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Kestrel

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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.

Advice on Kestrel and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 255.1K views

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Replies (2)
Recommends
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 152.6K views
Recommends
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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Pros of Kestrel
Pros of Apache Spark
    Be the first to leave a pro
    • 58
      Open-source
    • 48
      Fast and Flexible
    • 7
      One platform for every big data problem
    • 6
      Easy to install and to use
    • 6
      Great for distributed SQL like applications
    • 3
      Works well for most Datascience usecases
    • 2
      Machine learning libratimery, Streaming in real
    • 2
      In memory Computation
    • 0
      Interactive Query

    Sign up to add or upvote prosMake informed product decisions

    Cons of Kestrel
    Cons of Apache Spark
      Be the first to leave a con
      • 3
        Speed

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Kestrel?
      What companies use Apache Spark?
      See which teams inside your own company are using Kestrel or Apache Spark.
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      What tools integrate with Kestrel?
      What tools integrate with Apache Spark?
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        Mar 24 2021 at 12:57PM

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