Indexes play a crucial role in improving query performance when accessing data in a Db2 database. By creating indexes on specific columns, the Db2 optimizer can more quickly locate the desired data, reducing the need for full table scans and enabling faster query execution. Understanding and implementing efficient indexing strategies can greatly enhance the overall performance of a database and the applications that access it.
One of the first things you need to do is to understand the type of indexes used by Db2, which are B-tree indexes. B-tree indexes are commonly used and efficient for many situations. The general idea behind B-tree indexing is to improve the process of accessing data by making it easier to search through data faster. A B-tree stores data such that each node contains keys in ascending order. Each of these keys has two references to another two child nodes. The left side child node keys are less than the current keys, and the right side child node keys are more than the current keys.
The first important factor in efficient indexing is carefully selecting the columns to be indexed. Not all columns require an index, and indexing too many columns can lead to unnecessary overhead. It is essential to identify the columns frequently used in WHERE clauses, JOIN conditions, or ORDER BY and create indexes on those columns. This targeted approach ensures that the indexes are focused on the most critical areas and deliver optimal query performance.
Regular index maintenance is vital for sustaining performance gains. Over time, indexes can become fragmented or outdated due to data modifications. Fragmented indexes can hinder query performance, so it is important to periodically monitor and address index fragmentation. DB2 provides utilities to reorganize or rebuild indexes, reducing fragmentation and improving query response times.
Considerations for index fragmentation include setting appropriate free space, which is the percentage of space used on each index page when data is loaded. It is important to strike a balance between minimizing space consumption and reducing index maintenance.
Furthermore, it is crucial to monitor the impact of index usage on overall system performance. Query plans and performance monitoring tools provide insights into index usage and can identify situations where indexes are not effectively utilized. Identifying unused or underutilized indexes allows for their removal or modification, reducing storage overhead and improving overall database performance.
The following list of 10 steps can be used to ensure that you are using an effective indexing strategy for your Db2 databases and applications:
1. Index by workload, not by object
Many people make the mistake of just guessing at some indexes to create when they are creating other database objects (like tables and table spaces). But without an idea of how the tables are going to be accessed these guesses are usually wrong – at least some of them.
Indexes should be built to optimize the access of your SQL queries. To properly create an optimal set of indexes requires a list of the SQL to be used, an estimate of the frequency that each SQL statement will be executed, and the importance of each query. Only then can the delicate balancing act of creating the right indexes to optimize the right queries most of the time be made.
If you are doing it any other way, you are doing it wrong.
2. Build indexes based on predicates
3. Index most-heavily used queries
Numbers 2 and 3 can be thought of as corollaries to Number 1… that is, these are the aspects of application workload that need to be examined to produce appropriate and effective indexes.
Look at the predicates of the queries in the workload and work to create a set of indexes that match up to most (if not all) of them. When it is not practical to build all of the indexes you have identified, then it makes sense to look at the queries that will be used most often and optimize them first.
4. Index "important" queries
The more important the query, the more you might want to tune by index creation. If you are coding a query that the CIO will run every day, you want to make sure it delivers optimal performance. So building indexes for that particular query is important. On the other hand, a query for a clerk might not necessarily be weighted as high, so that query might have to make do with the indexes that already exist.
Of course, the decision should depend on the application’s importance to the business - not just on the user’s importance. For example, if that clerk runs a query hundreds of times a day that impacts revenue, but the CIO runs his query infrequently, then perhaps the clerk's query is more important.
5. Index to avoid sorting (GROUP BY, ORDER BY)
In addition to building indexes to optimize data access, indexes can be used to avoid sorting. The GROUP BY and ORDER BY clauses tend to invoke sorts, which can cause performance slowdowns. By indexing on the columns specified in these clauses, the optimizer can use an index to avoid a sort, and thereby potentially improve performance.
6. Create indexes for uniqueness (PK, U)
Some indexes are required to make the database schema valid. For example, Db2 requires that unique indexes be created when unique and primary key constraints exist. These indexes are not optional.
7. Create indexes for foreign keys
Even if not required, creating indexes for each foreign key can optimize the performance when accessing and enforcing referential constraints (RI – referential integrity).
8. Consider adding columns for index-only access
Sometimes it can be advantageous to include additional columns in an index to increase the chances of index-only access. With index-only access, all of the data needed to satisfy the query can be found in the index alone — without having to read data from the tablespace.
For example, suppose that there is an index on the DEPTNO column of the DEPT table. The following query may use this index:
SELECT DEPTNAME |
The index could be used to access only those columns with a DEPTNO greater than D00, but then Db2 would need to access the data in the table space to return the DEPTNAME. If you added DEPTNAME to the index, that is, create the index on (DEPTNO, DEPTNAME) then all of the data needed for this query exists in the index and additional I/O to the table space would not be needed. This technique is sometimes referred to as index overloading.
Of course, this is not always a good idea. You have to take into account whether other queries use the index and how it might negatively impact their performance.
It is also worth mentioning index include columns, which allows you to define additional (included) columns that are not part of the actual key but are available in the index. So we can create an index like this:
CREATE INDEX IXNAME
ON DEPT ( DEPTNO )
INCLUDE ( DEPTNAME )An example of a bad practice would be to place an artificial limit on indexing. There should be no arbitrary limit on the number of indexes that you can create for any database table. Indexes are undoubtedly one of the most important factors in creating efficient queries. Relational optimizers rely on indexes to build fast access paths to data. Without indexes data must be scanned – and that can be a long, inefficient means by which to retrieve your data. When a rule such as this exists, it usually is stated something like this...“Each table can have at most five indexes created for it” — or — “Do not create more than three indexes for any single table in the database.” These are bad standards.
If you already have three indexes, or five indexes, or even a dozen indexes -- and another index will improve performance -- why would you arbitrarily want to avoid creating that index? Item 10 below discusses one reason, but otherwise, the downside is only that you will need to manage the index.
Anyway, a good indexing standard, if you choose to have one, should read something like this: “Create indexes as necessary to support your database queries. Limitations on creating new indexes should only be entertained when they begin significantly to impede the efficiency of data modification.”
Which brings us to…
10. Be aware of I/U/D implications
Db2 must automatically maintain every index you create. This means every INSERT and every DELETE to an indexed table will insert and delete not just from the table, but also from its indexes.
Additionally, when you UPDATE the value of a column that has been defined in an index, the DBMS must also update the index. So, indexes speed the process of retrieval but slow down modification.
So the general rule of thumb should be "Index until it hurts... and then back off the least effective index to make it no longer hurt." Sure, that is easier said than done, but it is a valid philosophy to follow.
Summary
In conclusion, efficient indexing strategies are vital for enhancing query performance in IBM Db2. By understanding the role of indexes in query execution and following best practices such as selecting appropriate index types, carefully choosing indexed columns, addressing index fragmentation, and monitoring index usage, database administrators can optimize the database's performance. Effective indexing improves query response times, reduces resource consumption, and contributes to a DB2 environment with high performance!
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