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Kafka
Kafka

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Sandglass

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Kafka vs Sandglass: What are the differences?

What is Kafka? Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

What is Sandglass? Distributed, scalable, persistent time-sorted message queue. A distributed, horizontally scalable, persistent, time ordered message queue. Developed to support asynchronous tasks and message scheduling which makes it suitable for usage as a task queue.

Kafka and Sandglass can be categorized as "Message Queue" tools.

Some of the features offered by Kafka are:

  • Written at LinkedIn in Scala
  • Used by LinkedIn to offload processing of all page and other views
  • Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)

On the other hand, Sandglass provides the following key features:

  • Horizontal scalability
  • Highly available
  • Persistent storage

Kafka and Sandglass are both open source tools. Kafka with 12.7K GitHub stars and 6.81K forks on GitHub appears to be more popular than Sandglass with 1.52K GitHub stars and 40 GitHub forks.

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 Sandglass?

A distributed, horizontally scalable, persistent, time ordered message queue. Developed to support asynchronous tasks and message scheduling which makes it suitable for usage as a task queue.
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        What are some alternatives to Kafka and Sandglass?
        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.
        RabbitMQ
        RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
        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.
        See all alternatives
        Decisions about Kafka and Sandglass
        Adam Rabinovitch
        Adam Rabinovitch
        Global Technical Recruiting Lead & Engineering Evangelist at Beamery | 3 upvotes 156.9K 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.

        See more
        Conor Myhrvold
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber | 4 upvotes 100.5K 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)

        See more
        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).

        See more
        Fr茅d茅ric MARAND
        Fr茅d茅ric MARAND
        Core Developer at OSInet | 2 upvotes 88.6K 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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        Interest over time
        Reviews of Kafka and Sandglass
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        How developers use Kafka and Sandglass
        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 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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