DBMS > Kinetica vs. PouchDB vs. RocksDB vs. Solr vs. Splunk

System Properties Comparison Kinetica vs. PouchDB vs. RocksDB vs. Solr vs. Splunk

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

JavaScript DBMS with an API inspired by CouchDB

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A widely used distributed, scalable search engine based on Apache Lucene

Analytics Platform for Big Data

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

Technical documentation

docs.kinetica.com

pouchdb.com/guides

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

docs.splunk.com/Documentation/Splunk

Developer

Kinetica

Apache Software Foundation

Facebook, Inc.

Apache Software Foundation

Splunk Inc.

Initial release

2012

2013

2006

2003

Current release

7.1, August 2021

7.1.1, June 2019

9.2.1, May 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2

commercial

Limited free edition and free developer edition available

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++

JavaScript

C++

Java

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

via views

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Solr Parallel SQL Interface

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

C++ API
Java API

Java API
RESTful HTTP/JSON API

HTTP REST

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

JavaScript

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

View functions in JavaScript

Java plugins

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

optimistic locking

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

yes

Access rights for users and roles

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