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
A set of tools to analyse the output from TraDIS analyses

QuaTradis (Quadram TraDis) A set of tools to analyse the output from TraDIS analyses Contents Introduction Installation Required dependencies Bioconda

Quadram Institute Bioscience 2 Feb 16, 2022
Pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.

weightedcalcs weightedcalcs is a pandas-based Python library for calculating weighted means, medians, standard deviations, and more. Features Plays we

Jeremy Singer-Vine 98 Dec 31, 2022
Analyse the limit order book in seconds. Zoom to tick level or get yourself an overview of the trading day.

Analyse the limit order book in seconds. Zoom to tick level or get yourself an overview of the trading day. Correlate the market activity with the Apple Keynote presentations.

2 Jan 04, 2022
A program that uses an API and a AI model to get info of sotcks

Stock-Market-AI-Analysis I dont mind anyone using this code but please give me credit A program that uses an API and a AI model to get info of stocks

1 Dec 17, 2021
Integrate bus data from a variety of sources (batch processing and real time processing).

Purpose: This is integrate bus data from a variety of sources such as: csv, json api, sensor data ... into Relational Database (batch processing and r

1 Nov 25, 2021
Tools for working with MARC data in Catalogue Bridge.

catbridge_tools Tools for working with MARC data in Catalogue Bridge. Borrows heavily from PyMarc

1 Nov 11, 2021
Scraping and analysis of leetcode-compensations page.

Leetcode compensations report Scraping and analysis of leetcode-compensations page.

utsav 96 Jan 01, 2023
An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks

qgrid Qgrid is a Jupyter notebook widget which uses SlickGrid to render pandas DataFrames within a Jupyter notebook. This allows you to explore your D

Quantopian, Inc. 2.9k Jan 08, 2023
Weather analysis with Python, SQLite, SQLAlchemy, and Flask

Surf's Up Weather analysis with Python, SQLite, SQLAlchemy, and Flask Overview The purpose of this analysis was to examine weather trends (precipitati

Art Tucker 1 Sep 05, 2021
Python tools for querying and manipulating BIDS datasets.

PyBIDS is a Python library to centralize interactions with datasets conforming BIDS (Brain Imaging Data Structure) format.

Brain Imaging Data Structure 180 Dec 18, 2022
A Python package for modular causal inference analysis and model evaluations

Causal Inference 360 A Python package for inferring causal effects from observational data. Description Causal inference analysis enables estimating t

International Business Machines 506 Dec 19, 2022
Python for Data Analysis, 2nd Edition

Python for Data Analysis, 2nd Edition Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media Buy

Wes McKinney 18.6k Jan 08, 2023
Incubator for useful bioinformatics code, primarily in Python and R

Collection of useful code related to biological analysis. Much of this is discussed with examples at Blue collar bioinformatics. All code, images and

Brad Chapman 560 Jan 03, 2023
Statistical Rethinking: A Bayesian Course Using CmdStanPy and Plotnine

Statistical Rethinking: A Bayesian Course Using CmdStanPy and Plotnine Intro This repo contains the python/stan version of the Statistical Rethinking

Andrés Suárez 3 Nov 08, 2022
WAL enables programmable waveform analysis.

This repro introcudes the Waveform Analysis Language (WAL). The initial paper on WAL will appear at ASPDAC'22 and can be downloaded here: https://www.

Institute for Complex Systems (ICS), Johannes Kepler University Linz 40 Dec 13, 2022
ForecastGA is a Python tool to forecast Google Analytics data using several popular time series models.

ForecastGA is a tool that combines a couple of popular libraries, Atspy and googleanalytics, with a few enhancements.

JR Oakes 36 Jan 03, 2023
A Streamlit web-app for a data-science project that aims to evaluate if the answer to a question is helpful.

How useful is the aswer? A Streamlit web-app for a data-science project that aims to evaluate if the answer to a question is helpful. If you want to l

1 Dec 17, 2021
DaDRA (day-druh) is a Python library for Data-Driven Reachability Analysis.

DaDRA (day-druh) is a Python library for Data-Driven Reachability Analysis. The main goal of the package is to accelerate the process of computing estimates of forward reachable sets for nonlinear dy

2 Nov 08, 2021
Feature engineering and machine learning: together at last

Feature engineering and machine learning: together at last! Lambdo is a workflow engine which significantly simplifies data analysis by unifying featu

Alexandr Savinov 14 Sep 15, 2022
Get mutations in cluster by querying from LAPIS API

Cluster Mutation Script Get mutations appearing within user-defined clusters. Usage Clusters are defined in the clusters dict in main.py: clusters = {

neherlab 1 Oct 22, 2021