DBMS > Apache Impala vs. GridDB vs. OrientDB vs. SQLite

System Properties Comparison Apache Impala vs. GridDB vs. OrientDB vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Multi-model DBMS (Document, Graph, Key/Value)

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Document store

Key-value store
Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.griddb.net

www.orientdb.com/docs/last/index.html

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Toshiba Corporation

OrientDB LTD; CallidusCloud; SAP

Dwayne Richard Hipp

Initial release

2013

2010

2000

Current release

4.1.0, June 2022

5.1, August 2022

3.2.29, March 2024

3.46.0 (23 May 2024), May 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

Apache version 2

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

All OS with a Java JDK (>= JDK 6)

server-less

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

dynamic column types

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language, no joins

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java, Javascript

Triggers

yes

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication

none

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

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency within container, eventual consistency across containers

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Access rights for users and roles; record level security configurable

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

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