DBMS > Kinetica vs. RocksDB vs. Solr vs. Stardog

System Properties Comparison Kinetica vs. RocksDB vs. Solr vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

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

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

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	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

Technical documentation

docs.kinetica.com

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

docs.stardog.com

Developer

Kinetica

Facebook, Inc.

Apache Software Foundation

Stardog-Union

Initial release

2012

2013

2006

2010

Current release

7.1, August 2021

9.2.1, May 2024

9.6.1, May 2024

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

Java

Server operating systems

Linux

All OS with a Java VM

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

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

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

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

All search fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

Solr Parallel SQL Interface

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

Java API
RESTful HTTP/JSON API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Java plugins

user defined functions and aggregates, HTTP Server extensions in Java

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

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

yes

optimistic locking

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

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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