Apache Spark vs Spring Batch

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Spring Batch

Stacks184

Followers250

Votes0

GitHub Stars2.9K

Forks2.5K

Apache Spark vs Spring Batch: What are the differences?

Key Differences between Apache Spark and Spring Batch

Apache Spark and Spring Batch are both popular frameworks used for Big Data processing and batch processing, but they have some key differences that set them apart.

Processing Paradigm: Apache Spark is a distributed computing framework that offers in-memory processing capabilities, allowing for faster data processing, while Spring Batch is a lightweight framework that focuses on batch processing and is ideal for handling large volumes of data.
Data Processing Model: Spark operates on a data processing model called Resilient Distributed Datasets (RDD), which allows for parallel processing and fault tolerance. Spring Batch, on the other hand, follows a step-by-step approach to process data in chunks or batches, making it suitable for sequential processing.
Programming Languages: Apache Spark supports multiple programming languages such as Scala, Java, Python, and R, giving developers the flexibility to choose their preferred language. Spring Batch primarily uses Java as its programming language.
Integration with Ecosystem: Apache Spark integrates well with other Big Data tools and frameworks like Hadoop, Hive, and HBase, making it a comprehensive solution for Big Data processing. Spring Batch, on the other hand, is part of the larger Spring ecosystem, integrating seamlessly with other Spring framework components.
Real-time Vs Batch Processing: While both frameworks can handle batch processing, Spark also provides real-time stream processing capabilities through its structured streaming API. Spring Batch focuses primarily on batch processing and does not provide native support for real-time processing.
Data Manipulation: Apache Spark provides a wide range of built-in libraries and APIs for data manipulation and analysis, including SQL queries, machine learning algorithms, and graph processing. Spring Batch, on the other hand, focuses on data import/export, transformation, and business logic, without the extensive data manipulation capabilities offered by Spark.

In Summary, Apache Spark is a distributed computing framework that excels in in-memory processing and real-time stream processing, with extensive data manipulation capabilities, while Spring Batch is a lightweight framework specialized in batch processing for large volumes of data.

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Advice on Apache Spark, Spring Batch

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.

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Detailed Comparison

Apache Spark	Spring Batch
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.	It is designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. It also provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management.
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	Transaction management; Chunk based processing; Declarative I/O
Statistics
GitHub Stars 42.2K	GitHub Stars 2.9K
GitHub Forks 28.9K	GitHub Forks 2.5K
Stacks 3.1K	Stacks 184
Followers 3.5K	Followers 250
Votes 140	Votes 0
Pros & Cons
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	No community feedback yet
Integrations
No integrations available	Spring Boot MongoDB

What are some alternatives to Apache Spark, Spring Batch?

Node.js

Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

Rails

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

Laravel

It is a web application framework with expressive, elegant syntax. It attempts to take the pain out of development by easing common tasks used in the majority of web projects, such as authentication, routing, sessions, and caching.

.NET

.NET is a general purpose development platform. With .NET, you can use multiple languages, editors, and libraries to build native applications for web, mobile, desktop, gaming, and IoT for Windows, macOS, Linux, Android, and more.

ASP.NET Core

A free and open-source web framework, and higher performance than ASP.NET, developed by Microsoft and the community. It is a modular framework that runs on both the full .NET Framework, on Windows, and the cross-platform .NET Core.

Symfony

It is written with speed and flexibility in mind. It allows developers to build better and easy to maintain websites with PHP..

Spring

A key element of Spring is infrastructural support at the application level: Spring focuses on the "plumbing" of enterprise applications so that teams can focus on application-level business logic, without unnecessary ties to specific deployment environments.

Spring Boot

Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can "just run". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss. Most Spring Boot applications need very little Spring configuration.

Android SDK

Android provides a rich application framework that allows you to build innovative apps and games for mobile devices in a Java language environment.

Related Comparisons

Apache Spark vs Spring Batch: What are the differences?

Key Differences between Apache Spark and Spring Batch

Apache Spark and Spring Batch are both popular frameworks used for Big Data processing and batch processing, but they have some key differences that set them apart.

Processing Paradigm: Apache Spark is a distributed computing framework that offers in-memory processing capabilities, allowing for faster data processing, while Spring Batch is a lightweight framework that focuses on batch processing and is ideal for handling large volumes of data.
Data Processing Model: Spark operates on a data processing model called Resilient Distributed Datasets (RDD), which allows for parallel processing and fault tolerance. Spring Batch, on the other hand, follows a step-by-step approach to process data in chunks or batches, making it suitable for sequential processing.
Programming Languages: Apache Spark supports multiple programming languages such as Scala, Java, Python, and R, giving developers the flexibility to choose their preferred language. Spring Batch primarily uses Java as its programming language.
Integration with Ecosystem: Apache Spark integrates well with other Big Data tools and frameworks like Hadoop, Hive, and HBase, making it a comprehensive solution for Big Data processing. Spring Batch, on the other hand, is part of the larger Spring ecosystem, integrating seamlessly with other Spring framework components.
Real-time Vs Batch Processing: While both frameworks can handle batch processing, Spark also provides real-time stream processing capabilities through its structured streaming API. Spring Batch focuses primarily on batch processing and does not provide native support for real-time processing.
Data Manipulation: Apache Spark provides a wide range of built-in libraries and APIs for data manipulation and analysis, including SQL queries, machine learning algorithms, and graph processing. Spring Batch, on the other hand, focuses on data import/export, transformation, and business logic, without the extensive data manipulation capabilities offered by Spark.

Apache Spark vs Spring Batch

Overview

Apache Spark vs Spring Batch: What are the differences?