DBMS > Amazon DocumentDB vs. ReductStore vs. SiteWhere vs. Spark SQL

System Properties Comparison Amazon DocumentDB vs. ReductStore vs. SiteWhere vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

M2M integration platform for persisting/querying time series data

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

Primary database model

Document store

Time Series DBMS

Relational DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/documentdb

github.com/reductstore
www.reduct.store

github.com/sitewhere/sitewhere

spark.apache.org/sql

Technical documentation

aws.amazon.com/documentdb/resources

www.reduct.store/docs

sitewhere1.sitewhere.io/index.html

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

Developer

ReductStore LLC

SiteWhere

Apache Software Foundation

Initial release

2019

2023

2010

2014

Current release

1.9, March 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Open Source

Common Public Attribution License Version 1.0

Open Source

Apache 2.0

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

C++, Rust

Java

Scala

Server operating systems

hosted

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

predefined scheme

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

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

HTTP API

HTTP REST

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C++
JavaScript (Node.js)
Python
Rust

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

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.

User concepts

Access control

Access rights for users and roles

Users with fine-grained authorization concept

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