DBMS > DolphinDB vs. LevelDB vs. Transwarp StellarDB vs. XTDB

System Properties Comparison DolphinDB vs. LevelDB vs. Transwarp StellarDB vs. XTDB

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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.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A distributed graph DBMS built for enterprise-level graph applications

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Time Series DBMS
Vector DBMS

Key-value store

Graph DBMS

Document store

Secondary database models

Relational DBMS

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

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

www.dolphindb.com

github.com/google/leveldb

www.transwarp.cn/en/product/stellardb

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

github.com/google/leveldb/blob/main/doc/index.md

www.xtdb.com/docs

Developer

DolphinDB, Inc

Google

Transwarp

Juxt Ltd.

Initial release

2018

2011

2019

Current release

v2.00.4, January 2022

1.23, February 2021

1.19, September 2021

License

Commercial or Open Source

commercial

free community version available

Open Source

BSD

commercial

Open Source

MIT License

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++

Clojure

Server operating systems

Linux
Windows

Illumos
Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

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

OpenCypher

HTTP REST
JDBC

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Clojure
Java

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

yes

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