DBMS > DolphinDB vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. Rockset vs. STSdb

System Properties Comparison DolphinDB vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. Rockset vs. STSdb

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.

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Key-value store

Secondary database models

Relational DBMS

Spatial DBMS

Relational DBMS
Search engine

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

www.dolphindb.com

machbase.com

azure.microsoft.com/services/cosmos-db

rockset.com

github.com/STSSoft/STSdb4

Technical documentation

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

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

docs.rockset.com

Developer

DolphinDB, Inc

Machbase

Microsoft

Rockset

STS Soft SC

Initial release

2018

2013

2014

2019

2011

Current release

v2.00.4, January 2022

V8.0, August 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

free community version available

commercial

free test version available

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Linux
Windows

Linux
macOS
Windows

hosted

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

dynamic typing

yes

primitive types and user defined types (classes)

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.

ingestion from XML files supported

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Read-only SQL queries, including JOINs

APIs and other access methods

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

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP REST

.NET Client API

Supported programming languages

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

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Go
Java
JavaScript (Node.js)
Python

C#
Java

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Multi-item ACID transactions with snapshot isolation within a partition

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

volatile and lookup table

User concepts

Access control

Administrators, Users, Groups

simple password-based access control

Access rights can be defined down to the item level

Access rights for users and organizations can be defined via Rockset console

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