DBMS > GBase vs. Google Cloud Datastore vs. Spark SQL vs. SpatiaLite vs. Transbase

System Properties Comparison GBase vs. Google Cloud Datastore vs. Spark SQL vs. SpatiaLite vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Spark SQL is a component on top of 'Spark Core' for structured data processing

Spatial extension of SQLite

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Relational DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.gbase.cn

cloud.google.com/datastore

spark.apache.org/sql

www.gaia-gis.it/fossil/libspatialite/index

www.transaction.de/en/products/transbase.html

Technical documentation

cloud.google.com/datastore/docs

spark.apache.org/docs/latest/sql-programming-guide.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

www.transaction.de/en/products/transbase/features.html

Developer

General Data Technology Co., Ltd.

Google

Apache Software Foundation

Alessandro Furieri

Transaction Software GmbH

Initial release

2004

2008

2014

2008

1987

Current release

GBase 8a, GBase 8s, GBase 8c

3.5.0 ( 2.13), September 2023

5.0.0, August 2020

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

commercial

free development license

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, Java, Python

Scala

C++

C and C++

Server operating systems

Linux

hosted

Linux
OS X
Windows

server-less

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

Standard with numerous extensions

SQL-like query language (GQL)

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

using Google App Engine

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

none

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

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

yes

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

yes

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

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More resources

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