Output

Saving to a file

Pass outfile as a constructor argument or set it as a class attribute:

from microbench import MicroBench

# As a constructor argument
bench = MicroBench(outfile='/home/user/results.jsonl')

# Or as a class attribute
class MyBench(MicroBench):
    outfile = '/home/user/results.jsonl'

Results are written in JSONL format (one JSON object per line). When outfile is a path string, each write opens the file with O_APPEND, which guarantees atomic appends on POSIX filesystems. Multiple processes can safely write to the same file simultaneously — a common pattern when running benchmark jobs across cluster nodes.

Read results back with pandas:

import pandas
results = pandas.read_json('/home/user/results.jsonl', lines=True)

Or via get_results():

results = bench.get_results()

In-memory buffer

If no outfile is specified, results are written to an in-memory io.StringIO buffer. This is the default and is useful for interactive sessions or testing:

bench = MicroBench()

@bench
def my_function():
    pass

my_function()

results = bench.get_results()

Redis output

Redis is useful when a shared filesystem is not available, such as on cloud or HPC clusters. Requires redis-py.

Inherit from MicroBenchRedis and set redis_connection and redis_key as class attributes:

from microbench import MicroBenchRedis

class RedisBench(MicroBenchRedis):
    redis_connection = {'host': 'redis-host', 'port': 6379}
    redis_key = 'microbench:mykey'

bench = RedisBench()

@bench
def my_function():
    pass

my_function()

results = bench.get_results()

Results are appended to a Redis list using RPUSH and read back with LRANGE.