DBMS > AlaSQL vs. Apache IoTDB vs. Hawkular Metrics vs. Infobright vs. Microsoft Azure Synapse Analytics

System Properties Comparison AlaSQL vs. Apache IoTDB vs. Hawkular Metrics vs. Infobright vs. Microsoft Azure Synapse Analytics

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Document store
Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.42
Rank	#264	Overall
	#42	Document stores
	#122	Relational DBMS

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.88
Rank	#198	Overall
	#92	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Website

alasql.org

iotdb.apache.org

www.hawkular.org

ignitetech.com/softwarelibrary/infobrightdb

azure.microsoft.com/services/synapse-analytics

Technical documentation

github.com/AlaSQL/alasql

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.microsoft.com/azure/synapse-analytics

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation

Community supported by Red Hat

Ignite Technologies Inc.; formerly InfoBright Inc.

Microsoft

Initial release

2014

2018

2014

2005

2016

Current release

1.1.0, April 2023

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

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

JavaScript

Java

C++

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux
Windows

hosted

Data scheme

schema-free

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

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

SQL-like query language

yes

APIs and other access methods

JavaScript API

JDBC
Native API

HTTP REST

ADO.NET
JDBC
ODBC

Supported programming languages

JavaScript

C
C#
C++
Go
Java
Python
Scala

Go
Java
Python
Ruby

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
Java
PHP

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

based on Cassandra

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

selectable replication factor

based on Cassandra

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

yes

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

yes

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