DBMS > Bangdb vs. CockroachDB vs. Datomic vs. EsgynDB vs. RocksDB

System Properties Comparison Bangdb vs. CockroachDB vs. Datomic vs. EsgynDB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	0.15
Rank	#334	Overall
	#45	Document stores
	#31	Graph DBMS
	#31	Time Series DBMS

Score	6.10
Rank	#58	Overall
	#33	Relational DBMS

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Website

bangdb.com

www.cockroachlabs.com

www.datomic.com

www.esgyn.cn

rocksdb.org

Technical documentation

docs.bangdb.com

www.cockroachlabs.com/docs

docs.datomic.com

github.com/facebook/rocksdb/wiki

Developer

Sachin Sinha, BangDB

Cockroach Labs

Cognitect

Esgyn

Facebook, Inc.

Initial release

2012

2015

2012

2015

2013

Current release

BangDB 2.0, October 2021

23.1.1, May 2023

1.0.6735, June 2023

8.11.4, April 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0, commercial license available

commercial

limited edition free

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java, Clojure

C++, Java

C++

Server operating systems

Linux

Linux
macOS
Windows

All OS with a Java VM

Linux

Data scheme

schema-free

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes, wire compatible with PostgreSQL

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC

RESTful HTTP API

ADO.NET
JDBC
ODBC

C++ API
Java API

Supported programming languages

C
C#
C++
Java
Python

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

Clojure
Java

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

Java Stored Procedures

Triggers

yes, Notifications (with Streaming only)

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication using RAFT

none

But extensive use of caching in the application peers

Multi-source replication between multi datacenters

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

yes

recommended only for testing and development

yes

User concepts

Access control

yes (enterprise version only)

Role-based access control

fine grained access rights according to SQL-standard

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