DBMS > Alibaba Cloud Table Store vs. Apache Druid vs. BigchainDB vs. Microsoft Azure Cosmos DB vs. Riak KV

System Properties Comparison Alibaba Cloud Table Store vs. Apache Druid vs. BigchainDB vs. Microsoft Azure Cosmos DB vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Globally distributed, horizontally scalable, multi-model database service

Distributed, fault tolerant key-value store

Primary database model

Wide column store

Relational DBMS
Time Series DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

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

Secondary database models

Spatial DBMS

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

Score	0.31
Rank	#297	Overall
	#11	Wide column stores

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

www.alibabacloud.com/product/table-store

druid.apache.org

www.bigchaindb.com

azure.microsoft.com/services/cosmos-db

Technical documentation

www.alibabacloud.com/help/en/tablestore

druid.apache.org/docs/latest/design

bigchaindb.readthedocs.io/en/latest

learn.microsoft.com/azure/cosmos-db

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

Developer

Alibaba

Apache Software Foundation and contributors

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2016

2012

2016

2014

2009

Current release

29.0.1, April 2024

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

AGPL v3

commercial

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Python

Erlang

Server operating systems

hosted

Linux
OS X
Unix

Linux

hosted

Linux
OS X

Data scheme

schema-free

yes

schema-less columns are supported

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

restricted

SQL

Support of SQL

SQL for querying

SQL-like query language

APIs and other access methods

HTTP API

JDBC
RESTful HTTP/JSON API

CLI Client
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP API
Native Erlang Interface

Supported programming languages

Java
Python

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Server-side scripts

Stored procedures

JavaScript

Erlang

Triggers

JavaScript

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

manual/auto, time-based

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, via HDFS, S3 or other storage engines

selectable replication factor

yes

Implicit feature of the cloud service

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

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

User concepts

Access control

Access rights based on subaccounts and tokens

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes

Access rights can be defined down to the item level

yes, using Riak Security

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