DBMS > Alibaba Cloud Table Store vs. Couchbase vs. Datomic vs. Firebase Realtime Database vs. HEAVY.AI

System Properties Comparison Alibaba Cloud Table Store vs. Couchbase vs. Datomic vs. Firebase Realtime Database vs. HEAVY.AI

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

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Spatial DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Website

www.alibabacloud.com/product/table-store

www.couchbase.com

www.datomic.com

firebase.google.com/products/realtime-database

github.com/heavyai/heavydb
www.heavy.ai

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.couchbase.com

docs.datomic.com

firebase.google.com/docs/database

docs.heavy.ai

Developer

Alibaba

Couchbase, Inc.

Cognitect

Google

acquired by Google 2014

HEAVY.AI, Inc.

Initial release

2016

2011

2012

2016

Current release

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

1.0.6735, June 2023

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

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

commercial

limited edition free

commercial

Open Source

Apache Version 2; enterprise edition available

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

Java, Clojure

C++ and CUDA

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

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

HTTP API

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

Supported programming languages

Java
Python

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Clojure
Java

Java
JavaScript
Objective-C

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

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

yes

Transaction Functions

limited functionality with using 'rules'

Triggers

yes

via the TAP protocol

By using transaction functions

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Automatic Sharding

none

But extensive use of caching in the application peers

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication

including cross data center replication
Source-replica replication

none

But extensive use of caching in the application peers

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

Ephemeral buckets

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights based on subaccounts and tokens

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

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

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