Azure Event Hubs 和 IoTDB 集成

通过 Telegraf(由 InfluxData 构建的开源数据连接器)提供支持,实现强大的性能和简单的集成。

info

这不是实时大规模查询的推荐配置。为了进行查询和压缩优化、高速摄取和高可用性,您可能需要考虑 Azure Event Hubs 和 InfluxDB

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时间序列数据库
来源:DB Engines

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贡献者

目录

强大的性能,无限的扩展

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,它会更有价值。InfluxDB 是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 从 Azure Event Hubs 和 Azure IoT Hub 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。

此插件将 Telegraf 指标保存到 Apache IoTDB 后端,支持会话连接和数据插入。

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 支持物联网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、物联网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。

IoTDB

Apache IoTDB(物联网数据库)是一种物联网原生数据库,具有高性能的数据管理和分析能力,可部署在边缘和云端。其轻量级架构、高性能和丰富的功能集使其非常适合物联网工业领域的大规模数据存储、高速数据摄取和复杂分析。IoTDB 与 Apache Hadoop、Spark 和 Flink 深度集成,进一步增强了其处理大规模数据和复杂处理任务的能力。

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"

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"

输入和输出集成示例

Azure Event Hubs

  1. 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。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。

  2. 事件驱动的数据处理工作流:利用此插件触发响应于从 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。

  3. 与分析平台集成:实施此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,从而为决策提供依据。

  4. 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。

IoTDB

  1. 实时物联网监控:利用 IoTDB 插件收集来自各种物联网设备的传感器数据,并将其保存在 Apache IoTDB 后端中,从而促进对环境条件(如温度和湿度)的实时监控。此用例使组织能够分析随时间变化的趋势并根据历史数据做出明智的决策,同时还可以利用 IoTDB 的高效存储和查询功能。

  2. 智慧农业数据采集:使用 IoTDB 插件收集来自部署在田地中的智慧农业传感器的指标。通过将湿度水平、养分含量和大气条件传输到 IoTDB,农民可以访问有关最佳种植和浇水计划的详细见解,从而提高作物产量和资源管理水平。

  3. 能源消耗分析:利用 IoTDB 插件跟踪来自整个公用事业网络的智能电表的能源消耗指标。这种集成支持分析以识别使用高峰并预测未来的消耗模式,最终支持节能措施和改进的公用事业管理。

  4. 自动化工业设备监控:使用此插件收集制造工厂中机器的操作指标,并将其存储在 IoTDB 中进行分析。此设置可以帮助识别效率低下、预测性维护需求和操作异常,从而确保最佳性能并最大限度地减少意外停机时间。

反馈

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强大的性能,无限的扩展

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,它会更有价值。InfluxDB 是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

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