Azure Monitor 和 OpenSearch 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

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 Azure Monitor 和 InfluxDB 集成

50 亿+

Telegraf 下载量

#1

时序数据库
来源:DB Engines

10 亿+

InfluxDB 下载量

2,800+

贡献者

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,它会更有价值。使用 InfluxDB,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

使用 Azure Monitor API 从 Azure 资源收集指标。

OpenSearch 输出插件允许用户使用 HTTP 将指标直接发送到 OpenSearch 实例,从而促进 OpenSearch 生态系统内有效的数据管理和分析。

集成详情

Azure Monitor

Azure Monitor Telegraf 插件专为使用 Azure Monitor API 从各种 Azure 资源收集指标而设计。用户必须提供特定的凭据,例如 client_id、client_secret、tenant_id 和 subscription_id,以进行身份验证并访问其 Azure 资源。此外,该插件还支持从单个资源和资源组或订阅收集指标的功能,从而实现根据用户需求量身定制的灵活且可扩展的指标收集。此插件非常适合利用 Azure 云基础设施的组织,可以深入了解资源在一段时间内的性能和利用率,从而促进云资源的积极管理和优化。

OpenSearch

OpenSearch Telegraf 插件通过 HTTP 与 OpenSearch 数据库集成,从而实现指标的简化收集和存储。作为一个专为 OpenSearch 2.x 版本设计的强大工具,该插件提供了强大的功能,同时通过原始的 Elasticsearch 插件提供与 1.x 的兼容性。此插件有助于在 OpenSearch 中创建和管理索引,自动管理模板并确保数据结构化以有效地进行分析。该插件支持各种配置选项,例如索引名称、身份验证、运行状况检查和值处理,从而可以根据不同的操作要求进行定制。它的功能使其对于希望利用 OpenSearch 的强大功能进行指标存储和查询的组织至关重要。

配置

Azure Monitor

# Gather Azure resources metrics from Azure Monitor API
[[inputs.azure_monitor]]
  # can be found under Overview->Essentials in the Azure portal for your application/service
  subscription_id = "<>"
  # can be obtained by registering an application under Azure Active Directory
  client_id = "<>"
  # can be obtained by registering an application under Azure Active Directory.
  # If not specified Default Azure Credentials chain will be attempted:
  # - Environment credentials (AZURE_*)
  # - Workload Identity in Kubernetes cluster
  # - Managed Identity
  # - Azure CLI auth
  # - Developer Azure CLI auth
  client_secret = "<>"
  # can be found under Azure Active Directory->Properties
  tenant_id = "<>"
  # Define the optional Azure cloud option e.g. AzureChina, AzureGovernment or AzurePublic. The default is AzurePublic.
  # cloud_option = "AzurePublic"

  # resource target #1 to collect metrics from
  [[inputs.azure_monitor.resource_target]]
    # can be found under Overview->Essentials->JSON View in the Azure portal for your application/service
    # must start with 'resourceGroups/...' ('/subscriptions/xxxxxxxx-xxxx-xxxx-xxx-xxxxxxxxxxxx'
    # must be removed from the beginning of Resource ID property value)
    resource_id = "<>"
    # the metric names to collect
    # leave the array empty to use all metrics available to this resource
    metrics = [ "<>", "<>" ]
    # metrics aggregation type value to collect
    # can be 'Total', 'Count', 'Average', 'Minimum', 'Maximum'
    # leave the array empty to collect all aggregation types values for each metric
    aggregations = [ "<>", "<>" ]

  # resource target #2 to collect metrics from
  [[inputs.azure_monitor.resource_target]]
    resource_id = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # resource group target #1 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.resource_group_target]]
    # the resource group name
    resource_group = "<>"

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      # the resource type
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # resource group target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.resource_group_target]]
    resource_group = "<>"

    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # subscription target #1 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # subscription target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]
</code></pre>

OpenSearch

[[outputs.opensearch]]
  ## URLs
  ## The full HTTP endpoint URL for your OpenSearch instance. Multiple URLs can
  ## be specified as part of the same cluster, but only one URLs is used to
  ## write during each interval.
  urls = ["http://node1.os.example.com:9200"]

  ## Index Name
  ## Target index name for metrics (OpenSearch will create if it not exists).
  ## This is a Golang template (see https://pkg.go.dev/text/template)
  ## You can also specify
  ## metric name (`{{.Name}}`), tag value (`{{.Tag "tag_name"}}`), field value (`{{.Field "field_name"}}`)
  ## If the tag does not exist, the default tag value will be empty string "".
  ## the timestamp (`{{.Time.Format "xxxxxxxxx"}}`).
  ## For example: "telegraf-{{.Time.Format \"2006-01-02\"}}-{{.Tag \"host\"}}" would set it to telegraf-2023-07-27-HostName
  index_name = ""

  ## Timeout
  ## OpenSearch client timeout
  # timeout = "5s"

  ## Sniffer
  ## Set to true to ask OpenSearch a list of all cluster nodes,
  ## thus it is not necessary to list all nodes in the urls config option
  # enable_sniffer = false

  ## GZIP Compression
  ## Set to true to enable gzip compression
  # enable_gzip = false

  ## Health Check Interval
  ## Set the interval to check if the OpenSearch nodes are available
  ## Setting to "0s" will disable the health check (not recommended in production)
  # health_check_interval = "10s"

  ## Set the timeout for periodic health checks.
  # health_check_timeout = "1s"
  ## HTTP basic authentication details.
  # username = ""
  # password = ""
  ## HTTP bearer token authentication details
  # auth_bearer_token = ""

  ## Optional TLS Config
  ## 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"
  ## Send the specified TLS server name via SNI
  # tls_server_name = "kubernetes.example.com"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## Template Config
  ## Manage templates
  ## Set to true if you want telegraf to manage its index template.
  ## If enabled it will create a recommended index template for telegraf indexes
  # manage_template = true

  ## Template Name
  ## The template name used for telegraf indexes
  # template_name = "telegraf"

  ## Overwrite Templates
  ## Set to true if you want telegraf to overwrite an existing template
  # overwrite_template = false

  ## Document ID
  ## If set to true a unique ID hash will be sent as
  ## sha256(concat(timestamp,measurement,series-hash)) string. It will enable
  ## data resend and update metric points avoiding duplicated metrics with
  ## different id's
  # force_document_id = false

  ## Value Handling
  ## Specifies the handling of NaN and Inf values.
  ## This option can have the following values:
  ##    none    -- do not modify field-values (default); will produce an error
  ##               if NaNs or infs are encountered
  ##    drop    -- drop fields containing NaNs or infs
  ##    replace -- replace with the value in "float_replacement_value" (default: 0.0)
  ##               NaNs and inf will be replaced with the given number, -inf with the negative of that number
  # float_handling = "none"
  # float_replacement_value = 0.0

  ## Pipeline Config
  ## To use a ingest pipeline, set this to the name of the pipeline you want to use.
  # use_pipeline = "my_pipeline"

  ## Pipeline Name
  ## Additionally, you can specify a tag name using the notation (`{{.Tag "tag_name"}}`)
  ## which will be used as the pipeline name (e.g. "{{.Tag \"os_pipeline\"}}").
  ## If the tag does not exist, the default pipeline will be used as the pipeline.
  ## If no default pipeline is set, no pipeline is used for the metric.
  # default_pipeline = ""

输入和输出集成示例

Azure Monitor

  1. 动态资源监控:使用 Azure Monitor 插件根据特定标准(如标签或资源类型)动态收集 Azure 资源的指标。组织可以自动化加载和卸载资源指标的过程,从而根据资源利用率模式更好地跟踪和优化性能。

  2. 多云监控集成:使用集中式监控解决方案将从 Azure Monitor 收集的指标与其他云提供商集成。这使组织可以查看和分析跨多个云部署的性能数据,从而全面了解资源性能和成本,并简化操作。

  3. 异常检测和警报:结合机器学习算法,利用通过 Azure Monitor 插件收集的指标来检测资源利用率的异常。通过建立基准性能指标并在出现偏差时自动发出警报,组织可以降低风险并在性能问题升级之前解决它们。

  4. 历史性能分析:通过将数据馈送到数据仓库解决方案中,使用收集的 Azure 指标进行历史分析。这使组织能够跟踪随时间变化的趋势,从而可以根据历史性能数据进行详细的报告和决策。

OpenSearch

  1. 时序数据的动态索引:利用 OpenSearch Telegraf 插件为时序指标动态创建索引,确保数据以有组织的方式存储,从而有利于基于时间的查询。通过使用 Go 模板定义索引模式,用户可以利用该插件创建每日或每月索引,这可以大大简化数据管理和长期检索,从而提高分析性能。

  2. 多租户应用程序的集中式日志记录:在多租户应用程序中实施 OpenSearch 插件,其中每个租户的日志都发送到单独的索引。这可以在保持数据隔离的同时,对每个租户进行有针对性的分析和监控。通过利用索引名称模板功能,用户可以自动创建租户特定的索引,这不仅简化了流程,而且还增强了租户数据的安全性和可访问性。

  3. 与机器学习集成以进行异常检测:将 OpenSearch 插件与机器学习工具结合使用,以自动检测指标数据中的异常。通过配置插件以将实时指标发送到 OpenSearch,用户可以将机器学习模型应用于传入的数据流,以识别异常值或异常模式,从而促进主动监控和快速补救措施。

  4. 使用 OpenSearch 增强监控仪表板:使用从 OpenSearch 收集的指标来创建实时仪表板,以提供对系统性能的深入了解。通过将指标馈送到 OpenSearch,组织可以利用 OpenSearch Dashboards 可视化关键性能指标,使运营团队能够快速评估健康状况和性能,并做出数据驱动的决策。

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

查看入门方法

相关集成

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HTTP 插件从一个或多个 HTTP(S) 端点收集指标。它支持各种身份验证方法和数据格式的配置选项。

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Kafka 和 InfluxDB 集成

此插件从 Kafka 读取消息,并允许基于这些消息创建指标。它支持各种配置,包括不同的 Kafka 设置和消息处理选项。

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Kinesis 和 InfluxDB 集成

Kinesis 插件允许从 AWS Kinesis 流读取指标。它支持多种输入数据格式,并提供 DynamoDB 的检查点功能,以实现可靠的消息处理。

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