DBMS > Apache Impala vs. Badger vs. BaseX vs. MySQL vs. Spark SQL

System Properties Comparison Apache Impala vs. Badger vs. BaseX vs. MySQL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Widely used open source RDBMS

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

Primary database model

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

impala.apache.org

github.com/dgraph-io/badger

basex.org

www.mysql.com

spark.apache.org/sql

Technical documentation

impala.apache.org/impala-docs.html

godoc.org/github.com/dgraph-io/badger

docs.basex.org

dev.mysql.com/doc

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DGraph Labs

BaseX GmbH

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2013

2017

2007

1995

2014

Current release

4.1.0, June 2022

10.7, August 2023

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

BSD license

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

C and C++

Scala

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

XQuery supports types

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Triggers

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Multi-source replication
Source-replica replication

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

multiple readers, single writer

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept on 4 levels

Users with fine-grained authorization concept

no user groups or roles

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