DBMS > DolphinDB vs. JanusGraph vs. Solr vs. Tkrzw vs. Yanza

System Properties Comparison DolphinDB vs. JanusGraph vs. Solr vs. Tkrzw vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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.

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Time Series DBMS for IoT Applications

Primary database model

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	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

yanza.com

Technical documentation

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

docs.janusgraph.org

solr.apache.org/resources.html

Developer

DolphinDB, Inc

Linux Foundation; originally developed as Titan by Aurelius

Apache Software Foundation

Mikio Hirabayashi

Yanza

Initial release

2018

2017

2006

2020

2015

Current release

v2.00.4, January 2022

0.6.3, February 2023

9.6.1, May 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

C++

Server operating systems

Linux
Windows

Linux
OS X
Unix
Windows

All OS with a Java VM

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

Linux
macOS

Windows

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

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

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Java API
RESTful HTTP/JSON API

HTTP API

Supported programming languages

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

Clojure
Java
Python

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

C++
Java
Python
Ruby

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

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
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

yes

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

Administrators, Users, Groups

User authentification and security via Rexster Graph Server

yes

More information provided by the system vendor

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More resources

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