DBMS > Apache Impala vs. Couchbase vs. EsgynDB vs. Riak KV

System Properties Comparison Apache Impala vs. Couchbase vs. EsgynDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Distributed, fault tolerant key-value store

Primary database model

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

Secondary database models

Document store

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	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

impala.apache.org

www.couchbase.com

www.esgyn.cn

Technical documentation

impala.apache.org/impala-docs.html

docs.couchbase.com

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Couchbase, Inc.

Esgyn

OpenSource, formerly Basho Technologies

Initial release

2013

2011

2015

2009

Current release

4.1.0, June 2022

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

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

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

commercial

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C++

C, C++, Go and Erlang

C++, Java

Erlang

Server operating systems

Linux

Linux
OS X
Windows

Linux

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

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

yes

APIs and other access methods

JDBC
ODBC

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

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

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

yes

user defined functions and integration of map-reduce

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

Java Stored Procedures

Erlang

Triggers

yes

via the TAP protocol

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic Sharding

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication between multi datacenters

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

fine grained access rights according to SQL-standard

yes, using Riak Security

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