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SQL Indexes

An SQL index is an independently stored data structure that belongs to a table. A table can have zero or more indexes. When used appropriately, an SQL index can speed up your query. SQL indexes are primarily used with WHERE, JOIN or ORDER BY clauses to optimize the access of few records. Indexes are not typically used in analytical environments where queries access big portions of the involved tables.

In these examples, there is an index on table population with a key on the last_name column. This query uses the index:

SELECT ssn, last_name 
FROM population 
WHERE last_name = ‘Smith1234567’

This query cannot use the index as the WHERE clause is using the first_name column:

SELECT ssn, last_name 
FROM population 
WHERE first_name = ‘John1234567’

Cost of SQL Indexes

Since indexes are data structures associated with a table, all changes to the table must be also applied to the index. Inserts, deletes, and updates to tables with indexes are slower as they perform more write operations.

Before creating an index, evaluate the cost benefit relationship. You should only create an index where the benefit exceeds the cost. To identify the indexes that have the best benefit:

  1. Identify frequently executed queries with slow response times.
  2. Identify which queries can be accelerated using an index.
  3. Evaluate cost-benefit ratio of the proposed index.

Verify Indexes are being Used

If you have a slow query with an index, verify the index is actually being used. Once technique is the EXPLAIN statement. Review Show Me the Best Execution Plan in the Query Optimization tutorial.

Here is the query:

SELECT ssn, last_name 
FROM population 
WHERE lower ( last_name ) = ‘smith 10000002’

Run the query with the EXPLAIN execution plan:

Query Using EXPLAIN Execution Plan

In the EXPLAIN output you see it ran a Sequential Scan instead of the expected Index Scan on the last_name column. What happened? The problem is the lower() function. You cannot have a function or operation on a column if you want to use it as an index.

Refactor your query:

SELECT ssn, last_name 
FROM population 
WHERE  last_name = ‘smith 10000002’

Run the EXPLAIN execution plan on the refactored query:

Refactored Query Using EXPLAIN Execution Plan

The expected Index scan is used.

SQL Indexes Based on Expressions

As you learned, if you use a function or operation in the WHERE clause on the index key column, the index is not used in the query. Instead, a sequential scan is run.

To solve this limitation, some databases offer a special Functional index to support an expression as the index key.

In the PostgreSQL dialect you can use CREATE INDEX to create a functional index using the expression lower(last_name):

CREATE INDEX functional_index
ON population ( lower(last_name) )

Run the EXPLAIN execution plan on the query:

Indexes Based on Expressions

Success! The query execution plan is using an index scan.

Query Refactorization

In SQL, there are many ways to write a query to return the same results. You can refactor a slow query to use different clauses or conditions while returning the same results.

Here are some refactor techniques to try and improve query performance.

Reduce the Record Quantity

A slow query which returns a few records may actually be processing many records in the early stages of the query, with the more restrictive filters (to reduce the result set) applied at the end of the query processing.
In this case, you can use a Common Table Expression (CTE) to apply the restrictive filter first, then execute the query on the CTE table.

In these examples, the query is used to obtain the first last_name in each zip_code.

Here is the standard query, with a 7 second execution time:

Query Without Using CTE

Here is the refactored query using a CTE to reduce the number of records. The execution time is 5.4 seconds.

Query Using CTE

Avoid Correlated Subqueries

A correlated subquery is a subquery that refers to columns of the outer query. The problem with correlated subqueries is the high number of times it must be executed: once for each row processed by the outer query. Avoid correlated subqueries whenever possible.

This example uses a correlated subquery to obtain the zip_code for cities with a population of 1000 or above. The execution time is 1 minute 37 seconds

Query With Correlated Subquery

Here is the refactored query without using the correlated subquery. The execution time is 17.2 seconds.

Query Without Correlated Subquery

Consider a Materialized View

SQL views obtain the view records online using in the FROM clause. The online calculation of the view records can affect the query performance. Materialized views can improve query performance in some cases. There are considerations with using materialized views. For example, the view must be refreshed frequently for accurate results.

For this region population example, data refreshed daily is highly accurate.

This view groups any zip_code that starts with the same 3 digits and obtains the population in the region:

CREATE VIEW region_population AS
SELECT substring(zip_code, 1, 7) AS region, count(*) AS pop
  FROM  population
GROUP BY substring(zip_code, 1,7)

This is the query to obtain the population for the regions in the range City200 to City250:

SELECT region, pop 
FROM   region_population 
WHERE  region BETWEEN ‘City200’ AND ‘Citi250’

When the query is executed, the view region_population records are calculated as part of the query, adding extra time to the query execution time.

If you create a materialized view, the view records are not calculated each time the query is run. This example creates the materialized view:

CREATE MATERIALIZED VIEW region_population AS
SELECT substring(zip_code, 1, 7) AS region, count(*) AS pop
  FROM  population
GROUP BY substring(zip_code, 1,7)

Execute the query:

SELECT region, pop 
FROM   region_population 
WHERE  region BETWEEN ‘City200’ AND ‘Citi250’

The query is faster without having to calculate the view records.

Add Directives to the Planner

This is a non-standard technique offered by only a few SQL database engines.

Some database engines support adding execution directives in the query. For example, you can specify an index to use, or an index to not use, and what table to read first.

For example, this Oracle query includes a directive for which index to use:

SELECT /*+ INDEX (employees emp_department_ix)*/ employee_id, department_id
  FROM employees 
  WHERE department_id > 50;

The optimizer directive is included as a comment, enclosed with /* */ to specify the index emp_department_ix of the table employees.

Closing Words

In this lesson you learned about indexes, functional indexes, and techniques to refactor your queries to take advantage of indexes. Keep going, learn SQL and increase your skills!