Optimizing SQLite Queries with Cross Joins and Materialized CTEs

Understanding the Performance Discrepancy Between SQLite and PostgreSQL

The core issue revolves around a SQL query that performs significantly slower in SQLite compared to PostgreSQL. The query involves multiple Common Table Expressions (CTEs) and cross joins, which are used to generate and filter combinations of rows from a series of numbers. The query calculates fifth powers of integers and performs joins to find matching sums and differences of these powers. While PostgreSQL handles this query efficiently, SQLite struggles, leading to slow execution times.

The query structure is as follows:

WITH t AS (
    SELECT i, i*i*i*i*i AS i5 
    FROM (SELECT value+1 i FROM generate_series(1,150))
),
t3 AS (
    SELECT a.i ai, b.i bi, c.i ci, a.i5 + b.i5 + c.i5 i5 
    FROM t a, t b, t c 
    WHERE a.i < b.i AND b.i < c.i
),
t45 AS (
    SELECT d.i di, e.i ei, e.i5 - d.i5 i5 
    FROM t d, t e 
    WHERE d.i < e.i
)
SELECT ai, bi, ci, di, ei 
FROM t3, t45 
WHERE t3.i5 = t45.i5 AND t3.ci < di;

The query generates a series of numbers, calculates their fifth powers, and then performs cross joins to find combinations of these powers that satisfy specific conditions. The performance bottleneck arises due to the combinatorial nature of the joins and the lack of optimization in SQLite’s query planner for such scenarios.

Causes of Slow Query Performance in SQLite

The slow performance of the query in SQLite can be attributed to several factors:

1. Inefficient Query Planning: SQLite’s query planner may not optimize the execution plan effectively for complex queries involving multiple CTEs and cross joins. Unlike PostgreSQL, which has a more sophisticated query planner, SQLite may generate suboptimal plans that result in excessive computation.

2. Repeated Computation of CTEs: Without materialization, SQLite may recompute the CTEs multiple times during query execution. This is particularly problematic for queries with nested CTEs, as the same calculations are repeated unnecessarily.

3. Cross Join Overhead: Cross joins generate a Cartesian product of the involved tables, leading to a significant increase in the number of rows processed. In this query, the cross joins between t3 and t45 result in a large intermediate result set, which is then filtered. SQLite may struggle to handle such large intermediate results efficiently.

4. Version-Specific Behavior: The performance of the query varies across different versions of SQLite. For example, version 3.37 executes the query slowly, while version 3.39 performs better. This inconsistency suggests that the query planner’s behavior and optimizations have evolved over time, leading to varying performance outcomes.

5. Lack of Indexing: The query does not utilize indexes, as it operates on generated data rather than existing tables. This absence of indexing further exacerbates the performance issues, as SQLite cannot leverage index-based optimizations.

Resolving Performance Issues with Materialized CTEs and Version-Specific Optimizations

To address the performance issues, the following troubleshooting steps and solutions can be implemented:

1. Materializing CTEs

Materializing CTEs can significantly improve query performance by preventing repeated computations. The MATERIALIZED keyword instructs SQLite to compute the CTE once and store the result, which can then be reused throughout the query. This approach reduces redundant calculations and speeds up execution.

The modified query with materialized CTEs is as follows:

WITH t AS MATERIALIZED (
    SELECT i, i*i*i*i*i AS i5 
    FROM (SELECT value+1 i FROM generate_series(1,150))
),
t3 AS MATERIALIZED (
    SELECT a.i ai, b.i bi, c.i ci, a.i5 + b.i5 + c.i5 i5 
    FROM t a, t b, t c 
    WHERE a.i < b.i AND b.i < c.i
),
t45 AS MATERIALIZED (
    SELECT d.i di, e.i ei, e.i5 - d.i5 i5 
    FROM t d, t e 
    WHERE d.i < e.i
)
SELECT ai, bi, ci, di, ei 
FROM t3, t45 
WHERE t3.i5 = t45.i5 AND t3.ci < di;

This modification ensures that each CTE is computed only once, reducing the overall computation time.

2. Version-Specific Considerations

The performance of the query varies across SQLite versions due to differences in the query planner’s behavior. For example:

• Version 3.37: Executes the query slowly, generating a plan with 144 rows.
• Version 3.39: Executes the query faster, generating a plan with 123 rows.
• Version 3.45: Fails to execute the query, resulting in an error.

To ensure consistent performance, it is recommended to use a version of SQLite that supports the MATERIALIZED keyword and provides efficient query planning. Testing the query across multiple versions can help identify the most suitable version for the specific use case.

3. Analyzing Query Plans

Examining the query plan can provide insights into the execution strategy and identify potential bottlenecks. The EXPLAIN command can be used to generate the query plan:

EXPLAIN QUERY PLAN
WITH t AS MATERIALIZED (
    SELECT i, i*i*i*i*i AS i5 
    FROM (SELECT value+1 i FROM generate_series(1,150))
),
t3 AS MATERIALIZED (
    SELECT a.i ai, b.i bi, c.i ci, a.i5 + b.i5 + c.i5 i5 
    FROM t a, t b, t c 
    WHERE a.i < b.i AND b.i < c.i
),
t45 AS MATERIALIZED (
    SELECT d.i di, e.i ei, e.i5 - d.i5 i5 
    FROM t d, t e 
    WHERE d.i < e.i
)
SELECT ai, bi, ci, di, ei 
FROM t3, t45 
WHERE t3.i5 = t45.i5 AND t3.ci < di;

The query plan reveals the steps taken by SQLite to execute the query, including the order of joins and the use of intermediate results. Comparing the plans across different versions can help identify optimizations and inefficiencies.

4. Alternative Approaches

If materialization and version-specific optimizations do not yield satisfactory results, alternative approaches can be considered:

• Precomputing Data: Precompute the fifth powers and store them in a table with appropriate indexes. This reduces the computational overhead during query execution.
• Breaking Down the Query: Split the query into smaller, more manageable parts. Execute each part separately and combine the results programmatically.
• Using Temporary Tables: Store intermediate results in temporary tables and perform joins on these tables. This approach can reduce the complexity of the query and improve performance.

5. Testing and Benchmarking

Thorough testing and benchmarking are essential to validate the effectiveness of the optimizations. Compare the execution times and query plans across different versions of SQLite and alternative approaches. Use tools like sqlite3‘s .timer command to measure execution times accurately.

By understanding the causes of the performance issues and implementing the suggested solutions, the query can be optimized to run efficiently in SQLite. Materializing CTEs, analyzing query plans, and considering version-specific optimizations are key steps in achieving this goal. Additionally, exploring alternative approaches and conducting rigorous testing can further enhance performance and ensure consistent results across different environments.