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Kafka
Kafka

3.6K
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460
RabbitMQ
RabbitMQ

4.6K
3.2K
+ 1
453
Redis
Redis

14.6K
9.7K
+ 1
3.8K

What is Kafka?

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

What is RabbitMQ?

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

What is Redis?

Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
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What are some alternatives to Kafka, RabbitMQ, and Redis?
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.
Amazon Kinesis
Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Akka
Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM.
Apache Storm
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.
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Decisions about Kafka, RabbitMQ, and Redis
HAProxy
HAProxy
Varnish
Varnish
Tornado
Tornado
Django
Django
Redis
Redis
RabbitMQ
RabbitMQ
nginx
nginx
Memcached
Memcached
MySQL
MySQL
Python
Python
Node.js
Node.js

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.

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StackShare Editors
StackShare Editors
Redis
Redis
Go
Go
Kafka
Kafka

As of 2017, Slack was handling a peak of 1.4 billion jobs a day, (33,000 jobs every second). Until recently, Slack had continued to depend on their initial job queue implementation system based on Redis. While it had allowed them to grow exponentially and diversify their services, they soon outgrew the existing system. Also, dequeuing jobs required memory that was unavailable. Allowing job workers to scale up further burdened Redis, slowing the entire system.

Slack decided to use Kafka to ease the process and allow them to scale up without getting rid of the existing architecture. To build on it, they added Kafka in front of Redis leaving the existing queuing interface in place. A stateless service called Kafkagate was developed in Go to enqueue jobs to Kafka. It exposes an HTTP POST interface with each request comprising a topic, partition, and content. Kafkagate's design reduces latency while writing jobs and allows greater flexibility in job queue design. JQRelay, a stateless service, is used to relay jobs from a Kafka topic to Redis. It ensures only one relay process is assigned to each topic, failures are self-healing, and job-specific errors are corrected by re-enqueuing the job to Kafka. The new system was rolled out by double writing all jobs to both Redis and Kafka, with JQRelay operating in 'shadow mode' - dropping all jobs after reading it from Kafka. Jobs were verified by being tracked at each part of the system through its lifetime. By using durable storage and JQRelay, the enqueuing rate could be paused or adjusted to give Redis the necessary breathing room and make Slack a much more resilient service.

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James Cunningham
James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 107.5K views
atSentrySentry
RabbitMQ
RabbitMQ
Celery
Celery
#MessageQueue

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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Marc Bollinger
Marc Bollinger
Infra & Data Eng Manager at Lumosity · | 4 upvotes · 52.9K views
atLumosityLumosity
Pulsar
Pulsar
Redis
Redis
Heron
Heron
Apache Storm
Apache Storm
Scala
Scala
Kafka
Kafka
Ruby
Ruby
Node.js
Node.js

Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.

We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.

We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.

To find out more, read our 2017 engineering blog post about the migration!

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Adam Rabinovitch
Adam Rabinovitch
Global Technical Recruiting Lead & Engineering Evangelist at Beamery · | 3 upvotes · 157.4K views
atBeameryBeamery
Kafka
Kafka
Redis
Redis
Elasticsearch
Elasticsearch
MongoDB
MongoDB
RabbitMQ
RabbitMQ
Go
Go
Node.js
Node.js
Kubernetes
Kubernetes
#Microservices

Beamery runs a #microservices architecture in the backend on top of Google Cloud with Kubernetes There are a 100+ different microservice split between Node.js and Go . Data flows between the microservices over REST and gRPC and passes through Kafka RabbitMQ as a message bus. Beamery stores data in MongoDB with near-realtime replication to Elasticsearch . In addition, Beamery uses Redis for various memory-optimized tasks.

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Tim Abbott
Tim Abbott
Founder at Zulip · | 10 upvotes · 61.9K views
atZulipZulip
Redis
Redis
Python
Python
RabbitMQ
RabbitMQ

We've been using RabbitMQ as Zulip's queuing system since we needed a queuing system. What I like about it is that it scales really well and has good libraries for a wide range of platforms, including our own Python. So aside from getting it running, we've had to put basically 0 effort into making it scale for our needs.

However, there's several things that could be better about it: * It's error messages are absolutely terrible; if ever one of our users ends up getting an error with RabbitMQ (even for simple things like a misconfigured hostname), they always end up needing to get help from the Zulip team, because the errors logs are just inscrutable. As an open source project, we've handled this issue by really carefully scripting the installation to be a failure-proof configuration (in this case, setting the RabbitMQ hostname to 127.0.0.1, so that no user-controlled configuration can break it). But it was a real pain to get there and the process of determining we needed to do that caused a significant amount of pain to folks installing Zulip. * The pika library for Python takes a lot of time to startup a RabbitMQ connection; this means that Zulip server restarts are more disruptive than would be ideal. * It's annoying that you need to run the rabbitmqctl management commands as root.

But overall, I like that it has clean, clear semanstics and high scalability, and haven't been tempted to do the work to migrate to something like Redis (which has its own downsides).

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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 5 upvotes · 120.6K views
atUber TechnologiesUber Technologies
Kafka Manager
Kafka Manager
Kafka
Kafka
GitHub
GitHub
Apache Spark
Apache Spark
Hadoop
Hadoop

Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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Roman Bulgakov
Roman Bulgakov
Senior Back-End Developer, Software Architect at Chemondis GmbH · | 3 upvotes · 10.5K views
Kafka
Kafka

I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

Downsides of using Kafka are: - you have to deal with Zookeeper - you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)

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RabbitMQ
RabbitMQ
Kafka
Kafka

The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.

I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.

For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).

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Frédéric MARAND
Frédéric MARAND
Core Developer at OSInet · | 2 upvotes · 91.5K views
atOSInetOSInet
RabbitMQ
RabbitMQ
Beanstalkd
Beanstalkd
Kafka
Kafka

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.

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John Kodumal
John Kodumal
CTO at LaunchDarkly · | 15 upvotes · 132.8K views
atLaunchDarklyLaunchDarkly
Kafka
Kafka
Amazon Kinesis
Amazon Kinesis
Redis
Redis
Amazon EC2
Amazon EC2
Amazon ElastiCache
Amazon ElastiCache
Consul
Consul
Patroni
Patroni
TimescaleDB
TimescaleDB
PostgreSQL
PostgreSQL
Amazon RDS
Amazon RDS

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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Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 5 upvotes · 31.2K views
atFresha EngineeringFresha Engineering
Redis
Redis
RabbitMQ
RabbitMQ
PostgreSQL
PostgreSQL

Initially we had just 1 monolithic application with a PostgreSQL database (picked for performance, community & flexibility to work with GIS data), but as we've developed more features, it was clear that some stuff is relatively independent from the rest of the platform - it made sense to split the application into loosely coupled, asynchronously communicated services. As a communication broker we've used RabbitMQ (wrapped in our custom, ProtoBuff-based wrapper). To reduce some excessive inter-process (& inter-dyno) communication, we've applied Redis as a tool to keep short-lived, not-persistent information (but not as a cheap caching workaround for any kind of performance issues ;>).

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Django
Django
Redis
Redis

I use Redis because, based on the case studies I have reviewed, it appears to be the most performant cache database for my Django projects. The ease of configuration and deployment is also a big plus.

Using both higher level view caching as well as low-level QuerySet caching with Redis has allowed me to improve HTTP request times by an order of magnitude.

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Michael Mota
Michael Mota
CEO & Founder at AlterEstate · | 4 upvotes · 9.7K views
atAlterEstateAlterEstate
Django
Django
RabbitMQ
RabbitMQ
Celery
Celery

Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.

We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.

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Interest over time
Reviews of Kafka, RabbitMQ, and Redis
Review ofRabbitMQRabbitMQ

I developed one of the largest queue based medical results delivery systems in the world, 18,000+ queues and still growing over a decade later all using MQSeries, later called Websphere MQ. When I left that company I started using RabbitMQ after doing some research on free offerings.. it works brilliantly and is incredibly flexible from small scale single instance use to large scale multi-server - multi-site architectures.

If you can think in queues then RabbitMQ should be a viable solution for integrating disparate systems.

Review ofRedisRedis

Redis is a good caching tool for a cluster, but our application had performance issues while using Aws Elasticache Redis since some page had 3000 cache hits per a page load and Redis just couldn't quickly process them all in once + latency and object deseialization time - page load took 8-9 seconds. We create a custom hybrid caching based on Redis and EhCache which worked great for our goals. Check it out on github, it's called HybriCache - https://github.com/batir-akhmerov/hybricache.

How developers use Kafka, RabbitMQ, and Redis
Avatar of Pinterest
Pinterest uses KafkaKafka

http://media.tumblr.com/d319bd2624d20c8a81f77127d3c878d0/tumblr_inline_nanyv6GCKl1s1gqll.png

Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.

Avatar of Cloudcraft
Cloudcraft uses RedisRedis

Redis is used for storing all ephemeral (that's data you don't necessarily want to store permanently) user data, such as mapping of session IDs (stored in cookies) to current session variables at Cloudcraft.co. The many datastructures supported by Redis also makes it an excellent caching and realtime statistics layer. It doesn't hurt that the author, Antirez, is the nicest guy ever! These days, I would be really hard pressed to find any situation where I would pick something like Memcached over Redis.

Avatar of Trello
Trello uses RedisRedis

Trello uses Redis for ephemeral data that needs to be shared between server processes but not persisted to disk. Things like the activity level of a session or a temporary OpenID key are stored in Redis, and the application is built to recover gracefully if any of these (or all of them) are lost. We run with allkeys-lru enabled and about five times as much space as its actual working set needs, so Redis automatically discards data that hasn’t been accessed lately, and reconstructs it when necessary.

Avatar of Stack Exchange
Stack Exchange uses RedisRedis

The UI has message inbox that is sent a message when you get a new badge, receive a message, significant event, etc. Done using WebSockets and is powered by redis. Redis has 2 slaves, SQL has 2 replicas, tag engine has 3 nodes, elastic has 3 nodes - any other service has high availability as well (and exists in both data centers).

Avatar of Brandon Adams
Brandon Adams uses RedisRedis

Redis makes certain operations very easy. When I need a high-availability store, I typically look elsewhere, but for rapid development with the ability to land on your feet in prod, Redis is great. The available data types make it easy to build non-trivial indexes that would require complex queries in postgres.

Avatar of Kent Steiner
Kent Steiner uses RedisRedis

I use Redis for cacheing, data storage, mining and augmentation, proprietary distributed event system for disparate apps and services to talk to each other, and more. Redis has some very useful native data types for tracking, slicing and dicing information.

Avatar of Cloudify
Cloudify uses RabbitMQRabbitMQ

The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings.

Avatar of Chris Saylor
Chris Saylor uses RabbitMQRabbitMQ

Rabbit acts as our coordinator for all actions that happen during game time. All worker containers connect to rabbit in order to receive game events and emit their own events when applicable.

Avatar of Clarabridge Engage
Clarabridge Engage uses RabbitMQRabbitMQ

Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.

Avatar of Analytical Informatics
Analytical Informatics uses RabbitMQRabbitMQ

RabbitMQ is the enterprise message bus for our platform, providing infrastructure for managing our ETL queues, real-time event notifications for applications, and audit logging.

Avatar of Packet
Packet uses RabbitMQRabbitMQ

RabbitMQ is an all purpose queuing service for our stack. We use it for user facing jobs as well as keeping track of behind the scenes jobs.

Avatar of Coolfront Technologies
Coolfront Technologies uses KafkaKafka

Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.

Avatar of ShareThis
ShareThis uses KafkaKafka

We are using Kafka as a message queue to process our widget logs.

Avatar of Christopher Davison
Christopher Davison uses KafkaKafka

Used for communications and triggering jobs across ETL systems

Avatar of theskyinflames
theskyinflames uses KafkaKafka

Used as a integration middleware by messaging interchanging.

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