DBMS > Axibase vs. Couchbase vs. RethinkDB vs. Spark SQL

System Properties Comparison Axibase vs. Couchbase vs. RethinkDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Time Series DBMS

Document store

Relational DBMS

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	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.couchbase.com

rethinkdb.com

spark.apache.org/sql

Technical documentation

docs.couchbase.com

rethinkdb.com/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Axibase Corporation

Couchbase, Inc.

The Linux Foundation

since July 2017

Apache Software Foundation

Initial release

2013

2011

2009

2014

Current release

15585

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

2.4.1, August 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

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

Open Source

Apache Version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C, C++, Go and Erlang

C++

Scala

Server operating systems

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

string, binary, float, bool, date, geometry

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-like query language

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

JDBC
ODBC

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

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

Triggers

yes

via the TAP protocol

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic Sharding

Sharding

range based

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

User concepts

Access control

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

yes

users and table-level permissions

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