DBMS > AgensGraph vs. AnzoGraph DB vs. ClickHouse vs. Sphinx

System Properties Comparison AgensGraph vs. AnzoGraph DB vs. ClickHouse vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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.

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Graph DBMS
Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Search engine

Secondary database models

Time Series DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

bitnine.net/agensgraph

cambridgesemantics.com/anzograph

clickhouse.com

sphinxsearch.com

Technical documentation

bitnine.net/documentation

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

clickhouse.com/docs

sphinxsearch.com/docs

Developer

Bitnine Global Inc.

Cambridge Semantics

Clickhouse Inc.

Sphinx Technologies Inc.

Initial release

2016

2018

2016

2001

Current release

2.1, December 2018

2.3, January 2021

v24.4.2.141-stable, June 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free trial version available

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence 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.

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.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

Server operating systems

Linux
OS X
Windows

Linux

FreeBSD
Linux
macOS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

depending on used data model

Schema-free and OWL/RDFS-schema support

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.

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

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

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language (SphinxQL)

APIs and other access methods

Cypher Query Language
JDBC

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

Proprietary protocol

Supported programming languages

C
Java
JavaScript
Python

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Automatic sharding

key based and custom

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication in MPP-Cluster

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

none

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

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

not needed in graphs

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

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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.

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