Turbocharge PostgreSQL: Writing Lightning-Fast SQL Queries
PostgreSQL is a powerful open-source relational database known for its reliability and feature-richness. However, achieving optimal query performance can be challenging, especially as data volumes grow. Fortunately, by leveraging techniques like indexing, query optimization, and advanced PostgreSQL features, it's possible to dramatically improve the speed of SQL queries and ensure your application remains performant at scale.
Using Hash-Based Indexes for Speed
Hash indexes in PostgreSQL can provide significant performance benefits for certain types of queries, especially those involving equality predicates on large tables. Unlike B-tree indexes, which are optimized for range scans and sorting, hash indexes excel at rapid lookup of exact matches.
Best Use Case : WHERE clauses that use the `=` operator on indexed columns.
The key advantage of hash indexes is their ??(1) average lookup time, compared to the ??(log??) lookup time of B-tree indexes. In practice, this can translate to speedups of 10-60% for equality searches on large tables with hash indexes.
limitations of hash indexes: They do not support range searches, inequality operators like [ <>, !=, >, <, >=, <= ], or sorting operations. Additionally, hash indexes are not WAL-logged, meaning they are not crash-safe and may need to be rebuilt after a database crash. Hash indexes also do not enforce uniqueness constraints, so they cannot be used as primary keys or unique indexes.
Despite these caveats, hash indexes can be a powerful tool for accelerating certain PostgreSQL queries. One common use case is indexing large text columns that are frequently searched by equality, such as URLs or email addresses.
Use the CREATE INDEX command with the USING HASH option:
CREATE INDEX idx_hash ON table_name USING HASH (column_name);
Keep in mind that hash indexes do not automatically stay up-to-date with changes to the indexed column. Inserts, updates, and deletes will require manually reindexing the affected hash indexes using the REINDEX command.In summary, hash indexes are a specialized but effective indexing technique in PostgreSQL for accelerating exact-match queries on large datasets. By carefully applying them to the right use cases and columns, it's possible to achieve significant speedups and space savings compared to B-tree indexes.
Optimizing DISTINCT Queries with RECURSIVE CTEs
Using Recursive Common Table Expressions (CTEs) can make some DISTINCT queries run much faster in PostgreSQL. Imagine you have a big list and you want to find unique values. Normally, you would have to look at every single item in the list. But with a recursive CTE, you can jump from one unique value to the next, skipping over duplicates.
This “skip scan” method is much quicker than checking the whole list. The trick is to set up the recursive CTE to find the first unique value, then keep finding the next unique value that’s bigger than the last one, until you’ve gone through all the values you need.
For example:
Dataset:
Let’s assume we have a table employees with columns department and employee_id.
Objective:
We want to find the distinct employee_id for each department.
With Recursive CTE
? It finds distinct employee_ids for each department efficiently.
WITH RECURSIVE distinct_employees AS (
(
SELECT department, employee_id
FROM employees
ORDER BY department, employee_id
LIMIT 1
)
UNION ALL
(
SELECT e.department, e.employee_id
FROM distinct_employees de, LATERAL (
SELECT emp.department, emp.employee_id
FROM employees emp
WHERE (emp.department, emp.employee_id) > (de.department, de.employee_id)
ORDER BY emp.department, emp.employee_id
LIMIT 1
) e
)
)
SELECT department, array_agg(employee_id)
FROM distinct_employees
GROUP BY department;
Without CTE
? It finds distinct employee_ids but might be slower for larger tables.
SELECT department, array_agg(DISTINCT employee_id)
FROM employees
GROUP BY department;
Leveraging Prepared Statements for Faster Execution
Using prepared statements in PostgreSQL is a great way to make your queries run faster. Instead of parsing and planning a query every time you run it, PostgreSQL does this work once and reuses the plan for future executions with different values. This reduces the overhead of repeatedly parsing and planning, making query execution quicker, especially for complex and frequently run queries. For example, you prepare a statement to find products below a certain stock level once and then execute it with different stock levels as needed, saving significant time. This method can improve performance by up to 10 times for certain types of queries.
How to Use Prepared Statements in PostgreSQL
1. Prepare the Statement:
PREPARE get_low_stock AS
SELECT * FROM products WHERE stock < $1;
2. Execute the Statement with Different Values:
EXECUTE get_low_stock(10);
EXECUTE get_low_stock(20);
Stage 1 Predicates Boost Performance
When optimizing SQL queries in PostgreSQL, it's important to understand the difference between stage 1 and stage 2 predicates. Stage 1 predicates, also known as "sargable" predicates, can be evaluated directly against an index, allowing the query optimizer to drastically reduce the number of rows that need to be scanned. In contrast, stage 2 predicates cannot leverage indexes and must be evaluated against each individual row returned by the stage 1 predicates.Some common examples of stage 1 predicates include:
? Equality comparisons (e.g., COL = value)
? Range comparisons (e.g., COL > value, COL BETWEEN value1 AND value2)
? LIKE comparisons with a constant prefix (e.g., COL LIKE 'prefix%')
? IN lists (e.g., COL IN (value1, value2, value3))
? IS NULL checks (e.g., COL IS NULL)
Stage 2 predicates, on the other hand, involve things like:
? Inequality comparisons (e.g., COL `>` value)
? LIKE comparisons without a constant prefix (e.g., COL LIKE '%substring%')
? Complex expressions (e.g., UPPER(COL) = 'VALUE')
? Subqueries (e.g., COL IN (SELECT ...))
To illustrate the performance impact, consider a query with both stage 1 and stage 2 predicates:
SELECT *
FROM large_table
WHERE
indexed_col = 42 -- stage 1
AND
unindexed_col > 100 -- stage 2
PostgreSQL can use the index on indexed_col to quickly find the rows where indexed_col = 42. But it must then scan each of those rows to evaluate the stage 2 predicate unindexed_col > 100. If the stage 1 predicate is not selective enough, this can result in a lot of unnecessary row scanning.
In general, it's best to structure queries to maximize the use of stage 1 predicates and minimize stage 2 predicates.
This may involve:
By carefully designing queries and indexes around stage 1 predicates, it's possible to significantly improve the performance of read-heavy PostgreSQL workloads.