DBMS > 4D vs. Couchbase vs. Google Cloud Datastore vs. HBase

System Properties Comparison 4D vs. Couchbase vs. Google Cloud Datastore vs. HBase

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Editorial information provided by DB-Engines

Name

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Relational DBMS

Document store

Wide column store

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

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Website

www.4d.com

www.couchbase.com

cloud.google.com/datastore

hbase.apache.org

Technical documentation

developer.4d.com

docs.couchbase.com

cloud.google.com/datastore/docs

hbase.apache.org/book.html

Developer

4D, Inc

Couchbase, Inc.

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

1984

2011

2008

Current release

v20, April 2023

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

2.3.4, January 2021

License

Commercial or Open Source

commercial

Open Source

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

commercial

Open Source

Apache version 2

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

Server operating systems

OS X
Windows

Linux
OS X
Windows

hosted

Linux
Unix
Windows

using Cygwin

Data scheme

yes

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

yes, details here

options to bring your own types, AVRO

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

SQL-like query language (GQL)

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

Supported programming languages

4D proprietary IDE
PHP

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

yes

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

using Google App Engine

yes

Coprocessors in Java

Triggers

yes

via the TAP protocol

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Single row ACID (across millions of columns)

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.

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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