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  5. Apache Spark vs Kestrel

Apache Spark vs Kestrel

OverviewDecisionsComparisonAlternatives

Overview

Kestrel
Kestrel
Stacks37
Followers58
Votes0
Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K

Apache Spark vs Kestrel: What are the differences?

Apache Spark and Kestrel are two popular technologies in the field of big data processing. Below are the key differences between Apache Spark and Kestrel:

1. **Processing model**: Apache Spark is designed for in-memory processing, making it faster for iterative workloads, while Kestrel focuses on real-time message queuing and processing for handling large volumes of data at scale.
2. **Ease of use**: Apache Spark provides a high-level API which allows users to easily write complex data processing logic, while Kestrel is more low-level and requires more manual configuration and management.
3. **Fault tolerance**: Apache Spark comes with built-in mechanisms for fault tolerance through its resilient distributed datasets (RDDs) and lineage tracking, whereas Kestrel relies on external tools or custom implementations for fault tolerance.
4. **Supported languages**: Apache Spark supports multiple programming languages like Scala, Java, and Python, making it versatile for various use cases, while Kestrel is mainly focused on supporting Scala and Java, limiting its flexibility in multi-language environments.
5. **Use cases**: Apache Spark is suited for batch processing, iterative algorithms, real-time stream processing, and machine learning applications, while Kestrel is best used for building real-time data pipelines, message queuing, and event-driven architectures.
6. **Resource management**: Apache Spark comes with its own resource manager (like YARN or Mesos) for resource allocation and job scheduling, whereas Kestrel relies on external tools for resource management and coordination.

In Summary, Apache Spark and Kestrel differ in their processing model, ease of use, fault tolerance mechanisms, supported languages, use cases, and resource management approaches. Each technology caters to different needs in the big data processing landscape.

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Advice on Kestrel, Apache Spark

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

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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Detailed Comparison

Kestrel
Kestrel
Apache Spark
Apache Spark

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.

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.

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
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;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars
-
GitHub Stars
42.2K
GitHub Forks
-
GitHub Forks
28.9K
Stacks
37
Stacks
3.1K
Followers
58
Followers
3.5K
Votes
0
Votes
140
Pros & Cons
No community feedback yet
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
Cons
  • 4
    Speed

What are some alternatives to Kestrel, Apache Spark?

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

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.

Amazon SQS

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.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

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.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

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