Azure Monitor and IoTDB Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
5B+
Telegraf downloads
#1
Time series database
Source: DB Engines
1B+
Downloads of InfluxDB
2,800+
Contributors
Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Input and output integration overview
Gather metrics from Azure resources using the Azure Monitor API.
This plugin saves Telegraf metrics to an Apache IoTDB backend, supporting session connection and data insertion.
Integration details
Azure Monitor
The Azure Monitor Telegraf plugin is specifically designed for gathering metrics from various Azure resources using the Azure Monitor API. Users must provide specific credentials such as client_id
, client_secret
, tenant_id
, and subscription_id
to authenticate and gain access to their Azure resources. Additionally, the plugin supports functionality to collect metrics from both individual resources and resource groups or subscriptions, allowing for flexible and scalable metric collection tailored to user needs. This plugin is ideal for organizations leveraging Azure cloud infrastructure, providing crucial insights into resource performance and utilization over time, facilitating proactive management and optimization of cloud resources.
IoTDB
Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Its light-weight architecture, high performance, and rich feature set create a perfect fit for massive data storage, high-speed data ingestion, and complex analytics in the IoT industrial fields. IoTDB deeply integrates with Apache Hadoop, Spark, and Flink, which further enhances its capabilities in handling large scale data and sophisticated processing tasks.
Configuration
Azure Monitor
# Gather Azure resources metrics from Azure Monitor API
[[inputs.azure_monitor]]
# can be found under Overview->Essentials in the Azure portal for your application/service
subscription_id = "<>"
# can be obtained by registering an application under Azure Active Directory
client_id = "<>"
# can be obtained by registering an application under Azure Active Directory.
# If not specified Default Azure Credentials chain will be attempted:
# - Environment credentials (AZURE_*)
# - Workload Identity in Kubernetes cluster
# - Managed Identity
# - Azure CLI auth
# - Developer Azure CLI auth
client_secret = "<>"
# can be found under Azure Active Directory->Properties
tenant_id = "<>"
# Define the optional Azure cloud option e.g. AzureChina, AzureGovernment or AzurePublic. The default is AzurePublic.
# cloud_option = "AzurePublic"
# resource target #1 to collect metrics from
[[inputs.azure_monitor.resource_target]]
# can be found under Overview->Essentials->JSON View in the Azure portal for your application/service
# must start with 'resourceGroups/...' ('/subscriptions/xxxxxxxx-xxxx-xxxx-xxx-xxxxxxxxxxxx'
# must be removed from the beginning of Resource ID property value)
resource_id = "<>"
# the metric names to collect
# leave the array empty to use all metrics available to this resource
metrics = [ "<>", "<>" ]
# metrics aggregation type value to collect
# can be 'Total', 'Count', 'Average', 'Minimum', 'Maximum'
# leave the array empty to collect all aggregation types values for each metric
aggregations = [ "<>", "<>" ]
# resource target #2 to collect metrics from
[[inputs.azure_monitor.resource_target]]
resource_id = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# resource group target #1 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.resource_group_target]]
# the resource group name
resource_group = "<>"
# defines the resources to collect metrics from
[[inputs.azure_monitor.resource_group_target.resource]]
# the resource type
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# defines the resources to collect metrics from
[[inputs.azure_monitor.resource_group_target.resource]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# resource group target #2 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.resource_group_target]]
resource_group = "<>"
[[inputs.azure_monitor.resource_group_target.resource]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# subscription target #1 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.subscription_target]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# subscription target #2 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.subscription_target]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
</code></pre>
IoTDB
[[outputs.iotdb]]
## Configuration of IoTDB server connection
host = "127.0.0.1"
# port = "6667"
## Configuration of authentication
# user = "root"
# password = "root"
## Timeout to open a new session.
## A value of zero means no timeout.
# timeout = "5s"
## Configuration of type conversion for 64-bit unsigned int
## IoTDB currently DOES NOT support unsigned integers (version 13.x).
## 32-bit unsigned integers are safely converted into 64-bit signed integers by the plugin,
## however, this is not true for 64-bit values in general as overflows may occur.
## The following setting allows to specify the handling of 64-bit unsigned integers.
## Available values are:
## - "int64" -- convert to 64-bit signed integers and accept overflows
## - "int64_clip" -- convert to 64-bit signed integers and clip the values on overflow to 9,223,372,036,854,775,807
## - "text" -- convert to the string representation of the value
# uint64_conversion = "int64_clip"
## Configuration of TimeStamp
## TimeStamp is always saved in 64bits int. timestamp_precision specifies the unit of timestamp.
## Available value:
## "second", "millisecond", "microsecond", "nanosecond"(default)
# timestamp_precision = "nanosecond"
## Handling of tags
## Tags are not fully supported by IoTDB.
## A guide with suggestions on how to handle tags can be found here:
## https://iotdb.apache.org/UserGuide/Master/API/InfluxDB-Protocol.html
##
## Available values are:
## - "fields" -- convert tags to fields in the measurement
## - "device_id" -- attach tags to the device ID
##
## For Example, a metric named "root.sg.device" with the tags `tag1: "private"` and `tag2: "working"` and
## fields `s1: 100` and `s2: "hello"` will result in the following representations in IoTDB
## - "fields" -- root.sg.device, s1=100, s2="hello", tag1="private", tag2="working"
## - "device_id" -- root.sg.device.private.working, s1=100, s2="hello"
# convert_tags_to = "device_id"
## Handling of unsupported characters
## Some characters in different versions of IoTDB are not supported in path name
## A guide with suggetions on valid paths can be found here:
## for iotdb 0.13.x -> https://iotdb.apache.org/UserGuide/V0.13.x/Reference/Syntax-Conventions.html#identifiers
## for iotdb 1.x.x and above -> https://iotdb.apache.org/UserGuide/V1.3.x/User-Manual/Syntax-Rule.html#identifier
##
## Available values are:
## - "1.0", "1.1", "1.2", "1.3" -- enclose in `` the world having forbidden character
## such as @ $ # : [ ] { } ( ) space
## - "0.13" -- enclose in `` the world having forbidden character
## such as space
##
## Keep this section commented if you don't want to sanitize the path
# sanitize_tag = "1.3"
Input and output integration examples
Azure Monitor
-
Dynamic Resource Monitoring: Use the Azure Monitor plugin to dynamically gather metrics from Azure resources based on specific criteria like tags or resource types. Organizations can automate the process of loading and unloading resource metrics, enabling better performance tracking and optimization based on resource utilization patterns.
-
Multi-Cloud Monitoring Integration: Integrate metrics collected from Azure Monitor with other cloud providers using a centralized monitoring solution. This allows organizations to view and analyze performance data across multiple cloud deployments, providing a holistic overview of resource performance and costs, and streamlining operations.
-
Anomaly Detection and Alerting: Leverage the metrics gathered via the Azure Monitor plugin in conjunction with machine learning algorithms to detect anomalies in resource utilization. By establishing baseline performance metrics and automatically alerting on deviations, organizations can mitigate risks and address performance issues before they escalate.
-
Historical Performance Analysis: Use the collected Azure metrics to conduct historical analysis by feeding the data into a data warehousing solution. This enables organizations to track trends over time, allowing for detailed reporting and decision-making based on historical performance data.
IoTDB
-
Real-Time IoT Monitoring: Utilize the IoTDB plugin to gather sensor data from various IoT devices and save it in an Apache IoTDB backend, facilitating real-time monitoring of environmental conditions such as temperature and humidity. This use case enables organizations to analyze trends over time and make informed decisions based on historical data, while also utilizing IoTDB’s efficient storage and querying capabilities.
-
Smart Agriculture Data Collection: Use the IoTDB plugin to collect metrics from smart agriculture sensors deployed in fields. By transmitting moisture levels, nutrient content, and atmospheric conditions to IoTDB, farmers can access detailed insights into optimal planting and watering schedules, thus improving crop yields and resource management.
-
Energy Consumption Analytics: Leverage the IoTDB plugin to track energy consumption metrics from smart meters across a utility network. This integration enables analytics to identify peaks in usage and predict future consumption patterns, ultimately supporting energy conservation initiatives and improved utility management.
-
Automated Industrial Equipment Monitoring: Use this plugin to gather operational metrics from machinery in a manufacturing plant and store them in IoTDB for analysis. This setup can help identify inefficiencies, predictive maintenance needs, and operational anomalies, ensuring optimal performance and minimizing unexpected downtimes.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Related Integrations
Related Integrations
HTTP and InfluxDB Integration
The HTTP plugin collects metrics from one or more HTTP(S) endpoints. It supports various authentication methods and configuration options for data formats.
View IntegrationKafka and InfluxDB Integration
This plugin reads messages from Kafka and allows the creation of metrics based on those messages. It supports various configurations including different Kafka settings and message processing options.
View IntegrationKinesis and InfluxDB Integration
The Kinesis plugin allows for reading metrics from AWS Kinesis streams. It supports multiple input data formats and offers checkpointing features with DynamoDB for reliable message processing.
View Integration