DBMS > CockroachDB vs. DolphinDB vs. GridGain vs. Microsoft SQL Server

System Properties Comparison CockroachDB vs. DolphinDB vs. GridGain vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

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.

GridGain is an in-memory computing platform, built on Apache Ignite

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

www.cockroachlabs.com

www.dolphindb.com

www.gridgain.com

www.microsoft.com/en-us/sql-server

Technical documentation

www.cockroachlabs.com/docs

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

www.gridgain.com/docs/index.html

learn.microsoft.com/en-US/sql/sql-server

Developer

Cockroach Labs

DolphinDB, Inc

GridGain Systems, Inc.

Microsoft

Initial release

2015

2018

2007

1989

Current release

23.1.1, May 2023

v2.00.4, January 2022

GridGain 8.5.1

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

free community version available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, C++, .Net

C++

Server operating systems

Linux
macOS
Windows

Linux
Windows

Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

dynamic schema

yes

Typing

predefined data types such as float or date

yes

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

SQL

Support of SQL

yes, wire compatible with PostgreSQL

SQL-like query language

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

JDBC

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

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

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

horizontal partitioning

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

yes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

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

User concepts

Access control

Role-based access control

Administrators, Users, Groups

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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