Unleash Query Speed: 5 Ways EXPLAIN ANALYZE Optimizes Your Composite Indexes

You’ve heard that indexes speed up database queries. You might even have already created composite indexes on your tables, hoping to improve performance for queries that filter on multiple columns. That’s a great start!

But how do you know if your composite index is actually being used effectively by your database’s optimizer? Are your multi-column indexes configured correctly for your most important queries? This is where a powerful tool comes in: EXPLAIN ANALYZE.

While EXPLAIN shows you the database’s planned execution strategy for a query, EXPLAIN ANALYZE takes it a step further. It actually runs the query and reports back on what really happened, including execution times and the number of rows processed at each step. This real-world insight is invaluable for EXPLAIN ANALYZE composite index tuning.

Let’s explore how to use EXPLAIN ANALYZE to diagnose and fix performance issues related to your composite indexes.

Composite Indexes: A Quick Recap

Just to make sure we’re on the same page, a composite index is an index created on two or more columns of a table. For example, on an orders table, you might create a composite index on (customer_id, order_date). This helps queries that filter by both customer_id and order_date find data faster than using single-column indexes or scanning the whole table.

Why Just EXPLAIN Isn’t Enough (Introducing ANALYZE)

EXPLAIN is helpful. It shows you the steps the database optimizer thinks it will take to run your query. It provides estimated costs and estimated numbers of rows.

However, these are just estimates. If the database statistics are outdated, or if the data distribution is different than expected, the optimizer’s plan might be far from optimal.

EXPLAIN ANALYZE runs the query, collects actual runtime statistics, and adds them to the plan output. This removes the guesswork. You see exactly which steps took the longest, how many rows were actually processed, and whether the database’s initial estimates were accurate.

What EXPLAIN ANALYZE Shows You (Key Metrics)

When you run EXPLAIN ANALYZE (syntax varies slightly by database, e.g., EXPLAIN ANALYZE SELECT ... in PostgreSQL/MySQL, EXPLAIN PLAN FOR ... SELECT ...; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY); in Oracle), you get a detailed plan. The key things to look for are:

• Plan Nodes: These are the operations the database performs (e.g., Seq Scan, Index Scan, Hash Join, Sort).
• Costs: Two numbers, e.g., (cost=0.00..10.50 rows=10 width=100). The first is the estimated startup cost, the second is the total estimated cost. ANALYZE adds the actual total time.
• Rows: (rows=10 width=100). The first is the estimated number of rows this node will output. ANALYZE adds actual rows=10. Comparing these shows you if the optimizer guessed correctly.
• Timing: (actual time=0.050..0.100 rows=10 loops=1). The first is the actual time to get the first row, the second is the total actual time spent in this node. This is crucial for finding bottlenecks.
• Loops: How many times this plan node was executed.
• Filters/Conditions: Details about the WHERE clause conditions being applied at this step (e.g., Index Cond: (customer_id = 100), Filter: (order_date >= '...')).

Using EXPLAIN ANALYZE to Tune Composite Indexes: 5 Key Strategies

Here’s how to leverage this information to specifically optimize your composite indexes:

Strategy 1: Check if Your Composite Index is Being Used At All

This is the most basic check. You created a composite index, but is the database using it for the query you intended?

• What to Look For: In the EXPLAIN ANALYZE output, you want to see an Index Scan or Index Only Scan operation on your composite index. If you see a Seq Scan (or Table Scan in some databases) on the table involved in your composite index columns, the index is not being used for this query’s primary access path.
• Why it Happens: The query conditions might not match the index’s leading columns (leftmost prefix rule!), statistics might be outdated, or the table might be too small for the index to be beneficial.
• Example (PostgreSQL syntax):
  • Bad: -> Seq Scan on orders (actual time=50.000..150.000 rows=1000000 loops=1)
  • Good: -> Index Scan using orders_cust_date_idx on orders (actual time=0.100..0.500 rows=50 loops=1)

Strategy 2: Verify the Right Composite Index is Used

Sometimes, the database has multiple possible indexes (single column or composite) it could use, and it picks one. EXPLAIN ANALYZE shows you which one it chose. Is it the one you designed specifically for this multi-column filter?

• What to Look For: The Index Scan using index_name line. Confirm index_name is your intended composite index.
• Why it Happens: The optimizer estimates the cost of using different indexes and picks the lowest. Its estimates might be wrong, or another index might genuinely be better for the specific form of this query (e.g., it only uses the first column of your composite index).
• How to Tune: If the wrong index is picked and your intended composite index should be faster, update statistics, reconsider the column order in your composite index, or, as a last resort for testing, use an optimizer hint to force the intended index.

Strategy 3: Assess How Much of the Composite Index is Used (Leftmost Prefix)

A composite index on (A, B, C) is excellent for WHERE A=x AND B=y AND C=z. It’s still good for WHERE A=x AND B=y. It can be okay for WHERE A=x. But it’s generally poor for WHERE B=y AND C=z. EXPLAIN ANALYZE helps you see if the database is using the full power of your composite index.

• What to Look For: Examine the Index Cond: and Filter: clauses within the Index Scan node. Index Cond means the condition is used to navigate the index tree (very efficient). Filter means the condition is applied after reading index entries (less efficient).
• Why it Happens: The columns in your WHERE clause don’t align with the leading columns of the composite index, or the optimizer determined it was cheaper to scan a portion of the index and then filter.
• Example (PostgreSQL syntax):
  • Index: (customer_id, order_date)
  • Query: WHERE customer_id = 100 AND order_date >= '2024-01-01'
  • Good: -> Index Scan using orders_cust_date_idx on orders (actual time=...) Index Cond: (customer_id = 100) – The order_date condition is likely applied implicitly during the scan.
  • Less Ideal (if order_date was first in index): -> Index Scan using orders_date_cust_idx on orders (actual time=...) Index Cond: (order_date >= '2024-01-01') Filter: (customer_id = 100) – The customer_id is filtered after the date scan. Reordering the index to (customer_id, order_date) might be better.

Strategy 4: Spot Mismatched Estimates (Cardinality Issues)

Compare the rows= estimate with the actual rows= value at each node. Significant differences, especially early in the plan, indicate the optimizer had poor cardinality estimates.

• What to Look For: Large discrepancies between estimated and actual row counts. For example, (rows=10000 actual rows=1000000).
• Why it Happens: Outdated table/index statistics are the most common cause. Skewed data distribution can also fool the optimizer.
• How to Tune: Update your table and index statistics regularly. This gives the optimizer accurate information to choose the best plan and utilize your composite indexes correctly.

Strategy 5: Identify Expensive Operations Post-Index Scan

Even if your composite index is used, look at the steps that happen after the Index Scan. Is there a very costly Sort or Hash Join operation?

• What to Look For: Plan nodes after the index scan with high actual time.
• Why it Happens: The index might not be “covering” the query (not including all columns needed by the SELECT list), forcing costly lookups to the table. Or the query requires sorting on columns not in the index order.
• How to Tune: Consider adding frequently selected columns to your composite index (as included columns in SQL Server, or simply adding to the key in MySQL/PostgreSQL if appropriate). This can create a covering index, avoiding table lookups. Review the ORDER BY clause – can your composite index be reordered or extended to match the sort order?

How to Run EXPLAIN ANALYZE

The basic syntax in PostgreSQL and MySQL is:

EXPLAIN ANALYZE
SELECT column1, column2
FROM your_table
WHERE columnA = 'value1' AND columnB > 100;

In Oracle, it’s a two-step process:

EXPLAIN PLAN FOR
SELECT column1, column2
FROM your_table
WHERE columnA = 'value1' AND columnB > 100;

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(FORMAT => 'ALLSTATS LAST')); -- Use ALLSTATS LAST after running the query

• PostgreSQL EXPLAIN Documentation
• MySQL EXPLAIN Documentation
• Oracle EXPLAIN PLAN Documentation

Interpreting Complex Plans

EXPLAIN ANALYZE output can be nested. Read it from the innermost node (the indented ones, often starting with table access like Seq Scan or Index Scan) outwards. The output shows the flow of data and operations. The total time reported at the very top is the total execution time for the query.

Iteration is Key: Tune, Analyze, Repeat

Performance tuning with EXPLAIN ANALYZE is an iterative process:

1. Identify a slow query (using slow query logs or monitoring).
2. Run EXPLAIN ANALYZE on the slow query.
3. Analyze the output using the strategies above. Identify bottlenecks and potential index issues.
4. Make a change (e.g., create a new composite index, modify an existing one, update statistics).
5. Run EXPLAIN ANALYZE again on the same query to see the impact of your change.
6. Repeat until the query performance is satisfactory.

Conclusion

Creating composite indexes is a great step towards better database performance, but without verifying their effectiveness, you might be missing out on significant speed gains. EXPLAIN ANALYZE is an essential tool in your tuning toolkit, providing real-world data on how your queries are executed.

By using EXPLAIN ANALYZE to check if your composite index is used, verify the right one is chosen, understand how much of the index is utilized, spot bad estimates, and identify post-index bottlenecks, you can unleash the true query speed your composite indexes can provide.

Don’t guess whether your indexes are working – know for sure with EXPLAIN ANALYZE!

What are your favorite tips for using EXPLAIN ANALYZE? Share them in the comments below!