Citus vs RocksDB

Overview

RocksDB

Stacks141

Followers290

Votes11

GitHub Stars30.9K

Forks6.6K

Citus

Stacks60

Followers124

Votes11

GitHub Stars12.0K

Forks736

Citus vs RocksDB: What are the differences?

Developers describe Citus as "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. On the other hand, RocksDB is detailed as "Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team". 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.

Citus and RocksDB can be categorized as "Databases" 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, RocksDB provides the following key features:

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

"Multi-core Parallel Processing" is the top reason why over 3 developers like Citus, while over 2 developers mention "Very fast" as the leading cause for choosing RocksDB.

Citus and RocksDB are both open source tools. RocksDB with 14.3K GitHub stars and 3.12K forks on GitHub appears to be more popular than Citus with 3.64K GitHub stars and 273 GitHub forks.

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Advice on RocksDB, Citus

Feb 9, 2022

Needs adviceon

Milvus

HBase

RocksDB

I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

174k views174k

Comments

Masked

Jun 29, 2021

Needs advice

There'd be a couple of thousands of customers with a similar data structure and a medium number of transactions per day, but the data volume is pretty high (Each customer has around 1 or 2 GB so it would sum up to roughly 2TB). The usage pattern is both read and write-heavy (writes are mostly made through a Windows app, but read operations are made by the user), and I need the historical data for analysis and aggregation. The data model is not join-heavy as is not join-free. If the solution is fully ACID, the better, but must be Highly Available and Horizontally Scalable.

Also, the budget is not so high, and I'd rather be using a handful (at most 5) of cheap to medium-sized servers (2 CPU cores and 4GB RAM).

7.65k views7.65k

Comments

Detailed Comparison

RocksDB	Citus
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.	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.
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	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 30.9K	GitHub Stars 12.0K
GitHub Forks 6.6K	GitHub Forks 736
Stacks 141	Stacks 60
Followers 290	Followers 124
Votes 11	Votes 11
Pros & Cons
Pros 5 Very fast 3 Made by Facebook 2 Consistent performance 1 Ability to add logic to the database layer where needed	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 RocksDB, 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, RocksDB provides the following key features:

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

"Multi-core Parallel Processing" is the top reason why over 3 developers like Citus, while over 2 developers mention "Very fast" as the leading cause for choosing RocksDB.

Citus vs RocksDB