DBMS > Blazegraph vs. CouchDB vs. MySQL vs. Splice Machine vs. Trino

System Properties Comparison Blazegraph vs. CouchDB vs. MySQL vs. Splice Machine vs. Trino

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

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

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Widely used open source RDBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Graph DBMS
RDF store

Document store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	4.99
Rank	#65	Overall
	#36	Relational DBMS

Website

Technical documentation

wiki.blazegraph.com

docs.couchdb.org/en/stable

dev.mysql.com/doc

splicemachine.com/how-it-works

trino.io/broadcast
trino.io/docs/current

Social network pages

Developer

Blazegraph

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Oracle

since 2010, originally MySQL AB, then Sun

Splice Machine

Trino Software Foundation

Initial release

2006

2005

1995

2014

2012

2020 rebranded from PrestoSQL

Current release

2.1.5, March 2019

3.3.3, December 2023

8.4.0, April 2024

3.1, March 2021

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

Apache version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

Erlang

C and C++

Java

Server operating systems

Linux
OS X
Windows

Android
BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
macOS

for devlopment

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

Secondary indexes

yes

via views

yes

depending on connected data-source

SQL

Support of SQL

SPARQL is used as query language

yes

with proprietary extensions

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

proprietary syntax

yes

Java

yes, depending on connected data-source

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

atomic operations within a single document possible

ACID

not for MyISAM storage engine

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

table locks or row locks depending on storage engine

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

depending on connected data-source

In-memory capabilities

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

yes

User concepts

Access control

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

Access rights for users can be defined per database

Users with fine-grained authorization concept

no user groups or roles

Access rights for users, groups and roles according to SQL-standard

SQL standard access control

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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