pgbenchmark 0.0.9

A Python package to benchmark query performance and comparison on PostgreSQL Database

pgbenchmark

Python package to benchmark query performance on a PostgreSQL database. It allows you to measure the execution time of queries over multiple runs, providing detailed metrics about each run's performance.

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Installation

pip install pgbenchmark

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Example

import psycopg2
from pgbenchmark import Benchmark

conn = psycopg2.connect(
    "<< YOUR CONNECTION >>"
)

benchmark = Benchmark(db_connection=conn, number_of_runs=1000)
benchmark.set_sql("./test.sql")

for result in benchmark:
    # {'run': X, 'sent_at': <DATETIME WITH MS>, 'duration': '0.000064'}
    pass

""" View Summary """
print(benchmark.get_execution_results())
# {'runs': 1000, 'min_time': '0.00005', 'max_time': '0.000287', 'avg_time': '0.000072'}

You can also pass raw SQL as a String, instead of file

benchmark.set_sql("SELECT 1;")

It also supports SQLAlchemy connection engine

engine = create_engine("postgresql+psycopg2://.......")
conn = engine.connect()

# Set up benchmark class
benchmark = Benchmark(db_connection=conn, number_of_runs=5)

Example with Parallel or Threaded execution

⚠️ Please be careful. If you are running on Linux, pgbenchmark will load your cores on 100% !!!⚠️

from pgbenchmark import ParallelBenchmark  # <<-------- NEW IMPORT

conn_params = {
    "dbname": "postgres",
    "user": "postgres",
    "password": "",
    "host": "localhost",
    "port": "5432"
}

n_procs = 20  # Number of Processes (Cores basically)
n_runs_per_proc = 1_000

parallel_bench_pg = ParallelBenchmark(
    num_processes=n_procs,
    number_of_runs=n_runs_per_proc,
    db_connection_info=conn_params
)

parallel_bench_pg.set_sql("SELECT * from information_schema.tables;")  # Same as before

""" Unfortunately, as of now, you can't get execution results on the fly. """

parallel_bench_pg.run()  # RUN THE BENCHMARK 

results_pg = parallel_bench_pg.get_execution_results()
print(results_pg)

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