DBMS > Apache Impala vs. Google Cloud Datastore vs. RDF4J vs. SAP Adaptive Server vs. STSdb

System Properties Comparison Apache Impala vs. Google Cloud Datastore vs. RDF4J vs. SAP Adaptive Server vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

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

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	37.12
Rank	#25	Overall
	#17	Relational DBMS

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Website

impala.apache.org

cloud.google.com/datastore

rdf4j.org

www.sap.com/products/technology-platform/sybase-ase.html

github.com/STSSoft/STSdb4

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/datastore/docs

rdf4j.org/documentation

help.sap.com/docs/SAP_ASE

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

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

SAP, Sybase

STS Soft SC

Initial release

2013

2008

2004

1987

2011

Current release

4.1.0, June 2022

16.0

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

GPLv2, commercial license available

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++

Java

C and C++

Server operating systems

Linux

hosted

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Windows

Data scheme

yes

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

primitive types and user defined types (classes)

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 DML and DDL statements

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC
ODBC

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

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

Java
PHP
Python

C
C++
Cobol
Java
Java
Perl
PHP
Python

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

using Google App Engine

yes

Java and Transact-SQL

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication using Paxos

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

fine grained access rights according to SQL-standard

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