DBMS > Hawkular Metrics vs. KairosDB vs. Microsoft Azure Synapse Analytics vs. Stardog vs. ToroDB

System Properties Comparison Hawkular Metrics vs. KairosDB vs. Microsoft Azure Synapse Analytics vs. Stardog vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Distributed Time Series DBMS based on Cassandra or H2

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

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

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	0.71
Rank	#230	Overall
	#20	Time Series DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.hawkular.org

github.com/kairosdb/kairosdb

azure.microsoft.com/services/synapse-analytics

www.stardog.com

github.com/torodb/server

Technical documentation

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

kairosdb.github.io

docs.microsoft.com/azure/synapse-analytics

docs.stardog.com

Developer

Community supported by Red Hat

Microsoft

Stardog-Union

8Kdata

Initial release

2014

2013

2016

2010

2016

Current release

1.2.2, November 2018

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

AGPL-V3

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

C++

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux
macOS
Windows

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

HTTP REST

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Go
Java
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

C#
Java
PHP

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

Transact SQL

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via Hawkular Alerting

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

based on Cassandra

Sharding, horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

selectable replication factor

based on Cassandra

yes

Multi-source replication in HA-Cluster

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency in HA-Cluster

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

yes

relationships in graphs

Transaction concepts

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

ACID

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

simple password-based access control

yes

Access rights for users and roles

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

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

Expo: Real Time A/B Testing and Monitoring with Spark Streaming and Kafka at Walmart Labs
24 May 2019, InfoQ.com

provided by Google News

General Available: Azure Synapse Runtime for Apache Spark 3.4 is now GA | Azure updates
8 April 2024, Microsoft

Migrate Microsoft Azure Synapse Analytics to Amazon Redshift using AWS SCT | Amazon Web Services
18 October 2023, AWS Blog

Azure Synapse vs. Databricks: Data Platform Comparison 2024
26 March 2024, eWeek

Azure Synapse Runtime for Apache Spark 3.2 End of Support | Azure updates
22 March 2024, Microsoft

Azure Synapse Link for Cosmos DB: New Analytics Capabilities
10 November 2023, InfoQ.com

provided by Google News

Share this page