DBMS > Couchbase vs. etcd vs. Google BigQuery vs. IBM Cloudant

System Properties Comparison Couchbase vs. etcd vs. Google BigQuery vs. IBM Cloudant

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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

A distributed reliable key-value store

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

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

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Website

www.couchbase.com

etcd.io
github.com/etcd-io/etcd

cloud.google.com/bigquery

www.ibm.com/products/cloudant

Technical documentation

docs.couchbase.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

Developer

Couchbase, Inc.

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Initial release

2011

2010

Current release

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

3.4, August 2019

License

Commercial or Open Source

Open Source

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

Open Source

Apache Version 2.0

commercial

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

Erlang

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
Windows

experimental

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

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

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
JSON over HTTP

RESTful HTTP/JSON API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

Server-side scripts

Stored procedures

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

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

Triggers

yes

via the TAP protocol

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

User concepts

Access control

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per database

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