DBMS > DolphinDB vs. Newts vs. Quasardb vs. RocksDB vs. Solr

System Properties Comparison DolphinDB vs. Newts vs. Quasardb vs. RocksDB vs. Solr

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.

Time Series DBMS based on Cassandra

Distributed, high-performance timeseries database

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Time Series DBMS

Key-value store

Search engine

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.dolphindb.com

opennms.github.io/newts

quasar.ai

rocksdb.org

solr.apache.org

Technical documentation

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

github.com/OpenNMS/newts/wiki

doc.quasar.ai/master

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

DolphinDB, Inc

OpenNMS Group

quasardb

Facebook, Inc.

Apache Software Foundation

Initial release

2018

2014

2009

2013

2006

Current release

v2.00.4, January 2022

3.14.1, January 2024

9.2.1, May 2024

9.6.1, May 2024

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

BSD

Open Source

Apache Version 2

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++

Java

C++

Java

Server operating systems

Linux
Windows

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

integer and binary

yes

supports customizable data types and automatic typing

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

with tags

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

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

HTTP REST
Java API

HTTP API

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

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

Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

based on Cassandra

Sharding

consistent hashing

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Source-replica replication with selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

yes

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Administrators, Users, Groups

Cryptographically strong user authentication and audit trail

yes

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