DBMS > Hive vs. LevelDB vs. MySQL vs. Riak TS vs. Teradata

System Properties Comparison Hive vs. LevelDB vs. MySQL vs. Riak TS vs. Teradata

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Widely used open source RDBMS

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

hive.apache.org

github.com/google/leveldb

www.mysql.com

www.teradata.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

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

dev.mysql.com/doc

www.tiot.jp/riak-docs/riak/ts/latest

docs.teradata.com

Developer

Apache Software Foundation

initially developed by Facebook

Google

Oracle

since 2010, originally MySQL AB, then Sun

Open Source, formerly Basho Technologies

Teradata

Initial release

2012

2011

1995

2015

1984

Current release

3.1.3, April 2022

1.23, February 2021

8.4.0, April 2024

3.0.0, September 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

C and C++

Erlang

Server operating systems

All OS with a Java VM

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

hosted
Linux

Data scheme

yes

schema-free

yes

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

restricted

yes

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes, limited

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Native Erlang Interface

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

Supported programming languages

C++
Java
PHP
Python

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Erlang

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

pre-commit hooks and post-commit hooks

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

selectable replication factor

none

Multi-source replication
Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

links between datasets can be stored

yes

Transaction concepts

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

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 rights for users, groups and roles

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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