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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Airflow vs Redis

Airflow vs Redis

OverviewDecisionsComparisonAlternatives

Overview

Redis
Redis
Stacks62.0K
Followers46.5K
Votes3.9K
GitHub Stars42
Forks6
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Redis: What are the differences?

<Write Introduction here>
  1. Workflow Orchestration vs. In-Memory Data Structure: Airflow is a platform used for orchestrating complex workflows, scheduling tasks, and monitoring their execution, while Redis is an in-memory data structure store used for caching, messaging, and as a database.
  2. Distributed Task Execution vs. Key-Value Storage: Airflow allows you to execute tasks on a distributed set of workers, enabling parallel task execution, whereas Redis stores data as key-value pairs, offering fast data retrieval for various use cases.
  3. Built-in Scheduler vs. No Scheduler: Airflow comes with a built-in scheduler that manages task execution based on dependencies and predefined schedules, whereas Redis does not have a scheduler and relies on external systems for task scheduling.
  4. Support for DAGs vs. No Native Support for DAGs: Airflow natively supports Directed Acyclic Graphs (DAGs) to define workflows and dependencies between tasks, while Redis does not have native support for DAGs and requires additional setup for structuring workflows.
  5. Persistent Storage vs. Volatile Storage: Airflow uses a persistent database to store metadata related to tasks, executions, and configurations, ensuring durability, whereas Redis stores data in-memory, making it volatile and potentially losing data on restarts.
  6. Execution Flow Control vs. Simple Data Operations: Airflow provides advanced features for controlling task execution flow, such as branching, triggering tasks based on external events, and retries, whereas Redis focuses on simple data operations like set, get, and publish-subscribe for messaging.
In Summary, Airflow is designed for orchestrating workflows and managing task dependencies with a built-in scheduler, while Redis is suited for in-memory data storage and fast data operations without native support for workflow orchestration.

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Advice on Redis, 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

Redis
Redis
Airflow
Airflow

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

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.

-
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
42
GitHub Stars
-
GitHub Forks
6
GitHub Forks
-
Stacks
62.0K
Stacks
1.7K
Followers
46.5K
Followers
2.8K
Votes
3.9K
Votes
128
Pros & Cons
Pros
  • 888
    Performance
  • 542
    Super fast
  • 514
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
Cons
  • 15
    Cannot query objects directly
  • 3
    No secondary indexes for non-numeric data types
  • 1
    No WAL
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward

What are some alternatives to Redis, Airflow?

Hazelcast

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Aerospike

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

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.

SAP HANA

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

VoltDB

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

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.

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