DBMS > ClickHouse vs. EsgynDB vs. InfinityDB vs. NuoDB vs. SQream DB

System Properties Comparison ClickHouse vs. EsgynDB vs. InfinityDB vs. NuoDB vs. SQream DB

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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A Java embedded Key-Value Store which extends the Java Map interface

NuoDB is a webscale distributed database that supports SQL and ACID transactions

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

Primary database model

Relational DBMS

Key-value store

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	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

clickhouse.com

www.esgyn.cn

boilerbay.com

www.3ds.com/nuodb-distributed-sql-database

sqream.com

Technical documentation

clickhouse.com/docs

boilerbay.com/infinitydb/manual

doc.nuodb.com

docs.sqream.com

Developer

Clickhouse Inc.

Esgyn

Boiler Bay Inc.

Dassault Systèmes

originally NuoDB, Inc.

SQream Technologies

Initial release

2016

2015

2002

2013

2017

Current release

v24.4.1.2088-stable, May 2024

4.0

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

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.

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++

C++, Java

Java

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

FreeBSD
Linux
macOS

Linux

All OS with a Java VM

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

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

ADO.NET
JDBC
ODBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
ODBC

.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

All languages supporting JDBC/ODBC/ADO.Net

Java

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Java, SQL

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

none

data is dynamically stored/cached on the nodes where it is read/written

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.

Multi-source replication between multi datacenters

none

yes

Managed transparently by NuoDB

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

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.

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

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