DBMS > Apache Impala vs. Derby vs. DolphinDB vs. SiteWhere

System Properties Comparison Apache Impala vs. Derby vs. DolphinDB vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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.

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Relational DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

impala.apache.org

db.apache.org/derby

www.dolphindb.com

github.com/sitewhere/sitewhere

Technical documentation

impala.apache.org/impala-docs.html

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

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

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

DolphinDB, Inc

SiteWhere

Initial release

2013

1997

2018

2010

Current release

4.1.0, June 2022

10.17.1.0, November 2023

v2.00.4, January 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2

commercial

free community version available

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Java

Server operating systems

Linux

All OS with a Java VM

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

JDBC

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

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

Java

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate 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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Administrators, Users, Groups

Users with fine-grained authorization concept

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More resources

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