DBMS > Elasticsearch vs. NSDb vs. STSdb vs. WakandaDB

System Properties Comparison Elasticsearch vs. NSDb vs. STSdb vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

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

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

www.elastic.co/elasticsearch

nsdb.io

github.com/STSSoft/STSdb4

wakanda.github.io

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

nsdb.io/Architecture

wakanda.github.io/doc

Developer

Elastic

STS Soft SC

Wakanda SAS

Initial release

2010

2017

2011

2012

Current release

8.6, January 2023

4.0.8, September 2015

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache Version 2.0

Open Source

GPLv2, commercial license available

Open Source

AGPLv3, extended 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

Java, Scala

C++, JavaScript

Server operating systems

All OS with a Java VM

Linux
macOS

Windows

Linux
OS X
Windows

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

primitive types and user defined types (classes)

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

All search fields are automatically indexed

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP/JSON API

gRPC
HTTP REST
WebSocket

.NET Client API

RESTful HTTP API

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Java
Scala

C#
Java

JavaScript

Server-side scripts

Stored procedures

yes

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

Memcached and Redis integration

User concepts

Access control

yes

More information provided by the system vendor

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More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2017
2 January 2018, Paul Andlinger, Matthias Gelbmann

Elasticsearch moved into the top 10 most popular database management systems
3 July 2017, Matthias Gelbmann

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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Recent citations in the news

Apache Doris for Log and Time Series Data Analysis in NetEase: Why Not Elasticsearch and InfluxDB?
5 June 2024, hackernoon.com

8 Powerful Alternatives to Elasticsearch
25 April 2024, Yahoo Finance

Splunk vs Elasticsearch | A Comparison and How to Choose
12 January 2024, SentinelOne

Introducing Elasticsearch Vector Database to Azure OpenAI Service On Your Data (Preview)
26 March 2024, insider.govtech.com

Netflix Uses Elasticsearch Percolate Queries to Implement Reverse Searches Efficiently
29 April 2024, InfoQ.com

provided by Google News

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