Modbus 和 AWS Timestream 集成

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

info

对于大规模实时查询,这不是推荐的配置。为了获得查询和压缩优化、高速摄取和高可用性,您可能需要考虑 Modbus 和 InfluxDB

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时间序列数据库
来源:DB Engines

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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间序列数据时,它都更有价值。借助 InfluxDB,由 Telegraf 构建的排名第一的时间序列平台,可随之扩展。

查看入门方法

输入和输出集成概述

Modbus 插件允许您使用各种通信方法从 Modbus 设备收集数据,从而增强您监控和控制工业流程的能力。

AWS Timestream Telegraf 插件使用户能够将指标直接发送到 Amazon 的 Timestream 服务,该服务专为时间序列数据管理而设计。此插件为身份验证、数据组织和保留设置提供了多种配置选项。

集成详情

Modbus

Modbus 插件通过 Modbus TCP 或 Modbus RTU/ASCII 收集离散输入、线圈、输入寄存器和保持寄存器。

AWS Timestream

此插件旨在高效地将指标写入 Amazon 的 Timestream 服务,这是一种针对物联网和运营应用程序优化的时间序列数据库。借助此插件,Telegraf 可以发送从各种来源收集的数据,并支持灵活的配置,用于身份验证、数据组织和保留管理。它利用凭证链进行身份验证,允许各种方法,例如 Web 身份、承担角色和共享配置文件。用户可以定义指标在 Timestream 中的组织方式——是使用单个表还是多个表,以及控制磁存储和内存存储的保留期等方面。一个关键特性是它能够处理多指标记录,从而实现高效的数据摄取并帮助减少多次写入的开销。在错误处理方面,该插件包含用于解决与数据写入期间的 AWS 错误相关的常见问题的机制,例如用于限制的重试逻辑以及根据需要创建表的能力。

配置

Modbus

[[inputs.modbus]]
  name = "Device"
  slave_id = 1
  timeout = "1s"
  configuration_type = "register"
  discrete_inputs = [
    { name = "start", address = [0]},
    { name = "stop", address = [1]},
    { name = "reset", address = [2]},
    { name = "emergency_stop", address = [3]},
  ]
  coils = [
    { name = "motor1_run", address = [0]},
    { name = "motor1_jog", address = [1]},
    { name = "motor1_stop", address = [2]},
  ]
  holding_registers = [
    { name = "power_factor", byte_order = "AB", data_type = "FIXED", scale=0.01, address = [8]},
    { name = "voltage", byte_order = "AB", data_type = "FIXED", scale=0.1, address = [0]},
    { name = "energy", byte_order = "ABCD", data_type = "FIXED", scale=0.001, address = [5,6]},
    { name = "current", byte_order = "ABCD", data_type = "FIXED", scale=0.001, address = [1,2]},
    { name = "frequency", byte_order = "AB", data_type = "UFIXED", scale=0.1, address = [7]},
    { name = "power", byte_order = "ABCD", data_type = "UFIXED", scale=0.1, address = [3,4]},
    { name = "firmware", byte_order = "AB", data_type = "STRING", address = [5, 6, 7, 8, 9, 10, 11, 12]},
  ]
  input_registers = [
    { name = "tank_level", byte_order = "AB", data_type = "INT16", scale=1.0, address = [0]},
    { name = "tank_ph", byte_order = "AB", data_type = "INT16", scale=1.0, address = [1]},
    { name = "pump1_speed", byte_order = "ABCD", data_type = "INT32", scale=1.0, address = [3,4]},
  ]

AWS Timestream

[[outputs.timestream]]
  ## Amazon Region
  region = "us-east-1"

  ## Amazon Credentials
  ## Credentials are loaded in the following order:
  ## 1) Web identity provider credentials via STS if role_arn and web_identity_token_file are specified
  ## 2) Assumed credentials via STS if role_arn is specified
  ## 3) explicit credentials from 'access_key' and 'secret_key'
  ## 4) shared profile from 'profile'
  ## 5) environment variables
  ## 6) shared credentials file
  ## 7) EC2 Instance Profile
  #access_key = ""
  #secret_key = ""
  #token = ""
  #role_arn = ""
  #web_identity_token_file = ""
  #role_session_name = ""
  #profile = ""
  #shared_credential_file = ""

  ## Endpoint to make request against, the correct endpoint is automatically
  ## determined and this option should only be set if you wish to override the
  ## default.
  ##   ex: endpoint_url = "http://localhost:8000"
  # endpoint_url = ""

  ## Timestream database where the metrics will be inserted.
  ## The database must exist prior to starting Telegraf.
  database_name = "yourDatabaseNameHere"

  ## Specifies if the plugin should describe the Timestream database upon starting
  ## to validate if it has access necessary permissions, connection, etc., as a safety check.
  ## If the describe operation fails, the plugin will not start
  ## and therefore the Telegraf agent will not start.
  describe_database_on_start = false

  ## Specifies how the data is organized in Timestream.
  ## Valid values are: single-table, multi-table.
  ## When mapping_mode is set to single-table, all of the data is stored in a single table.
  ## When mapping_mode is set to multi-table, the data is organized and stored in multiple tables.
  ## The default is multi-table.
  mapping_mode = "multi-table"

  ## Specifies if the plugin should create the table, if the table does not exist.
  create_table_if_not_exists = true

  ## Specifies the Timestream table magnetic store retention period in days.
  ## Check Timestream documentation for more details.
  ## NOTE: This property is valid when create_table_if_not_exists = true.
  create_table_magnetic_store_retention_period_in_days = 365

  ## Specifies the Timestream table memory store retention period in hours.
  ## Check Timestream documentation for more details.
  ## NOTE: This property is valid when create_table_if_not_exists = true.
  create_table_memory_store_retention_period_in_hours = 24

  ## Specifies how the data is written into Timestream.
  ## Valid values are: true, false
  ## When use_multi_measure_records is set to true, all of the tags and fields are stored
  ## as a single row in a Timestream table.
  ## When use_multi_measure_record is set to false, Timestream stores each field in a
  ## separate table row, thereby storing the tags multiple times (once for each field).
  ## The recommended setting is true.
  ## The default is false.
  use_multi_measure_records = "false"

  ## Specifies the measure_name to use when sending multi-measure records.
  ## NOTE: This property is valid when use_multi_measure_records=true and mapping_mode=multi-table
  measure_name_for_multi_measure_records = "telegraf_measure"

  ## Specifies the name of the table to write data into
  ## NOTE: This property is valid when mapping_mode=single-table.
  # single_table_name = ""

  ## Specifies the name of dimension when all of the data is being stored in a single table
  ## and the measurement name is transformed into the dimension value
  ## (see Mapping data from Influx to Timestream for details)
  ## NOTE: This property is valid when mapping_mode=single-table.
  # single_table_dimension_name_for_telegraf_measurement_name = "namespace"

  ## Only valid and optional if create_table_if_not_exists = true
  ## Specifies the Timestream table tags.
  ## Check Timestream documentation for more details
  # create_table_tags = { "foo" = "bar", "environment" = "dev"}

  ## Specify the maximum number of parallel go routines to ingest/write data
  ## If not specified, defaulted to 1 go routines
  max_write_go_routines = 25

  ## Please see README.md to know how line protocol data is mapped to Timestream
  ##

输入和输出集成示例

Modbus

  1. 基本用法:要从单个设备读取数据,请使用设备名称和 IP 地址配置它,指定从站 ID 和感兴趣的寄存器。
  2. 多个请求:您可以通过指定多个 [[inputs.modbus.request]] 部分,在单个配置中定义多个请求以从不同的 Modbus 从站设备获取数据。
  3. 数据处理:利用缩放功能将原始 Modbus 读数转换为有用的指标,并根据需要调整单位转换。

AWS Timestream

  1. 物联网数据指标:使用 Timestream 插件将来自物联网设备的实时指标发送到 Timestream,从而可以快速分析和可视化传感器数据。通过将设备读数组织成时间序列格式,用户可以跟踪趋势、识别异常并根据设备性能简化运营决策。

  2. 应用程序性能监控:将 Timestream 与应用程序监控工具结合使用,以随时间推移发送有关服务性能的指标。此集成使工程师能够对应用程序性能进行历史分析,将其与业务指标相关联,并根据随时间推移查看的使用模式优化资源分配。

  3. 自动数据归档:配置 Timestream 插件以将数据写入 Timestream,同时管理保留期。此设置可以自动化归档策略,确保根据预定义的标准保留旧数据。这对于合规性和历史分析特别有用,使企业能够以最少的人工干预来维护其数据生命周期。

  4. 多应用程序指标聚合:利用 Timestream 插件将来自多个应用程序的指标聚合到 Timestream 中。通过创建统一的性能指标数据库,组织可以获得跨各种服务的整体洞察力,提高系统范围性能的可见性,并促进跨应用程序故障排除。

反馈

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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,由 Telegraf 构建的排名第一的时间序列平台,可随之扩展。

查看入门方法

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