DBMS > Google Cloud Datastore vs. ObjectBox vs. TDSQL for MySQL vs. TimesTen vs. Yanza

System Properties Comparison Google Cloud Datastore vs. ObjectBox vs. TDSQL for MySQL vs. TimesTen vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Extremely fast embedded database for small devices, IoT and Mobile

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

In-Memory RDBMS compatible to Oracle

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	0.85
Rank	#207	Overall
	#97	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

cloud.google.com/datastore

objectbox.io

www.tencentcloud.com/products/dcdb

www.oracle.com/database/technologies/related/timesten.html

yanza.com

Technical documentation

cloud.google.com/datastore/docs

docs.objectbox.io

www.tencentcloud.com/document/product/1042

docs.oracle.com/database/timesten-18.1

Developer

Google

ObjectBox Limited

Tencent

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Yanza

Initial release

2008

2017

2013

1998

2015

Current release

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

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

Proprietary native API

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API

Supported programming languages

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C#
C++
Java
PHP
Python
Ruby

C
C++
Java
PL/SQL

any language that supports HTTP calls

Server-side scripts

Stored procedures

using Google App Engine

yes

PL/SQL

Triggers

Callbacks using the Google Apps Engine

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

online/offline synchronization between client and server

Multi-source replication
Source-replica replication

none

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 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

Immediate Consistency or Eventual Consistency depending on configuration

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

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

yes

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

More information provided by the system vendor

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