DBMS > Apache Impala vs. GridDB vs. JaguarDB vs. MaxDB vs. Splunk

System Properties Comparison Apache Impala vs. GridDB vs. JaguarDB vs. MaxDB vs. Splunk

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Scalable in-memory time series database optimized for IoT and Big Data

Performant, highly scalable DBMS for AI and IoT applications

A robust and reliable RDBMS optimized to run all major SAP solutions

Analytics Platform for Big Data

Primary database model

Relational DBMS

Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

Search engine

Secondary database models

Document store

Key-value store
Relational DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.griddb.net

www.jaguardb.com/support.html

maxdb.sap.com/documentation

docs.splunk.com/Documentation/Splunk

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Toshiba Corporation

DataJaguar, Inc.

SAP, acquired from Software AG (Adabas-D) in 1997

Splunk Inc.

Initial release

2013

2015

1984

2003

Current release

4.1.0, June 2022

5.1, August 2022

3.3 July 2023

7.9.10.12, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

GPL V3.0

commercial

Limited community edition free

commercial

Limited free edition and free developer edition available

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++

the server part. Clients available in other languages

C++

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL92, SQL-like TQL (Toshiba Query Language)

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC
WebDAV

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

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

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

.Net
C#
Java
Perl
PHP
Python

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users can be defined per database

rights management via user accounts

fine grained access rights according to SQL-standard

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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