DBMS > Hypertable vs. MySQL vs. Solr vs. Spark SQL vs. Trafodion

System Properties Comparison Hypertable vs. MySQL vs. Solr vs. Spark SQL vs. Trafodion

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used open source RDBMS

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

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Wide column store

Relational DBMS

Key/Value like access via memcached API

Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

dev.mysql.com/doc

solr.apache.org/resources.html

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

trafodion.apache.org/documentation.html

Developer

Hypertable Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

2009

1995

2006

2014

Current release

0.9.8.11, March 2016

8.4.0, April 2024

9.6.0, April 2024

3.5.0 ( 2.13), September 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

C++

C and C++

Java

Scala

C++, Java

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

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

Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

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

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

restricted

only exact value or prefix value scans

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

with proprietary extensions

Solr Parallel SQL Interface

SQL-like DML and DDL statements

yes

APIs and other access methods

C++ API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

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

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

Java
Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

proprietary syntax

Java plugins

Java Stored Procedures

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

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

Multi-source replication
Source-replica replication

yes

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate 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

ACID

not for MyISAM storage engine

optimistic locking

ACID

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

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

yes

fine grained access rights according to SQL-standard

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