DBMS > EsgynDB vs. Solr vs. Stardog vs. TimescaleDB vs. VoltDB

System Properties Comparison EsgynDB vs. Solr vs. Stardog vs. TimescaleDB vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

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

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

Technical documentation

solr.apache.org/resources.html

docs.stardog.com

docs.timescale.com

docs.voltdb.com

Developer

Esgyn

Apache Software Foundation

Stardog-Union

Timescale

VoltDB Inc.

Initial release

2015

2006

2010

2017

2010

Current release

9.6.0, April 2024

7.3.0, May 2020

2.15.0, May 2024

11.3, April 2022

License

Commercial or Open Source

commercial

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

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java

Java, C++

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

Linux
OS X
Windows

Linux
OS X

for development

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

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

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

yes

full PostgreSQL SQL syntax

yes

only a subset of SQL 99

APIs and other access methods

ADO.NET
JDBC
ODBC

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

Java plugins

user defined functions and aggregates, HTTP Server extensions in Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Java

Triggers

yes

User configurable commands triggered on index changes

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, across time and space (hash partitioning) attributes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

Multi-source replication in HA-Cluster

Source-replica replication with hot standby and reads on replicas

Multi-source replication
Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

optimistic locking

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Access rights for users and roles

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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