DBMS > DuckDB vs. Impala vs. Spark SQL

System Properties Comparison DuckDB vs. Impala vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Analytic DBMS for Hadoop

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	4.02
Rank	#93	Overall
	#49	Relational DBMS

Score	18.24
Rank	#37	Overall
	#23	Relational DBMS

Score	19.23
Rank	#36	Overall
	#22	Relational DBMS

Website

duckdb.org

www.cloudera.com/products/open-source/apache-hadoop/impala.html

spark.apache.org/sql

Technical documentation

duckdb.org/docs

docs.cloudera.com/documentation/enterprise/latest/topics/impala.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Cloudera

Apache Software Foundation

Initial release

2018

2013

2014

Current release

0.9, September 2023

4.1.0, June 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

Server operating systems

server-less

Linux

Linux
OS X
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
JDBC

JDBC
ODBC

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

All languages supporting JDBC/ODBC

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

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

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

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More resources

Recent citations in the news

Spatial Data Management For GIS and Data Scientists
24 November 2023, iProgrammer

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13 July 2023, hackernoon.com

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10 November 2014, InformationWeek

Cloudera aims to bring real-time queries to Hadoop, big data
24 October 2012, ZDNet

Unravel Data Adds Native Support for Impala and Kafka
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