How do you optimize tables for specific queries?

Question

1. What are the patterns you use to determine the frequent queries?
2. How do you select the optimization factors?
3. What are the types of changes one can make?

Answer 1

Your question is a bit vague. Which DB platform?

If we are talking about SQL Server:

1. Use the Dynamic Management Views. Use SQL Profiler. Install the SP2 and the performance dashboard reports.
2. 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.
3. 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.

Answer 2

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.

Answer 3

1. 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.

Answer 4

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.

Answer 5

If your table is predominantly read, place a clustered index on the table.

Answer 6

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.

Answer 7

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.

Answer 8

indices on PKs and FKs and one thing that always helps PARTITIONING...

Answer 9

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.