DBMS > DolphinDB vs. FatDB vs. Google Cloud Spanner vs. Tibero

System Properties Comparison DolphinDB vs. FatDB vs. Google Cloud Spanner vs. Tibero

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

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 .NET NoSQL DBMS that can integrate with and extend SQL Server.

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 secure RDBMS, designed for easy portability from Oracle

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

Secondary database models

Relational DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

www.dolphindb.com

cloud.google.com/spanner

us.tmaxsoft.com/products/tibero

Technical documentation

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

cloud.google.com/spanner/docs

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

DolphinDB, Inc

FatCloud

Google

TmaxSoft

Initial release

2018

2012

2017

2003

Current release

v2.00.4, January 2022

6, April 2015

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 and Assembler

Server operating systems

Linux
Windows

Windows

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

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

SQL-like query language

Via inetgration in SQL Server

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

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

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC (using protocol buffers) API
JDBC

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

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

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

via applications

Persistent Stored Procedure (PSM)

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

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

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

planned for next version

User concepts

Access control

Administrators, Users, Groups

Can implement custom security layer via applications

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

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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