DBMS > Apache Impala vs. JaguarDB vs. MySQL vs. Solr vs. Spark SQL

System Properties Comparison Apache Impala vs. JaguarDB vs. MySQL vs. Solr vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Performant, highly scalable DBMS for AI and IoT applications

Widely used open source RDBMS

A widely used distributed, scalable search engine based on Apache Lucene

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

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Search engine

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

www.jaguardb.com/support.html

dev.mysql.com/doc

solr.apache.org/resources.html

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DataJaguar, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2013

2015

1995

2006

2014

Current release

4.1.0, June 2022

3.3 July 2023

8.4.0, April 2024

9.6.0, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPL V3.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

the server part. Clients available in other languages

C and C++

Java

Scala

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Windows

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

with proprietary extensions

Solr Parallel SQL Interface

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

optimistic locking

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

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

yes

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