pgbenchmark 0.1.8

A Python package to benchmark query performance 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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Please consider

1. Main purpose of this library is to easily microbenchmark queries without boilerplate.
2. This tool is not for Database Administrators (Yet).
3. There's a lot of re-thinking and work in progress ongoing on this project
4. Most of the things will be backwards-compatible, but some things might deprecate/break in future releases.
5. Since I'm developing this library as I go, mostly for my personal use, code here and there is sub-optimal.

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Installation

pip install pgbenchmark

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Example

For ParallelBenchmark, please scroll down....

import psycopg2
from pgbenchmark import Benchmark

conn = psycopg2.connect(
    dbname="postgres",
    user="postgres",
    password="  << Your Password >> ",
    host="localhost",
    port="5432"
)

benchmark = Benchmark(db_connection=conn, number_of_runs=1000)
benchmark.set_sql("SELECT 1;")

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': '0.000576',
#      'max_time': '0.014741',
#      'avg_time': '0.0007',
#      'median_time': '0.000642',
#      'percentiles': {'p25': '0.000612',
#                      'p50': '0.000642',
#                      'p75': '0.000696',
#                      'p99': '0.001331'}
#      }

You can also pass SQL file, instead of query string

benchmark.set_sql("./test.sql")

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Interactive | No-Code Mode

Simply run in your terminal:

pgbenchmark

You'll see the ouput

[ http://127.0.0.1:8000 ] Click to open pgbenchmark Interface

Configuration on the right, rest is very intuitive.

Pause and Resume buttons are not working for now :(

More Examples

Standard 'Benchmark' class allow all kinds of connections

1. Providing Nothing at all. Benchmark will use standard default factory values

from pgbenchmark import Benchmark

benchmark = Benchmark(number_of_runs=1000)
benchmark.set_sql("SELECT 1;")

for iteration in benchmark:
    pass

2. Providing Connection Details as Dict.

from pgbenchmark import Benchmark

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

benchmark = Benchmark(db_connection=params, number_of_runs=1000)
benchmark.set_sql("SELECT 1;")

for iteration in benchmark:
    pass

3. Psycopg2 connection object directly

from pgbenchmark import Benchmark

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

benchmark = Benchmark(db_connection=params, number_of_runs=1000)
benchmark.set_sql("SELECT 1;")

for iteration in benchmark:
    pass

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Example with Parallel execution

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

from pgbenchmark import ParallelBenchmark

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

# --- Configuration ---
N_PROCS = 20
N_RUNS_PER_PROC = 1000
SQL_QUERY = "SELECT 1;"

parallel_bench = ParallelBenchmark(
    num_processes=N_PROCS,
    number_of_runs=N_RUNS_PER_PROC,
    db_connection_info=pg_conn_params
)
parallel_bench.set_sql(SQL_QUERY)

if __name__ == '__main__':

    print("===================== Simply `run()` and get results at the end ==============================")

    parallel_bench.run()

    print("===================== Or... Iterate Live and get results per-process =========================")
    for result_from_process in parallel_bench.iter_successful_results():
        print(result_from_process)

    final_results = parallel_bench.get_execution_results()

Example with Template Engine

From version 0.1.0 pgbenchmark supports simple Template Engine for queries.

To emulate "real" scenarios, with different random or pre-defined queries, you can use set_sql_formatter method
to generate queries. Same syntax as Jinja2 using {{ [X] }} for variables.

def generate_random_value():
    return round(random.randint(10, 1000), 2)


N_RUNS_PER_PROC = 1000

SQL_QUERY = "SELECT {{random_value}};"

# ....

parallel_bench.set_sql(SQL_QUERY)

"""===================== use `ANY` function to generate values for your query =============================="""
parallel_bench.set_sql_formatter(for_placeholder="random_value", generator=generate_random_value)

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