DBMS > Apache Drill vs. IBM Db2 vs. PostgreSQL vs. Riak TS

System Properties Comparison Apache Drill vs. IBM Db2 vs. PostgreSQL vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Common in IBM host environments, 2 different versions for host and Windows/Linux

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

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

Primary database model

Document store
Relational DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Time Series DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

drill.apache.org

www.ibm.com/products/db2

www.postgresql.org

Technical documentation

drill.apache.org/docs

www.ibm.com/docs/en/db2

www.postgresql.org/docs

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

Developer

Apache Software Foundation

IBM

PostgreSQL Global Development Group

www.postgresql.org/developer

Open Source, formerly Basho Technologies

Initial release

2012

1983

host version

1989

1989: Postgres, 1996: PostgreSQL

2015

Current release

1.20.3, January 2023

12.1, October 2016

16.3, May 2024

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free version is available

Open Source

BSD

Open Source

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

Erlang

Server operating systems

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows
z/OS

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X

Data scheme

schema-free

yes

schema-free

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

standard with numerous extensions

yes, limited

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API
Native Erlang Interface

Supported programming languages

C++

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
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

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

partitioning by range, list and (since PostgreSQL 11) by hash

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

other methods possible by using 3rd party extensions

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

User concepts

Access control

Depending on the underlying data source

fine grained access rights according to SQL-standard

More information provided by the system vendor

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