Dec/090
Oracle Performance Tuning Tactics Part 2: Using Indexes
To recap, the “weapons” available in your “arsenal” for performance tuning are as follows:- Full table scans ; Indexes; Joins; Views; De-normalization; Oracle Stored Procedures; Sub-queries.
2. Indexes
There are several types of indexes available with Oracle databases:
* normal/B-Tree (balanced/ binary tree) indexes;
* bitmap indexes (which literally use a map of bits to represent the whole index and are
* function-based indexes (which are based on the value of an expression instead of the value of a column);
* partitioned indexes (tables and indexes may be partitioned separately)
* domain indexes (application-specific indexes created on complex data types such as spatial data).
This article only discusses bitmap and normal (b-tree) indexes.
2.1 B-Tree Indexes
B-Tree Indexes are most appropriate when retrieving a small amount of data from a very large table. The bigger the table and the lower the number of rows that you want to retrieve then the more effective the index.
If you think of the printed material in the real world, this makes perfect sense, you wouldn’t bother to create an index for a short article, but you would certainly want one for a lengthy reference book. The reasons are analogous too. When reading a short article it would take longer to find the areas of interest using the index then it would to read the whole article. It’s the same when you’re querying the database – for a small table with Oracle’s multi-block read feature of Oracle, the whole table would be read in one go, so by going to the index first, the database would have to read more data overall.
Indexes may improve read access to data and update operations (including insert and delete) using sub-queries. However update performance will be impaired because every time a row is inserted into, deleted from or updated (if the indexed column(s) is/are updated) in the database, the indexes have to be adjusted accordingly, thereby increasing the amount of i/o that has to performed.
Therefore having many indexes is ideal when the database is mostly read only, but if there are a high proportion of inserts and deletes and only a few read operations, then you would find that adding more indexes would degrade rather than improve performance. You also need to ensure that the column(s) you are proposing to index is/are used frequently as the limiting conditions for a query, if not you will waste a lot of space and degrade database performance.
Another factor to consider with b-tree indexes in Oracle databases, is how specific they are – their cardinality. The ideal b-tree index is one that refers to only one row in the table, as a primary key does by definition. This reduces considerably the amount of i/o required to retrieve the data, but if the index value relates to many rows because it is not selective, then a lot more data will be retrieved resulting in unnecessary i/o.
Dec/090
Oracle Performance Tuning Part 1: Using Full Table Scans
It is a common misconception that all SQL queries on all tables in Oracle databases should be index driven. In fact, using full-table scans can improve performance in two scenarios: when querying very small tables and when querying very large tables.
The Effect of Full Table Scans When Querying Small Tables
Let’s suppose your using your Oracle database to run in-house designed and built HR application. Consider a reference table such as a list of department ids and the associated department names. Even a large company is likely to have only a few departments – HR, Sales, Marketing, Finance, IT, so the table is going to be quite small.
Now let’s suppose the table has just 2 columns – department id and department name – with an index on the department id. To find the department name for a given id, we would have to read the index and then read the table, but because the table is so small and because Oracle reads multiple database blocks in one read operation the whole table is scanned in just one read, so however efficient the index, by using it we will be performing unnecessary i/o.
In this case, therefore, a full table scan is faster than an index scan and table lookup. The exception to this of course is when the table has been created as an index-only table (available since Oracle 
which means that the whole table is stored in a B-tree structure (although you may have pointers to overflow areas).
The Effect of Full Table Scans When Querying Very Large Tables
Let’s look at using this technique for querying very large tables in your Oracle database. Surely they should use an index? Otherwise you might have to read thousands of blocks. It is correct to say that a full table scan of a very large table could read many thousands of data blocks, but as we shall see it may be better to do this than to perform an index scan and table lookup.
The situation when the a full table scan is very likely to perform better than an index scan and table lookup is when you are retrieving 10% or more of the data in the table and it may perform better even when you are retrieving as little as 1% of the table data. Of course if you only want to retrieve one row in the table, then you would want to use an index.
Index Scan And Table Lookup Vs. Full Table Scan For Very Large Tables in Oracle Databases
Let’s look at the 2 scenarios then – retrieving 10% of the table data by index scan and table lookup vs. full table scan.
To make the maths easy, assume our table has 10,000,000 rows with 10 data rows per block and 100 index entries per block. Therefore to read 10% of the table via an index scan and table lookup, we would have to read 10,000 (1,000,000/100) index blocks plus 100,000 (1,000,000/10) data blocks. That’s 110,000 blocks in total.
However this assumes that the data is stored in order which means that we only retrieve the blocks of data that we want. If the data is not sorted then the worst case is that we would have to read 1 block for each row of data i.e. 1,000,000 blocks, which would would give us a worst case total of 1,010,000 blocks.
For a full table scan the maths is easy: (10,000,000 rows)/(10 rows per block) = 1,000,000 blocks. This is less than the worst case scenario for an indexed read, but more than the best case scenario for an indexed read. This would seem to suggest that if you sort your data before loading, an indexed read would be faster than a full-table scan.
Whilst it is true that pre-sorting the data of very large table will improve performance , it is not necessarily correct that the read via the index will be better overall. We also need to take into account what happens to the blocks stored in the buffer cache of the Oracle SGA and the impact this will have on other users of the database.
Let’s look at the effect on the buffer cache of reading many data blocks via an index. As we know, data and index blocks are stored in the buffer cache by Oracle for reuse by other queries by being marked as least recently used when we do an indexed read. However, those data blocks read by a full table scan are quickly aged out of the buffer cache, because they are not marked as least recently used.
What this means is that the large number of blocks (1,010,000) of index and table data read via the indexed read of our table will be saved in the SGA flushing practically all other data from it – which will obviously have an effect on other users.
Conversely, when a full table scan is performed only the last blocks read are held in the SGA (the actual number is determined by the multi-block read count) so the impact on other users of the database would be minimal.
Summary
To decide whether or not a full-table scan would be better than an indexed read, for a large table you need to consider what proportion of the table the query will retrieve from your Oracle database and consider the likely effect of that on other users. The denser the data, the more efficient a full table scan is for very large tables, but generally if you’re reading more than 1-10% of a very large table, a full table scan would be more efficient than an index scan and table lookup.
For small tables you will get much better performance from your Oracle database by caching the table in its entirety (so that it is always in memory), or by using an index-organised table then you will by relying on indexes. Having said that, every table should have a primary key index to guarantee uniqueness, but you don’t have to use it.
Dec/090
Oracle Performance Tuning Overview
There isn’t the time or space to provide an exhaustive study – whole books have been written about Oracle performance tuning. Instead we’ll just consider a few of the most important tactics and there’ll be suggestions for further reading, so you can expand your knowledge of this important subject.
One point that needs to be made clear is that performance tuning is not an exact science – you can’t always predict that a certain tactic or technique will improve performance. Every change has to be tested, because whilst it may improve performance in one area, it may also degrade performance in another area. Adding an index is a classic example of this – it may improve query performance but the performance of inserts and deletes become slower because the index entries also have to be updated.
The other thing to remember about performance tuning is that you need to decide on your goals before you start – do you need a 10% decrease in response time for queries ? Is adding new records or updating existing records 20% slower than targets ?
You also need to be aware that improving response time is not the only aim that you might have – you might want to reduce memory usage instead – which might mean having to redesign or rewrite some stored procedures. All these issues need to be considered before you start looking at performance.
Let’s start with a list of the topics that we’ll cover:
Use of full-table scans
How and when to use indexes.
How you can optimise joins.
How to use views to get a high performance database.
Why your database should NOT be normalised.
How to use stored procedures to sky-rocket Oracle performance.
How to use sub queries to boost Oracle performance
Oct/090
very basic sql optimization in mysql
It’s important to know how to write your queries fast. And there are also ways to do your queries faster Inside mysql, called Indexes. They help mysql do the work faster, but you need to tell how…
When you start getting large tables you can sometimes end up with slow running queries.
It’s important to optimize your queries and tables from the beginning, so you don’t end up with slow queries one year later when your tables have increased.
Badly created tables and queries is hard to find when you only have limited data in your tables. But there are tools to use to get a hint on if you will have problems later…
First the basics behind the scen in databases. Let’s say you are combining three tables with a join. Then mysql has to look through all three tables and then find a way to combine them in the correct way.
Take this sql query that will find the person “donald duck” and information connected to him in three tables:
SELECT a.id
FROM a, b, c
WHERE a.id = b.a_id
AND b.id = c.b_id
AND c.name = “donald duck”
Let’s say there are 2 rows in each table. What mysql does now is to start looking through all tables from the top down to the bottom and try to find matches.
The important thing to know about this is some math. Either you understand it or you just trust me that it is correct…
To combine all three tables, mysql needs to go through 2*2*2 combinations. This is 8 combinations to find the correct one.
Doesn’t sound that much of a deal, but let’s say we have 500 rows in the tables each. This gives our beloved database 500*500*500 = 1 250 000 different combinations to go through.
Luckily there are some smart people that have found a way to deal with this
It’s called “Indexes” and they help the database to cut down on the number of rows it has to go through to find the match.
The first thing you should ALWAYS do is to make the id in your tables to a PRIMARY key. Then you get that column indexed.
Look at indexes as a way to decrease the number of rows to look through to find the match. You put an index on a seperate column and you should only put it on columns that need it, otherwise your queries will go slower instead.
A basic rule that works very well is that you should put indexes on columns that your “search on” and combine in JOIN’s.
So if we look at the query above, what columns might be interesting to put indexes on?
First of all, make a.id, b.id, c.id PRIMARY keys and they get indexed automaticly.
After that I would set b.a_id and c.b_id as indexes. That is because they are used to combine the tables. If we can decrease the number of rows that needs to be looked through there, the queries will be faster.
The last column that might be interesting is c.name.
A summary:
- Make the id in each table PRIMARY
- Make columns that are “foreign keys” Indexed.
- If you have any other columns that are use in the WHERE clause, make this Indexed aswell.
This is a simplified guide on how to fasten your sql queries, but very good to know the basics.
I
will soon write a tutorial on how to check if your queries are slow or fast, and what to do about it…
Jul/090
What is a Database
Definitions:
A database is a collection of information organized into interrelated tables of data and specifications of data objects.
A table in a relational database is a predefined format of rows and columns that define an entity.
Database tables are composed of individual columns corresponding to the attributes of the object.
In a relational database, a row consists of one set of attributes (or one tuple) corresponding to one instance of the entity that a table schema describes.
Also Known As: Record
A single data item related to a database object. The database schema associates one or more attributes with each database entity.
Also Known As: field, column
A database record consists of one set of tuples for a given relational table. In a relational database, records correspond to rows in each table.
Databases are designed to offer an organized mechanism for storing, managing and retrieving information. They do so through the use of tables. If you’re familiar with spreadsheets like Microsoft Excel, you’re probably already accustomed to storing data in tabular form. It’s not much of a stretch to make the leap from spreadsheets to databases. Let’s take a look.
Database Tables
Just like Excel tables, database tables consist of columns and rows. Each column contains a different type of attribute and each row corresponds to a single record. For example, imagine that we were building a database table that contained names and telephone numbers. We’d probably set up columns named “FirstName”, “LastName” and “TelephoneNumber.” Then we’d simply start adding rows underneath those columns that contained the data we’re planning to store.
If we were building a table of contact information for our business that has 50 employees, we’d wind up with a table that contains 50 rows.
Databases and Spreadsheets
At this point, you’re probably asking yourself an obvious question – if a database is so much like a spreadsheet, why can’t I just use a spreadsheet? Databases are actually much more powerful than spreadsheets in the way you’re able to manipulate data. Here are just a few of the actions that you can perform on a database that would be difficult if not impossible to perform on a spreadsheet:
* Retrieve all records that match certain criteria
* Update records in bulk
* Cross-reference records in different tables
* Perform complex aggregate calculations
Jul/090
What is Oracle PLSQL
PL/SQL is Oracle’s SQL++ programming language providing structure and flow control extensions to SQL. The name PLSQL is derived from the term “Procedural Language extensions to SQL”.
On its own, SQL enables you to specify what you want done but not how it is done. However, you often need more control over how data is retrieved and manipulated and this is where PL/SQL comes in.
The procedural capabilities combined with standard SQL in Oracle PLSQL gives developers far more control of how their SQL statements interact with the database and makes using PL/SQL an excellent alternative to developing applications in other languages such as Java or C or VB.
The language itself is modeled on Ada, so Java/C/C++ programmers will find the syntax rather strange and probably won’t like the single”=” being used for comparison, but anyone who’s used Pascal or Ada or Modula2 will fell right at home.
PL/SQL is not a pure object-oriented language like Java or Ada, but it does support some obect-oriented features such as classification, polymorphism and, in the later versions, inheritance.
Jun/090
Advanced SQL – Sub-Queries
This advanced SQL tutorial focuses on the design of more complex SQL statements and the strategies available for implementing them, it concentrates on sub queries and joins because they are often inter-changeable, and views because these are often used to hide the complexity of queries involving sub-queries and joins.
As this is an advanced tutorial there is some consideration of performance issues, but this aspect is more thoroughly explored in our series on Oracle performance tuning.
This article talks about the following:
- Introduction to Sub-queries
- Use of sub-queries.
- Nested sub queries
- Correlated sub-queries.
Introduction to Sub-queries
Sub queries are also known as nested queries and are used to answer multi-part questions. Sub queries and joins are often interchangeable and in fact the Oracle optimiser may well treat a query containing a sub-query exactly as if it were a join.
Let’s use a trivial example of finding the names of everybody who works in the same department as a person called Jones to illustrate this point. The SQL could be written using a sub query as follows:
SELECT name FROM emp WHERE dept_no =
(SELECT dept_no FROM emp WHERE name = ‘JONES’)
or as a join statement, like this:-
SELECT e1.name FROM emp e1,emp e2
WHERE e1.dept_no = e2.dept_no AND e2name = ‘JONES’
With a trivial example like this there would probably be very little difference in terms of performance of the SQL for such a simple query, but with more complex queries there could well be performance implications. For this reason it is always worth trying a few variations of the SQL for a query and examining the execution plans before deciding on a particular approach, unless they’re very simple queries.
Learn more about Oracle performance tuning here.
Non Correlated Sub-Queries
There are, in fact, two types of sub query: correlated and non-correlated. The example shown above is a non-correlated sub query. The difference between them is that a correlated sub query refers to a column from a table in the parent query, whereas a non-correlated sub query doesn’t. This means that a non-correlated sub query is executed just once for the whole SQL statement, whereas correlated sub queries are executed once per row in the parent query.
Uses of Sub Queries
The most common use of sub queries is in the WHERE clause of queries to define the limiting condition for the rows returned (i.e. what value(s) the rows must have to be of interest), as in the previous example. However, they can also be used in other parts of the query. Specifically, sub queries can be used:
* to define the limiting conditions for SELECT, UPDATE and DELETE statements in the following clauses:-
- WHERE
- HAVING
- START WITH
Instead of a table name in
- INSERT statements
- UPDATE statements
- DELETE
- statements the FROM clause of SELECT statements
* To define the set of rows to be created in the target table of a CREATE TABLE AS or INSERT INTO sql statement.
* To define the set of rows to be included by a view or a snapshot in a CREATE VIEW or CREATE SNAPSHOT statement.
* To provide the new values for the specified columns in an UPDATE statement
The first example of sub query in SQL shown above, used a simple equality expression as we were interested in only one row, but we can also use the sub query to provide a set of rows.
For example, to find the names of all employees in the same departments as Smith and Jones, we could use the following SQL statement :-
SELECT name FROM emp WHERE dept_no IN
(SELECT dept_no FROM emp WHERE name = ‘JONES’ OR name = ‘SMITH’)
In fact, the original example could also return more than one row from the sub query if there were two or more people that were called Jones working in different departments. In the first example a run-time SQL error would be generated in that case, because the first example, by using ‘=’, specified that the sub query should produce no more than one row (it is perfectly legitimate for a sub query to return no rows).
We can reverse the question to ask for the names of all the employees that are NOT in the same department as Jones, To do this, the sense of the sub query just has to be reversed by prefixing it with ‘NOT’ or ‘!’. Again depending on whether there might be more than one Jones, we would either use ‘IN’ or ‘=’
SELECT name FROM emp WHERE dept_no NOT IN
( SELECT dept_no FROM emp WHERE name = ‘JONES’)
Or
SELECT name FROM emp WHERE dept_no !=
( SELECT dept_no FROM emp WHERE name = ‘JONES’)
Nested Sub-Queries
The SQL syntax allows queries to be nested, meaning that a sub query itself can contain a sub query, enabling very complex queries to be built as there is no syntacttical limit to the level of besting. However, very complex queries should be avoided as they are difficult to understand and to maintain and may not perform that well either.
For example, the SQL statement to find the departments that have employees with a salary higher than the average employee salary could be written as:
SELECT name FROM dept
WHERE id IN
(
SELECT dept_id FROM emp
WHERE sal >
(SELECT avg(sal)FROM emp)
)
Any of the other comparison operators instead of ‘=’ or ‘IN’ such as ‘>’, or ‘<’ can also be used with a sub query.
Sub Queries In The From Clause
The examples so far in this advanced SQL tutorial all had sub queries in the where clause, but sub queries can also be used in the from clause instead of a table name. In these circumstances the sub query acts as if it had been predefined as a view.
For example, the following SQL statement returns the amount of used space, the free space and the total allocated space for all tablespaces in a database.
SELECT ts.tablespace_name
,ROUND(fs.mbytes,2) “Free (Mbytes)”
FROM dba_tablespaces ts
,(
SELECT tablespace_name
,SUM(bytes)/1024/1024 mbytes
FROM dba_free_space
GROUP BY tablespace_name
) fs
WHERE ts.tablespace_name = fs.tablespace_name
Note that the sub query is given an alias so that results can be used in the main body of the query.
Sub Queries That Return No Rows
Up until now the queries shown have all been expected to produce a result, but when creating tables, it can be very useful to write the SQL to use a sub query which will not return any rows – when just the table structure is required and not any of the data.
In the following example we create a copy of the policy table with no rows:
CREATE TABLE new_policy AS
SELECT * from policy WHERE 1=0;
The sub query returns no data but does return the column names and data types to the ‘create table’ statement.
Correlated Sub-Queries
As we’ve seen already, there are two types of sub query: correlated and non-correlated. We’ve already looked at non-correlated sub queries (see advanced SQL tutorial part 1). All of the examples of sub queries up until now have been non-correlated sub queries.
Just like non-correlated sub queries, correlated sub queries are used to answer multi-part questions, but they are most often used to check for existence or absence of matching records in the parent table and the related table in the sub query.
A correlated sub query refers to a column from a table in the parent query. As mentioned in part 1 sub-queries (both correlated and non-correlated) and joins are usually interchangeable. However the SQL may be significantly faster when a correlated sub-query is used as correlated sub queries refer to a column from their parent queries, they are executed once per row in the parent query whereas non-correlated sub queries are executed once for the whole statement.
For example, using the emp and dept tables from before, to find out which departments have no employees assigned to them, we can write the SQL statement in 3 different ways – as a non-correlated sub query, as an outer join, or as a correlated sub-query.
Example 1 – non-correlated sub query
SELECT dept.name FROM dept
WHERE dept.id NOT IN
(
SELECT dept_id
FROM emp
WHERE dept_id IS NOT NULL
)
Example 2- outer join
SELECT dept.name FROM dept,emp
WHERE emp.dept_id (+) = dept.id
Example 3 – correlated sub query
SELECT dept.name FROM dept
WHERE NOT EXISTS (SELECT dept_id
FROM emp
WHERE emp.dept_id = dept.id)
The second example is an outer join SQL statement which may produce differnt reults to the other 2 queries as it returns both matching rows and the non-matching rows on one side of the join. In this case the query would return the names of departments which have no employees assigned to them plus the names of those departments that do have employees assigned to them.
The first and the third SQL statements would produce exactly the same results, but the first would probably be slower than the third if the dept_id column in the emp table were indexed (depending on the sizes of the tables).
The first SQL statement can not use any indexes – the where clause of the sub query is just checking for NOT NULL rows – so a full table scan would be performed. Also the sub query would be executed once for each row in the dept table.
On the other hand, the sub query in the third example can use the index and since only the dept_id is returned by the sub query, there is no need for any subsequent table access. For these reasons, the third query would normally perform better than the first.
As you can see there are nearly always several ways in which the SQL for a query may be written, and it is therefore best to try alternative SQL statements particularly for complex queries before deciding on the preferred one.