DBMS > Apache Impala vs. LevelDB vs. PostgreSQL vs. Stardog

System Properties Comparison Apache Impala vs. LevelDB vs. PostgreSQL vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Widely used open source RDBMS

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Graph DBMS
RDF 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	13.77
Rank	#40	Overall
	#24	Relational DBMS

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

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

impala.apache.org

github.com/google/leveldb

www.postgresql.org

www.stardog.com

Technical documentation

impala.apache.org/impala-docs.html

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

www.postgresql.org/docs

docs.stardog.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

PostgreSQL Global Development Group

www.postgresql.org/developer

Stardog-Union

Initial release

2013

2011

1989

1989: Postgres, 1996: PostgreSQL

2010

Current release

4.1.0, June 2022

1.23, February 2021

16.3, May 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD

Open Source

BSD

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

Server operating systems

Linux

Illumos
Linux
OS X
Windows

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

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

yes

standard with numerous extensions

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC

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

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

All languages supporting JDBC/ODBC

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

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.

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication in HA-Cluster

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

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

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

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Access rights for users and roles

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