DBMS > 4D vs. Apache Impala vs. Atos Standard Common Repository vs. GridDB vs. Kyligence Enterprise

System Properties Comparison 4D vs. Apache Impala vs. Atos Standard Common Repository vs. GridDB vs. Kyligence Enterprise

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Application development environment with integrated database management system

Analytic DBMS for Hadoop

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

A distributed analytics engine for big data, built on top of Apache Kylin

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

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	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Website

www.4d.com

impala.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

griddb.net

kyligence.io/kyligence-enterprise

Technical documentation

developer.4d.com

impala.apache.org/impala-docs.html

docs.griddb.net

Developer

4D, Inc

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Atos Convergence Creators

Toshiba Corporation

Kyligence, Inc.

Initial release

1984

2013

2016

2013

2016

Current release

v20, April 2023

4.1.0, June 2022

1703

5.1, August 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

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

commercial

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++

Java

Server operating systems

OS X
Windows

Linux

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

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

yes

close to SQL 92

SQL-like DML and DDL statements

SQL92, SQL-like TQL (Toshiba Query Language)

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

LDAP

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC

All languages with LDAP bindings

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

yes

Source-replica 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

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID at container level

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

Users and groups

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

LDAP bind authentication

Access rights for users can be defined per database

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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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