DBMS > BigchainDB vs. mSQL vs. Sadas Engine vs. SAP HANA vs. Solr

System Properties Comparison BigchainDB vs. mSQL vs. Sadas Engine vs. SAP HANA vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

In-memory, column based data store. Available as appliance or cloud service

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

Primary database model

Document store

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.bigchaindb.com

hughestech.com.au/products/msql

www.sadasengine.com

www.sap.com/products/hana.html

solr.apache.org

Technical documentation

bigchaindb.readthedocs.io/en/latest

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

help.sap.com/hana

solr.apache.org/resources.html

Developer

Hughes Technologies

SADAS s.r.l.

SAP

Apache Software Foundation

Initial release

2016

1994

2006

2010

2006

Current release

4.4, October 2021

8.0

2.0 SPS07 (April 4, 2023), April 2023

9.6.0, April 2024

License

Commercial or Open Source

Open Source

AGPL v3

commercial

free licenses can be provided

commercial

free trial version available

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Python

C++

Java

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

AIX
Linux
Windows

Appliance or cloud-service

All OS with a Java VM

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

Data scheme

schema-free

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

Solr Parallel SQL Interface

APIs and other access methods

CLI Client
RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Server-side scripts

Stored procedures

SQLScript, R

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

managed by 'Learn by Usage'

yes

User concepts

Access control

yes

Access rights for users, groups and roles according to SQL-standard

yes

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