DBMS > Apache Jena - TDB vs. Bangdb vs. etcd vs. HugeGraph vs. Yanza

System Properties Comparison Apache Jena - TDB vs. Bangdb vs. etcd vs. HugeGraph vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

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

A distributed reliable key-value store

A fast-speed and highly-scalable Graph DBMS

Time Series DBMS for IoT Applications

Primary database model

RDF store

Document store
Graph DBMS
Time Series DBMS

Key-value store

Graph DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	3.75
Rank	#84	Overall
	#3	RDF stores

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

jena.apache.org/documentation/tdb/index.html

bangdb.com

etcd.io
github.com/etcd-io/etcd

github.com/hugegraph
hugegraph.apache.org

yanza.com

Technical documentation

jena.apache.org/documentation/tdb/index.html

docs.bangdb.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

hugegraph.apache.org/docs

Developer

Apache Software Foundation

originally developed by HP Labs

Sachin Sinha, BangDB

Baidu

Yanza

Initial release

2000

2012

2018

2015

Current release

4.9.0, July 2023

BangDB 2.0, October 2021

3.4, August 2019

0.9

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C, C++

Java

Server operating systems

All OS with a Java VM

Linux

FreeBSD
Linux
Windows

experimental

Linux
macOS
Unix

Windows

Data scheme

yes

RDF Schemas

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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

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

secondary, composite, nested, reverse, geospatial

yes

also supports composite index and range index

SQL

Support of SQL

SQL like support with command line tool

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

Proprietary protocol
RESTful HTTP API

gRPC
JSON over HTTP

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

Supported programming languages

Java

C
C#
C++
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Groovy
Java
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Triggers

yes

via event handler

yes, Notifications (with Streaming only)

yes, watching key changes

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

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

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes

depending on used storage backend, e.g. Cassandra and HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

TDB Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

Access control via Jena Security

yes (enterprise version only)

Users, roles and permissions

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