Skip to main content

Airless is a package that aims to build a serverless and lightweight orchestration platform, creating workflows of multiple tasks being executed on FaaS platform

Project description

Airless

PyPI version

Airless is a package that aims to build a serverless and lightweight orchestration platform, creating workflows of multiple tasks being executed on Google Cloud Functions

Why not just use Apache Airflow?

Airflow is the industry standard when we talk about job orchestration and worflow management. However, in some cases, we believe it may not be the best solution. I would like to highlight 3 main cases we face that Airflow struggles to handle.

  • Serverless

At the beginning of a project we want to avoid dealing with infrastructure since it demands time and it has a fixed cost to reserve an instance to run Airflow. Since we didn't have that many jobs, it didn't make sense to have an instance of Airflow up 24-7.

When the project starts to get bigger and, if we use Airflow's instance to run the tasks, we start facing performance issues on the workflow.

In order to avoid this problems we decided to build a 100% serverless platform.

  • Parallel processing

The main use case we designed Airless for is for data scrappers. The problem with data scrappers is that normally you want them to process a lot of tasks in parallel, for instance, first you want to fetch a website and collect all links in that page and send them forward for another task to be executed and then that task does the same and so on and so forth.

Building this workflow that does not know before hand how many tasks are going to be executed is something hard be built on Airflow.

  • Data sharing between tasks

In order to built this massive parallel processing workflow that we explained on the previous topic, we need to be able to dynamically create and send data to the next task. So use the data from the first task as a trigger and an input data for the next tasks.

How it works

Airless builts its workflows based on Google Cloud Functions, Google Pub/Sub and Google Cloud Scheduler.

  1. Everything starts with the Cloud Scheduler, which is a serverless product from Google Cloud that is able to publish a message to a Pub/Sub with a cron scheduler
  2. When a message is published to a Pub/Sub it can trigger a Cloud Function and get executed with that message as an input
  3. This Cloud Functions is able to publish as many messages as it wants to as many Pub/Sub topics as it wants
  4. Repeat from 2

Preparation

Environment variables

  • ENV
  • GCP_PROJECT
  • PUBSUB_TOPIC_ERROR
  • LOG_LEVEL
  • PUBSUB_TOPIC_EMAIL_SEND
  • PUBSUB_TOPIC_SLACK_SEND
  • BIGQUERY_DATASET_ERROR
  • BIGQUERY_TABLE_ERROR
  • EMAIL_SENDER_ERROR
  • EMAIL_RECIPIENTS_ERROR
  • SLACK_CHANNELS_ERROR

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

airless-0.0.60.dev26.tar.gz (22.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

airless-0.0.60.dev26-py3-none-any.whl (28.2 kB view details)

Uploaded Python 3

File details

Details for the file airless-0.0.60.dev26.tar.gz.

File metadata

  • Download URL: airless-0.0.60.dev26.tar.gz
  • Upload date:
  • Size: 22.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.7

File hashes

Hashes for airless-0.0.60.dev26.tar.gz
Algorithm Hash digest
SHA256 dda8be7f7ed8584337f414c339918ab2ec65845a6def5810bd86a28004a9dbd5
MD5 be400b210ff0d90bd3659a1d68d88051
BLAKE2b-256 3be00ae6a0f5a9874145f0957a7de78d0b655aef00667cc591bb7dc1e8c0f501

See more details on using hashes here.

File details

Details for the file airless-0.0.60.dev26-py3-none-any.whl.

File metadata

  • Download URL: airless-0.0.60.dev26-py3-none-any.whl
  • Upload date:
  • Size: 28.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.7

File hashes

Hashes for airless-0.0.60.dev26-py3-none-any.whl
Algorithm Hash digest
SHA256 57474fc1dcb85357f9fb1d6359531101877d128410b5649960b83b1dafad1768
MD5 6fd64d87a3f59bbad7dddc15d545527b
BLAKE2b-256 f206f3c77c1628fd5b67710f09a59f8da548e4365d9e633502668315ea0ecd71

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page