DBMS > Apache Impala vs. Couchbase vs. InfluxDB vs. RDF4J

System Properties Comparison Apache Impala vs. Couchbase vs. InfluxDB vs. RDF4J

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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

DBMS for storing time series, events and metrics

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

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

Spatial DBMS

with GEO package

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	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

impala.apache.org

www.couchbase.com

www.influxdata.com/products/influxdb-overview

rdf4j.org

Technical documentation

impala.apache.org/impala-docs.html

docs.couchbase.com

docs.influxdata.com/influxdb

rdf4j.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Couchbase, Inc.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2013

2011

2013

2004

Current release

4.1.0, June 2022

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

2.7.6, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

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

Open Source

MIT-License; commercial enterprise version available

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C++

C, C++, Go and Erlang

Java

Server operating systems

Linux

Linux
OS X
Windows

Linux
OS X

through Homebrew

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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 DML and DDL statements

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

SQL-like query language

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

HTTP API
JSON over UDP

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
PHP
Python

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++

yes

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic Sharding

Sharding

in enterprise version only

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

in enterprise version only

none

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

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

Ephemeral buckets

yes

Depending on used storage engine

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.

simple rights management via user accounts

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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