DBMS > Alibaba Cloud Table Store vs. Couchbase vs. Google BigQuery vs. jBASE

System Properties Comparison Alibaba Cloud Table Store vs. Couchbase vs. Google BigQuery vs. jBASE

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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

Large scale data warehouse service with append-only tables

A robust multi-value DBMS comprising development tools and middleware

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

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Website

www.alibabacloud.com/product/table-store

www.couchbase.com

cloud.google.com/bigquery

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.couchbase.com

cloud.google.com/bigquery/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

Developer

Alibaba

Couchbase, Inc.

Google

Rocket Software (formerly Zumasys)

Initial release

2016

2011

2010

1991

Current release

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

5.7

License

Commercial or Open Source

commercial

Open Source

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

commercial

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

Server operating systems

hosted

Linux
OS X
Windows

hosted

AIX
Linux
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

Embedded SQL for jBASE in BASIC

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/JSON API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

Java
Python

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Basic
Jabbascript
Java

Server-side scripts

Stored procedures

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

user defined functions

in JavaScript

yes

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Automatic Sharding

none

Sharding

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

yes

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

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

ACID

Since BigQuery is designed for querying data

ACID

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

Ephemeral buckets

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.

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

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