Database Table Partitioning: Faster Queries & Data Management

Database Table Partitioning: Faster Queries & Data Management

We implement database table partitioning. The problem of data growth is one of the most common causes of performance degradation. In a recent project, a client's events table had grown to 500 million rows, and queries were hanging for 30 seconds. We implemented monthly range partitioning — response time dropped to 50 ms, and storage costs decreased by 40% due to archiving old partitions.

In another project, a log table consumed 1.5 TB. After daily partitioning, the active set shrank to 50 GB, and TTFB dropped from 3 seconds to 200 ms. Without partitioning, every SELECT scans the entire table, inflating LCP and INP. Partition pruning eliminates unnecessary partitions, accelerating queries by 5–20x depending on selectivity. A typical query for the last month with range partitioning on a date column runs 10x faster than a full scan.

Our engineers have 10+ years of experience in database optimization and have successfully delivered over 50 partitioning projects. We guarantee a correctly working solution.

What Problems Does Table Partitioning Solve?

• Performance degradation: Large tables without partitioning scan everything, increasing LCP and TTFB. Queries filtering by date or category suffer the most.
• Maintenance difficulties: Cleaning or archiving old data becomes a painful DELETE with locking.
• High storage costs: SSDs are expensive; storing all history on fast storage is wasteful.

How We Implement Table Partitioning

Stack and Tools

Partitioning Types Comparison

Concrete Case

Client: a marketing agency. Table events — 500 million rows, growing 10 million per month. Queries for last year's statistics were slow; backups were 200 GB.

We designed range partitioning on created_at with monthly partitions. Set up pg_partman: premake 3 partitions ahead, retention 12 months (auto-delete).

CREATE TABLE events ( id BIGSERIAL, user_id INTEGER NOT NULL, event_type VARCHAR(64) NOT NULL, payload JSONB, created_at TIMESTAMPTZ NOT NULL DEFAULT now() ) PARTITION BY RANGE (created_at); CREATE TABLE events_2023_01 PARTITION OF events FOR VALUES FROM ('2023-01-01') TO ('2023-02-01'); -- pg_partman: create schedule SELECT partman.create_parent( p_parent_table => 'public.events', p_control => 'created_at', p_type => 'native', p_interval => 'monthly', p_premake => 3 ); 

Result: queries sped up 10x, production size was only the latest 6 months, old data moved to object storage.

Why Choosing the Right Partition Key Matters

Partition pruning only works when the WHERE clause explicitly uses the key. A typical mistake is wrapping the key in a function: DATE(created_at) = '2023-01-15' — pruning disables. Correct: created_at >= '2023-01-15' AND created_at < '2023-01-16'. We verify this during testing.

How to Perform Zero-Downtime Migration?

1. Create a new partitioned table with the same columns.
2. Copy data in monthly batches (separate INSERT).
3. Switch via transaction: ALTER TABLE events RENAME TO events_old; ALTER TABLE events_partitioned RENAME TO events; — seconds.
4. Drop the old table after a week of monitoring.
5. Configure pg_partman for automatic management.

Our Work Process

1. Analysis — profile queries, identify slowest, assess data volume and growth rate.
2. Design — choose key and partitioning type (range, hash, list), number of partitions.
3. Implementation — write scripts, configure pg_partman, create historical partitions.
4. Testing — verify partition pruning, benchmark before/after.
5. Deployment — migrate using the described scheme, during business hours or minimal window.
6. Monitoring — set up alerts for missed partitions and threshold breaches.

Timelines and What's Included

Estimated timelines: from 3 to 10 business days. The cost is calculated individually. What is included:

• Partitioning schema documentation
• Scripts for partition creation and management
• Automated maintenance setup (pg_partman or equivalent)
• Monitoring and alerting instructions
• 3-month warranty on correct operation

Contact us for a free project assessment — we will analyze your workload and propose the optimal solution.

Common Mistakes in Table Partitioning

• Wrong key — pruning doesn't work, performance degrades
• Indexes created on parent but not on all partitions (PG < 11)
• No premake — new partitions aren't created on time
• In MySQL, unique keys must include the partition key

When Partitioning Is Not Needed

• Table smaller than 10–20 million rows — indexes suffice
• No clear key (data without temporal or categorical label)
• Queries don't filter by key — no benefit

More about PostgreSQL partitioning — official documentation

Contact us for a free project assessment. We will analyze your workload and propose the optimal solution.