DBMS > Amazon Redshift vs. Amazon SimpleDB vs. Apache Druid vs. LeanXcale vs. Splunk

System Properties Comparison Amazon Redshift vs. Amazon SimpleDB vs. Apache Druid vs. LeanXcale vs. Splunk

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Analytics Platform for Big Data

Primary database model

Relational DBMS

Key-value store

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Search engine

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.88
Rank	#139	Overall
	#24	Key-value stores

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	0.35
Rank	#283	Overall
	#41	Key-value stores
	#128	Relational DBMS

Score	88.71
Rank	#14	Overall
	#2	Search engines

Website

aws.amazon.com/redshift

aws.amazon.com/simpledb

druid.apache.org

www.leanxcale.com

www.splunk.com

Technical documentation

docs.aws.amazon.com/redshift

docs.aws.amazon.com/simpledb

druid.apache.org/docs/latest/design

docs.splunk.com/Documentation/Splunk

Developer

Amazon (based on PostgreSQL)

Amazon

Apache Software Foundation and contributors

LeanXcale

Splunk Inc.

Initial release

2012

2007

2012

2015

2003

Current release

29.0.1, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Limited free edition and free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

hosted

Linux
OS X
Unix

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

schema-less columns are supported

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

restricted

yes

All columns are indexed automatically

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

through Apache Derby

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
RESTful HTTP/JSON API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
Java
Scala

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding must be implemented in the application

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrent data updates can be detected by the application

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

fine grained access rights according to SQL-standard

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users and roles

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