DBMS > AllegroGraph vs. EJDB vs. Google BigQuery vs. Riak TS

System Properties Comparison AllegroGraph vs. EJDB vs. Google BigQuery vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Large scale data warehouse service with append-only tables

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Document store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

allegrograph.com

github.com/Softmotions/ejdb

cloud.google.com/bigquery

Technical documentation

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

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/bigquery/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Franz Inc.

Softmotions

Google

Open Source, formerly Basho Technologies

Initial release

2004

2012

2010

2015

Current release

8.0, December 2023

3.0.0, September 2022

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

GPLv2

commercial

Open Source

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

Erlang

Server operating systems

Linux
OS X
Windows

server-less

hosted

Linux
OS X

Data scheme

yes

RDF schemas

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

restricted

SQL

Support of SQL

SPARQL is used as query language

yes

yes, limited

APIs and other access methods

RESTful HTTP API
SPARQL

in-process shared library

RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

user defined functions

in JavaScript

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

with Federation

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

links between datasets can be stored

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

News

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17 April 2024

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3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

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