DBMS > Bangdb vs. Riak TS vs. Solr vs. WakandaDB

System Properties Comparison Bangdb vs. Riak TS vs. Solr vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

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

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

A widely used distributed, scalable search engine based on Apache Lucene

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Search engine

Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

bangdb.com

solr.apache.org

wakanda.github.io

Technical documentation

docs.bangdb.com

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

solr.apache.org/resources.html

wakanda.github.io/doc

Developer

Sachin Sinha, BangDB

Open Source, formerly Basho Technologies

Apache Software Foundation

Wakanda SAS

Initial release

2012

2015

2006

2012

Current release

BangDB 2.0, October 2021

3.0.0, September 2022

9.6.1, May 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Erlang

Java

C++, JavaScript

Server operating systems

Linux

Linux
OS X

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Windows

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

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

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL like support with command line tool

yes, limited

Solr Parallel SQL Interface

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C
C#
C++
Java
Python

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

JavaScript

Server-side scripts

Stored procedures

Erlang

Java plugins

yes

Triggers

yes, Notifications (with Streaming only)

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

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

yes (enterprise version only)

yes

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