DBMS > CockroachDB vs. Datomic vs. Heroic vs. PostgreSQL vs. WakandaDB

System Properties Comparison CockroachDB vs. Datomic vs. Heroic vs. PostgreSQL vs. WakandaDB

Editorial information provided by DB-Engines

Name

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Object oriented DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.cockroachlabs.com

www.datomic.com

github.com/spotify/heroic

www.postgresql.org

wakanda.github.io

Technical documentation

www.cockroachlabs.com/docs

docs.datomic.com

spotify.github.io/heroic

www.postgresql.org/docs

wakanda.github.io/doc

Developer

Cockroach Labs

Cognitect

Spotify

PostgreSQL Global Development Group

www.postgresql.org/developer

Wakanda SAS

Initial release

2015

2012

2014

1989

1989: Postgres, 1996: PostgreSQL

2012

Current release

23.1.1, May 2023

1.0.6735, June 2023

16.3, May 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

limited edition free

Open Source

Apache 2.0

Open Source

BSD

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java, Clojure

Java

C++, JavaScript

Server operating systems

Linux
macOS
Windows

All OS with a Java VM

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X
Windows

Data scheme

dynamic schema

yes

schema-free

yes

Typing

predefined data types such as float or date

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes, wire compatible with PostgreSQL

yes

standard with numerous extensions

APIs and other access methods

JDBC

RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API

Supported programming languages

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

Clojure
Java

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

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

partitioning by range, list and (since PostgreSQL 11) by hash

none

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

Source-replica replication

other methods possible by using 3rd party extensions

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

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Related products and services

3rd parties

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