Alternatives to Gearman logo

Alternatives to Gearman

RabbitMQ, Kafka, Celery, Redis, and Beanstalkd are the most popular alternatives and competitors to Gearman.
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What is Gearman and what are its top alternatives?

Gearman is an open-source, distributed job scheduler that helps developers with parallel processing, backgrounding, and load balancing of tasks. It offers features such as multi-language support, real-time status updates, and scalability. However, its limitations include potential complexity in setup and configuration for beginners.

  1. Celery: Celery is a distributed task queue built on Python that supports scheduling, execution, and monitoring of tasks. It offers features like real-time monitoring, task retries, and priority settings. Pros include a large community and compatibility with various message brokers, while cons include potential complexity in setting up and maintaining.
  2. Apache Kafka: Apache Kafka is a distributed event streaming platform that can be used for data processing, messaging, and real-time analytics. Its key features include fault tolerance, high-throughput processing, and scalability. Pros of using Apache Kafka include high performance and fault tolerance, while cons may involve learning curve and infrastructure requirements.
  3. RabbitMQ: RabbitMQ is an open-source message broker software that supports multiple messaging protocols. It offers features like message queuing, routing, and clustering. Pros of RabbitMQ include high availability, clustering support, and a wide variety of plugins. However, potential cons include performance limitations and complexity in configuration.
  4. Resque: Resque is a Redis-backed job queue management tool built on Ruby that allows for background processing of tasks. Key features include retries, plugins, and monitoring. Pros of using Resque include simplicity, flexibility, and compatibility with Ruby on Rails applications. Cons may involve performance limitations at scale.
  5. AWS Lambda: AWS Lambda is a serverless compute service that allows developers to run code without provisioning or managing servers. It offers features like automatic scaling, event-driven architecture, and pay-per-use pricing. Pros of AWS Lambda include ease of use, scalability, and integration with other AWS services. Cons may include vendor lock-in and potential limitations in running long-lived tasks.
  6. Sidekiq: Sidekiq is a background job processing tool for Ruby, built on Redis. It provides features like job retries, dashboard for monitoring, and concurrency control. Pros of Sidekiq include speed, reliability, and ease of use. However, it may require familiarity with Redis and potential configuration overhead.
  7. IronMQ: IronMQ is a hosted message queue service that supports cloud-based messaging for any application or service. Its key features include push and pull queues, message durability, and monitoring. Pros of IronMQ include ease of setup, scalability, and reliability. Cons may involve cost considerations and dependency on a third-party service.
  8. Beanstalkd: Beanstalkd is a simple, fast work queue service that supports delaying, prioritizing, and organizing jobs. It offers features like job burying, real-time monitoring, and protocol support. Pros of Beanstalkd include speed, simplicity, and reliability. Cons may involve limited features compared to more robust solutions.
  9. Kubernetes Jobs: Kubernetes Jobs are a way to create and manage pods that run to completion in a Kubernetes cluster. Key features include parallelism, retries, and scaling. Pros of using Kubernetes Jobs include integration with Kubernetes ecosystem, scalability, and fault-tolerance. Cons may involve complexity in setup and management for beginners.
  10. Dask: Dask is a flexible parallel computing library for analytic computing in Python. It offers task scheduling, parallel execution, and integration with NumPy and Pandas. Pros of Dask include scalability, integration with existing Python libraries, and support for complex computations. Cons may involve learning curve and potential configuration overhead.

Top Alternatives to Gearman

  • 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. ...

  • Celery
    Celery

    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. ...

  • Redis
    Redis

    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. ...

  • Beanstalkd
    Beanstalkd

    Beanstalks's interface is generic, but was originally designed for reducing the latency of page views in high-volume web applications by running time-consuming tasks asynchronously. ...

  • Laravel
    Laravel

    It is a web application framework with expressive, elegant syntax. It attempts to take the pain out of development by easing common tasks used in the majority of web projects, such as authentication, routing, sessions, and caching. ...

  • ZeroMQ
    ZeroMQ

    The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more. ...

  • MySQL
    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

Gearman alternatives & related posts

RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
  • 235
    It's fast and it works with good metrics/monitoring
  • 80
    Ease of configuration
  • 60
    I like the admin interface
  • 52
    Easy to set-up and start with
  • 22
    Durable
  • 19
    Standard protocols
  • 19
    Intuitive work through python
  • 11
    Written primarily in Erlang
  • 9
    Simply superb
  • 7
    Completeness of messaging patterns
  • 4
    Reliable
  • 4
    Scales to 1 million messages per second
  • 3
    Better than most traditional queue based message broker
  • 3
    Distributed
  • 3
    Supports MQTT
  • 3
    Supports AMQP
  • 2
    Clear documentation with different scripting language
  • 2
    Better routing system
  • 2
    Inubit Integration
  • 2
    Great ui
  • 2
    High performance
  • 2
    Reliability
  • 2
    Open-source
  • 2
    Runs on Open Telecom Platform
  • 2
    Clusterable
  • 2
    Delayed messages
  • 1
    Supports Streams
  • 1
    Supports STOMP
  • 1
    Supports JMS
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 · 1.7M 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

See more

Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

See more
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
  • 92
    Scalable
  • 86
    High-Performance
  • 66
    Durable
  • 38
    Publish-Subscribe
  • 19
    Simple-to-use
  • 18
    Open source
  • 12
    Written in Scala and java. Runs on JVM
  • 9
    Message broker + Streaming system
  • 4
    KSQL
  • 4
    Avro schema integration
  • 4
    Robust
  • 3
    Suport Multiple clients
  • 2
    Extremely good parallelism constructs
  • 2
    Partioned, replayable log
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Fun
  • 1
    Flexible
CONS OF KAFKA
  • 32
    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging

related Kafka posts

Nick Rockwell
SVP, Engineering at Fastly · | 46 upvotes · 3.9M views

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

See more
Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.1M views

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

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Celery logo

Celery

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Distributed task queue
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PROS OF CELERY
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    Task queue
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    Python integration
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    Django integration
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    Scheduled Task
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    Publish/subsribe
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    Various backend broker
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    Easy to use
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    Great community
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    Workflow
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    Free
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    Dynamic
CONS OF CELERY
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    Sometimes loses tasks
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    Depends on broker

related Celery posts

James Cunningham
Operations Engineer at Sentry · | 21 upvotes · 360.1K views

Sentry started as (and remains) an open-source project, growing out of an error logging tool built in 2008. That original build nine years ago was Django and Celery (Python’s asynchronous task codebase), with PostgreSQL as the database and Redis as the power behind Celery.

We displayed a truly shrewd notion of branding even then, giving the project a catchy name that companies the world over remain jealous of to this day: django-db-log. For the longest time, Sentry’s subtitle on GitHub was “A simple Django app, built with love.” A slightly more accurate description probably would have included Starcraft and Soylent alongside love; regardless, this captured what Sentry was all about.

#MessageQueue #InMemoryDatabases

See more
James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.7M 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

See more
Redis logo

Redis

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Open source (BSD licensed), in-memory data structure store
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PROS OF REDIS
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    Performance
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    Super fast
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    Ease of use
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    In-memory cache
  • 324
    Advanced key-value cache
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    Open source
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    Easy to deploy
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    Stable
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    Free
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    Fast
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    High-Performance
  • 40
    High Availability
  • 35
    Data Structures
  • 32
    Very Scalable
  • 24
    Replication
  • 22
    Great community
  • 22
    Pub/Sub
  • 19
    "NoSQL" key-value data store
  • 16
    Hashes
  • 13
    Sets
  • 11
    Sorted Sets
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    NoSQL
  • 10
    Lists
  • 9
    Async replication
  • 9
    BSD licensed
  • 8
    Bitmaps
  • 8
    Integrates super easy with Sidekiq for Rails background
  • 7
    Keys with a limited time-to-live
  • 7
    Open Source
  • 6
    Lua scripting
  • 6
    Strings
  • 5
    Awesomeness for Free
  • 5
    Hyperloglogs
  • 4
    Transactions
  • 4
    Outstanding performance
  • 4
    Runs server side LUA
  • 4
    LRU eviction of keys
  • 4
    Feature Rich
  • 4
    Written in ANSI C
  • 4
    Networked
  • 3
    Data structure server
  • 3
    Performance & ease of use
  • 2
    Dont save data if no subscribers are found
  • 2
    Automatic failover
  • 2
    Easy to use
  • 2
    Temporarily kept on disk
  • 2
    Scalable
  • 2
    Existing Laravel Integration
  • 2
    Channels concept
  • 2
    Object [key/value] size each 500 MB
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    Simple
CONS OF REDIS
  • 15
    Cannot query objects directly
  • 3
    No secondary indexes for non-numeric data types
  • 1
    No WAL

related Redis posts

Russel Werner
Lead Engineer at StackShare · | 32 upvotes · 2.6M views

StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

#StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

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Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 10.7M views

Our whole DevOps stack consists of the following tools:

  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively Git as revision control system
  • SourceTree as Git GUI
  • Visual Studio Code as IDE
  • CircleCI for continuous integration (automatize development process)
  • Prettier / TSLint / ESLint as code linter
  • SonarQube as quality gate
  • Docker as container management (incl. Docker Compose for multi-container application management)
  • VirtualBox for operating system simulation tests
  • Kubernetes as cluster management for docker containers
  • Heroku for deploying in test environments
  • nginx as web server (preferably used as facade server in production environment)
  • SSLMate (using OpenSSL) for certificate management
  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
  • PostgreSQL as preferred database system
  • Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
  • Scalability: All-in-one framework for distributed systems.
  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
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Beanstalkd logo

Beanstalkd

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A simple, fast work queue
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+ 1
74
PROS OF BEANSTALKD
  • 23
    Fast
  • 12
    Free
  • 12
    Does one thing well
  • 9
    Scalability
  • 8
    Simplicity
  • 3
    External admin UI developer friendly
  • 3
    Job delay
  • 2
    Job prioritization
  • 2
    External admin UI
CONS OF BEANSTALKD
    Be the first to leave a con

    related Beanstalkd posts

    Frédéric MARAND
    Core Developer at OSInet · | 2 upvotes · 233.8K views

    I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.

    So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.

    I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.

    See more
    Laravel logo

    Laravel

    27.9K
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    A PHP Framework For Web Artisans
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    PROS OF LARAVEL
    • 553
      Clean architecture
    • 392
      Growing community
    • 370
      Composer friendly
    • 344
      Open source
    • 324
      The only framework to consider for php
    • 220
      Mvc
    • 210
      Quickly develop
    • 168
      Dependency injection
    • 156
      Application architecture
    • 143
      Embraces good community packages
    • 73
      Write less, do more
    • 71
      Orm (eloquent)
    • 66
      Restful routing
    • 57
      Database migrations & seeds
    • 55
      Artisan scaffolding and migrations
    • 41
      Great documentation
    • 40
      Awesome
    • 30
      Awsome, Powerfull, Fast and Rapid
    • 29
      Build Apps faster, easier and better
    • 28
      Eloquent ORM
    • 26
      Promotes elegant coding
    • 26
      JSON friendly
    • 26
      Modern PHP
    • 25
      Most easy for me
    • 24
      Easy to learn, scalability
    • 23
      Beautiful
    • 22
      Blade Template
    • 21
      Test-Driven
    • 15
      Security
    • 15
      Based on SOLID
    • 13
      Cool
    • 13
      Clean Documentation
    • 13
      Easy to attach Middleware
    • 12
      Simple
    • 12
      Convention over Configuration
    • 11
      Easy Request Validatin
    • 10
      Simpler
    • 10
      Easy to use
    • 10
      Fast
    • 9
      Get going quickly straight out of the box. BYOKDM
    • 9
      Its just wow
    • 8
      Laravel + Cassandra = Killer Framework
    • 8
      Simplistic , easy and faster
    • 8
      Friendly API
    • 7
      Less dependencies
    • 7
      Super easy and powerful
    • 6
      Great customer support
    • 6
      Its beautiful to code in
    • 5
      Speed
    • 5
      Eloquent
    • 5
      Composer
    • 5
      Minimum system requirements
    • 5
      Laravel Mix
    • 5
      Easy
    • 5
      The only "cons" is wrong! No static method just Facades
    • 5
      Fast and Clarify framework
    • 5
      Active Record
    • 5
      Php7
    • 4
      Ease of use
    • 4
      Laragon
    • 4
      Laravel casher
    • 4
      Easy views handling and great ORM
    • 4
      Laravel Forge and Envoy
    • 4
      Cashier with Braintree and Stripe
    • 3
      Laravel Passport
    • 3
      Laravel Spark
    • 3
      Intuitive usage
    • 3
      Laravel Horizon and Telescope
    • 3
      Laravel Nova
    • 3
      Rapid development
    • 2
      Laravel Vite
    • 2
      Scout
    • 2
      Deployment
    • 1
      Succint sintax
    CONS OF LARAVEL
    • 54
      PHP
    • 33
      Too many dependency
    • 23
      Slower than the other two
    • 17
      A lot of static method calls for convenience
    • 15
      Too many include
    • 13
      Heavy
    • 9
      Bloated
    • 8
      Laravel
    • 7
      Confusing
    • 5
      Too underrated
    • 4
      Not fast with MongoDB
    • 1
      Slow and too much big
    • 1
      Not using SOLID principles
    • 1
      Difficult to learn

    related Laravel posts

    I need to build a web application plus android and IOS apps for an enterprise, like an e-commerce portal. It will have intensive use of MySQL to display thousands (40-50k) of live product information in an interactive table (searchable, filterable), live delivery tracking. It has to be secure, as it will handle information on customers, sales, inventory. Here is the technology stack: Backend: Laravel 7 Frondend: Vue.js, React or AngularJS?

    Need help deciding technology stack. Thanks.

    See more
    David Watson
    at Realtime App Solutions · | 15 upvotes · 102.2K views

    Coming from a non-web development environment background, I was a bit lost a first and bewildered by all the varying tools and platforms, and spent much too long evaluating before eventualy deciding on Laravel as the main core of my development.

    But as I started development with Laravel that lead me into discovering Vue.js for creating beautiful front-end components that were easy to configure and extend, so I decided to standardise on Vue.js for most of my front-end development.

    During my search for additional Vue.js components, a chance comment in a @laravel forum , led me to discover Quasar Framework initially for it's wide range of in-built components ... but once, I realised that Quasar Framework allowed me to use the same codebase to create apps for SPA, PWA, iOS, Android, and Electron then I was hooked.

    So, I'm now using mainly just Quasar Framework for all the front-end, with Laravel providing a backend API service to the Front-end apps.

    I'm deploying this all to DigitalOcean droplets via service called Moss.sh which deploys my private GitHub repositories directly to DigitalOcean in realtime.

    See more
    ZeroMQ logo

    ZeroMQ

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    71
    Fast, lightweight messaging library that allows you to design complex communication system without much effort
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    PROS OF ZEROMQ
    • 23
      Fast
    • 20
      Lightweight
    • 11
      Transport agnostic
    • 7
      No broker required
    • 4
      Low level APIs are in C
    • 4
      Low latency
    • 1
      Open source
    • 1
      Publish-Subscribe
    CONS OF ZEROMQ
    • 5
      No message durability
    • 3
      Not a very reliable system - message delivery wise
    • 1
      M x N problem with M producers and N consumers

    related ZeroMQ posts

    Shared insights
    on
    MongoDBMongoDBZeroMQZeroMQSpring BootSpring Boot

    In our Spring Boot application, which encompasses various projects, we employ ZeroMQ (ZMQ) for communication via a req/resp pattern. Recently, I observed that data is persisted in the MongoDB database before being transmitted to other applications. I've identified a method to monitor changes to the database, and I'm contemplating whether to utilize this monitoring approach to detect changes and execute the necessary instructions.

    Which approach is more advisable in this scenario: leveraging the database monitoring mechanism or sticking with the current ZMQ req/resp communication?

    Essentially, I'm seeking guidance on whether to rely on database monitoring for change detection and subsequent actions or to continue with the existing ZMQ communication pattern.

    See more
    Meili Triantafyllidi
    Software engineer at Digital Science · | 6 upvotes · 471.6K views
    Shared insights
    on
    Amazon SQSAmazon SQSRabbitMQRabbitMQZeroMQZeroMQ

    Hi, we are in a ZMQ set up in a push/pull pattern, and we currently start to have more traffic and cases that the service is unavailable or stuck. We want to: * Not loose messages in services outages * Safely restart service without losing messages (ZeroMQ seems to need to close the socket in the receiver before restart manually)

    Do you have experience with this setup with ZeroMQ? Would you suggest RabbitMQ or Amazon SQS (we are in AWS setup) instead? Something else?

    Thank you for your time

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    MySQL logo

    MySQL

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    The world's most popular open source database
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    PROS OF MYSQL
    • 800
      Sql
    • 679
      Free
    • 562
      Easy
    • 528
      Widely used
    • 490
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 104
      Great community
    • 79
      Secure
    • 75
      Full-text indexing and searching
    • 26
      Fast, open, available
    • 16
      Reliable
    • 16
      SSL support
    • 15
      Robust
    • 9
      Enterprise Version
    • 7
      Easy to set up on all platforms
    • 3
      NoSQL access to JSON data type
    • 1
      Relational database
    • 1
      Easy, light, scalable
    • 1
      Sequel Pro (best SQL GUI)
    • 1
      Replica Support
    CONS OF MYSQL
    • 16
      Owned by a company with their own agenda
    • 3
      Can't roll back schema changes

    related MySQL posts

    Nick Rockwell
    SVP, Engineering at Fastly · | 46 upvotes · 3.9M views

    When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

    So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

    React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

    Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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    Tim Abbott

    We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

    We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

    And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

    I can't recommend it highly enough.

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