simple way to build the declarative and destributed data pipelines with python

Overview

unipipeline

simple way to build the declarative and distributed data pipelines.

Why you should use it

  • Declarative strict config
  • Scaffolding
  • Fully typed
  • Python support 3.6+
  • Brokers support
    • kafka
    • rabbitmq
    • inmemory simple pubsub
  • Interruption handling = safe user code transactions
  • CLI

How to Install

$ pip3 install unipipeline

Example

# dag.yml
---

service:
  name: "example"
  echo_colors: true
  echo_level: error


external:
  service_name: {}


brokers:
  default_broker:
    import_template: "unipipeline.brokers.uni_memory_broker:UniMemoryBroker"

  ender_broker:
    import_template: "example.brokers.uni_log_broker:LogBroker"


messages:
  __default__:
    import_template: "example.messages.{{name}}:{{name|camel}}"

  input_message: {}

  inetermediate_message: {}

  ender_message: {}


cron:
  my_super_task:
    worker: my_super_cron_worker
    when: 0/1 * * * *

  my_mega_task:
    worker: my_super_cron_worker
    when: 0/2 * * * *

  my_puper_task:
    worker: my_super_cron_worker
    when: 0/3 * * * *


waitings:
  __default__:
    import_template: example.waitings.{{name}}_wating:{{name|camel}}Waiting

  common_db: {}


workers:
  __default__:
    import_template: "example.workers.{{name}}:{{name|camel}}"

  my_super_cron_worker:
    input_message: uni_cron_message

  input_worker:
    input_message: input_message
    waiting_for:
      - common_db

  intermediate_first_worker:
    input_message: inetermediate_message
    output_workers:
      - ender_second_worker
    waiting_for:
      - common_db

  intermediate_second_worker:
    input_message: inetermediate_message
    external: service_name
    output_workers:
      - ender_frist_worker

  ender_frist_worker:
    input_message: ender_message

  ender_second_worker:
    input_message: ender_message
    broker: ender_broker
    waiting_for:
      - common_db

Get Started

  1. create ./unipipeline.yml such as example above

  2. run cli command

unipipeline -f ./unipipeline.yml scaffold

It should create all structure of your workers, brokers and so on

  1. remove error raising from workers

  2. correct message structure for make more usefull

  3. correct broker connection (if need)

  4. run cli command to run your consumer

unipipeline -f ./unipipeline.yml consume input_worker

or with python

from unipipeline import Uni
u = Uni(f'./unipipeline.yml')
u.init_consumer_worker(f'input_worker')
u.initialize()
u.start_consuming()
  1. produce some message to the message broker by your self or with tools
unipipeline -f ./unipipeline.yml produce --worker input_worker --data='{"some": "prop"}'

or with python

# main.py
from unipipeline import Uni

u = Uni(f'./unipipeline.yml')
u.init_producer_worker(f'input_worker')
u.initialize()
u.send_to(f'input_worker', dict(some='prop'))

Definition

Service

service:
  name: some_name       # need for health-check file name
  echo_level: warning   # level of uni console logs (debug, info, warning, error)
  echo_colors: true     # show colors in console

External

external:
  some_name_of_external_service: {}
  • no props

  • it needs for declarative grouping the external workers with service

Worker

workers:
  __default__:                                        # each worker get this default props if defined
    retry_max_count: 10
    
  some_worker_name:
    retry_max_count: 3                                # just counter. message move to /dev/null if limit has reached 
    retry_delay_s: 1                                  # delay before retry
    topic: "{{name}}"                                 # template string
    error_payload_topic: "{{topic}}__error__payload"  # template string
    error_topic: "{{topic}}__error"                   # template string
    broker: "default_broker"                          # broker name. reference to message transport 
    external: null                                    # name of external service. reference in this config file 
    ack_after_success: true                           # automatic ack after process message
    waiting_for:                                      # list of references
      - some_waiting_name                             # name of block. this worker must wait for connection of this external service if need
    output_workers:                                   # list of references
      - some_other_worker_name                        # allow worker sending messages to this worker
    
    inport_template: "some.module.hierarchy.to.worker.{{name}}:{{name|camel}}OfClass"   # required module and classname for import

    input_message: "name_of_message"                  # required reference of input message type 

Waiting

waitings:
  some_blocked_service_name:
    retry_max_count: 3                         # the same semantic as worker.retry_max_count
    retry_delay_s: 10                          # the same semantic as worker.retry_delay_s
    import_template: "some.module:SomeClass"   # required. the same semantic as worker.import_template

Broker

brokers:
  some_name_of_broker:
    retry_max_count: 3                         # the same semantic as worker.retry_max_count
    retry_delay_s: 10                          # the same semantic as worker.retry_delay_s
    content_type: application/json             # content type
    compression: null                          # compression (null, application/x-gzip, application/x-bz2, application/x-lzma)
    import_template: "some.module:SomeClass"   # required. the same semantic as worker.import_template

Message

messages:
  name_of_message:
    import_template: "some.module:SomeClass"   # required. the same semantic as worker.import_template

build in messages:

messages:
  uni_cron_message:
    import_template: unipipeline.messages.uni_cron_message:UniCronMessage

CLI

unipipeline

usage: unipipeline --help

UNIPIPELINE: simple way to build the declarative and distributed data pipelines. this is cli tool for unipipeline

positional arguments:
  {check,scaffold,init,consume,cron,produce}
                        sub-commands
    check               check loading of all modules
    scaffold            create all modules and classes if it is absent. no args
    init                initialize broker topics for workers
    consume             start consuming workers. connect to brokers and waiting for messages
    cron                start cron jobs, That defined in config file
    produce             publish message to broker. send it to worker

optional arguments:
  -h, --help            show this help message and exit
  --config-file CONFIG_FILE, -f CONFIG_FILE
                        path to unipipeline config file (default: ./unipipeline.yml)
  --verbose [VERBOSE]   verbose output (default: false)

unipipeline check

usage: 
    unipipeline -f ./unipipeline.yml check
    unipipeline -f ./unipipeline.yml --verbose=yes check

check loading of all modules

optional arguments:
  -h, --help  show this help message and exit

unipipeline init

usage: 
    unipipeline -f ./unipipeline.yml init
    unipipeline -f ./unipipeline.yml --verbose=yes init
    unipipeline -f ./unipipeline.yml --verbose=yes init --workers some_worker_name_01 some_worker_name_02

initialize broker topics for workers

optional arguments:
  -h, --help            show this help message and exit
  --workers INIT_WORKERS [INIT_WORKERS ...], -w INIT_WORKERS [INIT_WORKERS ...]
                        workers list for initialization (default: [])

unipipeline scaffold

usage: 
    unipipeline -f ./unipipeline.yml scaffold
    unipipeline -f ./unipipeline.yml --verbose=yes scaffold

create all modules and classes if it is absent. no args

optional arguments:
  -h, --help  show this help message and exit

unipipeline consume

usage: 
    unipipeline -f ./unipipeline.yml consume
    unipipeline -f ./unipipeline.yml --verbose=yes consume
    unipipeline -f ./unipipeline.yml consume --workers some_worker_name_01 some_worker_name_02
    unipipeline -f ./unipipeline.yml --verbose=yes consume --workers some_worker_name_01 some_worker_name_02

start consuming workers. connect to brokers and waiting for messages

optional arguments:
  -h, --help            show this help message and exit
  --workers CONSUME_WORKERS [CONSUME_WORKERS ...], -w CONSUME_WORKERS [CONSUME_WORKERS ...]
                        worker list for consuming

unipipeline produce

usage: 
    unipipeline -f ./unipipeline.yml produce --worker some_worker_name_01 --data {"some": "json", "value": "for worker"}
    unipipeline -f ./unipipeline.yml --verbose=yes produce --worker some_worker_name_01 --data {"some": "json", "value": "for worker"}
    unipipeline -f ./unipipeline.yml produce --alone --worker some_worker_name_01 --data {"some": "json", "value": "for worker"}
    unipipeline -f ./unipipeline.yml --verbose=yes produce --alone --worker some_worker_name_01 --data {"some": "json", "value": "for worker"}

publish message to broker. send it to worker

optional arguments:
  -h, --help            show this help message and exit
  --alone [PRODUCE_ALONE], -a [PRODUCE_ALONE]
                        message will be sent only if topic is empty
  --worker PRODUCE_WORKER, -w PRODUCE_WORKER
                        worker recipient
  --data PRODUCE_DATA, -d PRODUCE_DATA
                        data for sending

unipipeline cron

usage: 
    unipipeline -f ./unipipeline.yml cron
    unipipeline -f ./unipipeline.yml --verbose=yes cron

start cron jobs, That defined in config file

optional arguments:
  -h, --help  show this help message and exit

Contributing

TODO LIST

  1. RPC Gateways: http, tcp, udp
  2. Close/Exit uni by call method
  3. Async producer
  4. Common Error Handling
  5. Async get_answer
  6. Server of Message layout
  7. Prometheus api
  8. req/res Sdk
  9. request tasks result registry
  10. Async consumer
  11. Async by default
  12. Multi-threading start with run-groups
Owner
aliaksandr-master
aliaksandr-master
Pip install minimal-pandas-api-for-polars

Minimal Pandas API for Polars Install From PyPI: pip install minimal-pandas-api-for-polars Example Usage (see tests/test_minimal_pandas_api_for_polars

Austin Ray 6 Oct 16, 2022
The repo for mlbtradetrees.com. Analyze any trade in baseball history!

The repo for mlbtradetrees.com. Analyze any trade in baseball history!

7 Nov 20, 2022
This repository contains some analysis of possible nerdle answers

Nerdle Analysis https://nerdlegame.com/ This repository contains some analysis of possible nerdle answers. Here's a quick overview: nerdle.py contains

0 Dec 16, 2022
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang

Uber Open Source 1.6k Dec 29, 2022
Hue Editor: Open source SQL Query Assistant for Databases/Warehouses

Hue Editor: Open source SQL Query Assistant for Databases/Warehouses

Cloudera 759 Jan 07, 2023
pyETT: Python library for Eleven VR Table Tennis data

pyETT: Python library for Eleven VR Table Tennis data Documentation Documentation for pyETT is located at https://pyett.readthedocs.io/. Installation

Tharsis Souza 5 Nov 19, 2022
Unsub is a collection analysis tool that assists libraries in analyzing their journal subscriptions.

About Unsub is a collection analysis tool that assists libraries in analyzing their journal subscriptions. The tool provides rich data and a summary g

9 Nov 16, 2022
Gaussian processes in TensorFlow

Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow

GPflow 1.7k Jan 06, 2023
Data cleaning tools for Business analysis

Datacleaning datacleaning tools for Business analysis This program is made for Vicky's work. You can use it, too. 数据清洗 该数据清洗工具是为了商业分析 这个程序是为了Vicky的工作而

Lin Jian 3 Nov 16, 2021
NumPy and Pandas interface to Big Data

Blaze translates a subset of modified NumPy and Pandas-like syntax to databases and other computing systems. Blaze allows Python users a familiar inte

Blaze 3.1k Jan 05, 2023
A Python package for the mathematical modeling of infectious diseases via compartmental models

A Python package for the mathematical modeling of infectious diseases via compartmental models. Originally designed for epidemiologists, epispot can be adapted for almost any type of modeling scenari

epispot 12 Dec 28, 2022
DefAP is a program developed to facilitate the exploration of a material's defect chemistry

DefAP is a program developed to facilitate the exploration of a material's defect chemistry. A large number of features are provided and rapid exploration is supported through the use of autoplotting

6 Oct 25, 2022
🧪 Panel-Chemistry - exploratory data analysis and build powerful data and viz tools within the domain of Chemistry using Python and HoloViz Panel.

🧪📈 🐍. The purpose of the panel-chemistry project is to make it really easy for you to do DATA ANALYSIS and build powerful DATA AND VIZ APPLICATIONS within the domain of Chemistry using using Python a

Marc Skov Madsen 97 Dec 08, 2022
Techdegree Data Analysis Project 2

Basketball Team Stats Tool In this project you will be writing a program that reads from the "constants" data (PLAYERS and TEAMS) in constants.py. Thi

2 Oct 23, 2021
Helper tools to construct probability distributions built from expert elicited data for use in monte carlo simulations.

Elicited Helper tools to construct probability distributions built from expert elicited data for use in monte carlo simulations. Credit to Brett Hoove

Ryan McGeehan 3 Nov 04, 2022
BasstatPL is a package for performing different tabulations and calculations for descriptive statistics.

BasstatPL is a package for performing different tabulations and calculations for descriptive statistics. It provides: Frequency table constr

Angel Chavez 1 Oct 31, 2021
A stock analysis app with streamlit

StockAnalysisApp A stock analysis app with streamlit. You select the ticker of the stock and the app makes a series of analysis by using the price cha

Antonio Catalano 50 Nov 27, 2022
Working Time Statistics of working hours and working conditions by industry and company

Working Time Statistics of working hours and working conditions by industry and company

Feng Ruohang 88 Nov 04, 2022
A real data analysis and modeling project - restaurant inspections

A real data analysis and modeling project - restaurant inspections Jafar Pourbemany 9/27/2021 This project represents data analysis and modeling of re

Jafar Pourbemany 2 Aug 21, 2022
PyTorch implementation for NCL (Neighborhood-enrighed Contrastive Learning)

NCL (Neighborhood-enrighed Contrastive Learning) This is the official PyTorch implementation for the paper: Zihan Lin*, Changxin Tian*, Yupeng Hou* Wa

RUCAIBox 73 Jan 03, 2023