目录
输入和输出集成概述
此插件允许通过 Stackdriver Monitoring API 从 Google Cloud 服务收集监控数据。它旨在通过收集相关指标来帮助用户监控其云基础设施的性能和健康状况。
Dynatrace 插件允许用户将 Telegraf 收集的指标直接发送到 Dynatrace 进行监控和分析。此集成增强了系统和应用程序的可观测性,为性能和运行状况提供了宝贵的见解。
集成详情
Google Cloud Stackdriver
Stackdriver Telegraf 插件允许用户使用 Cloud Monitoring API v3 从 Google Cloud Monitoring 查询时序数据。借助此插件,用户可以轻松地将 Google Cloud 监控指标集成到其监控堆栈中。此 API 提供了关于 Google Cloud 中运行的资源和应用程序的大量见解,包括性能、正常运行时间和运行指标。该插件支持各种配置选项来过滤和优化检索到的数据,使用户能够根据其特定需求自定义其监控设置。此集成有助于更顺畅地维护云资源的健康和性能,并协助团队根据历史和当前性能统计数据做出数据驱动的决策。
Dynatrace
Telegraf 的 Dynatrace 插件有助于通过 Dynatrace Metrics API V2 将指标传输到 Dynatrace 平台。此插件可以在两种模式下运行:它可以与 Dynatrace OneAgent 一起运行,后者自动化身份验证;或者它可以在独立配置中运行,对于没有 OneAgent 的环境,需要手动指定 URL 和 API 令牌。除非显式配置为使用可用的配置选项将某些指标视为增量计数器,否则该插件主要将指标报告为计量器。此功能使用户能够自定义发送到 Dynatrace 的指标的行为,从而利用该平台的强大功能进行全面的性能监控和可观测性。对于用户而言,确保 Dynatrace 和 Telegraf 都符合版本要求至关重要,从而在与 Dynatrace 生态系统集成时优化兼容性和性能。
配置
Google Cloud Stackdriver
[[inputs.stackdriver]]
## GCP Project
project = "erudite-bloom-151019"
## Include timeseries that start with the given metric type.
metric_type_prefix_include = [
"compute.googleapis.com/",
]
## Exclude timeseries that start with the given metric type.
# metric_type_prefix_exclude = []
## Most metrics are updated no more than once per minute; it is recommended
## to override the agent level interval with a value of 1m or greater.
interval = "1m"
## Maximum number of API calls to make per second. The quota for accounts
## varies, it can be viewed on the API dashboard:
## https://cloud.google.com/monitoring/quotas#quotas_and_limits
# rate_limit = 14
## The delay and window options control the number of points selected on
## each gather. When set, metrics are gathered between:
## start: now() - delay - window
## end: now() - delay
#
## Collection delay; if set too low metrics may not yet be available.
# delay = "5m"
#
## If unset, the window will start at 1m and be updated dynamically to span
## the time between calls (approximately the length of the plugin interval).
# window = "1m"
## TTL for cached list of metric types. This is the maximum amount of time
## it may take to discover new metrics.
# cache_ttl = "1h"
## If true, raw bucket counts are collected for distribution value types.
## For a more lightweight collection, you may wish to disable and use
## distribution_aggregation_aligners instead.
# gather_raw_distribution_buckets = true
## Aggregate functions to be used for metrics whose value type is
## distribution. These aggregate values are recorded in in addition to raw
## bucket counts; if they are enabled.
##
## For a list of aligner strings see:
## https://cloud.google.com/monitoring/api/ref_v3/rpc/google.monitoring.v3#aligner
# distribution_aggregation_aligners = [
# "ALIGN_PERCENTILE_99",
# "ALIGN_PERCENTILE_95",
# "ALIGN_PERCENTILE_50",
# ]
## Filters can be added to reduce the number of time series matched. All
## functions are supported: starts_with, ends_with, has_substring, and
## one_of. Only the '=' operator is supported.
##
## The logical operators when combining filters are defined statically using
## the following values:
## filter ::= {AND AND AND }
## resource_labels ::= {OR }
## metric_labels ::= {OR }
## user_labels ::= {OR }
## system_labels ::= {OR }
##
## For more details, see https://cloud.google.com/monitoring/api/v3/filters
#
## Resource labels refine the time series selection with the following expression:
## resource.labels. =
# [[inputs.stackdriver.filter.resource_labels]]
# key = "instance_name"
# value = 'starts_with("localhost")'
#
## Metric labels refine the time series selection with the following expression:
## metric.labels. =
# [[inputs.stackdriver.filter.metric_labels]]
# key = "device_name"
# value = 'one_of("sda", "sdb")'
#
## User labels refine the time series selection with the following expression:
## metadata.user_labels."" =
# [[inputs.stackdriver.filter.user_labels]]
# key = "environment"
# value = 'one_of("prod", "staging")'
#
## System labels refine the time series selection with the following expression:
## metadata.system_labels."" =
# [[inputs.stackdriver.filter.system_labels]]
# key = "machine_type"
# value = 'starts_with("e2-")'
</code></pre>
Dynatrace
[[outputs.dynatrace]]
## For usage with the Dynatrace OneAgent you can omit any configuration,
## the only requirement is that the OneAgent is running on the same host.
## Only setup environment url and token if you want to monitor a Host without the OneAgent present.
##
## Your Dynatrace environment URL.
## For Dynatrace OneAgent you can leave this empty or set it to "http://127.0.0.1:14499/metrics/ingest" (default)
## For Dynatrace SaaS environments the URL scheme is "https://{your-environment-id}.live.dynatrace.com/api/v2/metrics/ingest"
## For Dynatrace Managed environments the URL scheme is "https://{your-domain}/e/{your-environment-id}/api/v2/metrics/ingest"
url = ""
## Your Dynatrace API token.
## Create an API token within your Dynatrace environment, by navigating to Settings > Integration > Dynatrace API
## The API token needs data ingest scope permission. When using OneAgent, no API token is required.
api_token = ""
## Optional prefix for metric names (e.g.: "telegraf")
prefix = "telegraf"
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Optional flag for ignoring tls certificate check
# insecure_skip_verify = false
## Connection timeout, defaults to "5s" if not set.
timeout = "5s"
## If you want metrics to be treated and reported as delta counters, add the metric names here
additional_counters = [ ]
## In addition or as an alternative to additional_counters, if you want metrics to be treated and
## reported as delta counters using regular expression pattern matching
additional_counters_patterns = [ ]
## NOTE: Due to the way TOML is parsed, tables must be at the END of the
## plugin definition, otherwise additional config options are read as part of the
## table
## Optional dimensions to be added to every metric
# [outputs.dynatrace.default_dimensions]
# default_key = "default value"
输入和输出集成示例
Google Cloud Stackdriver
-
将云指标集成到自定义仪表板中:借助此插件,团队可以将来自 Google Cloud 的指标输入到个性化仪表板中,从而可以实时监控应用程序性能和资源利用率。通过自定义云指标的可视化表示,运营团队可以轻松识别趋势和异常,从而在问题升级之前进行主动管理。
-
自动化警报和分析:用户可以设置自动化警报机制,利用插件的指标来跟踪资源阈值。此功能使团队能够通过提供即时通知来快速响应性能下降或中断,从而缩短平均恢复时间并确保持续的运营效率。
-
跨平台资源比较:该插件可用于从各种 Google Cloud 服务中提取指标,并将它们与本地资源进行比较。这种跨平台可见性有助于组织就资源分配和扩展策略做出明智的决策,并优化云支出与本地基础设施的对比。
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用于容量规划的历史数据分析:通过随着时间的推移收集历史指标,该插件使团队能够进行彻底的容量规划。了解过去的性能趋势有助于准确预测资源需求,从而实现更好的预算编制和投资策略。
Dynatrace
-
云基础设施监控:利用 Dynatrace 插件来监控云基础设施设置,将来自 Telegraf 的实时指标馈送到 Dynatrace。此集成提供了资源利用率、应用程序性能和系统运行状况的整体视图,从而能够主动响应各种云环境中的性能问题。
-
自定义应用程序性能指标:通过配置 Dynatrace 输出插件以发送来自 Telegraf 的定制指标来实施自定义应用程序特定指标。通过利用额外的计数器和维度选项,开发团队可以获得与应用程序运营需求精确对齐的见解,从而实现有针对性的优化工作。
-
多环境指标管理:对于运行多个 Dynatrace 环境(例如,生产、暂存和开发)的组织,请使用此插件从单个 Telegraf 实例管理所有环境的指标。通过正确配置端点和 API 令牌,团队可以在整个 SDLC 中保持一致的监控实践,确保在开发过程的早期检测到性能异常。
-
基于指标变化的自动化警报:将 Dynatrace 输出插件与警报机制集成,该机制在特定指标超过定义的阈值时触发通知。此场景涉及配置额外的计数器来监控关键应用程序性能指标,从而能够快速采取补救措施以维持服务可用性和用户满意度。
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