DBMS > Google Cloud Datastore vs. openGauss vs. SAP HANA vs. SAP SQL Anywhere vs. SingleStore

System Properties Comparison Google Cloud Datastore vs. openGauss vs. SAP HANA vs. SAP SQL Anywhere vs. SingleStore

Editorial information provided by DB-Engines

Name

Description

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

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

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

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	1.11
Rank	#175	Overall
	#79	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	3.50
Rank	#81	Overall
	#44	Relational DBMS

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

cloud.google.com/datastore

gitee.com/opengauss
opengauss.org

www.sap.com/products/hana.html

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

www.singlestore.com

Technical documentation

cloud.google.com/datastore/docs

docs.opengauss.org/en
gitee.com/opengauss/docs

help.sap.com/hana

help.sap.com/docs/SAP_SQL_Anywhere

docs.singlestore.com

Developer

Google

Huawei and openGauss community

SAP

formerly Sybase

SingleStore Inc.

Initial release

2008

2019

2010

1992

2013

Current release

3.0, March 2022

2.0 SPS07 (April 4, 2023), April 2023

17, July 2015

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

commercial

free developer edition 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

C, C++, Java

C++, Go

Server operating systems

hosted

Linux

Appliance or cloud-service

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux

64 bit version required

Data scheme

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)

ANSI SQL 2011

yes

but no triggers and foreign keys

APIs and other access methods

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

JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

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

C
C++
Java

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

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

SQLScript, R

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

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash)

yes

none

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

yes

Source-replica replication

Database mirroring

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can define user-defined aggregate functions for map-reduce-style calculations

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

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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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)

Access rights for users, groups and roles according to SQL-standard

yes

fine grained access rights according to SQL-standard

Fine grained access control via users, groups and roles

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