DBMS > Hive vs. MySQL vs. Pinecone vs. Riak KV vs. SAP Advantage Database Server

System Properties Comparison Hive vs. MySQL vs. Pinecone vs. Riak KV vs. SAP Advantage Database Server

Editorial information provided by DB-Engines

Name

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Widely used open source RDBMS

A managed, cloud-native vector database

Distributed, fault tolerant key-value store

Low-cost RDBMS with access to ISAM and FoxPro data structures

Primary database model

Relational DBMS

Key/Value like access via memcached API

Vector DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

hive.apache.org

www.mysql.com

www.pinecone.io

www.sap.com/products/advantage-database-server.html

Technical documentation

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

dev.mysql.com/doc

docs.pinecone.io/docs/overview

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

Developer

Apache Software Foundation

initially developed by Facebook

Oracle

since 2010, originally MySQL AB, then Sun

Pinecone Systems, Inc

OpenSource, formerly Basho Technologies

Sybase, SAP

Initial release

2012

1995

2019

2009

1993

Current release

3.1.3, April 2022

8.4.0, April 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

Erlang

Server operating systems

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Linux
OS X

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API

HTTP API
Native Erlang Interface

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C++
Java
PHP
Python

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

Python

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

Delphi
Perl
PHP

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

selectable replication factor

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
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 data sets 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

In-memory capabilities

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

yes

caching of (temporary) tables can be configured

User concepts

Access control

Access rights for users, groups and roles

Users with fine-grained authorization concept

no user groups or roles

yes, using Riak Security

fine grained access rights according to SQL-standard

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