DBMS > Datomic vs. GridDB vs. OpenTSDB vs. Sphinx vs. SQLite

System Properties Comparison Datomic vs. GridDB vs. OpenTSDB vs. Sphinx vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Scalable in-memory time series database optimized for IoT and Big Data

Scalable Time Series DBMS based on HBase

Open source search engine for searching in data from different sources, e.g. relational databases

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Key-value store
Relational DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

Technical documentation

docs.datomic.com

docs.griddb.net

opentsdb.net/docs/build/html/index.html

sphinxsearch.com/docs

www.sqlite.org/docs.html

Developer

Cognitect

Toshiba Corporation

currently maintained by Yahoo and other contributors

Sphinx Technologies Inc.

Dwayne Richard Hipp

Initial release

2012

2013

2011

2001

2000

Current release

1.0.7075, December 2023

5.1, August 2022

3.5.1, February 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

LGPL

Open Source

GPL version 2, commercial licence available

Open Source

Public Domain

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

Java, Clojure

C++

Java

C++

Server operating systems

All OS with a Java VM

Linux

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

numeric data for metrics, strings for tags

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language (SphinxQL)

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HTTP API
Telnet API

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Clojure
Java

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Erlang
Go
Java
Python
R
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

Transaction Functions

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

based on HBase

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.