DBMS > Google Cloud Datastore vs. SAP HANA vs. SAP SQL Anywhere vs. SiriDB vs. Vitess

System Properties Comparison Google Cloud Datastore vs. SAP HANA vs. SAP SQL Anywhere vs. SiriDB vs. Vitess

Editorial information provided by DB-Engines

Name

Description

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

In-memory, column based data store. Available as appliance or cloud service

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Open Source Time Series DBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial 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	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

cloud.google.com/datastore

www.sap.com/products/hana.html

www.sap.com/products/technology-platform/sql-anywhere.html

siridb.com

vitess.io

Technical documentation

cloud.google.com/datastore/docs

help.sap.com/hana

help.sap.com/docs/SAP_SQL_Anywhere

docs.siridb.com

vitess.io/docs

Developer

Google

SAP

formerly Sybase

Cesbit

The Linux Foundation, PlanetScale

Initial release

2008

2010

1992

2017

2013

Current release

2.0 SPS07 (April 4, 2023), April 2023

17, July 2015

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Server operating systems

hosted

Appliance or cloud-service

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux

Docker
Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

Numeric data

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

APIs and other access methods

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

JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

using Google App Engine

SQLScript, R

yes, in C/C++, Java, .Net or Perl

yes

proprietary syntax

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

Source-replica replication

Database mirroring

yes

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 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 across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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

yes

fine grained access rights according to SQL-standard

simple rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

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