DBMS > AnzoGraph DB vs. ClickHouse vs. GBase vs. Hazelcast

System Properties Comparison AnzoGraph DB vs. ClickHouse vs. GBase vs. Hazelcast

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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

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

An analytical database for business intelligence with large customers in China.

A widely adopted in-memory data grid

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store

Secondary database models

Time Series DBMS

Document store

JSON support with IMDG 3.12

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

Score	0.27
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	16.20
Rank	#37	Overall
	#23	Relational DBMS

Score	1.04
Rank	#190	Overall
	#88	Relational DBMS

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Website

cambridgesemantics.com/anzograph

clickhouse.com

www.gbase.cn

hazelcast.com

Technical documentation

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

clickhouse.com/docs

hazelcast.org/imdg/docs

Developer

Cambridge Semantics

Clickhouse Inc.

Hazelcast

Initial release

2018

2016

2008

Current release

2.3, January 2021

v24.3.2.23-lts, April 2024

GBase 8a

5.3.6, November 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; commercial licenses 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.

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

C++

Java

Server operating systems

Linux

FreeBSD
Linux
macOS

All OS with a Java VM

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

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

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language

APIs and other access methods

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

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

C++
Java
Python

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

key based and custom

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

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

User concepts

Access control

Access rights for users and roles

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Role-based access control

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