DBMS > AllegroGraph vs. Google Cloud Datastore vs. SAP HANA vs. Sphinx

System Properties Comparison AllegroGraph vs. Google Cloud Datastore vs. SAP HANA vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Document store

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.84
Rank	#204	Overall
	#35	Document stores
	#18	Graph DBMS
	#7	RDF stores
	#10	Vector DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Website

allegrograph.com

cloud.google.com/datastore

www.sap.com/products/hana.html

sphinxsearch.com

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

cloud.google.com/datastore/docs

help.sap.com/hana

sphinxsearch.com/docs

Developer

Franz Inc.

Google

SAP

Sphinx Technologies Inc.

Initial release

2004

2008

2010

2001

Current release

8.0, December 2023

2.0 SPS07 (April 4, 2023), April 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Open Source

GPL version 2, commercial licence 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++

Server operating systems

Linux
OS X
Windows

hosted

Appliance or cloud-service

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

RDF schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

bulk load of XML files possible

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SPARQL is used as query language

SQL-like query language (GQL)

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API
SPARQL

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

JDBC
ODBC

Proprietary protocol

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

using Google App Engine

SQLScript, R

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

yes

none

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 configuration

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

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles and pluggable authentication

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

yes

More information provided by the system vendor

News

AllegroGraph Named a 2024 Trend Setting Product
16 September 2024

Gartner recognized Franz Inc. as a Key Neuro-Symbolic AI Provider in 2024 Hype Cycle for AI
10 September 2024

Next-Generation Cybersecurity: Integrating Knowledge Graphs and Neuro-Symbolic AI with STIX and TAXII
10 September 2024

Pruning Cycles in UMLS Metathesaurus: A Neuro Symbolic AI Approach
9 September 2024

Awesome AI Companies – Franz Inc.
17 July 2024

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