DBMS > DolphinDB vs. GridGain vs. Microsoft SQL Server vs. Transwarp ArgoDB vs. XTDB

System Properties Comparison DolphinDB vs. GridGain vs. Microsoft SQL Server vs. Transwarp ArgoDB vs. XTDB

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.

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

Microsofts flagship relational DBMS

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

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

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Document store

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial 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	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.12
Rank	#348	Overall
	#151	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.dolphindb.com

www.gridgain.com

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

www.transwarp.cn/en/product/argodb

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

Technical documentation

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

www.gridgain.com/docs/index.html

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

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

www.xtdb.com/docs

Developer

DolphinDB, Inc

GridGain Systems, Inc.

Microsoft

Transwarp

Juxt Ltd.

Initial release

2018

2007

1989

2019

Current release

v2.00.4, January 2022

GridGain 8.5.1

SQL Server 2022, November 2022

1.19, September 2021

License

Commercial or Open Source

commercial

free community version available

commercial

restricted free version is available

commercial

Open Source

MIT License

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++

Clojure

Server operating systems

Linux
Windows

Linux
OS X
Solaris
Windows

Linux
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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

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

yes

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

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

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

HTTP REST
JDBC

Supported programming languages

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

Clojure
Java

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

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

yes, each node contains all data

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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