jcrist

import sys
import importlib.metadata
import timeit
from dataclasses import dataclass

import msgspec
import orjson
from mashumaro.codecs.json import JSONEncoder, JSONDecoder
from mashumaro.codecs.orjson import ORJSONEncoder, ORJSONDecoder

jcrist

jcrist

jcrist

# This is a modified version of `orig_benchmark.py`, using different data to
# highlight performance differences.
import json
import random
import string
import timeit
from statistics import mean, stdev

import orjson
import simdjson

jcrist

from datetime import date
from enum import StrEnum, auto
from typing import Annotated

from msgspec import Struct, Meta


class OrderStatus(StrEnum):
    OPEN = auto()
    CLOSED = auto()

jcrist

"""A quick benchmark comparing how quickly `__init__` with default values runs
for various dataclass-like libraries.

We also compare against the time it takes to initialize a `dict` or `tuple`
with the same data, as a "low-bar" for pure-python implementations.
"""

import timeit

import attrs

jcrist

"""An example key-value store server and client implementation using msgspec
and asyncio.

Requests are serialized using the MessagePack protocol, as implemented by
msgspec. Additionally, all messages are length-prefix framed using a 32 bit
big-endian integer.

Note that this encoding isn't tied to either asyncio or msgspec - this could
just as easily be implemented using sockets and a different serialization
protocol. Length-prefix framing is useful in that respect - it separates the IO

jcrist

import ibis
from ibis import _

con = ibis.datasette.connect("https://scotrail.datasette.io/scotrail")
t = con.tables.announcements

def random(category):
    return (
        t.filter(_.Category == category)
        .sort_by(ibis.random())

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jcrist

In [1]: import ibis

In [2]: ibis.options.interactive = True  # enable interactive mode

In [3]: con = ibis.sqlite.connect("legislators.db")  # connect to a database

In [4]: legislators = con.tables["legislators"]  # access tables

In [5]: legislators.groupby("bio_gender").count()  # query using a dataframe-like API
Out[5]: