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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Stream Processing
  5. Airflow vs Storm

Airflow vs Storm

OverviewDecisionsComparisonAlternatives

Overview

Apache Storm
Apache Storm
Stacks207
Followers282
Votes25
GitHub Stars6.7K
Forks4.1K
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Storm: What are the differences?

Airflow vs Storm: Key Differences

Airflow and Storm are both widely used open-source systems for processing and managing large volumes of data. While they share some similarities, there are several key differences between the two.

  1. Processing Model: Airflow follows a batch processing model, where jobs are executed at predefined intervals. It provides a flexible and customizable workflow scheduling mechanism, supporting the execution of complex data pipelines with dependencies. On the other hand, Storm follows a real-time stream processing model, allowing for continuous processing of data in real-time. It is designed to handle high-velocity, event-driven data streams with low latency, making it suitable for applications that require fast data processing.

  2. Event-Driven vs Time-Driven: Storm is event-driven, meaning it processes data as soon as it becomes available. It supports high-velocity, low-latency processing by providing a distributed and fault-tolerant processing framework. In contrast, Airflow is time-driven, where jobs are scheduled to run periodically based on predefined schedules. It focuses more on orchestrating and managing workflows, enabling the execution of complex data pipelines.

  3. Data Processing Paradigm: Airflow primarily focuses on batch processing and supports various data processing paradigms, including batch, streaming, and machine learning. It provides a rich set of tools and functionalities for workflow management, task scheduling, and dependency tracking. Storm, on the other hand, is designed specifically for real-time stream processing. It provides a distributed and fault-tolerant stream processing engine with low-latency processing capabilities.

  4. Language Support: Airflow enables developers to write data pipelines using Python. It provides a Python-based interface for defining workflows, task dependencies, and data processing logic. Storm, on the other hand, supports multiple programming languages, including Java, Python, and Clojure. This allows developers to write stream processing topologies in the language of their choice.

  5. Scalability and Fault-Tolerance: Airflow is highly scalable and can handle large-scale data processing tasks by leveraging distributed computing resources. It provides fault-tolerance through task retries and failure handling mechanisms. Storm, on the other hand, is designed to be highly scalable and fault-tolerant out of the box. It distributes the processing across a cluster of machines and provides automatic failover and recovery.

  6. Community and Ecosystem: Airflow has a thriving open-source community and a growing ecosystem of plugins and extensions. It is widely used and has extensive documentation and community support. Storm, on the other hand, has a smaller community compared to Airflow. It still has active development and maintenance but may have a slightly smaller ecosystem of plugins and extensions.

In Summary, Airflow and Storm differ in their processing models, with Airflow focusing on batch processing and workflow management, while Storm excels in real-time stream processing. They also differ in their data processing paradigms, language support, scalability, fault-tolerance, and community ecosystem.

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Advice on Apache Storm, Airflow

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Comments

Detailed Comparison

Apache Storm
Apache Storm
Airflow
Airflow

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

Storm integrates with the queueing and database technologies you already use;Simple API;Scalable;Fault tolerant;Guarantees data processing;Use with any language;Easy to deploy and operate;Free and open source
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
GitHub Stars
6.7K
GitHub Stars
-
GitHub Forks
4.1K
GitHub Forks
-
Stacks
207
Stacks
1.7K
Followers
282
Followers
2.8K
Votes
25
Votes
128
Pros & Cons
Pros
  • 10
    Flexible
  • 6
    Easy setup
  • 4
    Event Processing
  • 3
    Clojure
  • 2
    Real Time
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward

What are some alternatives to Apache Storm, Airflow?

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Confluent

Confluent

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

Apache Beam

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Luigi

Luigi

It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

Unito

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

KSQL

KSQL

KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.

Shipyard

Shipyard

na

Heron

Heron

Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.

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