DBMS > Apache Impala vs. EXASOL vs. GridGain vs. OrigoDB

System Properties Comparison Apache Impala vs. EXASOL vs. GridGain vs. OrigoDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

GridGain is an in-memory computing platform, built on Apache Ignite

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store
Object oriented DBMS

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	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

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

Website

Technical documentation

impala.apache.org/impala-docs.html

www.exasol.com/resources

www.gridgain.com/docs/index.html

origodb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Exasol

GridGain Systems, Inc.

Robert Friberg et al

Initial release

2013

2000

2007

2009

under the name LiveDB

Current release

4.1.0, June 2022

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

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.

Implementation language

C++

Java, C++, .Net

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

C#
C++
Java
PHP
Python
Ruby
Scala

.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes (compute grid and cache interceptors can be used instead)

yes

Triggers

yes

yes (cache interceptors and events)

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes (replicated cache)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Hadoop integration

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

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

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

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

Security Hooks for custom implementations

Role based authorization

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More resources

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