DBMS > DolphinDB vs. JaguarDB vs. Manticore Search vs. Oracle Berkeley DB

System Properties Comparison DolphinDB vs. JaguarDB vs. Manticore Search vs. Oracle Berkeley DB

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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.

Performant, highly scalable DBMS for AI and IoT applications

Multi-storage database for search, including full-text search.

Widely used in-process key-value store

Primary database model

Time Series DBMS

Key-value store
Vector DBMS

Search engine

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Relational DBMS

Time Series DBMS

using the Manticore Columnar Library

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#15	Vector DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

www.dolphindb.com

www.jaguardb.com

manticoresearch.com

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

Technical documentation

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

www.jaguardb.com/support.html

manual.manticoresearch.com

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

Developer

DolphinDB, Inc

DataJaguar, Inc.

Manticore Software

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2018

2015

2017

1994

Current release

v2.00.4, January 2022

3.3 July 2023

6.0, February 2023

18.1.40, May 2020

License

Commercial or Open Source

commercial

free community version available

Open Source

GPL V3.0

Open Source

GPL version 2

Open Source

commercial license available

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++

the server part. Clients available in other languages

C++

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

Server operating systems

Linux
Windows

Linux

FreeBSD
Linux
macOS
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

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

JDBC
ODBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

Supported programming languages

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

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.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

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Synchronous replication based on Galera library

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

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

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

rights management via user accounts

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