DBMS > Apache Impala vs. Manticore Search vs. MySQL vs. SAP HANA vs. Stardog

System Properties Comparison Apache Impala vs. Manticore Search vs. MySQL vs. SAP HANA vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Multi-storage database for search, including full-text search.

Widely used open source RDBMS

In-memory, column based data store. Available as appliance or cloud service

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS

Search engine

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

impala.apache.org

manticoresearch.com

www.mysql.com

www.sap.com/products/hana.html

www.stardog.com

Technical documentation

impala.apache.org/impala-docs.html

manual.manticoresearch.com

dev.mysql.com/doc

help.sap.com/hana

docs.stardog.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Manticore Software

Oracle

since 2010, originally MySQL AB, then Sun

SAP

Stardog-Union

Initial release

2013

2017

1995

2010

Current release

4.1.0, June 2022

6.0, February 2023

8.4.0, April 2024

2.0 SPS07 (April 4, 2023), April 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPL version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

also available as a cloud based service

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Implementation language

C++

C and C++

Java

Server operating systems

Linux

FreeBSD
Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Appliance or cloud-service

Linux
macOS
Windows

Data scheme

yes

Fixed schema

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Import/export of XML data possible

Secondary indexes

yes

full-text index on all search fields

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

with proprietary extensions

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

All languages supporting JDBC/ODBC

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

proprietary syntax

SQLScript, R

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Synchronous replication based on Galera library

Multi-source replication
Source-replica replication

yes

Multi-source replication in HA-Cluster

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

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationships in graphs

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

not for MyISAM storage engine

ACID

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 Manticore index.

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

no user groups or roles

yes

Access rights for users and roles

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