DBMS > Blazegraph vs. HarperDB vs. Riak KV vs. Splice Machine

System Properties Comparison Blazegraph vs. HarperDB vs. Riak KV vs. Splice Machine

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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.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Distributed, fault tolerant key-value store

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

Primary database model

Graph DBMS
RDF store

Document store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

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	0.60
Rank	#244	Overall
	#38	Document stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

blazegraph.com

www.harperdb.io

splicemachine.com

Technical documentation

wiki.blazegraph.com

docs.harperdb.io/docs

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

splicemachine.com/how-it-works

Developer

Blazegraph

HarperDB

OpenSource, formerly Basho Technologies

Splice Machine

Initial release

2006

2017

2009

2014

Current release

2.1.5, March 2019

3.1, August 2021

3.2.0, December 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

free community edition available

Open Source

Apache version 2, commercial enterprise edition

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Node.js

Erlang

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X

Linux
OS X
Solaris
Windows

Data scheme

schema-free

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

yes

JSON data 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.

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SPARQL is used as query language

SQL-like data manipulation statements

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API
Native Erlang Interface

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

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

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

Erlang

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

no "single point of failure"

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

selectable replication factor

Multi-source replication
Source-replica replication

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

links between data sets can be stored

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

yes, using LMDB

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

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

Access rights for users and roles

yes, using Riak Security

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

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