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。
Sumo Logic 插件旨在促进将指标从 Telegraf 发送到 Sumo Logic 的 HTTP 源。通过使用此插件,用户可以在 Sumo Logic 平台上分析他们的指标数据,并利用各种输出数据格式。
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 监控指标集成到他们的监控堆栈中。此 API 提供了关于在 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健康和性能,并协助团队根据历史和当前的性能统计数据做出数据驱动的决策。
Sumo Logic
此插件有助于将指标传输到 Sumo Logic 的 HTTP 源,并采用指定的 HTTP 消息数据格式。Telegraf 必须是 1.16.0 或更高版本,可以发送以多种格式编码的指标,包括 graphite、carbon2 和 prometheus。这些格式对应于 Sumo Logic 识别的不同内容类型,确保指标被正确解释以进行分析。与 Sumo Logic 的集成允许用户利用全面的分析平台,从而能够从他们的指标数据中获得丰富的可视化效果和见解。该插件提供了配置选项,例如设置 HTTP 指标源的 URL、选择数据格式以及指定超时和请求大小等附加参数,从而增强了数据监控工作流程的灵活性和控制力。
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>
Sumo Logic
[[outputs.sumologic]]
## Unique URL generated for your HTTP Metrics Source.
## This is the address to send metrics to.
# url = "https://events.sumologic.net/receiver/v1/http/"
## Data format to be used for sending metrics.
## This will set the "Content-Type" header accordingly.
## Currently supported formats:
## * graphite - for Content-Type of application/vnd.sumologic.graphite
## * carbon2 - for Content-Type of application/vnd.sumologic.carbon2
## * prometheus - for Content-Type of application/vnd.sumologic.prometheus
##
## More information can be found at:
## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#content-type-headers-for-metrics
##
## NOTE:
## When unset, telegraf will by default use the influx serializer which is currently unsupported
## in HTTP Source.
data_format = "carbon2"
## Timeout used for HTTP request
# timeout = "5s"
## Max HTTP request body size in bytes before compression (if applied).
## By default 1MB is recommended.
## NOTE:
## Bear in mind that in some serializer a metric even though serialized to multiple
## lines cannot be split any further so setting this very low might not work
## as expected.
# max_request_body_size = 1000000
## Additional, Sumo specific options.
## Full list can be found here:
## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#supported-http-headers
## Desired source name.
## Useful if you want to override the source name configured for the source.
# source_name = ""
## Desired host name.
## Useful if you want to override the source host configured for the source.
# source_host = ""
## Desired source category.
## Useful if you want to override the source category configured for the source.
# source_category = ""
## Comma-separated key=value list of dimensions to apply to every metric.
## Custom dimensions will allow you to query your metrics at a more granular level.
# dimensions = ""
</code></pre>
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 的指标导入个性化仪表板,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。此功能允许团队通过提供即时通知来对性能下降或中断做出快速响应,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。这种跨平台可见性帮助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:通过随时间推移收集历史指标,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。
Sumo Logic
-
实时系统监控仪表板:利用 Sumo Logic 插件将来自服务器的性能指标持续馈送到 Sumo Logic 仪表板中。此设置允许技术团队实时可视化系统健康状况和负载,从而通过详细的图表和指标更快地识别任何性能瓶颈或系统故障。
-
自动化警报系统:配置插件以发送在 Sumo Logic 中为特定阈值(如 CPU 使用率或内存消耗)触发警报的指标。通过设置自动化警报,团队可以在问题升级为严重故障之前主动解决问题,从而显着提高响应时间和整体系统可靠性。
-
跨系统指标聚合:跨不同环境(开发、测试、生产)集成多个 Telegraf 实例,并使用此插件将所有指标导入中央 Sumo Logic 实例。这种聚合支持跨环境的全面分析,从而在整个软件开发生命周期中实现更好的监控和知情的决策。
-
带有维度跟踪的自定义指标:使用 Sumo Logic 插件发送自定义指标,其中包括标识基础设施各个方面的维度(例如,环境、服务类型)。这种精细的跟踪允许进行更定制的分析,使您的团队能够剖析跨不同应用程序层或业务功能的性能。
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