Amazon SQS vs Kafka vs Sparrow

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Amazon SQS
Amazon SQS

1.2K
618
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Kafka
Kafka

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+ 1
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Sparrow
Sparrow

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+ 1
0
- No public GitHub repository available -
- No public GitHub repository available -

What is 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.

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 Sparrow?

Sparrow keeps messages in memory, but persists them to disk, using Sqlite, when the queue is shutdown.
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          What are some alternatives to Amazon SQS, Kafka, and Sparrow?
          Amazon MQ
          Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud.
          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.
          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 SNS
          Amazon Simple Notification Service makes it simple and cost-effective to push to mobile devices such as iPhone, iPad, Android, Kindle Fire, and internet connected smart devices, as well as pushing to other distributed services. Besides pushing cloud notifications directly to mobile devices, SNS can also deliver notifications by SMS text message or email, to Simple Queue Service (SQS) queues, or to any HTTP endpoint.
          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.
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          How developers use Amazon SQS, Kafka, and Sparrow
          Avatar of Karma
          Karma uses Amazon SQSAmazon SQS

          In the beginning we thought we wanted to start using something like RabbitMQ or maybe Kafka or maybe ActiveMQ. Back then we only had a few developers and no ops people. That has changed now, but we didn't really look forward to setting up a queuing cluster and making sure that all works.

          What we did instead was we looked at what services Amazon offers to see if we can use those to build our own messaging system within those services. That's basically what we did. We wrote some clients in Ruby that can basically do the entire orchestration for us, and we run all our messaging on both SNS and SQS. Basically what you can do in Amazon services is you can use Amazon Simple Notification Service, so SNS, for creating topics and you can use queues to subscribe to these topics. That's basically all you need for a messaging system. You don't have to worry about scalability at all. That's what really appealed to us.

          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 Brandon Adams
          Brandon Adams uses Amazon SQSAmazon SQS

          This isn't exactly low-latency (10s to 100s of milliseconds), but it has good throughput and a simple API. There is good reliability, and there is no configuration necessary to get up and running. A hosted queue is important when trying to move fast.

          Avatar of Simple Merchant
          Simple Merchant uses Amazon SQSAmazon SQS

          SQS is the bridge between our new Lambda services and our incumbent Rails applications. Extremely easy to use when you're already using other AWS infrastructure.

          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 Olo
          Olo uses Amazon SQSAmazon SQS

          Primary message queue. Enqueueing operations revert to a local file-system-based queue when SQS is unavailable.

          Avatar of IndiTip
          IndiTip uses Amazon SQSAmazon SQS

          I can't afford to lose data if Dynamo throttles my writes, so everything goes into a message queue first.

          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.

          How much does Amazon SQS cost?
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