DBMS > CockroachDB vs. Datomic vs. FoundationDB vs. Hive vs. MySQL

System Properties Comparison CockroachDB vs. Datomic vs. FoundationDB vs. Hive vs. MySQL

Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Ordered key-value store. Core features are complimented by layers.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Widely used open source RDBMS

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.06
Rank	#185	Overall
	#31	Document stores
	#28	Key-value stores
	#85	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

www.cockroachlabs.com

www.datomic.com

github.com/apple/foundationdb

hive.apache.org

www.mysql.com

Technical documentation

www.cockroachlabs.com/docs

docs.datomic.com

apple.github.io/foundationdb

cwiki.apache.org/confluence/display/Hive/Home

dev.mysql.com/doc

Developer

Cockroach Labs

Cognitect

FoundationDB

Apache Software Foundation

initially developed by Facebook

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2012

2013

2012

1995

Current release

24.1.0, May 2024

1.0.7075, December 2023

6.2.28, November 2020

3.1.3, April 2022

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

limited edition free

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

C++

Java

C and C++

Server operating systems

Linux
macOS
Windows

All OS with a Java VM

Linux
OS X
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

dynamic schema

yes

schema-free

some layers support schemas

yes

Typing

predefined data types such as float or date

yes

some layers support typing

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes, wire compatible with PostgreSQL

supported in specific SQL layer only

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC

RESTful HTTP API

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

Clojure
Java

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

C++
Java
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Transaction Functions

in SQL-layer only

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

none

But extensive use of caching in the application peers

yes

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Linearizable consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

in SQL-layer only

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Role-based access control

Access rights for users, groups and roles

Users with fine-grained authorization concept

no user groups or roles

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