DBMS > Apache Impala vs. LeanXcale vs. MonetDB vs. Spark SQL

System Properties Comparison Apache Impala vs. LeanXcale vs. MonetDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A relational database management system that stores data in columns

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

www.monetdb.org/Documentation

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

LeanXcale

MonetDB BV

Apache Software Foundation

Initial release

2013

2015

2004

2014

Current release

4.1.0, June 2022

Dec2023 (11.49), December 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Mozilla Public License 2.0

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

Linux

FreeBSD
Linux
OS X
Solaris
Windows

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

SQL-like DML and DDL statements

yes

through Apache Derby

yes

SQL 2003 with some extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
Java
Scala

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication available in experimental status

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

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