Benchmarks

PGQueuer includes a built-in benchmarking tool for evaluating performance in your environment.

Key Observations

• Consistency: PGQueuer maintains consistent throughput across different job volumes.
• Peak throughput: ~6.4k–18k jobs per second depending on concurrency and driver.
• Scalability: Performance increases with concurrency.

Running the Benchmark

python3 tools/benchmark.py -dq 5 -eqbs 10

Example output:

Settings:
Timer:                  15.0 seconds
Dequeue:                5
Dequeue Batch Size:     10
Enqueue:                1
Enqueue Batch Size:     10

Queue size: 0
Queue size: 114
...
Jobs per Second: 18.35k

Drain Strategy

The drain strategy enqueues a fixed number of jobs and measures how long PGQueuer takes to empty the queue:

python3 tools/benchmark.py --strategy drain --jobs 50000

Use this when evaluating batch processing performance.

asyncpg vs psycopg Comparison

Both tests run with the same settings:

• Timer: 10.0 seconds
• Dequeue: 5 workers, batch size 10
• Enqueue: 1 worker, batch size 10

asyncpg

python3 tools/benchmark.py -d apg

140k job [00:09, 14.1k job/s]

• Total jobs: 140k
• Throughput: 14.1k jobs/s

psycopg

python3 tools/benchmark.py -d psy

85.1k job [00:10, 8.35k job/s]

• Total jobs: 85.1k
• Throughput: 8.35k jobs/s

Key Observations

• asyncpg achieves roughly double the throughput of psycopg under these conditions.
• The asynchronous nature of asyncpg allows better system resource utilization.
• For latency-sensitive or throughput-critical workloads, asyncpg or the asyncpg pool driver is recommended.