DBMS > Apache Impala vs. GBase vs. mSQL vs. OrigoDB vs. Stardog

System Properties Comparison Apache Impala vs. GBase vs. mSQL vs. OrigoDB vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Graph DBMS
RDF store

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

impala.apache.org

www.gbase.cn

hughestech.com.au/products/msql

origodb.com

www.stardog.com

Technical documentation

impala.apache.org/impala-docs.html

origodb.com/docs

docs.stardog.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

General Data Technology Co., Ltd.

Hughes Technologies

Robert Friberg et al

Stardog-Union

Initial release

2013

2004

1994

2009

under the name LiveDB

2010

Current release

4.1.0, June 2022

GBase 8a, GBase 8s, GBase 8c

4.4, October 2021

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free licenses can be provided

Open Source

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C, Java, Python

Java

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

Standard with numerous extensions

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC

.NET Client API
HTTP API
LINQ

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Domain Events

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

none

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

Source-replica replication

Multi-source replication in HA-Cluster

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

Eventual Consistency

Immediate Consistency

none

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

depending on model

yes

relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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, groups and roles

based on Apache Sentry and Kerberos

yes

Role based authorization

Access rights for users and roles

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More resources

Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

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provided by Google News

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