DBMS > Datomic vs. DolphinDB vs. Oracle Berkeley DB vs. TempoIQ vs. TimesTen

System Properties Comparison Datomic vs. DolphinDB vs. Oracle Berkeley DB vs. TempoIQ vs. TimesTen

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

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

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.

Widely used in-process key-value store

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.datomic.com

www.dolphindb.com

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

tempoiq.com (offline)

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

Technical documentation

docs.datomic.com

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

docs.oracle.com/cd/E17076_05/html/index.html

docs.oracle.com/database/timesten-18.1

Developer

Cognitect

DolphinDB, Inc

Oracle

originally developed by Sleepycat, which was acquired by Oracle

TempoIQ

Oracle, TimesTen Performance Software, HP

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

Initial release

2012

2018

1994

2012

1998

Current release

1.0.6735, June 2023

v2.00.4, January 2022

18.1.40, May 2020

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

limited edition free

commercial

free community version available

Open Source

commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Clojure

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

All OS with a Java VM

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

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

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

RESTful HTTP API

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

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Clojure
Java

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C#
Java
JavaScript

Node.js
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

PL/SQL

Triggers

By using transaction functions

yes

only for the SQL API

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

by means of logfiles and checkpoints

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

Administrators, Users, Groups

simple authentication-based access control

fine grained access rights according to SQL-standard

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