DBMS > Amazon DynamoDB vs. Blazegraph vs. Brytlyt vs. PostGIS vs. Postgres-XL

System Properties Comparison Amazon DynamoDB vs. Blazegraph vs. Brytlyt vs. PostGIS vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Spatial extension of PostgreSQL

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Document store
Key-value store

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

aws.amazon.com/dynamodb

Technical documentation

docs.aws.amazon.com/dynamodb

wiki.blazegraph.com

docs.brytlyt.io

postgis.net/documentation

www.postgres-xl.org/documentation

Developer

Amazon

Blazegraph

Brytlyt

Initial release

2012

2006

2016

2005

2014

since 2012, originally named StormDB

Current release

2.1.5, March 2019

5.0, August 2023

3.4.2, February 2024

10 R1, October 2018

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

extended commercial license available

commercial

Open Source

GPL v2.0

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++ and CUDA

Server operating systems

hosted

Linux
OS X
Windows

Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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

specific XML-type available, but no XML query functionality.

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

yes

distributed, parallel query execution

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

in PL/pgSQL

user defined functions

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

based on PostgreSQL

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

MVCC

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.

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Security and Authentication via Web Application Container (Tomcat, Jetty)

fine grained access rights according to SQL-standard

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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