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,它都会更有价值。InfluxDB 是排名第一的时间序列平台,旨在通过 Telegraf 进行扩展。
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 从 Azure Event Hubs 和 Azure IoT 中心消费数据,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。
Datadog Telegraf 插件能够将指标提交到 Datadog Metrics API,通过可靠的指标摄取过程促进高效的监控和数据分析。
集成详情
Azure Event Hubs
此插件充当 Azure Event Hubs 和 Azure IoT 中心的消费者,允许用户有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 中心支持 IoT 应用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。
Datadog
此插件写入 Datadog Metrics API,使用户能够发送指标以进行监控和性能分析。通过使用 Datadog API 密钥,用户可以将插件配置为与 Datadog 的 v1 API 建立连接。该插件支持各种配置选项,包括连接超时、HTTP 代理设置和数据压缩方法,确保适应不同的部署环境。将计数指标转换为速率的能力增强了 Telegraf 与 Datadog 代理的集成,尤其适用于依赖实时性能指标的应用程序。
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"
Datadog
[[outputs.datadog]]
## Datadog API key
apikey = "my-secret-key"
## Connection timeout.
# timeout = "5s"
## Write URL override; useful for debugging.
## This plugin only supports the v1 API currently due to the authentication
## method used.
# url = "https://app.datadoghq.com/api/v1/series"
## Set http_proxy
# use_system_proxy = false
# http_proxy_url = "http://localhost:8888"
## Override the default (none) compression used to send data.
## Supports: "zlib", "none"
# compression = "none"
## When non-zero, converts count metrics submitted by inputs.statsd
## into rate, while dividing the metric value by this number.
## Note that in order for metrics to be submitted simultaenously alongside
## a Datadog agent, rate_interval has to match the interval used by the
## agent - which defaults to 10s
# rate_interval = 0s
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 插件监控来自传感器和执行器等物联网设备的遥测数据。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。此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:利用此插件触发数据处理工作流,以响应从 Azure Event Hubs 接收的事件。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,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导入到 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 插件跨各种系统或平台同步数据流。通过从 Azure Event Hubs 消费数据并将其转发到数据库或云存储等其他系统,组织可以在其整个架构中保持一致且最新的信息,从而实现有凝聚力的数据策略。
Datadog
-
实时基础设施监控:使用 Datadog 插件实时监控服务器指标,方法是将 CPU 使用率和内存统计信息直接发送到 Datadog。这种集成使 IT 团队能够在集中式仪表板中可视化和分析系统性能指标,从而能够主动响应任何新出现的问题,例如资源瓶颈或服务器过载。
-
应用程序性能跟踪:利用此插件将特定于应用程序的指标(例如请求计数和错误率)提交到 Datadog。通过与应用程序监控工具集成,团队可以将基础设施指标与应用程序性能相关联,从而提供洞察力,使他们能够优化代码性能并改善用户体验。
-
指标中的异常检测:配置 Datadog 插件以发送指标,这些指标可以根据 Datadog 的机器学习功能检测到的异常模式触发警报和通知。这种主动监控有助于团队在客户受到影响之前迅速应对潜在的中断或性能下降。
-
与云服务集成:通过利用 Datadog 插件发送来自云资源的指标,IT 团队可以了解云应用程序性能。监控延迟和错误率等指标有助于确保满足服务级别协议 (SLA),并有助于优化跨云环境的资源分配。
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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,它都会更有价值。InfluxDB 是排名第一的时间序列平台,旨在通过 Telegraf 进行扩展。
查看入门方法