DBMS > AnzoGraph DB vs. Google Cloud Datastore vs. Linter vs. Oracle Berkeley DB

System Properties Comparison AnzoGraph DB vs. Google Cloud Datastore vs. Linter vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

RDBMS for high security requirements

Widely used in-process key-value store

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

cambridgesemantics.com/anzograph

cloud.google.com/datastore

linter.ru

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

cloud.google.com/datastore/docs

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Cambridge Semantics

Google

relex.ru

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2018

2008

1990

1994

Current release

2.3, January 2021

18.1.40, May 2020

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

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 and C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

hosted

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query language (GQL)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

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

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

C++
Java
Python

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

using Google App Engine

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

Callbacks using the Google Apps Engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication using Paxos

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

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

not needed in graphs

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

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 and roles

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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