DBMS > Apache Impala vs. HyperSQL vs. MarkLogic vs. MySQL vs. Sphinx

System Properties Comparison Apache Impala vs. HyperSQL vs. MarkLogic vs. MySQL vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Operational and transactional Enterprise NoSQL database

Widely used open source RDBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Key/Value like access via memcached API

Search engine

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	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

Technical documentation

impala.apache.org/impala-docs.html

hsqldb.org/web/hsqlDocsFrame.html

docs.marklogic.com

dev.mysql.com/doc

sphinxsearch.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

MarkLogic Corp.

Oracle

since 2010, originally MySQL AB, then Sun

Sphinx Technologies Inc.

Initial release

2013

2001

1995

2001

Current release

4.1.0, June 2022

2.7.2, June 2023

11.0, December 2022

8.4.0, April 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

based on BSD license

commercial

restricted free version is available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

C++

Java

C++

C and C++

C++

Server operating systems

Linux

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

Schema can be enforced

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.

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL92

yes

with proprietary extensions

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC
Java

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

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java, SQL

yes

via XQuery or JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

ACID

can act as a resource manager in an XA/JTA transaction

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

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, with Range Indexes

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

Role-based access control at the document and subdocument levels

Users with fine-grained authorization concept

no user groups or roles

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