DBMS > Google Cloud Datastore vs. Oracle vs. Oracle Coherence vs. SurrealDB vs. Tibero

System Properties Comparison Google Cloud Datastore vs. Oracle vs. Oracle Coherence vs. SurrealDB vs. Tibero

Editorial information provided by DB-Engines

Name

Description

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

Widely used RDBMS

Oracles in-memory data grid solution

A fully ACID transactional, developer-friendly, multi-model DBMS

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Document store

Relational DBMS

Key-value store

Document store
Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

cloud.google.com/datastore

www.oracle.com/database

www.oracle.com/java/coherence

surrealdb.com

us.tmaxsoft.com/products/tibero

Technical documentation

cloud.google.com/datastore/docs

docs.oracle.com/en/database

docs.oracle.com/en/middleware/standalone/coherence

surrealdb.com/docs

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

Developer

Google

Oracle

SurrealDB Ltd

TmaxSoft

Initial release

2008

1980

2007

2022

2003

Current release

23c, September 2023

14.1, August 2023

v1.5.0, May 2024

6, April 2015

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Open Source

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 and C++

Java

Rust

C and Assembler

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

All OS with a Java VM

Linux
macOS
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

with proprietary extensions

SQL-like query language

yes

APIs and other access methods

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JCache
JPA
RESTful HTTP API

GraphQL
RESTful HTTP API
WebSocket

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

Supported programming languages

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
C++
Java

Deno
Go
JavaScript (Node.js)
Rust

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

Server-side scripts

Stored procedures

using Google App Engine

PL/SQL

also stored procedures in Java possible

Persistent Stored Procedure (PSM)

Triggers

Callbacks using the Google Apps Engine

yes

Live Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

yes, with selectable consistency level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can be realized in PL/SQL

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

Eventual Consistency
Immediate Consistency

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

isolation level can be parameterized

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

planned for next version

User concepts

Access control

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

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

yes, based on authentication and database rules

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

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