Apache Spark vs Kafka

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Kafka: What are the differences?

Apache Spark and Kafka are two popular tools in the realm of big data processing. Apache Spark is a fast and general-purpose cluster computing system, while Kafka is a distributed streaming platform. Below are some key differences between Apache Spark and Kafka:

Processing Type: Apache Spark is primarily used for processing large datasets in batch or streaming modes, providing features for data manipulation, machine learning, and graph processing. On the other hand, Kafka is focused on real-time processing and messaging, allowing for the scalable and fault-tolerant handling of streams of data.
Data Storage: Apache Spark operates on data that is already stored in various sources like HDFS, HBase, or Cassandra, performing computations on that data in-memory. Kafka, however, acts as a buffer that collects and delivers data streams in real-time, providing a distributed and fault-tolerant storage mechanism for these streams.
Computing Model: Apache Spark utilizes a resilient distributed dataset (RDD) abstraction for distributing data across a cluster and performing parallel processing. In contrast, Kafka follows a publish-subscribe messaging model where producers publish data to topics and consumers subscribe to those topics to receive the data.

In Summary, Apache Spark specializes in high-speed data processing and analytics, while Kafka focuses on real-time data streaming and messaging.

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CLI (Node.js)

Manual

Advice on Kafka, Apache Spark

viradiya

Apr 12, 2020

Needs adviceon

AngularJS

ASP.NET Core

MSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k

Comments

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

576k views576k

Comments

Ishfaq

Feb 28, 2020

Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

804k views804k

Comments

Detailed Comparison

Kafka	Apache Spark
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	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 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);Supports both on-line as off-line processing	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 31.2K	GitHub Stars 42.2K
GitHub Forks 14.8K	GitHub Forks 28.9K
Stacks 24.2K	Stacks 3.1K
Followers 22.3K	Followers 3.5K
Votes 607	Votes 140
Pros & Cons
Pros 126 High-throughput 119 Distributed 92 Scalable 86 High-Performance 66 Durable Cons 32 Non-Java clients are second-class citizens 29 Needs Zookeeper 9 Operational difficulties 5 Terrible Packaging	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 Kafka, 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.

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

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 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

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

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

Distributed SQL Query Engine for Big Data

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 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.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Related Comparisons

Apache Spark vs Kafka: What are the differences?

Processing Type: Apache Spark is primarily used for processing large datasets in batch or streaming modes, providing features for data manipulation, machine learning, and graph processing. On the other hand, Kafka is focused on real-time processing and messaging, allowing for the scalable and fault-tolerant handling of streams of data.
Data Storage: Apache Spark operates on data that is already stored in various sources like HDFS, HBase, or Cassandra, performing computations on that data in-memory. Kafka, however, acts as a buffer that collects and delivers data streams in real-time, providing a distributed and fault-tolerant storage mechanism for these streams.
Computing Model: Apache Spark utilizes a resilient distributed dataset (RDD) abstraction for distributing data across a cluster and performing parallel processing. In contrast, Kafka follows a publish-subscribe messaging model where producers publish data to topics and consumers subscribe to those topics to receive the data.

In Summary, Apache Spark specializes in high-speed data processing and analytics, while Kafka focuses on real-time data streaming and messaging.

Apache Spark vs Kafka

Overview