目录
输入和输出集成概述
此插件可以通过 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运行状况。
此插件允许使用 Metrics API 将指标发送到 New Relic Insights,从而实现对应用程序性能的有效监控和分析。
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、正常运行时间和运营指标。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。这种集成有助于更顺畅地维护云资源的运行状况和性能,并帮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。
New Relic
此插件利用 Metrics API 将指标写入 New Relic Insights,Metrics API 提供了一种将时序数据发送到 New Relic 平台的强大机制。用户必须首先获取 Insights API 密钥才能验证和授权其数据提交。该插件旨在促进与 New Relic 的监控和分析功能轻松集成,支持各种指标类型并允许高效的数据处理。核心功能包括为指标添加前缀以更好地识别、API 请求的可自定义超时以及对代理设置的支持以增强连接性。用户必须根据其需求配置这些选项,以实现数据无缝流入 New Relic,从而进行全面的实时分析和洞察。
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>
New Relic
[[outputs.newrelic]]
## The 'insights_key' parameter requires a NR license key.
## New Relic recommends you create one
## with a convenient name such as TELEGRAF_INSERT_KEY.
## reference: https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#ingest-license-key
# insights_key = "New Relic License Key Here"
## Prefix to add to add to metric name for easy identification.
## This is very useful if your metric names are ambiguous.
# metric_prefix = ""
## Timeout for writes to the New Relic API.
# timeout = "15s"
## HTTP Proxy override. If unset use values from the standard
## proxy environment variables to determine proxy, if any.
# http_proxy = "http://corporate.proxy:3128"
## Metric URL override to enable geographic location endpoints.
# If not set use values from the standard
# metric_url = "https://metric-api.newrelic.com/metric/v1"
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:借助此插件,团队可以将来自 Google Cloud 的指标导入到个性化仪表板中,从而实现对应用程序性能和资源利用率的实时监控。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实现主动管理。
-
自动警报和分析:用户可以利用插件的指标设置自动警报机制,以跟踪资源阈值。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,并将其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。
-
用于容量规划的历史数据分析:通过长期收集历史指标,该插件使团队能够进行全面的容量规划。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和投资策略。
New Relic
-
应用程序性能监控:使用 New Relic Telegraf 插件将来自 Web 服务的应用程序性能指标发送到 New Relic Insights。通过集成此插件,开发人员可以收集诸如响应时间、错误率和吞吐量之类的数据,使团队能够实时监控应用程序的运行状况并在问题影响用户之前快速解决问题。此设置有助于主动管理应用程序性能和用户体验。
-
基础设施指标聚合:利用此插件聚合和发送来自各种服务器的系统级指标(CPU 使用率、内存消耗等)到 New Relic。这有助于系统管理员保持对基础设施性能的全面视图,从而促进容量规划和识别潜在瓶颈。通过在 New Relic 中集中指标,团队可以可视化长期趋势并就资源分配做出明智的决策。
-
多租户应用程序的动态指标命名:使用 metric_prefix 选项实现动态前缀,以区分多租户应用程序中的不同租户。通过配置插件以在指标名称中包含每个租户的唯一标识符,团队可以分析每个租户的使用模式和性能指标。这提供了对租户行为的宝贵见解,支持定制优化并提高不同客户群的服务质量。
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实时异常检测:将 New Relic 插件与警报机制相结合,以根据异常指标模式触发通知。通过发送诸如请求计数和响应时间之类的指标,团队可以在 New Relic 中设置阈值,当阈值被违反时,将自动警告责任方。这种用户驱动的方法支持对潜在问题做出即时响应,防止其升级为更大的事件。
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