DBMS > Amazon Redshift vs. Apache Impala vs. TerminusDB vs. Tibero vs. Vitess

System Properties Comparison Amazon Redshift vs. Apache Impala vs. TerminusDB vs. Tibero vs. Vitess

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

A secure RDBMS, designed for easy portability from Oracle

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store

Document store
RDF store

Document store
Spatial DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	0.22
Rank	#323	Overall
	#28	Graph DBMS

Score	1.81
Rank	#143	Overall
	#65	Relational DBMS

Score	1.04
Rank	#191	Overall
	#89	Relational DBMS

Website

aws.amazon.com/redshift

impala.apache.org

terminusdb.com

us.tmaxsoft.com/products/tibero

vitess.io

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

terminusdb.github.io/terminusdb/#

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

vitess.io/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DataChemist Ltd.

TmaxSoft

The Linux Foundation, PlanetScale

Initial release

2012

2013

2018

2003

2013

Current release

4.1.0, June 2022

11.0.0, January 2023

6, April 2015

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL V3

commercial

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Prolog, Rust

C and Assembler

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
Solaris
Windows

Docker
Linux
macOS

Data scheme

yes

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

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like query language (WOQL)

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

JavaScript
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes

Persistent Stored Procedure (PSM)

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Graph Partitioning

horizontal partitioning

by range, hash, list or composite

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Journaling Streams

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

in-memory journaling

yes

In-memory capabilities

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

yes

planned for next version

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

Users with fine-grained authorization concept

no user groups or roles

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