DBMS > Badger vs. HBase vs. LeanXcale vs. Newts vs. STSdb

System Properties Comparison Badger vs. HBase vs. LeanXcale vs. Newts vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Time Series DBMS based on Cassandra

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Key-value store

Wide column store

Key-value store
Relational DBMS

Time Series DBMS

Key-value store

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

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

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

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

github.com/dgraph-io/badger

hbase.apache.org

www.leanxcale.com

opennms.github.io/newts

github.com/STSSoft/STSdb4

Technical documentation

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

hbase.apache.org/book.html

github.com/OpenNMS/newts/wiki

Developer

DGraph Labs

Apache Software Foundation

Apache top-level project, originally developed by Powerset

LeanXcale

OpenNMS Group

STS Soft SC

Initial release

2017

2008

2015

2014

2011

Current release

2.3.4, January 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

Open Source

GPLv2, 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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux
OS X
Windows

Windows

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

yes

through Apache Derby

APIs and other access methods

Java API
RESTful HTTP API
Thrift

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP REST
Java API

.NET Client API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
Java
Scala

Java

C#
Java

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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