DBMS > ClickHouse vs. Datomic vs. HugeGraph vs. Interbase vs. InterSystems Caché

System Properties Comparison ClickHouse vs. Datomic vs. HugeGraph vs. Interbase vs. InterSystems Caché

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

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

A fast-speed and highly-scalable Graph DBMS

Light-weight proven RDBMS

originally from Borland

A multi-model DBMS and application server

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Secondary database models

Time Series DBMS

Document store

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Website

clickhouse.com

www.datomic.com

github.com/hugegraph
hugegraph.apache.org

www.embarcadero.com/products/interbase

www.intersystems.com/products/cache

Technical documentation

clickhouse.com/docs

docs.datomic.com

hugegraph.apache.org/docs

docs.embarcadero.com/products/interbase

docs.intersystems.com

Developer

Clickhouse Inc.

Cognitect

Baidu

Embarcadero

InterSystems

Initial release

2016

2012

2018

1984

1997

Current release

v24.4.2.141-stable, June 2024

1.0.7075, December 2023

0.9

InterBase 2020, December 2019

2018.1.4, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

Open Source

Apache Version 2.0

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.

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Implementation language

C++

Java, Clojure

Java

Server operating systems

FreeBSD
Linux
macOS

All OS with a Java VM

Linux
macOS
Unix

Android
iOS
Linux
OS X
Windows

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

depending on used data model

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.

export as XML data possible

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

APIs and other access methods

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

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

.NET Client API
JDBC
ODBC
RESTful HTTP API

Supported programming languages

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

Groovy
Java
Python

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C#
C++
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

asynchronous Gremlin script jobs

yes

Interbase procedure and trigger language

yes

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

Interbase Change Views

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

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

Users, roles and permissions

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

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