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

System Properties Comparison EsgynDB vs. Solr vs. TimescaleDB vs. TimesTen 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

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

In-Memory RDBMS compatible to Oracle

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	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.esgyn.cn

solr.apache.org

www.timescale.com

www.oracle.com/database/technologies/related/timesten.html

www.voltdb.com

Technical documentation

solr.apache.org/resources.html

docs.timescale.com

docs.oracle.com/database/timesten-18.1

docs.voltdb.com

Developer

Esgyn

Apache Software Foundation

Timescale

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

VoltDB Inc.

Initial release

2015

2006

2017

1998

2010

Current release

9.6.0, April 2024

2.15.0, May 2024

11 Release 2 (11.2.2.8.0)

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

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
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X

for development

Data scheme

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

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

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

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

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

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
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
C++
Java
PL/SQL

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

PL/SQL

Java

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, across time and space (hash partitioning) attributes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

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

by means of logfiles and checkpoints

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

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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More resources

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