DBMS > BigchainDB vs. Elasticsearch vs. Hawkular Metrics vs. MySQL vs. YottaDB

System Properties Comparison BigchainDB vs. Elasticsearch vs. Hawkular Metrics vs. MySQL vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Widely used open source RDBMS

A fast and solid embedded Key-value store

Primary database model

Key/Value like access via memcached API

Key-value store

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Spatial DBMS

Relational DBMS

using the Octo plugin

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

Score	0.76
Rank	#216	Overall
	#36	Document stores

Score	128.79
Rank	#8	Overall
	#1	Search engines

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Score	0.21
Rank	#309	Overall
	#43	Key-value stores

Website

www.bigchaindb.com

www.elastic.co/elasticsearch

www.hawkular.org

www.mysql.com

yottadb.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

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

www.hawkular.org/hawkular-metrics/docs/user-guide

dev.mysql.com/doc

yottadb.com/resources/documentation

Developer

Elastic

Community supported by Red Hat

Oracle

since 2010, originally MySQL AB, then Sun

YottaDB, LLC

Initial release

2016

2010

2014

1995

2001

Current release

8.6, January 2023

9.0.0, July 2024

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

Elastic License

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0

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

Python

Java

C and C++

Server operating systems

Linux

All OS with a Java VM

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux

Data scheme

schema-free

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

schema-free

yes

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

by using the Octo plugin

APIs and other access methods

CLI Client
RESTful HTTP API

Java API
RESTful HTTP/JSON API

HTTP REST

ADO.NET
JDBC
ODBC
Proprietary native API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

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

Go
Java
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

by using the 'percolation' feature

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes

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

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

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

Memcached and Redis integration

yes

User concepts

Access control

yes

Users with fine-grained authorization concept

no user groups or roles

Users and groups based on OS-security mechanisms

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