DBMS > Hawkular Metrics vs. JaguarDB vs. Microsoft Azure Table Storage vs. Postgres-XL

System Properties Comparison Hawkular Metrics vs. JaguarDB vs. Microsoft Azure Table Storage vs. Postgres-XL

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
Name	Hawkular Metrics  Xexclude from comparison	JaguarDB  Xexclude from comparison	Microsoft Azure Table Storage  Xexclude from comparison	Postgres-XL  Xexclude from comparison
Description	Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.	Performant, highly scalable DBMS for AI and IoT applications	A Wide Column Store for rapid development using massive semi-structured datasets	Based on PostgreSQL enhanced with MPP and write-scale-out cluster features
Primary database model	Time Series DBMS	Key-value store Vector DBMS	Wide column store	Relational DBMS
Secondary database models				Document store Spatial DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart	Score 0.08 Rank #366   Overall #39   Time Series DBMS	Score 0.06 Rank #381   Overall #59   Key-value stores #15   Vector DBMS	Score 4.04 Rank #77   Overall #6   Wide column stores	Score 0.53 Rank #254   Overall #117   Relational DBMS
Website	www.hawkular.org	www.jaguardb.com	azure.microsoft.com/­en-us/­services/­storage/­tables	www.postgres-xl.org
Technical documentation	www.hawkular.org/­hawkular-metrics/­docs/­user-guide	www.jaguardb.com/­support.html		www.postgres-xl.org/­documentation
Developer	Community supported by Red Hat	DataJaguar, Inc.	Microsoft
Initial release	2014	2015	2012	2014 since 2012, originally named StormDB
Current release		3.3 July 2023		10 R1, October 2018
License Commercial or Open Source	Open Source Apache 2.0	Open Source GPL V3.0	commercial	Open Source Mozilla public license
Cloud-based only Only available as a cloud service	no	no	yes	no
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language	Java	C++ the server part. Clients available in other languages		C
Server operating systems	Linux OS X Windows	Linux	hosted	Linux macOS
Data scheme	schema-free	yes	schema-free	yes
Typing predefined data types such as float or date	yes	yes	yes	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.	no	no	no	yes XML type, but no XML query functionality
Secondary indexes	no	yes	no	yes
SQL Support of SQL	no	A subset of ANSI SQL is implemented but no views, foreign keys, triggers	no	yes distributed, parallel query execution
APIs and other access methods	HTTP REST	JDBC ODBC	RESTful HTTP API	ADO.NET JDBC native C library ODBC streaming API for large objects
Supported programming languages	Go Java Python Ruby	C C++ Go Java JavaScript (Node.js) PHP Python Ruby Scala	.Net C# C++ Java JavaScript (Node.js) PHP Python Ruby	.Net C C++ Delphi Erlang Java JavaScript (Node.js) Perl PHP Python Tcl
Server-side scripts Stored procedures	no	no	no	user defined functions
Triggers	yes via Hawkular Alerting	no	no	yes
Partitioning methods Methods for storing different data on different nodes	Sharding based on Cassandra	Sharding	Sharding Implicit feature of the cloud service	horizontal partitioning
Replication methods Methods for redundantly storing data on multiple nodes	selectable replication factor based on Cassandra	Multi-source replication	yes implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce Offers an API for user-defined Map/Reduce methods	no	no	no	no
Consistency concepts Methods to ensure consistency in a distributed system	Eventual Consistency based on Cassandra Immediate Consistency based on Cassandra	Eventual Consistency	Immediate Consistency	Immediate Consistency
Foreign keys Referential integrity	no	no	no	yes
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	no	no	optimistic locking	ACID MVCC
Concurrency Support for concurrent manipulation of data	yes	yes	yes	yes
Durability Support for making data persistent	yes	yes	yes	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	no	no	no	no
User concepts Access control	no	rights management via user accounts	Access rights based on private key authentication or shared access signatures	fine grained access rights according to SQL-standard
More information provided by the system vendor We invite representatives of system vendors to contact us for updating and extending the system information, and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.
Related products and services We invite representatives of vendors of related products to contact us for presenting information about their offerings here.
More resources
	Hawkular Metrics	JaguarDB	Microsoft Azure Table Storage	Postgres-XL
Recent citations in the news	Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS 5 June 2019, DevClass provided by Google News		Working with Azure to Use and Manage Data Lakes 7 March 2024, Simplilearn How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB 9 July 2021, hackernoon.com How to use Azure Table storage in .Net 14 January 2019, InfoWorld Quick Guide to Azure Storage Pricing 16 May 2023, DevOps.com How to write data to Azure Table Store with an Azure Function 14 April 2017, Experts Exchange provided by Google News

Share this page