Azure Event Hubs and IoTDB Integration
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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.
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Input and output integration overview
The Azure Event Hubs Input Plugin allows Telegraf to consume data from Azure Event Hubs and Azure IoT Hub, enabling efficient data processing and monitoring of event streams from these cloud services.
This plugin saves Telegraf metrics to an Apache IoTDB backend, supporting session connection and data insertion.
Integration details
Azure Event Hubs
This plugin serves as a consumer for Azure Event Hubs and Azure IoT Hub, allowing users to ingest data streams from these platforms efficiently. Azure Event Hubs is a highly scalable data streaming platform and event ingestion service capable of receiving and processing millions of events per second, while Azure IoT Hub enables secure device-to-cloud and cloud-to-device communication in IoT applications. The Event Hub Input Plugin interacts seamlessly with these services, providing reliable message consumption and stream processing capabilities. Key features include dynamic management of consumer groups, message tracking to prevent data loss, and customizable settings for prefetch counts, user agents, and metadata handling. This plugin is designed to support a range of use cases, including real-time telemetry data collection, IoT data processing, and integration with various data analysis and monitoring tools within the broader Azure ecosystem.
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 Event Hubs
[[inputs.eventhub_consumer]]
## The default behavior is to create a new Event Hub client from environment variables.
## This requires one of the following sets of environment variables to be set:
##
## 1) Expected Environment Variables:
## - "EVENTHUB_CONNECTION_STRING"
##
## 2) Expected Environment Variables:
## - "EVENTHUB_NAMESPACE"
## - "EVENTHUB_NAME"
## - "EVENTHUB_KEY_NAME"
## - "EVENTHUB_KEY_VALUE"
## 3) Expected Environment Variables:
## - "EVENTHUB_NAMESPACE"
## - "EVENTHUB_NAME"
## - "AZURE_TENANT_ID"
## - "AZURE_CLIENT_ID"
## - "AZURE_CLIENT_SECRET"
## Uncommenting the option below will create an Event Hub client based solely on the connection string.
## This can either be the associated environment variable or hard coded directly.
## If this option is uncommented, environment variables will be ignored.
## Connection string should contain EventHubName (EntityPath)
# connection_string = ""
## Set persistence directory to a valid folder to use a file persister instead of an in-memory persister
# persistence_dir = ""
## Change the default consumer group
# consumer_group = ""
## By default the event hub receives all messages present on the broker, alternative modes can be set below.
## The timestamp should be in https://github.com/toml-lang/toml#offset-date-time format (RFC 3339).
## The 3 options below only apply if no valid persister is read from memory or file (e.g. first run).
# from_timestamp =
# latest = true
## Set a custom prefetch count for the receiver(s)
# prefetch_count = 1000
## Add an epoch to the receiver(s)
# epoch = 0
## Change to set a custom user agent, "telegraf" is used by default
# user_agent = "telegraf"
## To consume from a specific partition, set the partition_ids option.
## An empty array will result in receiving from all partitions.
# partition_ids = ["0","1"]
## Max undelivered messages
## This plugin uses tracking metrics, which ensure messages are read to
## outputs before acknowledging them to the original broker to ensure data
## is not lost. This option sets the maximum messages to read from the
## broker that have not been written by an output.
##
## This value needs to be picked with awareness of the agent's
## metric_batch_size value as well. Setting max undelivered messages too high
## can result in a constant stream of data batches to the output. While
## setting it too low may never flush the broker's messages.
# max_undelivered_messages = 1000
## Set either option below to true to use a system property as timestamp.
## You have the choice between EnqueuedTime and IoTHubEnqueuedTime.
## It is recommended to use this setting when the data itself has no timestamp.
# enqueued_time_as_ts = true
# iot_hub_enqueued_time_as_ts = true
## Tags or fields to create from keys present in the application property bag.
## These could for example be set by message enrichments in Azure IoT Hub.
# application_property_tags = []
# application_property_fields = []
## Tag or field name to use for metadata
## By default all metadata is disabled
# sequence_number_field = "SequenceNumber"
# enqueued_time_field = "EnqueuedTime"
# offset_field = "Offset"
# partition_id_tag = "PartitionID"
# partition_key_tag = "PartitionKey"
# iot_hub_device_connection_id_tag = "IoTHubDeviceConnectionID"
# iot_hub_auth_generation_id_tag = "IoTHubAuthGenerationID"
# iot_hub_connection_auth_method_tag = "IoTHubConnectionAuthMethod"
# iot_hub_connection_module_id_tag = "IoTHubConnectionModuleID"
# iot_hub_enqueued_time_field = "IoTHubEnqueuedTime"
## Data format to consume.
## Each data format has its own unique set of configuration options, read
## more about them here:
## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
data_format = "influx"
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 Event Hubs
-
Real-Time IoT Device Monitoring: Use the Azure Event Hubs Plugin to monitor telemetry data from IoT devices like sensors and actuators. By streaming device data into monitoring dashboards, organizations can gain insights into system performances, track usage patterns, and quickly respond to irregularities. This setup allows for proactive management of devices, improving operational efficiency and reducing downtime.
-
Event-Driven Data Processing Workflows: Leverage this plugin to trigger data processing workflows in response to events received from Azure Event Hubs. For instance, when a new event arrives, it can initiate data transformation, aggregation, or storage processes, allowing businesses to automate their workflows more effectively. This integration enhances responsiveness and streamlines operations across systems.
-
Integration with Analytics Platforms: Implement the plugin to funnel event data into analytics platforms like Azure Synapse or Power BI. By integrating real-time streaming data into analytics tools, organizations can perform comprehensive data analysis, drive business intelligence efforts, and create interactive visualizations that inform decision-making.
-
Cross-Platform Data Sync: Utilize the Azure Event Hubs Plugin to synchronize data streams across diverse systems or platforms. By consuming data from Azure Event Hubs and forwarding it to other systems like databases or cloud storage, organizations can maintain consistent and up-to-date information across their entire architecture, enabling cohesive data strategies.
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
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