DBMS > DolphinDB vs. Google Cloud Spanner vs. Hazelcast vs. Microsoft SQL Server vs. Tkrzw

System Properties Comparison DolphinDB vs. Google Cloud Spanner vs. Hazelcast vs. Microsoft SQL Server vs. Tkrzw

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.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A widely adopted in-memory data grid

Microsofts flagship relational DBMS

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Secondary database models

Relational DBMS

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS
Spatial DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

www.dolphindb.com

cloud.google.com/spanner

hazelcast.com

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

dbmx.net/tkrzw

Technical documentation

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

cloud.google.com/spanner/docs

hazelcast.org/imdg/docs

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

Developer

DolphinDB, Inc

Google

Hazelcast

Microsoft

Mikio Hirabayashi

Initial release

2018

2017

2008

1989

2020

Current release

v2.00.4, January 2022

5.3.6, November 2023

SQL Server 2022, November 2022

0.9.3, August 2020

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux
Windows

hosted

All OS with a Java VM

Linux
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

Query statements complying to ANSI 2011

SQL-like query language

yes

APIs and other access methods

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

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

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

Supported programming languages

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

Go
Java
JavaScript (Node.js)
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

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

Triggers

yes

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

Multi-source replication with 3 replicas for regional instances.

yes

Replicated Map

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

yes

ACID

Strict serializable isolation

one or two-phase-commit; repeatable reads; read commited

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

using specific database classes

User concepts

Access control

Administrators, Users, Groups

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

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