DBMS > Blazegraph vs. Riak TS vs. SWC-DB vs. Tarantool

System Properties Comparison Blazegraph vs. Riak TS vs. SWC-DB vs. Tarantool

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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.

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

A high performance, scalable Wide Column DBMS

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Wide column store

Document store
Key-value store
Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

with Tarantool/GIS extension

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	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Website

blazegraph.com

github.com/kashirin-alex/swc-db
www.swcdb.org

www.tarantool.io

Technical documentation

wiki.blazegraph.com

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

www.tarantool.io/en/doc

Developer

Blazegraph

Open Source, formerly Basho Technologies

Alex Kashirin

Initial release

2006

2015

2020

2008

Current release

2.1.5, March 2019

3.0.0, September 2022

0.5, April 2021

2.10.0, May 2022

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

GPL V3

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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

Erlang

C++

C and C++

Server operating systems

Linux
OS X
Windows

Linux
OS X

Linux

BSD
Linux
macOS

Data scheme

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

RDF literal types

string, double, decimal, uuid, integer, blob, boolean, datetime

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

yes, limited

SQL-like query language

Full-featured ANSI SQL support

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

HTTP API
Native Erlang Interface

Proprietary protocol
Thrift

Open binary protocol

Supported programming languages

.Net
C
C++
Java
JavaScript
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

C++

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

yes

Erlang

Lua, C and SQL stored procedures

Triggers

yes

pre-commit hooks and post-commit hooks

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

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

Eventual Consistency

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Relationships in Graphs

links between datasets can be stored

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

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

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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