DBMS > Apache Impala vs. PostgreSQL vs. TerarkDB

System Properties Comparison Apache Impala vs. PostgreSQL vs. TerarkDB

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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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

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.

Key-value store

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	10.08
Rank	#41	Overall
	#25	Relational DBMS

Score	652.16
Rank	#4	Overall
	#4	Relational DBMS

Score	0.00
Rank	#388	Overall
	#62	Key-value stores

Website

impala.apache.org

www.postgresql.org

github.com/bytedance/terarkdb

Technical documentation

impala.apache.org/impala-docs.html

www.postgresql.org/docs

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

PostgreSQL Global Development Group

www.postgresql.org/developer

ByteDance, originally Terark

Initial release

2013

1989

1989: Postgres, 1996: PostgreSQL

2016

Current release

4.1.0, June 2022

16.4, August 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Server operating systems

Linux

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

Data scheme

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

standard with numerous extensions

APIs and other access methods

JDBC
ODBC

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

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++
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.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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.

yes

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

More information provided by the system vendor

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More resources

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PASS Data Community Summit
Seattle, Washington, 4-8 November 2024

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