Chasing down some DB performance issues in a fairly typical EclipseLink/JPA application.
I am seeing frequent queries that are taking 25-100ms. These are simple queries, just selecting all columns from a table where its primary key is equal to a value. They shouldn't be slow.
I'm looking at the query time in the postgres log, using the log_min_duration_statement so this should eliminate any network or application overhead.
This query is not slow, but it is used very often.
Why would selecting * by primary key be slow?
Is this specific to postgres or is it a generic DB issue?
How can I speed this up? In general? For postgres?
Sample query from the pg log:
2010-07-28 08:19:08 PDT - LOG: duration: 61.405 ms statement: EXECUTE <unnamed> [PREPARE: SELECT coded_ele
ment_key, code_system, code_system_label, description, label, code, concept_key, alternate_code_key FROM coded
_element WHERE (coded_element_key = $1)]
Table has around 3.5 million rows.
I have also run EXPLAIN and EXPLAIN ANALYZE on this query, its only doing an index scan.
Select * makes your database work harder, and as a general rule, is a bad practice. There are tons of questions/answers on stackoverflow talking about that.
have you tried replacing * with the field names?
Could you be getting some kind of locking contention? What kind of locks are you taking when performing these queries?
Well, I don't know much about postgres SQL, so I'll give you a tip for MS SQL Server which might be applicable.
MS SQL Server has the concept of a "cluster index" which is the physical layout of the data on the disk. It's good to use on field where you'll be seeking a range between to values (date fields mostly). It's not much use if you're looking for a exact value (like a primary key lookup). However, sometimes the primary key index is inadvertantly set as a clustered index. This makes an index lookup into a table scan.
The the row unusually large or contain BLOBs and large binary fields?
Is this directly through console or is this query being run through some data access API like jdbc or ADO.NET? You mention JPA that looks like a data access API. For short queries, data access API become a larger percent of execution time-- creating the command, creating objects to hold the rows and cells, etc.
select * is almost always a very very bad idea.
If the order of the fields changes, it will break your code.
According to comments, this isn't really important given the abstraction library you're using.
You're probably returning more data from the table than you actually want. Selecting for the specific fields you want can save transfer time.
25ms is about the lower bound you're going to see on almost any kind of SQL query -- that's only two disk accesses! You might want to look into ways to reduce the number of times the query is run rather than trying to optimize the query.
Related
i found a in a table there are 50 thousands records and it takes one minute when we fetch data from sql server table just by issuing a sql. there are one primary key that means a already a cluster index is there. i just do not understand why it takes one minute. beside index what are the ways out there to optimize a table to get the data faster. in this situation what i need to do for faster response. also tell me how we can write always a optimize sql. please tell me all the steps in detail for optimization.
thanks.
The fastest way to optimize indexes in table is to use SQL Server Tuning Advisor. Take a look http://www.youtube.com/watch?v=gjT8wL92mqE <-- here
Select only the columns you need, rather than select *. If your table has some large columns e.g. OLE types or other binary data (maybe used for storing images etc) then you may be transferring vastly more data off disk and over the network than you need.
As others have said, an index is no help to you when you are selecting all rows (no where clause). Using an index would be slower in such cases because of the index read and table lookup for each row, vs full table scan.
If you are running select * from employee (as per question comment) then no amount of indexing will help you. It's an "Every column for every row" query: there is no magic for this.
Adding a WHERE won't help usually for select * query too.
What you can check is index and statistics maintenance. Do you do any? Here's a Google search
Or change how you use the data...
Edit:
Why a WHERE clause usually won't help...
If you add a WHERE that is not the PK..
you'll still need to scan the table unless you add an index on the searched column
then you'll need a key/bookmark lookup unless you make it covering
with SELECT * you need to add all columns to the index to make it covering
for a many hits, the index will probably be ignored to avoid key/bookmark lookups.
Unless there is a network issue or such, the issue is reading all columns not lack of WHERE
If you did SELECT col13 FROM MyTable and had an index on col13, the index will probably be used.
A SELECT * FROM MyTable WHERE DateCol < '20090101' with an index on DateCol but matched 40% of the table, it will probably be ignored or you'd have expensive key/bookmark lookups
Irrespective of the merits of returning the whole table to your application that does sound an unexpectedly long time to retrieve just 50000 rows of employee data.
Does your query have an ORDER BY or is it literally just select * from employee?
What is the definition of the employee table? Does it contain any particularly wide columns? Are you storing binary data such as their CVs or employee photo in it?
How are you issuing the SQL and retrieving the results?
What isolation level are your select statements running at (You can use SQL Profiler to check this)
Are you encountering blocking? Does adding NOLOCK to the query speed things up dramatically?
I know my questions will sound silly and probably nobody will have perfect answer but since I am in a complete dead-end with the situation it will make me feel better to post it here.
So...
I have a SQL Server Express database that's 500 Mb. It contains 5 tables and maybe 30 stored procedure. This database is use to store articles and is use for the Developer It web site. Normally the web pages load quickly, let's say 2 ou 3 sec. BUT, sqlserver process uses 100% of the processor for those 2 or 3 sec.
I try to find which stored procedure was the problem and I could not find one. It seems like every read into the table dans contains the articles (there are about 155,000 of them and 20 or so gets added every 15 minutes).
I added few indexes but without luck...
It is because the table is full text indexed ?
Should I have order with the primary key instead of date ? I never had any problems with ordering by dates....
Should I use dynamic SQL ?
Should I add the primary key into the URL of the articles ?
Should I use multiple indexes for separate columns or one big index ?
I you want more details or code bits, just ask for it.
Basically, every little hint is much appreciated.
Thanks.
If your index is not being used, then it usually indicates one of two problems:
Non-sargable predicate conditions, such as WHERE DATEPART(YY, Column) = <something>. Wrapping columns in a function will impair or eliminate the optimizer's ability to effectively use an index.
Non-covered columns in the output list, which is very likely if you're in the habit of writing SELECT * instead of SELECT specific_columns. If the index doesn't cover your query, then SQL Server needs to perform a RID/key lookup for every row, one by one, which can slow down the query so much that the optimizer just decides to do a table scan instead.
See if one of these might apply to your situation; if you're still confused, I'd recommend updating the question with more information about your schema, the data, and the queries that are slow. 500 MB is very small for a SQL database, so this shouldn't be slow. Also post what's in the execution plan.
Use SQL Profiler to capture a lot of typical queries used in your app. Then run the profiler results through index tuning wizard. That will tell you what indexes can be added to optimize.
Then look at the worst performing queries and analyze their execution plans manually.
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.
What techniques can be applied effectively to improve the performance of SQL queries? Are there any general rules that apply?
Use primary keys
Avoid select *
Be as specific as you can when building your conditional statements
De-normalisation can often be more efficient
Table variables and temporary tables (where available) will often be better than using a large source table
Partitioned views
Employ indices and constraints
Learn what's really going on under the hood - you should be able to understand the following concepts in detail:
Indexes (not just what they are but actually how they work).
Clustered indexes vs heap allocated tables.
Text and binary lookups and when they can be in-lined.
Fill factor.
How records are ghosted for update/delete.
When page splits happen and why.
Statistics, and how they effect various query speeds.
The query planner, and how it works for your specific database (for instance on some systems "select *" is slow, on modern MS-Sql DBs the planner can handle it).
The biggest thing you can do is to look for table scans in sql server query analyzer (make sure you turn on "show execution plan"). Otherwise there are a myriad of articles at MSDN and elsewhere that will give good advice.
As an aside, when I started learning to optimize queries I ran sql server query profiler against a trace, looked at the generated SQL, and tried to figure out why that was an improvement. Query profiler is far from optimal, but it's a decent start.
There are a couple of things you can look at to optimize your query performance.
Ensure that you just have the minimum of data. Make sure you select only the columns you need. Reduce field sizes to a minimum.
Consider de-normalising your database to reduce joins
Avoid loops (i.e. fetch cursors), stick to set operations.
Implement the query as a stored procedure as this is pre-compiled and will execute faster.
Make sure that you have the correct indexes set up. If your database is used mostly for searching then consider more indexes.
Use the execution plan to see how the processing is done. What you want to avoid is a table scan as this is costly.
Make sure that the Auto Statistics is set to on. SQL needs this to help decide the optimal execution. See Mike Gunderloy's great post for more info. Basics of Statistics in SQL Server 2005
Make sure your indexes are not fragmented. Reducing SQL Server Index Fragmentation
Make sure your tables are not fragmented. How to Detect Table Fragmentation in SQL Server 2000 and 2005
Use a with statment to handle query filtering.
Limit each subquery to the minimum number of rows possible.
then join the subqueries.
WITH
master AS
(
SELECT SSN, FIRST_NAME, LAST_NAME
FROM MASTER_SSN
WHERE STATE = 'PA' AND
GENDER = 'M'
),
taxReturns AS
(
SELECT SSN, RETURN_ID, GROSS_PAY
FROM MASTER_RETURNS
WHERE YEAR < 2003 AND
YEAR > 2000
)
SELECT *
FROM master,
taxReturns
WHERE master.ssn = taxReturns.ssn
A subqueries within a with statement may end up as being the same as inline views,
or automatically generated temp tables. I find in the work I do, retail data, that about 70-80% of the time, there is a performance benefit.
100% of the time, there is a maintenance benefit.
I think using SQL query analyzer would be a good start.
In Oracle you can look at the explain plan to compare variations on your query
Make sure that you have the right indexes on the table. if you frequently use a column as a way to order or limit your dataset an index can make a big difference. I saw in a recent article that select distinct can really slow down a query, especially if you have no index.
The obvious optimization for SELECT queries is ensuring you have indexes on columns used for joins or in WHERE clauses.
Since adding indexes can slow down data writes you do need to monitor performance to ensure you don't kill the DB's write performance, but that's where using a good query analysis tool can help you balanace things accordingly.
Indexes
Statistics
on microsoft stack, Database Engine Tuning Advisor
Some other points (Mine are based on SQL server, since each db backend has it's own implementations they may or may not hold true for all databases):
Avoid correlated subqueries in the select part of a statement, they are essentially cursors.
Design your tables to use the correct datatypes to avoid having to apply functions on them to get the data out. It is far harder to do date math when you store your data as varchar for instance.
If you find that you are frequently doing joins that have functions in them, then you need to think about redesigning your tables.
If your WHERE or JOIN conditions include OR statements (which are slower) you may get better speed using a UNION statement.
UNION ALL is faster than UNION if (And only if) the two statments are mutually exclusive and return the same results either way.
NOT EXISTS is usually faster than NOT IN or using a left join with a WHERE clause of ID = null
In an UPDATE query add a WHERE condition to make sure you are not updating values that are already equal. The difference between updating 10,000,000 records and 4 can be quite significant!
Consider pre-calculating some values if you will be querying them frequently or for large reports. A sum of the values in an order only needs to be done when the order is made or adjusted, rather than when you are summarizing the results of 10,000,000 million orders in a report. Pre-calculations should be done in triggers so that they are always up-to-date is the underlying data changes. And it doesn't have to be just numbers either, we havea calculated field that concatenates names that we use in reports.
Be wary of scalar UDFs, they can be slower than putting the code in line.
Temp table tend to be faster for large data set and table variables faster for small ones. In addition you can index temp tables.
Formatting is usually faster in the user interface than in SQL.
Do not return more data than you actually need.
This one seems obvious but you would not believe how often I end up fixing this. Do not join to tables that you are not using to filter the records or actually calling one of the fields in the select part of the statement. Unnecessary joins can be very expensive.
It is an very bad idea to create views that call other views that call other views. You may find you are joining to the same table 6 times when you only need to once and creating 100,000,00 records in an underlying view in order to get the 6 that are in your final result.
In designing a database, think about reporting not just the user interface to enter data. Data is useless if it is not used, so think about how it will be used after it is in the database and how that data will be maintained or audited. That will often change the design. (This is one reason why it is a poor idea to let an ORM design your tables, it is only thinking about one use case for the data.) The most complex queries affecting the most data are in reporting, so designing changes to help reporting can speed up queries (and simplify them) considerably.
Database-specific implementations of features can be faster than using standard SQL (That's one of the ways they sell their product), so get to know your database features and find out which are faster.
And because it can't be said too often, use indexes correctly, not too many or too few. And make your WHERE clauses sargable (Able to use indexes).
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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.