DBMS > Apache Impala vs. PostgreSQL vs. Riak TS vs. StarRocks

System Properties Comparison Apache Impala vs. PostgreSQL vs. Riak TS vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

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

Relational DBMS

Secondary database models

Document store

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	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

impala.apache.org

www.postgresql.org

www.starrocks.io

Technical documentation

impala.apache.org/impala-docs.html

www.postgresql.org/docs

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

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

PostgreSQL Global Development Group

www.postgresql.org/developer

Open Source, formerly Basho Technologies

The Linux Foundation

since Feb 2023

Initial release

2013

1989

1989: Postgres, 1996: PostgreSQL

2015

2020

Current release

4.1.0, June 2022

16.3, May 2024

3.0.0, September 2022

3.3, June 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Erlang

C++, Java

Server operating systems

Linux

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

Linux
OS X

Linux

Data scheme

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

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

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

standard with numerous extensions

yes, limited

yes

APIs and other access methods

JDBC
ODBC

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

HTTP API
Native Erlang Interface

JDBC
MySQL protocol

Supported programming languages

All languages supporting JDBC/ODBC

.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

Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

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

Erlang

user defined functions

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

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

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

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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