DBMS > Apache Doris vs. Couchbase vs. Netezza vs. Spark SQL

System Properties Comparison Apache Doris vs. Couchbase vs. Netezza vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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

Data warehouse and analytics appliance part of IBM PureSystems

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

Primary database model

Relational 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

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

www.couchbase.com

www.ibm.com/products/netezza

spark.apache.org/sql

Technical documentation

github.com/apache/doris/wiki

docs.couchbase.com

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

Developer

Apache Software Foundation, originally contributed from Baidu

Couchbase, Inc.

IBM

Apache Software Foundation

Initial release

2017

2011

2000

2014

Current release

1.2.2, February 2023

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

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

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

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

C, C++, Go and Erlang

Scala

Server operating systems

Linux

Linux
OS X
Windows

Linux

included in appliance

Linux
OS X
Windows

Data scheme

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

yes

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

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
MySQL client

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

JDBC
ODBC
OLE DB

JDBC
ODBC

Supported programming languages

Java

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

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

yes

Triggers

yes

via the TAP protocol

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Automatic Sharding

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of 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

User concepts

Access control

fine grained access rights according to SQL-standard

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

Users with fine-grained authorization concept

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