DBMS > atoti vs. Bangdb vs. RDF4J vs. Tarantool

System Properties Comparison atoti vs. Bangdb vs. RDF4J vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Converged and high performance database for device data, events, time series, document and graph

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

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

Primary database model

Object oriented DBMS

Document store
Graph DBMS
Time Series DBMS

RDF store

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

Technical documentation

docs.atoti.io

docs.bangdb.com

rdf4j.org/documentation

www.tarantool.io/en/doc

Developer

ActiveViam

Sachin Sinha, BangDB

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2004

2008

Current release

BangDB 2.0, October 2021

2.10.0, May 2022

License

Commercial or Open Source

commercial

free versions available

Open Source

BSD 3

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C, C++

Java

C and C++

Server operating systems

Linux

Linux
OS X
Unix
Windows

BSD
Linux
macOS

Data scheme

schema-free

yes

RDF Schemas

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: string, long, double, int, geospatial, stream, events

yes

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL like support with command line tool

Full-featured ANSI SQL support

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Open binary protocol

Supported programming languages

C
C#
C++
Java
Python

Java
PHP
Python

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

Server-side scripts

Stored procedures

Python

yes

Lua, C and SQL stored procedures

Triggers

yes, Notifications (with Streaming only)

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

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

selectable replication factor, Knob for CAP (enterprise version only)

none

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

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

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

Transaction concepts

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

ACID

Isolation support depends on the API used

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, multi-version concurrency control (MVCC)

yes, optimistic concurrency control

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

in-memory storage is supported as well

yes, write ahead logging

In-memory capabilities

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

yes

yes, run db with in-memory only mode

yes, full featured in-memory storage engine with persistence

User concepts

Access control

yes (enterprise version only)

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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