Apache Spark vs Citus

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Citus

Stacks60

Followers124

Votes11

GitHub Stars12.0K

Forks736

Citus vs Apache Spark: What are the differences?

What is Citus? Worry-free Postgres for SaaS. Built to scale out. Citus is worry-free Postgres for SaaS. Made to scale out, Citus is an extension to Postgres that distributes queries across any number of servers. Citus is available as open source, as on-prem software, and as a fully-managed service.

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.

Citus belongs to "Databases" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".

Some of the features offered by Citus are:

Multi-Node Scalable PostgreSQL
Built-in Replication and High Availability
Real-time Reads/Writes On Multiple Nodes

On the other hand, Apache Spark provides the following key features:

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

"Multi-core Parallel Processing" is the primary reason why developers consider Citus over the competitors, whereas "Open-source" was stated as the key factor in picking Apache Spark.

Citus 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 Citus with 3.64K GitHub stars and 273 GitHub forks.

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Advice on Apache Spark, Citus

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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Comments

Detailed Comparison

Apache Spark	Citus
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's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.
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	Multi-Node Scalable PostgreSQL;Built-in Replication and High Availability;Real-time Reads/Writes On Multiple Nodes;Multi-core Parallel Processing of Queries;Tenant isolation
Statistics
GitHub Stars 42.2K	GitHub Stars 12.0K
GitHub Forks 28.9K	GitHub Forks 736
Stacks 3.1K	Stacks 60
Followers 3.5K	Followers 124
Votes 140	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 6 Multi-core Parallel Processing 3 Drop-in PostgreSQL replacement 2 Distributed with Auto-Sharding
Integrations
No integrations available	.NET Loggly Java Rails Datadog Logentries Heroku Papertrail PostgreSQL Kafka

What are some alternatives to Apache Spark, Citus?

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

Some of the features offered by Citus are:

Multi-Node Scalable PostgreSQL
Built-in Replication and High Availability
Real-time Reads/Writes On Multiple Nodes

On the other hand, Apache Spark provides the following key features:

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

"Multi-core Parallel Processing" is the primary reason why developers consider Citus over the competitors, whereas "Open-source" was stated as the key factor in picking Apache Spark.

Apache Spark vs Citus