DBMS > Google Cloud Bigtable vs. PouchDB vs. Sadas Engine vs. SiriDB vs. XTDB

System Properties Comparison Google Cloud Bigtable vs. PouchDB vs. Sadas Engine vs. SiriDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

JavaScript DBMS with an API inspired by CouchDB

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open Source Time Series DBMS

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

Primary database model

Key-value store
Wide column store

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

cloud.google.com/bigtable

pouchdb.com

www.sadasengine.com

siridb.com

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

Technical documentation

cloud.google.com/bigtable/docs

pouchdb.com/guides

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.siridb.com

www.xtdb.com/docs

Developer

Google

Apache Software Foundation

SADAS s.r.l.

Cesbit

Juxt Ltd.

Initial release

2015

2012

2006

2017

2019

Current release

7.1.1, June 2019

8.0

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

commercial

free trial version available

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

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

Implementation language

JavaScript

C++

Clojure

Server operating systems

hosted

server-less, requires a JavaScript environment (browser, Node.js)

AIX
Linux
Windows

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

limited SQL, making use of Apache Calcite

APIs and other access methods

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

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC
Proprietary protocol

HTTP API

HTTP REST
JDBC

Supported programming languages

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

JavaScript

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

with a proxy-based framework, named couchdb-lounge

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

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

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

managed by 'Learn by Usage'

yes

User concepts

Access control

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

Access rights for users, groups and roles according to SQL-standard

simple rights management via user accounts

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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