DBMS > CouchDB vs. Google Cloud Bigtable vs. LeanXcale vs. SiriDB vs. XTDB

System Properties Comparison CouchDB vs. Google Cloud Bigtable vs. LeanXcale vs. SiriDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Open Source Time Series DBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store

Key-value store
Wide column store

Key-value store
Relational DBMS

Time Series DBMS

Document store

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

couchdb.apache.org

cloud.google.com/bigtable

www.leanxcale.com

siridb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigtable/docs

docs.siridb.com

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

LeanXcale

Cesbit

Juxt Ltd.

Initial release

2005

2015

2017

2019

Current release

3.3.3, December 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

MIT License

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Clojure

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data

yes, extensible-data-notation format

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

via views

yes

SQL

Support of SQL

yes

through Apache Derby

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP API

HTTP REST
JDBC

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
Java
Scala

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

Clojure
Java

Server-side scripts

Stored procedures

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

yes, each node contains all data

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 (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users can be defined per database

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

simple rights management via user accounts

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More resources

DB-Engines blog posts

Couchbase climbs up the DB-Engines Ranking, increasing its popularity by 10% every month
2 June 2014, Matthias Gelbmann

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Recent citations in the news

How to install the CouchDB NoSQL database on Debian Server 11
16 June 2022, TechRepublic

IBM Cloudant pulls plan to fund new foundational layer for CouchDB
15 March 2022, The Register

CouchDB 3.0 ends admin party era • DEVCLASS
27 February 2020, DevClass

Tracking Expenses with CouchDB and Angular — SitePoint
28 August 2014, SitePoint

Apache CouchDB: The Definitive Introduction
28 August 2014, InfoQ.com

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