DBMS > ClickHouse vs. HugeGraph vs. IBM Db2 Event Store vs. SQream DB

System Properties Comparison ClickHouse vs. HugeGraph vs. IBM Db2 Event Store vs. SQream DB

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Editorial information provided by DB-Engines

Name

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.

A fast-speed and highly-scalable Graph DBMS

Distributed Event Store optimized for Internet of Things use cases

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Graph DBMS

Event Store
Time Series DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

clickhouse.com

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2-event-store

sqream.com

Technical documentation

clickhouse.com/docs

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2-event-store

docs.sqream.com

Developer

Clickhouse Inc.

Baidu

IBM

SQream Technologies

Initial release

2016

2018

2017

Current release

v24.4.2.141-stable, June 2024

0.9

2.0

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

free developer edition available

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.

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

C and C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

FreeBSD
Linux
macOS

Linux
macOS
Unix

Linux

Linux, macOS, Windows for the developer addition

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

also supports composite index and range index

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

through the embedded Spark runtime

yes

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

.Net
JDBC
ODBC

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

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

yes

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

key based and custom

yes

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

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

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

Active-active shard replication

none

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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. 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

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