semvec/pypi-packages huggingface dataset:
rediswq 0.1
A Redis-based work queue
Requires Python: >=3.6, <4
Keywords: redis,distributed

# RedisWQ

This package is based on the `rediswq.py` file that the
[Kubernetes Tutorial for Fine Parallel Processing Using a Work Queue](https://kubernetes.io/docs/tasks/job/fine-parallel-processing-work-queue/) uses.

While the code itself seems to be quite solid, there are currently no tests
and no documentation available.

I'm hoping to add both in the future.
fastdb 1.0.1
Faster database operations using Python

# fastdb

## Firestore Example:

> NOTE: Make sure to export `GOOGLE_APPLICATION_CREDENTIALS` in environment variables which points to the service account file.

```py
from fastdb.firestore import Firestore

fs = Firestore()

user = fs.client.collection("users").document("4GH5IOyQVYCMMrdwRm0D").get()

```
learn-python-ms 0.1.6
This is description of learn python project.
Requires Python: >=3.8.0

Learning Java as Microservice
===========

Python provides such and such and so and so. You might find
it most useful for tasks involving <x> and also <y>. Typical usage
often looks like this::

    #!/usr/bin/env python

    from towelstuff import location
    from towelstuff import utils

    if utils.has_towel():
        print "Your towel is located:", location.where_is_my_towel()

(Note the double-colon and 4-space indent formatting above.)

Paragraphs are separated by blank lines. *Italics*, **bold**,
and ``monospace`` look like this.


A Section
=========

Lists look like this:

* First

* Second. Can be multiple lines
  but must be indented properly.

A Sub-Section
-------------

Numbered lists look like you'd expect:

1. hi there

2. must be going

Urls are http://like.this and links can be
written `like this <http://www.example.com/foo/bar>`_.
elasticsearch-logger 0.0.2
Library for logging to Elastic Search.
Keywords: logging,es,elastic search

# elasticsearch-logger
Simple package for logging to Elasticsearch. Expected usage is to use this package inside your k8s cluster.

## Installation
```
pip install elasticsearch_logger
```

## Example of usage
In this example we write 3 messages (1-3) to buffer and after that write to Elasticsearch running on address 127.0.0.   :9200 one-by-one (that should be changed to bulk write). Logger has 4 levels of severity (DEBUG, INFO, WARNING, ERROR) and writes messages in the following format: `appName:default-app-name appName.keyword:default-app-name env:default-env-name env.keyword:default-env-name payload:Hi 3 severity:INFO severity.keyword:INFO timestamp:Jun 30, 2021 @ 18:46:54.620 _index:app-name-logs-default-app-name-30-06-2021 _score: - _type:doc`. 

```
from elasticsearch_logger import ElasticLogger
mh_logger = ElasticLogger(
    buffer_size=3,
    index='app-name-logs',
    app_name='app_name',
    env='dev',
    port=9200,
    ip_addr='127.0.0.1'
)

mh_logger.info('Hi! 1')
mh_logger.info('Hi! 2')
mh_logger.info('Hi! 3')
mh_logger.info('Hi! 4')
```
target-localjson 0.1.1
A Singer Target for save data to local json files
Requires Python: >=3.8,<4.0

# target-localjson

## Intro

This Singer Target implements a simple way persist data as local json files.

## Install

pip install target-localjson

## Config

To execute the Tap in Sync Mode you have to provide a config.json file.

There is only one required key: dest_dir. It must be a string and represents the destination directory where json files will be persisted. 

For each stream received, the Target creates a subdirectory and puts the json files there.

**Requires write permission on dest_dir.**

## Run Target

e.g.

    tap-<your-choice> -c tap_sample_config.json | target-localjson -c sample_config.json