gNMI 和 AWS Timestream 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。

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

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,它会更有价值。使用 InfluxDB,第一时序平台,旨在与 Telegraf 一起扩展。

查看入门方法

输入和输出集成概述

gNMI (gRPC 网络管理接口) 输入插件使用 gNMI Subscribe 方法从网络设备收集遥测数据。它支持 TLS 以实现安全身份验证和数据传输。

AWS Timestream Telegraf 插件使用户能够将指标直接发送到 Amazon 的 Timestream 服务,该服务专为时序数据管理而设计。此插件提供各种配置选项,用于身份验证、数据组织和保留设置。

集成详情

gNMI

此输入插件与供应商无关,可以与任何支持 gNMI 规范的平台一起使用。它基于 gNMI Subscribe 方法消耗遥测数据,从而可以实时监控网络设备。

AWS Timestream

此插件旨在有效地将指标写入 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序数据库。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度量记录,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 AWS 错误相关的常见问题的机制,例如用于节流的重试逻辑以及根据需要创建表的能力。

配置

gNMI


[[inputs.gnmi]]
  ## Address and port of the gNMI GRPC server
  addresses = ["10.49.234.114:57777"]

  ## define credentials
  username = "cisco"
  password = "cisco"

  ## gNMI encoding requested (one of: "proto", "json", "json_ietf", "bytes")
  # encoding = "proto"

  ## redial in case of failures after
  # redial = "10s"

  ## gRPC Keepalive settings
  ## See https://pkg.go.dev/google.golang.org/grpc/keepalive
  ## The client will ping the server to see if the transport is still alive if it has
  ## not see any activity for the given time.
  ## If not set, none of the keep-alive setting (including those below) will be applied.
  ## If set and set below 10 seconds, the gRPC library will apply a minimum value of 10s will be used instead.
  # keepalive_time = ""

  ## Timeout for seeing any activity after the keep-alive probe was
  ## sent. If no activity is seen the connection is closed.
  # keepalive_timeout = ""

  ## gRPC Maximum Message Size
  # max_msg_size = "4MB"

  ## Enable to get the canonical path as field-name
  # canonical_field_names = false

  ## Remove leading slashes and dots in field-name
  # trim_field_names = false

  ## Guess the path-tag if an update does not contain a prefix-path
  ## Supported values are
  ##   none         -- do not add a 'path' tag
  ##   common path  -- use the common path elements of all fields in an update
  ##   subscription -- use the subscription path
  # path_guessing_strategy = "none"

  ## Prefix tags from path keys with the path element
  # prefix_tag_key_with_path = false

  ## Optional client-side TLS to authenticate the device
  ## 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"
  ## Password for the key file if it is encrypted
  # tls_key_pwd = ""
  ## Send the specified TLS server name via SNI
  # tls_server_name = "kubernetes.example.com"
  ## Minimal TLS version to accept by the client
  # tls_min_version = "TLS12"
  ## List of ciphers to accept, by default all secure ciphers will be accepted
  ## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values.
  ## Use "all", "secure" and "insecure" to add all support ciphers, secure
  ## suites or insecure suites respectively.
  # tls_cipher_suites = ["secure"]
  ## Renegotiation method, "never", "once" or "freely"
  # tls_renegotiation_method = "never"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## gNMI subscription prefix (optional, can usually be left empty)
  ## See: https://github.com/openconfig/reference/blob/master/rpc/gnmi/gnmi-specification.md#222-paths
  # origin = ""
  # prefix = ""
  # target = ""

  ## Vendor specific options
  ## This defines what vendor specific options to load.
  ## * Juniper Header Extension (juniper_header): some sensors are directly managed by
  ##   Linecard, which adds the Juniper GNMI Header Extension. Enabling this
  ##   allows the decoding of the Extension header if present. Currently this knob
  ##   adds component, component_id & sub_component_id as additional tags
  # vendor_specific = []

  ## YANG model paths for decoding IETF JSON payloads
  ## Model files are loaded recursively from the given directories. Disabled if
  ## no models are specified.
  # yang_model_paths = []

  ## Define additional aliases to map encoding paths to measurement names
  # [inputs.gnmi.aliases]
  #   ifcounters = "openconfig:/interfaces/interface/state/counters"

  [[inputs.gnmi.subscription]]
    ## Name of the measurement that will be emitted
    name = "ifcounters"

    ## Origin and path of the subscription
    ## See: https://github.com/openconfig/reference/blob/master/rpc/gnmi/gnmi-specification.md#222-paths
    ##
    ## origin usually refers to a (YANG) data model implemented by the device
    ## and path to a specific substructure inside it that should be subscribed
    ## to (similar to an XPath). YANG models can be found e.g. here:
    ## https://github.com/YangModels/yang/tree/master/vendor/cisco/xr
    origin = "openconfig-interfaces"
    path = "/interfaces/interface/state/counters"

    ## Subscription mode ("target_defined", "sample", "on_change") and interval
    subscription_mode = "sample"
    sample_interval = "10s"

    ## Suppress redundant transmissions when measured values are unchanged
    # suppress_redundant = false

    ## If suppression is enabled, send updates at least every X seconds anyway
    # heartbeat_interval = "60s"

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

gNMI

  1. 监控 Cisco 设备:使用 gNMI 插件从 Cisco IOS XR、NX-OS 或 IOS XE 设备收集遥测数据以进行性能监控。

  2. 实时网络洞察:借助 gNMI 插件,网络管理员可以深入了解实时指标,例如接口统计信息和 CPU 使用率。

  3. 安全数据采集:配置带有 TLS 设置的 gNMI 插件,以确保在从设备收集敏感遥测数据时进行安全通信。

  4. 灵活的数据处理:使用订阅选项自定义您要根据特定需求或要求收集的遥测数据。

  5. 错误处理:该插件包含故障排除选项,用于处理常见问题,例如缺少指标名称或 TLS 握手失败。

AWS Timestream

  1. 物联网数据指标:使用 Timestream 插件将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序格式,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:利用 Timestream 和应用程序监控工具,可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. 自动化数据存档:配置 Timestream 插件以将数据写入 Timestream,同时管理保留期。此设置可以自动化存档策略,确保根据预定义的标准保存旧数据。这对于合规性和历史分析尤其有用,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。通过创建性能指标的统一数据库,组织可以获得跨各种服务的整体洞察力,从而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

感谢您成为我们社区的一份子!如果您有任何一般性反馈或在这些页面上发现任何错误,我们欢迎并鼓励您提出意见。请在 InfluxDB 社区 Slack 中提交您的反馈。

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序数据时,它会更有价值。使用 InfluxDB,第一时序平台,旨在与 Telegraf 一起扩展。

查看入门方法

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