DBMS > Google Cloud Bigtable vs. HBase vs. Riak KV vs. SiriDB

System Properties Comparison Google Cloud Bigtable vs. HBase vs. Riak KV vs. SiriDB

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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.

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

Distributed, fault tolerant key-value store

Open Source Time Series DBMS

Primary database model

Key-value store
Wide column store

Wide column store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Time Series DBMS

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	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

cloud.google.com/bigtable

hbase.apache.org

siridb.com

Technical documentation

cloud.google.com/bigtable/docs

hbase.apache.org/book.html

www.tiot.jp/riak-docs/riak/kv/latest

docs.siridb.com

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

OpenSource, formerly Basho Technologies

Cesbit

Initial release

2015

2008

2009

2017

Current release

2.3.4, January 2021

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache version 2, commercial enterprise edition

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

Java

Erlang

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux
OS X

Linux

Data scheme

schema-free

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

Numeric data

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

restricted

yes

SQL

Support of SQL

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

HTTP API
Native Erlang Interface

HTTP API

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

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
Source-replica replication

selectable replication factor

yes

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)

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

Atomic single-row operations

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

User concepts

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

yes, using Riak Security

simple rights management via user accounts

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More resources

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