DBMS > HBase vs. IBM Db2 Event Store vs. MySQL vs. NuoDB vs. Solr

System Properties Comparison HBase vs. IBM Db2 Event Store vs. MySQL vs. NuoDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Distributed Event Store optimized for Internet of Things use cases

Widely used open source RDBMS

NuoDB is a webscale distributed database that supports SQL and ACID transactions

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

Primary database model

Wide column store

Event Store
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

hbase.apache.org

www.ibm.com/products/db2-event-store

www.mysql.com

www.3ds.com/nuodb-distributed-sql-database

solr.apache.org

Technical documentation

hbase.apache.org/book.html

www.ibm.com/docs/en/db2-event-store

dev.mysql.com/doc

doc.nuodb.com

solr.apache.org/resources.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Dassault Systèmes

originally NuoDB, Inc.

Apache Software Foundation

Initial release

2008

2017

1995

2013

2006

Current release

2.3.4, January 2021

2.0

8.4.0, April 2024

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free developer edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

limited edition free

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C++

Java

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux

Linux, macOS, Windows for the developer addition

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

All OS with a Java VM

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

Data scheme

schema-free, schema definition possible

yes

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

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

yes

through the embedded Spark runtime

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

Java API
RESTful HTTP API
Thrift

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

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

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

proprietary syntax

Java, SQL

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

data is dynamically stored/cached on the nodes where it is read/written

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Active-active shard replication

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

yes

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

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

not for MyISAM storage engine

ACID

tunable commit protocol

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

table locks or row locks depending on storage engine

yes

MVCC

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

Temporary table

yes

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Standard SQL roles/ privileges, Administrative Users

yes

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