DBMS > BigchainDB vs. Hawkular Metrics vs. Heroic vs. MySQL vs. Spark SQL

System Properties Comparison BigchainDB vs. Hawkular Metrics vs. Heroic vs. MySQL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Widely used open source RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.bigchaindb.com

www.hawkular.org

github.com/spotify/heroic

www.mysql.com

spark.apache.org/sql

Technical documentation

bigchaindb.readthedocs.io/en/latest

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

spotify.github.io/heroic

dev.mysql.com/doc

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Community supported by Red Hat

Spotify

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2016

2014

1995

2014

Current release

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

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.

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Implementation language

Python

Java

C and C++

Scala

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

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

via Elasticsearch

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
RESTful HTTP API

HTTP REST

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

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

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

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.

yes

User concepts

Access control

yes

Users with fine-grained authorization concept

no user groups or roles

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