DBMS > Apache Impala vs. Hypertable vs. Microsoft Azure Cosmos DB vs. SiteWhere

System Properties Comparison Apache Impala vs. Hypertable vs. Microsoft Azure Cosmos DB vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

An open source BigTable implementation based on distributed file systems such as Hadoop

Globally distributed, horizontally scalable, multi-model database service

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

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	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

impala.apache.org

azure.microsoft.com/services/cosmos-db

github.com/sitewhere/sitewhere

Technical documentation

impala.apache.org/impala-docs.html

learn.microsoft.com/azure/cosmos-db

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Hypertable Inc.

Microsoft

SiteWhere

Initial release

2013

2009

2014

2010

Current release

4.1.0, June 2022

0.9.8.11, March 2016

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

predefined scheme

Typing

predefined data types such as float or date

yes

JSON types

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

restricted

only exact value or prefix value scans

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC

C++ API
Thrift

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Perl
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication factor on file system level

yes

Implicit feature of the cloud service

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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, groups and roles

based on Apache Sentry and Kerberos

Access rights can be defined down to the item level

Users with fine-grained authorization concept

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