DBMS > AllegroGraph vs. ClickHouse vs. Datomic vs. GigaSpaces vs. Google Cloud Datastore

System Properties Comparison AllegroGraph vs. ClickHouse vs. Datomic vs. GigaSpaces vs. Google Cloud Datastore

Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

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.

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

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Document store

Secondary database models

Spatial DBMS

Time Series DBMS

Graph DBMS
Search engine

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

Score	1.13
Rank	#179	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Website

cloud.google.com/datastore

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

clickhouse.com/docs

docs.datomic.com

docs.gigaspaces.com/latest/landing.html

cloud.google.com/datastore/docs

Developer

Franz Inc.

Clickhouse Inc.

Cognitect

Gigaspaces Technologies

Google

Initial release

2004

2016

2012

2000

2008

Current release

8.0, December 2023

v24.4.1.2088-stable, May 2024

1.0.7075, December 2023

15.5, September 2020

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

Apache 2.0

commercial

limited edition free

Open Source

Apache Version 2; Commercial licenses available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

Java, Clojure

Java, C++, .Net

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
macOS

All OS with a Java VM

Linux
macOS
Solaris
Windows

hosted

Data scheme

yes

RDF schemas

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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.

bulk load of XML files possible

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

Close to ANSI SQL (SQL/JSON + extensions)

SQL-99 for query and DML statements

SQL-like query language (GQL)

APIs and other access methods

RESTful HTTP API
SPARQL

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

RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

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

Clojure
Java

.Net
C++
Java
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

yes

Transaction Functions

yes

using Google App Engine

Triggers

yes

By using transaction functions

yes, event driven architecture

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

with Federation

key based and custom

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

none

But extensive use of caching in the application peers

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

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