DBMS > Apache Phoenix vs. ClickHouse vs. Oracle Coherence vs. Sadas Engine vs. Teradata Aster

System Properties Comparison Apache Phoenix vs. ClickHouse vs. Oracle Coherence vs. Sadas Engine vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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.

Oracles in-memory data grid solution

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Platform for big data analytics on multistructured data sources and types

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	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

phoenix.apache.org

clickhouse.com

www.oracle.com/java/coherence

www.sadasengine.com

Technical documentation

phoenix.apache.org

clickhouse.com/docs

docs.oracle.com/en/middleware/standalone/coherence

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Apache Software Foundation

Clickhouse Inc.

Oracle

SADAS s.r.l.

Teradata

Initial release

2014

2016

2007

2006

2005

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

v24.4.1.2088-stable, May 2024

14.1, August 2023

8.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

free trial version 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.

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Implementation language

Java

C++

Java

C++

Server operating systems

Linux
Unix
Windows

FreeBSD
Linux
macOS

All OS with a Java VM

AIX
Linux
Windows

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

Close to ANSI SQL (SQL/JSON + extensions)

yes

APIs and other access methods

JDBC

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

JCache
JPA
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
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

.Net
C++
Java

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

yes

R packages

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Sharding

horizontal partitioning

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.

yes, with selectable consistency level

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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.

authentification to access the cache via certificates or http basic authentication

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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