DBMS > DolphinDB vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Rockset

System Properties Comparison DolphinDB vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Rockset

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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.

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Relational DBMS

Spatial DBMS
Time Series DBMS

Spatial DBMS

Relational DBMS
Search engine

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.82
Rank	#212	Overall
	#36	Document stores

Website

www.dolphindb.com

www.kinetica.com

azure.microsoft.com/services/cosmos-db

rockset.com

Technical documentation

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

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

docs.rockset.com

Developer

DolphinDB, Inc

Kinetica

Microsoft

Rockset

Initial release

2018

2012

2014

2019

Current release

v2.00.4, January 2022

7.1, August 2021

License

Commercial or Open Source

commercial

free community version available

commercial

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++

C, C++

C++

Server operating systems

Linux
Windows

Linux

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

dynamic 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.

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

SQL-like DML and DDL statements

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

JDBC
ODBC
RESTful HTTP API

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

HTTP REST

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

Implicit feature of the cloud service

yes

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

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

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

GPU vRAM or System RAM

User concepts

Access control

Administrators, Users, Groups

Access rights for users and roles on table level

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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