Apache Spark vs RocksDB

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes141

GitHub Stars42.2K

Forks28.9K

RocksDB

Stacks141

Followers290

Votes11

GitHub Stars30.9K

Forks6.6K

Apache Spark vs RocksDB: What are the differences?

Introduction

In this article, we will highlight the key differences between Apache Spark and RocksDB, two popular technologies in the data processing and storage domain.

Execution Model: Apache Spark is a distributed computing framework that operates on clusters and provides fault tolerance. It uses a data processing engine called RDD (Resilient Distributed Dataset) to process and distribute data across the cluster. On the other hand, RocksDB is an embedded key-value storage engine that is optimized for fast storage and retrieval of data on a single machine.
Data Processing: Apache Spark is designed for processing large-scale data in distributed clusters. It supports various operations such as Map, Reduce, Filter, Join, and others, making it suitable for complex data transformations and analytics tasks. In contrast, RocksDB focuses on efficient storage and retrieval of data. It provides fast and persistent key-value storage, making it ideal for applications that require high-performance data access.
Data Storage: Apache Spark utilizes a distributed file system (such as Hadoop HDFS or AWS S3) to store data across multiple nodes in a cluster. It enables fault tolerance and allows processing large datasets. In contrast, RocksDB is an embedded storage engine, meaning it operates directly on local storage of a single machine. It offers high-performance storage capabilities but lacks the distributed nature of Spark.
Supported Languages: Apache Spark offers APIs in multiple programming languages, including Scala, Java, Python, and R, providing developers with flexibility. It allows them to write code in their preferred language and interact with Spark's distributed computing capabilities. In comparison, RocksDB provides a C++ interface and additional language bindings, limiting the choice of programming languages for applications integrating with RocksDB.
Integration with Big Data Ecosystem: Apache Spark is a part of the Apache Hadoop ecosystem, which provides a wide range of tools and frameworks for big data processing. It seamlessly integrates with other Hadoop components such as HBase, Hive, and Kafka, allowing users to leverage the entire ecosystem for their data processing needs. On the other hand, RocksDB, being an embedded storage engine, does not have direct integration with the Hadoop ecosystem. It can be used as a local storage option within a larger data processing pipeline.
Use Cases: Apache Spark is commonly used for processing and analyzing large datasets in a distributed manner. It is well-suited for applications like data exploration, machine learning, streaming analytics, and batch processing. RocksDB, with its high-performance storage characteristics, is often employed in scenarios requiring low-latency access to a large amount of data, such as caching, real-time applications, and certain database systems.

In summary, Apache Spark is a distributed computing framework focused on data processing and analytics, while RocksDB is an embedded key-value storage engine optimized for fast storage and retrieval of data on a single machine. Spark operates in a distributed cluster environment, supports multiple programming languages, and integrates with the big data ecosystem. In contrast, RocksDB offers high-performance storage and is suitable for low-latency data access use cases.

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Advice on Apache Spark, RocksDB

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	RocksDB
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.	RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.
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	Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM;Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory;Scales linearly with number of CPUs so that it works well on ARM processors
Statistics
GitHub Stars 42.2K	GitHub Stars 30.9K
GitHub Forks 28.9K	GitHub Forks 6.6K
Stacks 3.1K	Stacks 141
Followers 3.5K	Followers 290
Votes 141	Votes 11
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 5 Very fast 3 Made by Facebook 2 Consistent performance 1 Ability to add logic to the database layer where needed

What are some alternatives to Apache Spark, RocksDB?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Apache Spark vs RocksDB: What are the differences?

Introduction

In this article, we will highlight the key differences between Apache Spark and RocksDB, two popular technologies in the data processing and storage domain.

Execution Model: Apache Spark is a distributed computing framework that operates on clusters and provides fault tolerance. It uses a data processing engine called RDD (Resilient Distributed Dataset) to process and distribute data across the cluster. On the other hand, RocksDB is an embedded key-value storage engine that is optimized for fast storage and retrieval of data on a single machine.
Data Processing: Apache Spark is designed for processing large-scale data in distributed clusters. It supports various operations such as Map, Reduce, Filter, Join, and others, making it suitable for complex data transformations and analytics tasks. In contrast, RocksDB focuses on efficient storage and retrieval of data. It provides fast and persistent key-value storage, making it ideal for applications that require high-performance data access.
Data Storage: Apache Spark utilizes a distributed file system (such as Hadoop HDFS or AWS S3) to store data across multiple nodes in a cluster. It enables fault tolerance and allows processing large datasets. In contrast, RocksDB is an embedded storage engine, meaning it operates directly on local storage of a single machine. It offers high-performance storage capabilities but lacks the distributed nature of Spark.
Supported Languages: Apache Spark offers APIs in multiple programming languages, including Scala, Java, Python, and R, providing developers with flexibility. It allows them to write code in their preferred language and interact with Spark's distributed computing capabilities. In comparison, RocksDB provides a C++ interface and additional language bindings, limiting the choice of programming languages for applications integrating with RocksDB.
Integration with Big Data Ecosystem: Apache Spark is a part of the Apache Hadoop ecosystem, which provides a wide range of tools and frameworks for big data processing. It seamlessly integrates with other Hadoop components such as HBase, Hive, and Kafka, allowing users to leverage the entire ecosystem for their data processing needs. On the other hand, RocksDB, being an embedded storage engine, does not have direct integration with the Hadoop ecosystem. It can be used as a local storage option within a larger data processing pipeline.
Use Cases: Apache Spark is commonly used for processing and analyzing large datasets in a distributed manner. It is well-suited for applications like data exploration, machine learning, streaming analytics, and batch processing. RocksDB, with its high-performance storage characteristics, is often employed in scenarios requiring low-latency access to a large amount of data, such as caching, real-time applications, and certain database systems.

Apache Spark vs RocksDB

Overview

Apache Spark vs RocksDB: What are the differences?