DBMS > 4D vs. Couchbase vs. GridGain vs. Microsoft Azure Data Explorer vs. Trafodion

System Properties Comparison 4D vs. Couchbase vs. GridGain vs. Microsoft Azure Data Explorer vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Application development environment with integrated database management system

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Fully managed big data interactive analytics platform

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

column oriented

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

www.4d.com

www.couchbase.com

www.gridgain.com

azure.microsoft.com/services/data-explorer

trafodion.apache.org

Technical documentation

developer.4d.com

docs.couchbase.com

www.gridgain.com/docs/index.html

docs.microsoft.com/en-us/azure/data-explorer

trafodion.apache.org/documentation.html

Developer

4D, Inc

Couchbase, Inc.

GridGain Systems, Inc.

Microsoft

Apache Software Foundation, originally developed by HP

Initial release

1984

2011

2007

2019

2014

Current release

v20, April 2023

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

GridGain 8.5.1

cloud service with continuous releases

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Go and Erlang

Java, C++, .Net

C++, Java

Server operating systems

OS X
Windows

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

hosted

Linux

Data scheme

yes

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

close to SQL 92

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

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

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

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

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

4D proprietary IDE
PHP

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

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

Yes, possible languages: KQL, Python, R

Java Stored Procedures

Triggers

yes

via the TAP protocol

yes (cache interceptors and events)

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes (replicated cache)

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Spark connector (open source): github.com/Azure/azure-kusto-spark

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Ephemeral buckets

yes

User concepts

Access control

Users and groups

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Security Hooks for custom implementations

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

More information provided by the system vendor

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.