minute!
SET NOCOUNT ON
Usually the first line inside my stored procedures, unless I actually need to use ##ROWCOUNT.
In SQL Server, use the nolock directive. It allows the select command to complete without having to wait - usually other transactions to finish.
SELECT * FROM Orders (nolock) where UserName = 'momma'
Remove cursors wherever the are not neceesary.
Remove function calls in Sprocs where a lot of rows will call the function.
My colleague used function calls (getting lastlogindate from userid as example) to return very wide recordsets.
Tasked with optimisation, I replaced the function calls in the sproc with the function's code: I got many sprocs' running time down from > 20 seconds to < 1.
Don't prefix Stored Procedure names with "sp_" because system procedures all start with "sp_", and SQL Server will have to search harder to find your procedure when it gets called.
Dirty reads -
set transaction isolation level read uncommitted
Prevents dead locks where transactional integrity isn't absolutely necessary (which is usually true)
I always go to SQL Profiler (if it's a stored procedure with a lot of nesting levels) or the query execution planner (if it's a few SQL statements with no nesting) first. 90% of the time you can find the problem immediately with one of these two tools.