Amazon RDS vs Citus

Overview

Amazon RDS

Stacks16.2K

Followers10.8K

Votes761

Citus

Stacks60

Followers124

Votes11

GitHub Stars12.0K

Forks736

Amazon RDS vs Citus: What are the differences?

Database Structure: Amazon RDS is a managed relational database service that supports various database engines like MySQL, PostgreSQL, Oracle, etc., whereas Citus is an extension to PostgreSQL that distributes tables across multiple nodes enabling horizontal scaling.
Scalability: Amazon RDS allows vertical scaling by increasing the compute power and storage of the instances, while Citus enables horizontal scaling by distributing data across multiple nodes for better performance.
Data Distribution: In Amazon RDS, data is stored in a single database instance, limiting the scalability options, while Citus distributes data across a cluster of nodes, allowing for parallel query processing and faster performance for analytical workloads.
Performance: Amazon RDS is suitable for transactional workloads due to its support for various RDBMS engines, whereas Citus is optimized for analytical workloads that require high performance and scalability for real-time data analysis.
Use Cases: Amazon RDS is commonly used for traditional OLTP applications, where data integrity is crucial, while Citus is preferred for real-time analytics, time-series data, and multi-tenant applications that require scalability and high availability.
Management Complexity: Amazon RDS simplifies database management tasks like backups, patching, and scaling through its managed service, while Citus requires more specialized knowledge to set up and manage distributed databases efficiently.

In Summary, Amazon RDS and Citus differ in terms of database structure, scalability options, data distribution, performance optimization, use cases, and management complexity, making each suitable for different types of applications and workloads.

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Detailed Comparison

Amazon RDS	Citus
Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.	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.
Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security	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 -	GitHub Stars 12.0K
GitHub Forks -	GitHub Forks 736
Stacks 16.2K	Stacks 60
Followers 10.8K	Followers 124
Votes 761	Votes 11
Pros & Cons
Pros 165 Reliable failovers 156 Automated backups 130 Backed by amazon 92 Db snapshots 87 Multi-availability	Pros 6 Multi-core Parallel Processing 3 Drop-in PostgreSQL replacement 2 Distributed with Auto-Sharding
Integrations
No integrations available	.NET Apache Spark Loggly Java Rails Datadog Logentries Heroku Papertrail PostgreSQL

What are some alternatives to Amazon RDS, 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

Amazon RDS vs Citus: What are the differences?

Database Structure: Amazon RDS is a managed relational database service that supports various database engines like MySQL, PostgreSQL, Oracle, etc., whereas Citus is an extension to PostgreSQL that distributes tables across multiple nodes enabling horizontal scaling.
Scalability: Amazon RDS allows vertical scaling by increasing the compute power and storage of the instances, while Citus enables horizontal scaling by distributing data across multiple nodes for better performance.
Data Distribution: In Amazon RDS, data is stored in a single database instance, limiting the scalability options, while Citus distributes data across a cluster of nodes, allowing for parallel query processing and faster performance for analytical workloads.
Performance: Amazon RDS is suitable for transactional workloads due to its support for various RDBMS engines, whereas Citus is optimized for analytical workloads that require high performance and scalability for real-time data analysis.
Use Cases: Amazon RDS is commonly used for traditional OLTP applications, where data integrity is crucial, while Citus is preferred for real-time analytics, time-series data, and multi-tenant applications that require scalability and high availability.
Management Complexity: Amazon RDS simplifies database management tasks like backups, patching, and scaling through its managed service, while Citus requires more specialized knowledge to set up and manage distributed databases efficiently.

Amazon RDS vs Citus

Overview