DBMS > DolphinDB vs. Solr vs. Stardog vs. TerminusDB

System Properties Comparison DolphinDB vs. Solr vs. Stardog vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Relational DBMS

Spatial DBMS

Document store
RDF store

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

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

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

Technical documentation

docs.dolphindb.cn/en/help200/index.html

solr.apache.org/resources.html

docs.stardog.com

terminusdb.github.io/terminusdb/#

Developer

DolphinDB, Inc

Apache Software Foundation

Stardog-Union

DataChemist Ltd.

Initial release

2018

2006

2010

2018

Current release

v2.00.4, January 2022

9.6.1, May 2024

7.3.0, May 2020

11.0.0, January 2023

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache Version 2

commercial

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

Open Source

GPL V3

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++

Java

Prolog, Rust

Server operating systems

Linux
Windows

All OS with a Java VM

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

Linux
macOS
Windows

Linux

Data scheme

yes

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

schema-free and OWL/RDFS-schema support

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

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 query language

Solr Parallel SQL Interface

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

SQL-like query language (WOQL)

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

.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

JavaScript
Python

Server-side scripts

Stored procedures

yes

Java plugins

user defined functions and aggregates, HTTP Server extensions in Java

yes

Triggers

yes

User configurable commands triggered on index changes

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in HA-Cluster

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

yes

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 journaling

In-memory capabilities

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

yes

User concepts

Access control

Administrators, Users, Groups

yes

Access rights for users and roles

Role-based access control

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More resources

DB-Engines blog posts

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