目录
强大的性能,无限的扩展能力
收集、组织和处理海量高速数据。 当您将任何数据视为时间序列数据时,它都更有价值。 借助 InfluxDB,这款排名第一的时间序列平台旨在与 Telegraf 一起扩展。
查看入门方法
输入和输出集成概述
Azure Event Hubs 输入插件允许 Telegraf 使用来自 Azure Event Hubs 和 Azure IoT Hub 的数据,从而实现来自这些云服务的事件流的高效数据处理和监控。
OpenSearch 输出插件允许用户使用 HTTP 将指标直接发送到 OpenSearch 实例,从而促进 OpenSearch 生态系统内有效的数据管理和分析。
集成详情
Azure Event Hubs
此插件充当 Azure Event Hubs 和 Azure IoT Hub 的消费者,允许用户高效地从这些平台摄取数据流。 Azure Event Hubs 是一个高度可扩展的数据流平台和事件摄取服务,能够每秒接收和处理数百万个事件,而 Azure IoT Hub 支持 IoT 应用程序中安全的设备到云和云到设备通信。 Event Hub 输入插件与这些服务无缝交互,提供可靠的消息消费和流处理能力。 主要功能包括消费者组的动态管理、防止数据丢失的消息跟踪以及用于预取计数、用户代理和元数据处理的可自定义设置。 此插件旨在支持各种用例,包括实时遥测数据收集、IoT 数据处理以及与更广泛的 Azure 生态系统中的各种数据分析和监控工具集成。
OpenSearch
OpenSearch Telegraf 插件通过 HTTP 与 OpenSearch 数据库集成,从而实现指标的简化收集和存储。 作为专为 2.x 及更高版本的 OpenSearch 版本设计的强大工具,该插件在提供强大功能的同时,通过原始 Elasticsearch 插件提供与 1.x 的兼容性。 此插件有助于在 OpenSearch 中创建和管理索引,自动管理模板并确保数据结构化高效地进行分析。 该插件支持各种配置选项,例如索引名称、身份验证、运行状况检查和值处理,从而可以根据不同的操作要求进行定制。 它的功能使其对于希望利用 OpenSearch 的强大功能进行指标存储和查询的组织至关重要。
配置
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"
OpenSearch
[[outputs.opensearch]]
## URLs
## The full HTTP endpoint URL for your OpenSearch instance. Multiple URLs can
## be specified as part of the same cluster, but only one URLs is used to
## write during each interval.
urls = ["http://node1.os.example.com:9200"]
## Index Name
## Target index name for metrics (OpenSearch will create if it not exists).
## This is a Golang template (see https://pkg.go.dev/text/template)
## You can also specify
## metric name (`{{.Name}}`), tag value (`{{.Tag "tag_name"}}`), field value (`{{.Field "field_name"}}`)
## If the tag does not exist, the default tag value will be empty string "".
## the timestamp (`{{.Time.Format "xxxxxxxxx"}}`).
## For example: "telegraf-{{.Time.Format \"2006-01-02\"}}-{{.Tag \"host\"}}" would set it to telegraf-2023-07-27-HostName
index_name = ""
## Timeout
## OpenSearch client timeout
# timeout = "5s"
## Sniffer
## Set to true to ask OpenSearch a list of all cluster nodes,
## thus it is not necessary to list all nodes in the urls config option
# enable_sniffer = false
## GZIP Compression
## Set to true to enable gzip compression
# enable_gzip = false
## Health Check Interval
## Set the interval to check if the OpenSearch nodes are available
## Setting to "0s" will disable the health check (not recommended in production)
# health_check_interval = "10s"
## Set the timeout for periodic health checks.
# health_check_timeout = "1s"
## HTTP basic authentication details.
# username = ""
# password = ""
## HTTP bearer token authentication details
# auth_bearer_token = ""
## Optional TLS Config
## Set to true/false to enforce TLS being enabled/disabled. If not set,
## enable TLS only if any of the other options are specified.
# tls_enable =
## Trusted root certificates for server
# tls_ca = "/path/to/cafile"
## Used for TLS client certificate authentication
# tls_cert = "/path/to/certfile"
## Used for TLS client certificate authentication
# tls_key = "/path/to/keyfile"
## Send the specified TLS server name via SNI
# tls_server_name = "kubernetes.example.com"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Template Config
## Manage templates
## Set to true if you want telegraf to manage its index template.
## If enabled it will create a recommended index template for telegraf indexes
# manage_template = true
## Template Name
## The template name used for telegraf indexes
# template_name = "telegraf"
## Overwrite Templates
## Set to true if you want telegraf to overwrite an existing template
# overwrite_template = false
## Document ID
## If set to true a unique ID hash will be sent as
## sha256(concat(timestamp,measurement,series-hash)) string. It will enable
## data resend and update metric points avoiding duplicated metrics with
## different id's
# force_document_id = false
## Value Handling
## Specifies the handling of NaN and Inf values.
## This option can have the following values:
## none -- do not modify field-values (default); will produce an error
## if NaNs or infs are encountered
## drop -- drop fields containing NaNs or infs
## replace -- replace with the value in "float_replacement_value" (default: 0.0)
## NaNs and inf will be replaced with the given number, -inf with the negative of that number
# float_handling = "none"
# float_replacement_value = 0.0
## Pipeline Config
## To use a ingest pipeline, set this to the name of the pipeline you want to use.
# use_pipeline = "my_pipeline"
## Pipeline Name
## Additionally, you can specify a tag name using the notation (`{{.Tag "tag_name"}}`)
## which will be used as the pipeline name (e.g. "{{.Tag \"os_pipeline\"}}").
## If the tag does not exist, the default pipeline will be used as the pipeline.
## If no default pipeline is set, no pipeline is used for the metric.
# default_pipeline = ""
输入和输出集成示例
Azure Event Hubs
-
实时物联网设备监控:使用 Azure Event Hubs 插件监控来自物联网设备(如传感器和执行器)的遥测数据。 通过将设备数据流式传输到监控仪表板,组织可以深入了解系统性能、跟踪使用模式并快速响应异常情况。 这种设置允许对设备进行主动管理,从而提高运营效率并减少停机时间。
-
事件驱动的数据处理工作流:利用此插件触发响应从 Azure Event Hubs 接收的事件的数据处理工作流。 例如,当新事件到达时,它可以启动数据转换、聚合或存储过程,从而使企业能够更有效地自动化其工作流。 这种集成增强了响应能力并简化了跨系统的运营。
-
与分析平台集成:实施该插件以将事件数据输送到 Azure Synapse 或 Power BI 等分析平台。 通过将实时流数据集成到分析工具中,组织可以执行全面的数据分析、推动商业智能工作并创建信息丰富的交互式可视化效果,从而为决策提供依据。
-
跨平台数据同步:利用 Azure Event Hubs 插件跨不同系统或平台同步数据流。 通过使用来自 Azure Event Hubs 的数据并将其转发到其他系统(如数据库或云存储),组织可以在其整个架构中维护一致且最新的信息,从而实现有凝聚力的数据策略。
OpenSearch
-
时间序列数据的动态索引:利用 OpenSearch Telegraf 插件为时间序列指标动态创建索引,确保数据以有序的方式存储,从而有助于基于时间的查询。 通过使用 Go 模板定义索引模式,用户可以利用该插件创建每日或每月索引,这可以大大简化数据管理和长期检索,从而提高分析性能。
-
多租户应用程序的集中式日志记录:在多租户应用程序中实施 OpenSearch 插件,其中每个租户的日志都发送到单独的索引。 这支持对每个租户进行有针对性的分析和监控,同时保持数据隔离。 通过利用索引名称模板功能,用户可以自动创建特定于租户的索引,这不仅简化了流程,还增强了租户数据的安全性和可访问性。
-
与机器学习集成以进行异常检测:将 OpenSearch 插件与机器学习工具结合使用,以自动检测指标数据中的异常。 通过配置插件以将实时指标发送到 OpenSearch,用户可以将机器学习模型应用于传入的数据流,以识别异常值或异常模式,从而促进主动监控和快速补救措施。
-
使用 OpenSearch 增强监控仪表板:使用从 OpenSearch 收集的指标创建实时仪表板,以深入了解系统性能。 通过将指标馈送到 OpenSearch,组织可以利用 OpenSearch 仪表板来可视化关键绩效指标,使运营团队能够快速评估运行状况和性能,并做出数据驱动的决策。
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强大的性能,无限的扩展能力
收集、组织和处理海量高速数据。 当您将任何数据视为时间序列数据时,它都更有价值。 借助 InfluxDB,这款排名第一的时间序列平台旨在与 Telegraf 一起扩展。
查看入门方法