DBMS > Couchbase vs. Google Cloud Datastore vs. H2GIS vs. Hawkular Metrics vs. STSdb

System Properties Comparison Couchbase vs. Google Cloud Datastore vs. H2GIS vs. Hawkular Metrics vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

Spatial extension of H2

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Relational DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

www.couchbase.com

cloud.google.com/datastore

www.h2gis.org

www.hawkular.org

github.com/STSSoft/STSdb4

Technical documentation

docs.couchbase.com

cloud.google.com/datastore/docs

www.h2gis.org/docs/home

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Couchbase, Inc.

Google

CNRS

Community supported by Red Hat

STS Soft SC

Initial release

2011

2008

2013

2014

2011

Current release

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

4.0.8, September 2015

License

Commercial or Open Source

Open Source

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

commercial

Open Source

LGPL 3.0

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

primitive types and user defined types (classes)

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

SQL-like query language (GQL)

yes

APIs and other access methods

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

HTTP REST

.NET Client API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

Java

Go
Java
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

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

using Google App Engine

yes

based on H2

Triggers

yes

via the TAP protocol

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

none

Sharding

based on Cassandra

none

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

yes

based on H2

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

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)

yes

based on H2

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