DBMS > Badger vs. LeanXcale vs. Oracle Rdb vs. Yanza

System Properties Comparison Badger vs. LeanXcale vs. Oracle Rdb vs. Yanza

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Editorial information provided by DB-Engines

Name

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

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Time Series DBMS for IoT Applications

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Website

github.com/dgraph-io/badger

www.leanxcale.com

www.oracle.com/database/technologies/related/rdb.html

yanza.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

DGraph Labs

LeanXcale

Oracle, originally developed by Digital Equipment Corporation (DEC)

Yanza

Initial release

2017

2015

1984

2015

Current release

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache 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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

HP Open VMS

Windows

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

through Apache Derby

yes

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP API

Supported programming languages

C
Java
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

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