DBMS > HBase vs. Hypertable vs. LevelDB vs. MySQL vs. Teradata

System Properties Comparison HBase vs. Hypertable vs. LevelDB vs. MySQL vs. Teradata

Editorial information provided by DB-Engines

Name

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

Description

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

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used open source RDBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Wide column store

Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

hbase.apache.org

github.com/google/leveldb

www.mysql.com

www.teradata.com

Technical documentation

hbase.apache.org/book.html

github.com/google/leveldb/blob/main/doc/index.md

dev.mysql.com/doc

docs.teradata.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Hypertable Inc.

Google

Oracle

since 2010, originally MySQL AB, then Sun

Teradata

Initial release

2008

2009

2011

1995

1984

Current release

2.3.4, January 2021

0.9.8.11, March 2016

1.23, February 2021

8.4.0, April 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

GNU version 3. Commercial license available

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

C and C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
OS X
Windows

an inofficial Windows port is available

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

hosted
Linux

Data scheme

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL 2016 + extensions

APIs and other access methods

Java API
RESTful HTTP API
Thrift

C++ API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
Perl
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

proprietary syntax

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor on file system level

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or 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

Single row ACID (across millions of columns)

ACID

not for MyISAM storage engine

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

with automatic compression on writes

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

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

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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