DBMS > Google Cloud Spanner vs. Hive vs. Quasardb vs. RDF4J vs. SAP HANA

System Properties Comparison Google Cloud Spanner vs. Hive vs. Quasardb vs. RDF4J vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Distributed, high-performance timeseries database

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

cloud.google.com/spanner

hive.apache.org

quasar.ai

rdf4j.org

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/spanner/docs

cwiki.apache.org/confluence/display/Hive/Home

doc.quasar.ai/master

rdf4j.org/documentation

help.sap.com/hana

Developer

Google

Apache Software Foundation

initially developed by Facebook

quasardb

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

SAP

Initial release

2017

2012

2009

2004

2010

Current release

3.1.3, April 2022

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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

Java

C++

Java

Server operating systems

hosted

All OS with a Java VM

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Appliance or cloud-service

Data scheme

yes

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC
Thrift

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

C++
Java
PHP
Python

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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

selectable replication factor

Source-replica replication with selectable replication factor

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

query execution via MapReduce

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

yes

Transient mode

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

Cryptographically strong user authentication and audit trail

yes

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