DBMS > AnzoGraph DB vs. Citus vs. Datomic vs. RocksDB vs. YottaDB

System Properties Comparison AnzoGraph DB vs. Citus vs. Datomic vs. RocksDB vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

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

A fast and solid embedded Key-value store

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store

Secondary database models

Document store

Relational DBMS

using the Octo plugin

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

cambridgesemantics.com/anzograph

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.citusdata.com

docs.datomic.com

github.com/facebook/rocksdb/wiki

yottadb.com/resources/documentation

Developer

Cambridge Semantics

Cognitect

Facebook, Inc.

YottaDB, LLC

Initial release

2018

2010

2012

2013

2001

Current release

2.3, January 2021

8.1, December 2018

1.0.6735, June 2023

9.2.1, May 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL, commercial license also available

commercial

limited edition free

Open Source

BSD

Open Source

AGPL 3.0

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

Java, Clojure

C++

Server operating systems

Linux

All OS with a Java VM

Linux

Docker
Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

standard, with numerous extensions

by using the Octo plugin

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

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

RESTful HTTP API

C++ API
Java API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

C++
Java
Python

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Clojure
Java

C
C++
Go
Java
Perl
Python
Ruby

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

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

yes

Transaction Functions

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

But extensive use of caching in the application peers

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

other methods possible by using 3rd party extensions

none

But extensive use of caching in the application peers

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

yes

optimistic locking

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

yes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

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