Alternatives to Celery logo

Alternatives to Celery

RabbitMQ, Kafka, Airflow, Cucumber, and Amazon SQS are the most popular alternatives and competitors to Celery.
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What is Celery and what are its top alternatives?

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Celery is a tool in the Message Queue category of a tech stack.
Celery is an open source tool with 20.6K GitHub stars and 4.4K GitHub forks. Here’s a link to Celery's open source repository on GitHub

Top Alternatives to Celery

  • RabbitMQ
    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • Airflow
    Airflow

    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. ...

  • Cucumber
    Cucumber

    Cucumber is a tool that supports Behaviour-Driven Development (BDD) - a software development process that aims to enhance software quality and reduce maintenance costs. ...

  • Amazon SQS
    Amazon SQS

    Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use. ...

  • ActiveMQ
    ActiveMQ

    Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License. ...

  • MQTT
    MQTT

    It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. ...

  • 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. ...

Celery alternatives & related posts

RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
  • 232
    It's fast and it works with good metrics/monitoring
  • 79
    Ease of configuration
  • 58
    I like the admin interface
  • 50
    Easy to set-up and start with
  • 20
    Durable
  • 18
    Standard protocols
  • 18
    Intuitive work through python
  • 10
    Written primarily in Erlang
  • 8
    Simply superb
  • 6
    Completeness of messaging patterns
  • 3
    Reliable
  • 3
    Scales to 1 million messages per second
  • 2
    Better than most traditional queue based message broker
  • 2
    Distributed
  • 2
    Supports AMQP
  • 1
    Inubit Integration
  • 1
    Open-source
  • 1
    Delayed messages
  • 1
    Supports MQTT
  • 1
    Runs on Open Telecom Platform
  • 1
    High performance
  • 1
    Reliability
  • 1
    Clusterable
  • 1
    Clear documentation with different scripting language
  • 1
    Great ui
  • 1
    Better routing system
CONS OF RABBITMQ
  • 9
    Too complicated cluster/HA config and management
  • 6
    Needs Erlang runtime. Need ops good with Erlang runtime
  • 5
    Configuration must be done first, not by your code
  • 4
    Slow

related RabbitMQ posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.5M views
Shared insights
on
CeleryCeleryRabbitMQRabbitMQ
at

As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

#MessageQueue

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Yogesh Bhondekar
Product Manager | SaaS | Traveller · | 15 upvotes · 262.6K views

Hi, I am building an enhanced web-conferencing app that will have a voice/video call, live chats, live notifications, live discussions, screen sharing, etc features. Ref: Zoom.

I need advise finalizing the tech stack for this app. I am considering below tech stack:

  • Frontend: React
  • Backend: Node.js
  • Database: MongoDB
  • IAAS: #AWS
  • Containers & Orchestration: Docker / Kubernetes
  • DevOps: GitLab, Terraform
  • Brokers: Redis / RabbitMQ

I need advice at the platform level as to what could be considered to support concurrent video streaming seamlessly.

Also, please suggest what could be a better tech stack for my app?

#SAAS #VideoConferencing #WebAndVideoConferencing #zoom #stack

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Kafka logo

Kafka

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
  • 126
    High-throughput
  • 119
    Distributed
  • 90
    Scalable
  • 84
    High-Performance
  • 65
    Durable
  • 37
    Publish-Subscribe
  • 19
    Simple-to-use
  • 17
    Open source
  • 11
    Written in Scala and java. Runs on JVM
  • 8
    Message broker + Streaming system
  • 4
    Avro schema integration
  • 4
    Robust
  • 4
    KSQL
  • 2
    Suport Multiple clients
  • 2
    Partioned, replayable log
  • 1
    Flexible
  • 1
    Extremely good parallelism constructs
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Fun
CONS OF KAFKA
  • 30
    Non-Java clients are second-class citizens
  • 28
    Needs Zookeeper
  • 8
    Operational difficulties
  • 3
    Terrible Packaging

related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.6M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

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Airflow logo

Airflow

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A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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PROS OF AIRFLOW
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    Features
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    Task Dependency Management
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    Cluster of workers
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    Beautiful UI
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    Extensibility
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    Open source
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    Python
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    Complex workflows
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    Good api
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    Custom operators
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    Apache project
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    Dashboard
CONS OF AIRFLOW
  • 2
    Running it on kubernetes cluster relatively complex
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    Open source - provides minimum or no support
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    Logical separation of DAGs is not straight forward
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    Observability is not great when the DAGs exceed 250

related Airflow posts

Shared insights
on
JenkinsJenkinsAirflowAirflow

I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

  1. Trigger Matillion ETL loads
  2. Trigger Attunity Replication tasks that have downstream ETL loads
  3. Trigger Golden gate Replication Tasks
  4. Shell scripts, wrappers, file watchers
  5. Event-driven schedules

I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

See more
Shared insights
on
AWS Step FunctionsAWS Step FunctionsAirflowAirflow

I am working on a project that grabs a set of input data from AWS S3, pre-processes and divvies it up, spins up 10K batch containers to process the divvied data in parallel on AWS Batch, post-aggregates the data, and pushes it to S3.

I already have software patterns from other projects for Airflow + Batch but have not dealt with the scaling factors of 10k parallel tasks. Airflow is nice since I can look at which tasks failed and retry a task after debugging. But dealing with that many tasks on one Airflow EC2 instance seems like a barrier. Another option would be to have one task that kicks off the 10k containers and monitors it from there.

I have no experience with AWS Step Functions but have heard it's AWS's Airflow. There looks to be plenty of patterns online for Step Functions + Batch. Do Step Functions seem like a good path to check out for my use case? Do you get the same insights on failing jobs / ability to retry tasks as you do with Airflow?

See more
Cucumber logo

Cucumber

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Simple, human collaboration.
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PROS OF CUCUMBER
  • 20
    Simple Syntax
  • 7
    Simple usage
  • 5
    Huge community
  • 3
    Nice report
CONS OF CUCUMBER
    Be the first to leave a con

    related Cucumber posts

    Benjamin Poon
    QA Manager - Engineering at HBC Digital · | 8 upvotes · 931K views

    For our digital QA organization to support a complex hybrid monolith/microservice architecture, our team took on the lofty goal of building out a commonized UI test automation framework. One of the primary requisites included a technical minimalist threshold such that an engineer or analyst with fundamental knowledge of JavaScript could automate their tests with greater ease. Just to list a few: - Nightwatchjs - Selenium - Cucumber - GitHub - Go.CD - Docker - ExpressJS - React - PostgreSQL

    With this structure, we're able to combine the automation efforts of each team member into a centralized repository while also providing new relevant metrics to business owners.

    See more
    Sarah Elson
    Product Growth at LambdaTest · | 4 upvotes · 416K views

    @producthunt LambdaTest Selenium JavaScript Java Python PHP Cucumber TeamCity CircleCI With this new release of LambdaTest automation, you can run tests across an Online Selenium Grid of 2000+ browsers and OS combinations to perform cross browser testing. This saves you from the pain of maintaining the infrastructure and also saves you the licensing costs for browsers and operating systems. #testing #Seleniumgrid #Selenium #testautomation #automation #webdriver #producthunt hunted

    See more
    Amazon SQS logo

    Amazon SQS

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    Fully managed message queuing service
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    PROS OF AMAZON SQS
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      Easy to use, reliable
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      Low cost
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      Simple
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      Doesn't need to maintain it
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      It is Serverless
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      Has a max message size (currently 256K)
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      Easy to configure with Terraform
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      Triggers Lambda
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      Delayed delivery upto 15 mins only
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      Delayed delivery upto 12 hours
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      JMS compliant
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      Support for retry and dead letter queue
    • 1
      D
    CONS OF AMAZON SQS
    • 2
      Has a max message size (currently 256K)
    • 2
      Proprietary
    • 2
      Difficult to configure
    • 1
      Has a maximum 15 minutes of delayed messages only

    related Amazon SQS posts

    Praveen Mooli
    Engineering Manager at Taylor and Francis · | 18 upvotes · 2.6M views

    We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

    To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

    To build #Webapps we decided to use Angular 2 with RxJS

    #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

    See more
    Tim Specht
    ‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 689K views

    In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

    While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

    In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

    #ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

    See more
    ActiveMQ logo

    ActiveMQ

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    A message broker written in Java together with a full JMS client
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    PROS OF ACTIVEMQ
    • 18
      Easy to use
    • 14
      Open source
    • 13
      Efficient
    • 10
      JMS compliant
    • 6
      High Availability
    • 5
      Scalable
    • 3
      Persistence
    • 3
      Support XA (distributed transactions)
    • 3
      Distributed Network of brokers
    • 1
      Highly configurable
    • 1
      Docker delievery
    • 0
      RabbitMQ
    CONS OF ACTIVEMQ
    • 1
      ONLY Vertically Scalable
    • 1
      Support
    • 1
      Low resilience to exceptions and interruptions
    • 1
      Difficult to scale

    related ActiveMQ posts

    I want to choose Message Queue with the following features - Highly Available, Distributed, Scalable, Monitoring. I have RabbitMQ, ActiveMQ, Kafka and Apache RocketMQ in mind. But I am confused which one to choose.

    See more
    Naushad Warsi
    software developer at klingelnberg · | 1 upvote · 694.4K views
    Shared insights
    on
    ActiveMQActiveMQRabbitMQRabbitMQ

    I use ActiveMQ because RabbitMQ have stopped giving the support for AMQP 1.0 or above version and the earlier version of AMQP doesn't give the functionality to support OAuth.

    If OAuth is not required and we can go with AMQP 0.9 then i still recommend rabbitMq.

    See more
    MQTT logo

    MQTT

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    A machine-to-machine Internet of Things connectivity protocol
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    PROS OF MQTT
    • 3
      Varying levels of Quality of Service to fit a range of
    • 1
      Very easy to configure and use with open source tools
    • 1
      Lightweight with a relatively small data footprint
    CONS OF MQTT
    • 1
      Easy to configure in an unsecure manner

    related MQTT posts

    Kindly suggest the best tool for generating 10Mn+ concurrent user load. The tool must support MQTT traffic, REST API, support to interfaces such as Kafka, websockets, persistence HTTP connection, auth type support to assess the support /coverage.

    The tool can be integrated into CI pipelines like Azure Pipelines, GitHub, and Jenkins.

    See more
    A Nielsen
    Fullstack Dev at ADTELA · | 2 upvotes · 23.6K views

    Hi Marc,

    For the com part, depending of more details not provided, i'd use SSE, OR i'd run either Mosquitto or RabbitMQ running on Amazon EC2 instances and leverage MQTT or amqp 's subscribe/publish features with my users running mqtt or amqp clients (tcp or websockets) somehow. (publisher too.. you don't say how and who gets to update the document(s).

    I find "a ton of end users", depending on how you define a ton (1k users ;) ?) and how frequent document updates are, that can mean a ton of ressources, can't cut it at some point, even using SSE

    how many, how big, how persistant do the document(s) have to be ? Db-wise,can't say for lack of details and context, yeah could also be Redis, any RDBMS or nosql or even static json files stored on an Amazon S3 bucket .. anything really

    Good luck!

    See more
    Apache NiFi logo

    Apache NiFi

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    A reliable system to process and distribute data
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    PROS OF APACHE NIFI
    • 15
      Visual Data Flows using Directed Acyclic Graphs (DAGs)
    • 8
      Free (Open Source)
    • 7
      Simple-to-use
    • 5
      Reactive with back-pressure
    • 5
      Scalable horizontally as well as vertically
    • 4
      Fast prototyping
    • 3
      Bi-directional channels
    • 2
      Data provenance
    • 2
      Built-in graphical user interface
    • 2
      End-to-end security between all nodes
    • 2
      Can handle messages up to gigabytes in size
    • 1
      Hbase support
    • 1
      Kudu support
    • 1
      Hive support
    • 1
      Slack integration
    • 1
      Support for custom Processor in Java
    • 1
      Lot of articles
    • 1
      Lots of documentation
    CONS OF APACHE NIFI
    • 2
      HA support is not full fledge
    • 2
      Memory-intensive

    related Apache NiFi posts

    I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

    For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

    See more