Voyz

import multiprocessing
import time
from collections import deque


class ThrottleBarrier():
    def __init__(
            self,
            counter: multiprocessing.Value,
            lock: multiprocessing.Lock,

kamui-fin

Interview Course

Research & Preparation

• Interview Preparation Checklist
• Last Minute Job Interview Tips
• Video Interview Notes
• How to Research a Company
• How to Create Practice Questions
• Have a Plan for Your Job Interview

nymous

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

vsajip

This set of files is to help you set up a socket listener for Python logging in a production environment.

The other files are:

Filename	Purpose
prepare.sh	A Bash script to prepare the environment for testing.
supervisor.conf	The Supervisor configuration file, which has entries for the listener and a multi-process web application.
ensure_app.sh	A Bash script to ensure that Supervisor is running with the above configuration.
log_listener.py	The socket listener program which receives log events and records them to a file.

skrawcz

# --- my_functions.py
import pandas as pd


def avg_3wk_spend(spend: pd.Series) -> pd.Series:
    """Rolling 3 week average spend."""
    return spend.rolling(3).mean()


def spend_per_signup(spend: pd.Series, signups: pd.Series) -> pd.Series:

AniketSaki

Apache-Python with Kafka source and sink

Reproduced from https://thecodinginterface.com/blog/kafka-source-sink-with-apache-flink-table-api/

PyFlink is compatible with Python>=3.5<3.9

Process:

1. Produce events and send to Kafka topic
2. Set up streaming service via PyFlink DataStream API
3. Read from Kafka source via PyFlink TABLE API
4. Process data

EricPostMaster

changjonathanc

❯ rm out.csv
❯ cat 1.py
from glob import glob
import mmap

files = glob("data/*")

files.sort(key=lambda x: int(x.split("/")[-1].split(".")[0]))
write_f = open("out.csv", "w+b")

tykurtz

GROKKING NOTES

I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.

So below I made a list of leetcode problems that are as close to grokking problems as possible.

Pattern: Sliding Window

• https://leetcode.com/problems/maximum-subarray/ # Close enough
• https://leetcode.com/problems/minimum-size-subarray-sum/

pvsune

import logging
from multiprocessing import Pool, TimeoutError


logging.basicConfig(level=logging.INFO)


def timeout(func, args=None, kwds=None, timeout=10):
    """Call function and wait until timeout.