I am new to sql but i am learning, I have a forum were people can search for a location using radius and latitudes, the site is loading pretty slow would indexing the search fields be appropriate? I have read that indexs should not be used on table that under go updates and creates yet I do not know what other things I can do to improve performance. I was thinking of maybe creating an identical table with the same information and using that only for searches with an index.
"Select * from threads where latitudes >= #ThirtyLatMin AND #ThirtyLatMax >= latitudes AND longitudes >= #ThirtyLonMin AND #ThirtyLonMax>=longitudes order by activities desc"
Any modern RDBMS should be able to handle a few indexes on a given table; while it's true that index maintenance has a cost during table INSERTs and UPDATEs, that cost is generally outweighed by appropriate index utilization during a SELECT statement (and other queries).
Some general tips, based on your question (and sample query):
Only return the data you need; using SELECT * in a query is usually a red flag when performance tuning, because it means the coder didn't take the time to spell out what was needed by the calling application. If you're returning a bunch of data that you're not using, you're holding your performance back.
Once you've specified the columns to be returned, try to "cover" them with an index; in other words, make an index column that contains all of the information necessary to satisfy the query. Note that this becomes a balancing act when dealing with multiple different queries (different columns) that hit the same table. Too many indexes on a table increases the cost (see header), and may eventually outweigh the benefits. Note that different RDBMS's have different ways of dealing with this; for example, Microsoft SQL Server uses an INCLUDE syntax to represent broad, multi-column indexes in a covering capacity without significant overhead.
Index not only the columns returned, but make the first column of your indexes be the columns used in your WHERE clause. From the example above, I'd put an index that started with latitude and longitude and included the remaining necessary columns.
Whenever possible, move sorting out of the database. SORTS are expensive operations, and unless you're using it to assist in filtering data (i.e, TOP N searches), you'll probably see an improvement by moving it out of SQL.
I have read that indexs should not be used on table that under go updates and creates
I would love to see where you have read that because that is incorrect. Indexes are crucial to performance.
Please go read http://use-the-index-luke.com for a good introduction to indexes in SQL.
Related
What is the difference between using Indexes in SQL vs Using the ORDER BY clause?
Since from what I understand , the Indexes arrange the specified column(s) in an ordered manner that helps the query engine in looking through the tables quickly (and hence prevents table scan).
My question - why can't the query engine simply use the ORDER BY for improving performance?
Thanks !
You put the tag as sql-server-2008 but the question has nothing to do with SQL server. This question will apply to all databases.
From wikipedia:
Indexing is a technique some storage engines use for improving
database performance. The many types of indexes share the common
property that they reduce the need to examine every entry when running
a query. In large databases, this can reduce query time/cost by orders
of magnitude. The simplest form of index is a sorted list of values
that can be searched using a binary search with an adjacent reference to the location of the entry, analogous to the index in the back of a book. The same data can have multiple indexes (an employee database could be indexed by last name and hire date).
From a related thread in StackExchange
In the SQL world, order is not an inherent property of a set of data.
Thus, you get no guarantees from your RDBMS that your data will come
back in a certain order -- or even in a consistent order -- unless you
query your data with an ORDER BY clause.
To answer why the indexes are necessary?
Note the bolded text about indexing regarding the reduction in the need to examine every entry. In the absence of an index when an ORDER BY is issued in SQL, every entry need to be examined which increases the number of entries.
ORDER BY is applied only when reading. A single column may be used in indexes in which case there could be several different kinds of ordering in sql query requests. It is not possible to define the indexes unless we understand how the query requests are made.
A lot of times indexes are added once new patterns of querying emerge so as to keep the query performant which mean index creation is driven by how you defined your ORDER BY in SQL.
Query engine which processes your SQL with/without ORDER BY, defines your execution plan and does not understand Storage of data. The Data retrieved from a query engine may be partly from memory if the data was in cache and partly/fully from disk. When reading from disk in the storage engine will uses the indexes to figure the quickly read data.
ORDER BY effects the performance of a query when reading. Index effects the performance of a query when doing all the Create, Read, Update and Delete operations.
A query engine may choose to use an index or totally ignore the index based on the data characteristics.
How costly would SELECT One, Two, Three be compared to SELECT One, Two, Three, ..... N-Column
If you have a sql query that has two or three tables joined together and is retrieving 100 rows of data, does performance have anything to say whether I should be selecting only the number of columns I need? Or should I write a query that just yanks all the columns..
If possible, could you help me understand what aspects of a query would be relatively costly compared to one another? Is it the joins? is it the large number of records pulled? is it the number of columns in the select statement?
Would 1 record vs 10 record vs 100 record matter?
As an extremely generalized version of ranking those factors you mention in terms of performance penalty and occurrence in the queries you write, I would say:
Joins - Especially when joining on tables with no indexes for the fields you're joining on and/or with tables that have a very large amount of data.
# of Rows / Amount of Data - Again, indexes mitigate this quite a bit, just make sure you have the right ones.
# of Fields - I would say the # of fields in the SELECT clause impact performance the least in most situations.
I would say any performance-driving property is always coupled with how much data you have - sure a join might be fast when your tables have 100 rows each, but when millions of rows are in the tables, you have to start thinking about more efficient design.
Several things impact the cost of a query.
First, are there appropriate indexes for it to use. Fields that are used in a join should almost always be indexed and foreign keys are not indexed by default, the designer of the database must create them. Fields used inthe the where clasues often need indexes as well.
Next, is the where clause sargable, in other words can it use the indexes even if you have the correct ones? A bad where clause can hurt a query far more than joins or extra columns. You can't get anything but a table scan if you use syntax that prevents the use of an index such as:
LIKE '%test'
Next, are you returning more data than you need? You should never return more columns than you need and you should not be using select * in production code as it has additional work to look up the columns as well as being very fragile and subject to create bad bugs as the structure changes with time.
Are you joining to tables you don't need to be joining to? If a table returns no columns in the select, is not used in the where and doesn't filter out any records if the join is removed, then you have an unnecessary join and it can be eliminated. Unnecessary joins are particularly prevalant when you use a lot of views, especially if you make the mistake of calling views from other views (which is a buig performance killer for may reasons) Sometimes if you trace through these views that call other views, you will see the same table joined to multiple times when it would not have been necessary if the query was written from scratch instead of using a view.
Not only does returning more data than you need cause the SQL Server to work harder, it causes the query to use up more of the network resources and more of the memory of the web server if you are holding the results in memory. It is an all arouns poor choice.
Finally are you using known poorly performing techniques when a better one is available. This would include the use of cursors when a set-based alternative is better, the use of correlated subqueries when a join would be better, the use of scalar User-defined functions, the use of views that call other views (especially if you nest more than one level. Most of these poor techniques involve processing row-by-agonizing-row which is generally the worst choice in a database. To properly query datbases you need to think in terms of data sets, not processing one row at a time.
There are plenty more things that affect performance of queries and the datbase, to truly get a grip onthis subject you need to read some books onthe subject. This is too complex a subject to fully discuss in a message board.
Or should I write a query that just yanks all the columns..
No. Just today there was another question about that.
If possible, could you help me understand what aspects of a query would be relatively costly compared to one another? Is it the joins? is it the large number of records pulled? is it the number of columns in the select statement?
Any useless join or data retrieval costs you time and should be avoided. Retrieving rows from a datastore is costly. Joins can be more or less costly depending on the context, amount of indexes defined... you can examine the query plan of each query to see the estimated cost for each step.
Selecting more columns/rows will have some performance impacts, but honestly why would you want to select more data than you are going to use anyway?
If possible, could you help me
understand what aspects of a query
would be relatively costly compared to
one another?
Build the query you need, THEN worry about optimizing it if the performance doesn't meet your expectations. You are putting the horse before the cart.
To answer the following:
How costly would SELECT One, Two,
Three be compared to SELECT One, Two,
Three, ..... N-Column
This is not a matter of the select performance but the amount of time it takes to fetch the data. Select * from Table and Select ID from Table preform the same but the fetch of the data will take longer. This goes hand in hand with the number of rows returned from a query.
As for understanding preformance here is a good link
http://www.dotnetheaven.com/UploadFile/skrishnasamy/SQLPerformanceTunning03112005044423AM/SQLPerformanceTunning.aspx
Or google tsql Performance
Joins have the potential to be expensive. In the worst case scenario, when no indexes can be used, they require O(M*N) time, where M and N are the number of records in the tables. To speed things up, you can CREATE INDEX on columns that are part of the join condition.
The number of columns has little effect on the time required to find rows, but slows things down by requiring more data to be sent.
What others are saying is all true.
But typically, if you are working with tables that already have good indexes, what's most important for performance is what goes into the WHERE statement. There you have to worry more about using a field that has no index or using a statement that can't me optimized.
The difference between SELECT One, Two, Three FROM ... and SELECT One,...,N FROM ... could be like the difference between day and night. To understand the problem, you need to understand the concept of a covering index:
A covering index is a special case
where the index itself contains the
required data field(s) and can return
the data.
As you add more unnecessary columns to the projection list you are forcing the query optimizer to lookup the newly added columns in the 'table' (really in the clustered index or in the heap). This can change an execution plan from an efficient narrow index range scan or seek into a bloated clustered index scan, which can result in differences of times from sub-second to +hours, depending on your data. So projecting unnecessary columns is often the most impacting factor of a query.
The number of records pulled is a more subtle issue. With a large number, a query can hit the index tipping point and choose, again, a clustered index scan over narrower index range scan and lookup. Now the fact that lookups into the clustered index are necessary to start with means the narrow index is not covering, which ultimately may be caused by projecting unnecessary column.
And finally, joins. The question here is joins, as opposed to what else? If a join is required, there is no alternative, and that's all there is to say about this.
Ultimately, query performance is driven by one factor alone: amount of IO. And the amount of IO is driven ultimately by the access paths available to satisfy the query. In other words, by the indexing of your data. It is impossible to write efficient queries on bad indexes. It is possible to write bad queries on good indexes, but more often than not the optimizer can compensate and come up with a good plan. You should spend all your effort in better understanding index design:
Designing Indexes
SQL Server Optimization
Short answer: Dont select more fields then you need - Search for "*" in both your sourcecode and your stored procedures ;)
You allways have to consider what parts of the query will cause which costs.
If you have a good DB design, joining a few tables is usually not expensive. (Make sure you have correct indices).
The main issue with "select *" is that it will cause unpredictable behavior in your results. If you write a query like that, AND access the fields with the columnindex, you will be locked into the DB-Schema forever.
Another thing to consider is the amount of data you have to consider. You might think its trivial, but the Version2.0 of your application suddenly adds a ProfilePicture to the User table. And now the query that will select 100 Users will suddenly use up several Megabyte of bandwith.
The second thing you should consider is the number of rows you return. SQL is very powerfull at sorting and grouping, so let SQL do his job, and dont move it to the client. Limit the amount of records you return. In most applications it makes no sense to return more then 100 rows to a user at once. You might let the user choose to load more, but make it a choice he has to make.
Finally, monitor your SQL Server. Run a profiler against it, and try to find your worst queries. A SQL Query should not take longer then half a second, if it does, something is most likely messed up (Yes... there are operation that can take much longer, but those should have a reason)
Edit:
Once you found the slow query, look at the execution plan... You will see which parts of the query are expensive, and which parts work well... The Optimizer is also a tool that can be used.
I suggest you consider your queries in terms of I/O first. Disk I/O on my SATA II system is 6Gb/sec. My DDR3 memory bandwidth is 12GB/sec. I can move items in memory 16 times faster than I can retrieve from disk. (Ref Wikipedia and Tom's hardware)
The difference between getting a few columns and all the columns for your 100 rows could be the dfference in getting a single 8K page from disk to getting two or more pages from disk. When the pages are finally in memory moving two columns or all columns to a hash table is faster than any measuring tool I have.
I value the advice of the others on this topic related to database design. The design of narrow indexes, using included columns to make covering indexes, avoiding table or index scans in favor of seeks by using an appropiate WHERE clause, narrow primary keys, etc is the diffenence between having a DBA title and being a DBA.
I am developing a little data warehouse system with a web interface where people can do filtered searches. There are current about 50 columns that people may wish to filter on, and about 2.5 million rows. A table scan is painfully slow. The trouble is that the range of queries I'm getting have no common prefixes.
Right now I'm using sqlite3, which will only use an index if there the columns required are the leftmost columns in that index. This seems to mean I'd need a lot of indexes. A quick glance at MySQL suggests it would also require many indexes for this kind of query.
My question is what indexing implementations are available for different database systems which can handle this kind of query on arbitrary combinations of columns?
I've prototyped my own indexing scheme; I store extra tables which list integer primary keys in my big table where each value for each column occur, and I keep enough statistics to be able to first examine the values with the smallest number of matches. It works okay; much better than a table scan but still a bit on the slow side, which is unsurprising for a first version in Python doing many SQL queries.
There are column-oriented databases that store data on a per-column base, where every column is its own index. They are a very good fit for Data Warehouse as they are extremly fast to read, but fairly slow to update.
Kickfire is such an example, which is a customized MySQL engine and has held the TPC-H benchmark top crown for a number of weeks, at an impressive system cost. Note that Kickfire is an appliance, sold as a hardware box.
Infobright would be another similar example, and has a free community edition that runs on Windows and Linux.
When there's too many indexes to create for a table I usually fall back on Full Text Search. Can't say if it will fit your scenario though.
SInce data warehouses are typically optimized for reading data not writing it, I would consider simply indexing all the columns. Yes this will slow down putting data into the warehouse, but typically that happens during non-peak hours and only once a day or less often.
One should only consider introducing "home grown" index structures, based on SQL tables, as a last resort, i.e. if there still exists [business-wise plausible] query cases not properly handled with an traditional index setting. For example if the list of such indexes were to become to big etc.
A few observations
You do not necessarily need indexes that include all of the columns that may be involved in one particular query; only the [collectively] selective ones may be required.
In other words if the query uses, for example, columns a, b, c and d, but if an index with a and b exists and if that produces, statistically only a few thousand rows, it may be acceptable to not introduce indexes with a, b and c (or and d or both), if c or d are not very plausible search criteria (used infrequently), and if their width is such that is would unduly burden the a+b index (or if there were other columns with a better fit for being "tacked-on" to the a+b index).
Aside from the obvious additional demand they put on disk storage, additional indexes, while possibly helping with SELECT (read) operations may also become an impediment with CUD (Create/Update/Delete) operations. It appears the context here is akin to a datawarehouse, where few [unscheduled] CUD operations take place, but it is good to keep this in mind.
See SQLite Optimizer for valuable insight into the way SQLite determines the way a particular query is executed.
Making a list of indexes
A tentative basis for the index scheme for this application may look like this:
[A] A single column index for every column in the table (save maybe the ones which are ridiculously unselective, say a "Married" column w/ "Y/N" values in it....)
[B] A two (or three) columns index for each the likely/common use case queries
[C] Additional two/three column indexes for the cases where some non-common query case involves a set of columns none of which is individually selective.
From this basis we then can define the actual list of indexes needed by:
Adding one (or a few) extra columns at the end of (and in a well thought out order...) to the [B] indexes above. Typically such columns are choosed because of their relative small width (they do grow the index unduly) and because they have a relative chance of being used in combination with the columns cited before them in the index.
Removing the [A] indexes which are generally equivalent to one or several [B] indexes. That is: columns which start with the same column, and for which the extra columns do no burden much the index.
reviewing the TREE of all possible (or all acceptable) cases, and marking off the branches adequately served with the indexes above. Then adding yet more indexes for the odd use cases not readily covered (if only with partial index scan + main table lookup for an acceptable number of rows).
In this situation, I find a hand-written tree structure a useful tool to help manage the otherwise unmanageable lists of possible combinations. Assuming a maximum of 4 search criteria selected from the 50 columns indicated in the question, we have in excess of 230,000 combinations to consider... The tree helps prune this rather quickly.
I want to know optimization techniques for databases that has nearly 80,000 records,
list of possibilities for optimizing
i am using for my mobile project in android platform
i use sqlite,i takes lot of time to retreive the data
Thanks
Well, with only 80,000 records and assuming your database is well designed and normalized, just adding indexes on the columns that you frequently use in your WHERE or ORDER BY clauses should be sufficient.
There are other more sophisticated techniques you can use (such as denormalizing certain tables, partitioning, etc.) but those normally only start to come into play when you have millions of records to deal with.
ETA:
I see you updated the question to mention that this is on a mobile platform - that could change things a bit.
Assuming you can't pare down the data set at all, one thing you might be able to do would be to try to partition the database a bit. The idea here is to take your one large table and split it into several smaller identical tables that each hold a subset of the data.
Which of those tables a given row would go into would depend on how you choose to partition it. For example, if you had a "customer_id" field that could range from 0 to 10,000 you might put customers 0 - 2500 in table1, 2,500 - 5,000 in table2, etc. splitting the one large table into 4 smaller ones. You would then have logic in your app that would figure out which table (or tables) to query to retrieve a given record.
You would want to partition your data in such a way that you generally only need to query one of the partitions at a time. Exactly how you would partition the data would depend on what fields you have and how you are using them, but the general idea is the same.
Create indexes
Delete indexes
Normalize
DeNormalize
80k rows isn't many rows these days. Clever index(es) with queries that utlise these indexes will serve you right.
Learn how to display query execution maps, then learn to understand what they mean, then optimize your indices, tables, queries accordingly.
Such a wide topic, which does depend on what you want to optimise for. But the basics:
indexes. A good indexing strategy is important, indexing the right columns that are frequently queried on/ordered by is important. However, the more indexes you add, the slower your INSERTs and UPDATEs will be so there is a trade-off.
maintenance. Keep indexes defragged and statistics up to date
optimised queries. Identify queries that are slow (using profiler/built-in information available from SQL 2005 onwards) and see if they could be written more efficiently (e.g. avoid CURSORs, used set-based operations where possible
parameterisation/SPs. Use parameterised SQL to query the db instead of adhoc SQL with hardcoded search values. This will allow better execution plan caching and reuse.
start with a normalised database schema, and then de-normalise if appropriate to improve performance
80,000 records is not much so I'll stop there (large dbs, with millions of data rows, I'd have suggested partitioning the data)
You really have to be more specific with respect to what you want to do. What is your mix of operations? What is your table structure? The generic advice is to use indices as appropriate but you aren't going to get much help with such a generic question.
Also, 80,000 records is nothing. It is a moderate-sized table and should not make any decent database break a sweat.
First of all, indexes are really a necessity if you want a well-performing database.
Besides that, though, the techniques depend on what you need to optimize for: Size, speed, memory, etc?
One thing that is worth knowing is that using a function in the where statement on the indexed field will cause the index not to be used.
Example (Oracle):
SELECT indexed_text FROM your_table WHERE upper(indexed_text) = 'UPPERCASE TEXT';
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.