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Aerospike
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Aerospike vs Kafka: What are the differences?

What is Aerospike? Flash-optimized in-memory open source NoSQL database. Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

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.

Aerospike and Kafka are primarily classified as "In-Memory Databases" and "Message Queue" tools respectively.

Some of the features offered by Aerospike are:

  • 99% of reads/writes complete in under 1 millisecond.
  • Predictable low latency at high throughput – second to none. Read the YCSB Benchmark.
  • The secret sauce? A thousand things done right. Server code in ‘C’ (not Java or Erlang) precisely tuned to avoid context switching and memory copies. Highly parallelized multi-threaded, multi-core, multi-cpu, multi-SSD execution.

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

  • 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)

"Ram and/or ssd persistence " is the primary reason why developers consider Aerospike over the competitors, whereas "High-throughput" was stated as the key factor in picking Kafka.

Aerospike and Kafka are both open source tools. It seems that Kafka with 12.5K GitHub stars and 6.7K forks on GitHub has more adoption than Aerospike with 282 GitHub stars and 52 GitHub forks.

According to the StackShare community, Kafka has a broader approval, being mentioned in 501 company stacks & 451 developers stacks; compared to Aerospike, which is listed in 30 company stacks and 9 developer stacks.

What is Aerospike?

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

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.
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    What are some alternatives to Aerospike and Kafka?
    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.
    Riak
    Riak is a distributed database designed to deliver maximum data availability by distributing data across multiple servers. As long as your client can reach one Riak server, it should be able to write data. In most failure scenarios, the data you want to read should be available, although it may not be the most up-to-date version of that data.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    Tarantool
    It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications
    See all alternatives
    Decisions about Aerospike and Kafka
    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 5 upvotes · 124.1K 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 )

    See more
    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 · 92K 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 Aerospike and Kafka
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    How developers use Aerospike and Kafka
    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 ShareThis
    ShareThis uses AerospikeAerospike

    Aerospike is used heavily in our real time kafka pipeline. We use it two ways: we harness the fast key-value store lookup and leveraging Aerospikes ACID capabilities through UDFs we could manage updates in real time.

    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.

    How much does Aerospike cost?
    How much does Kafka cost?
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