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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Citus vs TiDB

Citus vs TiDB

OverviewComparisonAlternatives

Overview

Citus
Citus
Stacks60
Followers124
Votes11
GitHub Stars12.0K
Forks736
TiDB
TiDB
Stacks76
Followers177
Votes28
GitHub Stars39.3K
Forks6.0K

Citus vs TiDB: What are the differences?

1. **Scalability**: Citus allows horizontal scaling by sharding data across multiple nodes, while TiDB offers horizontal scalability by sharding data across multiple nodes and providing automatic data rebalancing. 2. **Consistency Model**: Citus employs a strict serializable consistency model, ensuring ACID compliance for distributed transactions, whereas TiDB uses a hybrid consistency model combining the strengths of strict consistency and eventual consistency. 3. **Data Distribution**: Citus distributes data based on a sharding key chosen by the user, offering flexibility in data distribution strategies, whereas TiDB automatically partitions data using the Primary Key as the sharding key, simplifying data distribution for the user. 4. **Indexing**: Citus supports traditional indexing methods like B-tree, GIN, and GiST indexes for efficient query processing, while TiDB relies on a distributed Key-Value storage engine for indexing purposes, optimizing read-heavy workloads. 5. **Query Processing**: Citus leverages PostgreSQL's query planner and executor to process queries efficiently, supporting complex SQL queries, whereas TiDB has a unique query processing layer that translates SQL queries into a distributed execution plan for optimal performance. 6. **Storage Engine**: Citus uses PostgreSQL as its underlying storage engine, providing rich data types and advanced SQL functionalities, while TiDB uses TiKV as its distributed storage engine, enabling horizontal scalability and fault tolerance for data storage.

In Summary, Citus and TiDB differ in terms of scalability approaches, consistency models, data distribution strategies, indexing methods, query processing mechanisms, and storage engines.

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Detailed Comparison

Citus
Citus
TiDB
TiDB

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.

Inspired by the design of Google F1, TiDB supports the best features of both traditional RDBMS and NoSQL.

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
Horizontal scalability;Asynchronous schema changes;Consistent distributed transactions;Compatible with MySQL protocol;Written in Go;NewSQL over TiKV;Multiple storage engine support
Statistics
GitHub Stars
12.0K
GitHub Stars
39.3K
GitHub Forks
736
GitHub Forks
6.0K
Stacks
60
Stacks
76
Followers
124
Followers
177
Votes
11
Votes
28
Pros & Cons
Pros
  • 6
    Multi-core Parallel Processing
  • 3
    Drop-in PostgreSQL replacement
  • 2
    Distributed with Auto-Sharding
Pros
  • 9
    Open source
  • 7
    Horizontal scalability
  • 5
    Strong ACID
  • 3
    HTAP
  • 2
    Mysql Compatibility
Integrations
.NET
.NET
Apache Spark
Apache Spark
Loggly
Loggly
Java
Java
Rails
Rails
Datadog
Datadog
Logentries
Logentries
Heroku
Heroku
Papertrail
Papertrail
PostgreSQL
PostgreSQL
No integrations available

What are some alternatives to Citus, TiDB?

MongoDB

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

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

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

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

SQLite

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

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

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

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

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

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.

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