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Kestrel
Kestrel

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Apache Spark

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Kestrel vs Apache Spark: What are the differences?

Developers describe Kestrel as "Simple, distributed message queue system". Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM. On the other hand, Apache Spark is detailed as "Fast and general engine for large-scale data processing". 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.

Kestrel and Apache Spark are primarily classified as "Message Queue" and "Big Data" tools respectively.

Some of the features offered by Kestrel are:

  • Written by Robey Pointer
  • Starling clone written in Scala (a port of Starling from Ruby to Scala)
  • Queues are stored in memory, but logged on disk

On the other hand, Apache Spark provides the following key features:

  • Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
  • Write applications quickly in Java, Scala or Python
  • Combine SQL, streaming, and complex analytics

Kestrel and Apache Spark are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than Kestrel with 2.8K GitHub stars and 326 GitHub forks.

What is Kestrel?

Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM.

What is 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.
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        What are some alternatives to Kestrel and Apache Spark?
        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 is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
        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.
        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.
        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.
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        Decisions about Kestrel and Apache Spark
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        Reviews of Kestrel and Apache Spark
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        How developers use Kestrel and Apache Spark
        Avatar of Wei Chen
        Wei Chen uses Apache SparkApache Spark

        Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

        Avatar of Ralic Lo
        Ralic Lo uses Apache SparkApache Spark

        Used Spark Dataframe API on Spark-R for big data analysis.

        Avatar of Kalibrr
        Kalibrr uses Apache SparkApache Spark

        We use Apache Spark in computing our recommendations.

        Avatar of BrainFinance
        BrainFinance uses Apache SparkApache Spark

        As a part of big data machine learning stack (SMACK).

        Avatar of Dotmetrics
        Dotmetrics uses Apache SparkApache Spark

        Big data analytics and nightly transformation jobs.

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