Azure Event Hubs 和 TimescaleDB 集成

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

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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时序数据库
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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序数据时,它都会更有价值。InfluxDB 是排名第一的时序平台,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,从而能够高效地处理数据并监控来自这些云服务的事件流。

此输出插件提供了一种可靠高效的机制,用于将 Telegraf 收集的指标直接路由到 TimescaleDB。通过利用 PostgreSQL 强大的生态系统以及 TimescaleDB 的时序优化,它支持高性能数据摄取和高级查询功能。

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 Hub 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。

TimescaleDB

TimescaleDB 是一个开源时序数据库,作为 PostgreSQL 的扩展构建,旨在高效处理大规模、面向时间的数据。TimescaleDB 于 2017 年推出,是为了响应对能够管理海量数据、具有高插入速率和复杂查询的强大、可扩展解决方案日益增长的需求。通过利用 PostgreSQL 熟悉的 SQL 接口,并使用专门的时序功能对其进行增强,TimescaleDB 迅速在希望将时序功能集成到现有关系数据库中的开发人员中流行起来。它的混合方法使用户可以受益于 PostgreSQL 的灵活性、可靠性和生态系统,同时为时序数据提供优化的性能。

该数据库在需要快速摄取数据点并对历史时期进行复杂分析查询的环境中尤其有效。TimescaleDB 具有许多创新功能,例如透明地将数据分区为可管理块的超表和内置的连续聚合。这些功能可以显着提高查询速度和资源效率。

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"

TimescaleDB

# Publishes metrics to a TimescaleDB database
[[outputs.postgresql]]
  ## Specify connection address via the standard libpq connection string:
  ##   host=... user=... password=... sslmode=... dbname=...
  ## Or a URL:
  ##   postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
  ## See https://postgresql.ac.cn/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
  ##
  ## All connection parameters are optional. Environment vars are also supported.
  ## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
  ## All supported vars can be found here:
  ##  https://postgresql.ac.cn/docs/current/libpq-envars.html
  ##
  ## Non-standard parameters:
  ##   pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
  ##   pool_min_conns (default: 0) - Minimum size of connection pool.
  ##   pool_max_conn_lifetime (default: 0s) - Maximum connection age before closing.
  ##   pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
  ##   pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
  # connection = ""

  ## Postgres schema to use.
  # schema = "public"

  ## Store tags as foreign keys in the metrics table. Default is false.
  # tags_as_foreign_keys = false

  ## Suffix to append to table name (measurement name) for the foreign tag table.
  # tag_table_suffix = "_tag"

  ## Deny inserting metrics if the foreign tag can't be inserted.
  # foreign_tag_constraint = false

  ## Store all tags as a JSONB object in a single 'tags' column.
  # tags_as_jsonb = false

  ## Store all fields as a JSONB object in a single 'fields' column.
  # fields_as_jsonb = false

  ## Name of the timestamp column
  ## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
  # timestamp_column_name = "time"

  ## Type of the timestamp column
  ## Currently, "timestamp without time zone" and "timestamp with time zone"
  ## are supported
  # timestamp_column_type = "timestamp without time zone"

  ## Templated statements to execute when creating a new table.
  # create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }})''',
  # ]

  ## Templated statements to execute when adding columns to a table.
  ## Set to an empty list to disable. Points containing tags for which there is
  ## no column will be skipped. Points containing fields for which there is no
  ## column will have the field omitted.
  # add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## Templated statements to execute when creating a new tag table.
  # tag_table_create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
  # ]

  ## Templated statements to execute when adding columns to a tag table.
  ## Set to an empty list to disable. Points containing tags for which there is
  ## no column will be skipped.
  # tag_table_add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## The postgres data type to use for storing unsigned 64-bit integer values
  ## (Postgres does not have a native unsigned 64-bit integer type).
  ## The value can be one of:
  ##   numeric - Uses the PostgreSQL "numeric" data type.
  ##   uint8 - Requires pguint extension (https://github.com/petere/pguint)
  # uint64_type = "numeric"

  ## When using pool_max_conns > 1, and a temporary error occurs, the query is
  ## retried with an incremental backoff. This controls the maximum duration.
  # retry_max_backoff = "15s"

  ## Approximate number of tag IDs to store in in-memory cache (when using
  ## tags_as_foreign_keys). This is an optimization to skip inserting known
  ## tag IDs. Each entry consumes approximately 34 bytes of memory.
  # tag_cache_size = 100000

  ## Cut column names at the given length to not exceed PostgreSQL's
  ## 'identifier length' limit (default: no limit)
  ## (see https://postgresql.ac.cn/docs/current/limits.html)
  ## Be careful to not create duplicate column names!
  # column_name_length_limit = 0

  ## Enable & set the log level for the Postgres driver.
  # log_level = "warn" # trace, debug, info, warn, error, none

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。这种集成提高了响应能力并简化了跨系统的操作。

  3. 与分析平台集成:实施此插件以将事件数据注入到 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,以辅助决策。

  4. 跨平台数据同步:利用 Azure Event Hubs 插件跨不同的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维护一致且最新的信息,从而实现有凝聚力的数据策略。

TimescaleDB

  1. 实时物联网数据摄取:使用此插件实时收集和存储来自数千个物联网设备的传感器数据。此设置有助于即时分析,帮助组织监控运营效率并快速响应不断变化的条件。

  2. 云应用程序性能监控:利用此插件将来自分布式云应用程序的详细性能指标馈送到 TimescaleDB 中。这种集成支持实时仪表板和警报,使团队能够快速识别和缓解性能瓶颈。

  3. 历史数据分析和报告:实施一个系统,将长期指标存储在 TimescaleDB 中,以进行全面的历史分析。这种方法使企业能够执行趋势分析、生成详细报告并根据存档的时序数据做出数据驱动的决策。

  4. 自适应警报和异常检测:将此插件与自动化异常检测工作流集成。通过将指标持续流式传输到 TimescaleDB,机器学习模型可以分析数据模式并在发生异常时触发警报,从而提高系统可靠性和主动维护能力。

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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序数据时,它都会更有价值。InfluxDB 是排名第一的时序平台,旨在与 Telegraf 一起扩展。

查看入门方法

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