DBMS > CockroachDB vs. IRONdb vs. Realm vs. RocksDB vs. SAP SQL Anywhere

System Properties Comparison CockroachDB vs. IRONdb vs. Realm vs. RocksDB vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines 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.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

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

Score	6.10
Rank	#58	Overall
	#33	Relational DBMS

Score	7.71
Rank	#52	Overall
	#9	Document stores

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

Score	4.57
Rank	#78	Overall
	#43	Relational DBMS

Website

www.cockroachlabs.com

www.circonus.com/solutions/time-series-database/

realm.io

rocksdb.org

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

www.cockroachlabs.com/docs

docs.circonus.com/irondb/category/getting-started

realm.io/docs

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Cockroach Labs

Circonus LLC.

Realm, acquired by MongoDB in May 2019

Facebook, Inc.

SAP

formerly Sybase

Initial release

2015

2017

2014

2013

1992

Current release

23.1.1, May 2023

V0.10.20, January 2018

8.11.4, April 2024

17, July 2015

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

Open Source

BSD

commercial

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.

Implementation language

C and C++

C++

Server operating systems

Linux
macOS
Windows

Linux

Android
Backend: server-less
iOS
Windows

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

dynamic schema

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

JDBC

HTTP API

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes, in Lua

runs within the applications so server-side scripts are unnecessary

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Change Listeners

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

Automatic, metric affinity per node

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

configurable replication factor, datacenter aware

none

yes

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

In-Memory realm

yes

User concepts

Access control

Role-based access control

yes

fine grained access rights according to SQL-standard

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