DBMS > Derby vs. DolphinDB vs. Hive vs. RocksDB

System Properties Comparison Derby vs. DolphinDB vs. Hive vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Relational DBMS

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

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

Technical documentation

db.apache.org/derby/manuals/index.html

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

cwiki.apache.org/confluence/display/Hive/Home

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

DolphinDB, Inc

Apache Software Foundation

initially developed by Facebook

Facebook, Inc.

Initial release

1997

2018

2012

2013

Current release

10.17.1.0, November 2023

v2.00.4, January 2022

3.1.3, April 2022

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free community version available

Open Source

Apache Version 2

Open Source

BSD

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

C++

Java

C++

Server operating systems

All OS with a Java VM

Linux
Windows

All OS with a Java VM

Linux

Data scheme

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC

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

JDBC
ODBC
Thrift

C++ API
Java API

Supported programming languages

Java

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

C++
Java
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

Administrators, Users, Groups

Access rights for users, groups and roles

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