Azure Event Hubs and InfluxDB 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.
See Ways to Get Started
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.
The InfluxDB plugin writes metrics to the InfluxDB HTTP service, allowing for efficient storage and retrieval of time series data.
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.
InfluxDB
The InfluxDB Telegraf plugin serves to send metrics to the InfluxDB HTTP API, facilitating the storage and query of time series data in a structured manner. Integrating seamlessly with InfluxDB, this plugin provides essential features such as token-based authentication and support for multiple InfluxDB cluster nodes, ensuring reliable and scalable data ingestion. Through its configurability, users can specify options like organization, destination buckets, and HTTP-specific settings, providing flexibility to tailor how data is sent and stored. The plugin also supports secret management for sensitive data, which enhances security in production environments. This plugin is particularly beneficial in modern observability stacks where real-time analytics and storage of time series data are crucial.
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"
InfluxDB
[[outputs.influxdb]]
## The full HTTP or UDP URL for your InfluxDB instance.
##
## Multiple URLs can be specified for a single cluster, only ONE of the
## urls will be written to each interval.
# urls = ["unix:///var/run/influxdb.sock"]
# urls = ["udp://127.0.0.1:8089"]
# urls = ["http://127.0.0.1:8086"]
## Local address to bind when connecting to the server
## If empty or not set, the local address is automatically chosen.
# local_address = ""
## The target database for metrics; will be created as needed.
## For UDP url endpoint database needs to be configured on server side.
# database = "telegraf"
## The value of this tag will be used to determine the database. If this
## tag is not set the 'database' option is used as the default.
# database_tag = ""
## If true, the 'database_tag' will not be included in the written metric.
# exclude_database_tag = false
## If true, no CREATE DATABASE queries will be sent. Set to true when using
## Telegraf with a user without permissions to create databases or when the
## database already exists.
# skip_database_creation = false
## Name of existing retention policy to write to. Empty string writes to
## the default retention policy. Only takes effect when using HTTP.
# retention_policy = ""
## The value of this tag will be used to determine the retention policy. If this
## tag is not set the 'retention_policy' option is used as the default.
# retention_policy_tag = ""
## If true, the 'retention_policy_tag' will not be included in the written metric.
# exclude_retention_policy_tag = false
## Write consistency (clusters only), can be: "any", "one", "quorum", "all".
## Only takes effect when using HTTP.
# write_consistency = "any"
## Timeout for HTTP messages.
# timeout = "5s"
## HTTP Basic Auth
# username = "telegraf"
# password = "metricsmetricsmetricsmetrics"
## HTTP User-Agent
# user_agent = "telegraf"
## UDP payload size is the maximum packet size to send.
# udp_payload = "512B"
## Optional TLS Config for use on HTTP connections.
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## HTTP Proxy override, if unset values the standard proxy environment
## variables are consulted to determine which proxy, if any, should be used.
# http_proxy = "http://corporate.proxy:3128"
## Additional HTTP headers
# http_headers = {"X-Special-Header" = "Special-Value"}
## HTTP Content-Encoding for write request body, can be set to "gzip" to
## compress body or "identity" to apply no encoding.
# content_encoding = "gzip"
## When true, Telegraf will output unsigned integers as unsigned values,
## i.e.: "42u". You will need a version of InfluxDB supporting unsigned
## integer values. Enabling this option will result in field type errors if
## existing data has been written.
# influx_uint_support = false
## When true, Telegraf will omit the timestamp on data to allow InfluxDB
## to set the timestamp of the data during ingestion. This is generally NOT
## what you want as it can lead to data points captured at different times
## getting omitted due to similar data.
# influx_omit_timestamp = false
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.
InfluxDB
-
Real-Time System Monitoring: Utilize the InfluxDB plugin to capture and store metrics from a range of system components, such as CPU usage, memory consumption, and disk I/O. By pushing these metrics into InfluxDB, you can create a live dashboard that visualizes system performance in real time. This setup not only helps in identifying performance bottlenecks but also assists in proactive capacity planning by analyzing trends over time.
-
Performance Tracking for Web Applications: Automatically gather and push metrics related to web application performance, such as request durations, error rates, and user interactions, to InfluxDB. By employing this plugin in your monitoring stack, you can use the stored metrics to generate reports and analyses that help understand user behavior and application efficiency, thus guiding development and optimization efforts.
-
IoT Data Aggregation: Leverage the InfluxDB Telegraf plugin to collect sensor data from various IoT devices and store it in a centralized InfluxDB instance. This use case enables you to analyze trends and patterns in environmental or machine data over time, facilitating smarter decisions and predictive maintenance strategies. By integrating IoT data into InfluxDB, organizations can harness the power of historical data analysis to drive innovation and operational efficiency.
-
Analyzing Historical Metrics for Forecasting: Set up the InfluxDB plugin to send historical metric data into InfluxDB and use it to drive forecasting models. By analyzing past performance metrics, you can create predictive models that forecast future trends and demands. This application is particularly useful for business intelligence purposes, helping organizations prepare for fluctuations in resource needs based on historical usage patterns.
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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