Apache Spark vs Javalin

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Javalin

Stacks30

Followers64

Votes3

Javalin vs Apache Spark: What are the differences?

What is Javalin? Simple REST APIs for Java and Kotlin. Javalin started as a fork of the Spark framework but quickly turned into a ground-up rewrite influenced by express.js. Both of these web frameworks are inspired by the modern micro web framework grandfather: Sinatra, so if you’re coming from Ruby then Javalin shouldn’t feel too unfamiliar.

What is Apache Spark? 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.

Javalin can be classified as a tool in the "Microframeworks (Backend)" category, while Apache Spark is grouped under "Big Data Tools".

Javalin and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than Javalin with 3.06K GitHub stars and 257 GitHub forks.

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Advice on Apache Spark, Javalin

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

Juan José

May 1, 2020

Decided

I developed Hexagon heavily inspired in these great tools because of the following reasons:

Take full advantage of the Kotlin programming language without any strings attached to Java (as a language).
I wanted to be able to replace the HTTP server library used with different adapters (Jetty, Netty, etc.) and though right now there is only one, more are coming.
Have a complete tool to do full applications, though you can use other libraries, Hexagon comes with a dependency injection helper, settings loading from different sources and HTTP Client, so it comes with (batteries included).

Right now I'm using it for my pet projects, and I'm happy with it.

35.9k views35.9k

Comments

Detailed Comparison

Apache Spark	Javalin
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.	Javalin started as a fork of the Spark framework but quickly turned into a ground-up rewrite influenced by express.js. Both of these web frameworks are inspired by the modern micro web framework grandfather: Sinatra, so if you’re coming from Ruby then Javalin shouldn’t feel too unfamiliar.
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 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 30
Followers 3.5K	Followers 64
Votes 140	Votes 3
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	Pros 1 Does not require IDEA plugins 1 Rich support of template engines 1 Lightweight
Integrations
No integrations available	Kotlin Java

What are some alternatives to Apache Spark, Javalin?

ExpressJS

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Django REST framework

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Sails.js

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Sinatra

Sinatra is a DSL for quickly creating web applications in Ruby with minimal effort.

Lumen

Laravel Lumen is a stunningly fast PHP micro-framework for building web applications with expressive, elegant syntax. We believe development must be an enjoyable, creative experience to be truly fulfilling. Lumen attempts to take the pain out of development by easing common tasks used in the majority of web projects, such as routing, database abstraction, queueing, and caching.

Slim

Slim is easy to use for both beginners and professionals. Slim favors cleanliness over terseness and common cases over edge cases. Its interface is simple, intuitive, and extensively documented — both online and in the code itself.

Fastify

Fastify is a web framework highly focused on speed and low overhead. It is inspired from Hapi and Express and as far as we know, it is one of the fastest web frameworks in town. Use Fastify can increase your throughput up to 100%.

Falcon

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hapi

hapi is a simple to use configuration-centric framework with built-in support for input validation, caching, authentication, and other essential facilities for building web applications and services.

TypeORM

It supports both Active Record and Data Mapper patterns, unlike all other JavaScript ORMs currently in existence, which means you can write high quality, loosely coupled, scalable, maintainable applications the most productive way.