DBMS > Impala vs. Memcached vs. SAP HANA vs. Spark SQL

System Properties Comparison Impala vs. Memcached vs. SAP HANA vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

In-memory key-value store, originally intended for caching

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

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	18.03
Rank	#40	Overall
	#25	Relational DBMS

Score	24.95
Rank	#33	Overall
	#4	Key-value stores

Score	55.09
Rank	#21	Overall
	#13	Relational DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

www.cloudera.com/products/open-source/apache-hadoop/impala.html

www.memcached.org

www.sap.com/products/hana.html

spark.apache.org/sql

Technical documentation

docs.cloudera.com/documentation/enterprise/latest/topics/impala.html

github.com/memcached/memcached/wiki

help.sap.com/hana

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

Developer

Cloudera

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

SAP

Apache Software Foundation

Initial release

2013

2003

2010

2014

Current release

4.0.0, July 2021

1.6.15, March 2022

2.0 SPS05 (June 26, 2020), June 2020

3.2.0, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD license

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based 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
Unix
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Proprietary protocol

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Repcached, a Memcached patch, provides this functionallity

yes

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

yes

using SASL (Simple Authentication and Security Layer) protocol

yes

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