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间序列数据时,它都更有价值。借助 InfluxDB,排名第一的、旨在与 Telegraf 一起扩展的时间序列平台。
查看入门方法
输入和输出集成概述
此插件支持通过 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。
InfluxDB 插件将指标写入 InfluxDB HTTP 服务,从而实现时间序列数据的高效存储和检索。
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 查询时间序列数据。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 提供了有关 Google Cloud 中运行的资源和应用程序的丰富见解,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筛选和优化检索到的数据,使用户可以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。
InfluxDB
InfluxDB Telegraf 插件用于将指标发送到 InfluxDB HTTP API,从而以结构化方式促进时间序列数据的存储和查询。此插件与 InfluxDB 无缝集成,提供诸如基于令牌的身份验证以及对多个 InfluxDB 集群节点的支持等基本功能,从而确保可靠且可扩展的数据摄取。通过其可配置性,用户可以指定诸如组织、目标存储桶和 HTTP 特定设置之类的选项,从而灵活地定制数据的发送和存储方式。该插件还支持敏感数据的密钥管理,从而增强了生产环境中的安全性。此插件在现代可观测性堆栈中特别有用,在这些堆栈中,实时分析和时间序列数据的存储至关重要。
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>
InfluxDB
[[outputs.influxdb]]
## The full HTTP or UDP URL for your InfluxDB instance.
##
## Multiple URLs can be specified for a single cluster, only ONE of the
## urls will be written to each interval.
# urls = ["unix:///var/run/influxdb.sock"]
# urls = ["udp://127.0.0.1:8089"]
# urls = ["http://127.0.0.1:8086"]
## Local address to bind when connecting to the server
## If empty or not set, the local address is automatically chosen.
# local_address = ""
## The target database for metrics; will be created as needed.
## For UDP url endpoint database needs to be configured on server side.
# database = "telegraf"
## The value of this tag will be used to determine the database. If this
## tag is not set the 'database' option is used as the default.
# database_tag = ""
## If true, the 'database_tag' will not be included in the written metric.
# exclude_database_tag = false
## If true, no CREATE DATABASE queries will be sent. Set to true when using
## Telegraf with a user without permissions to create databases or when the
## database already exists.
# skip_database_creation = false
## Name of existing retention policy to write to. Empty string writes to
## the default retention policy. Only takes effect when using HTTP.
# retention_policy = ""
## The value of this tag will be used to determine the retention policy. If this
## tag is not set the 'retention_policy' option is used as the default.
# retention_policy_tag = ""
## If true, the 'retention_policy_tag' will not be included in the written metric.
# exclude_retention_policy_tag = false
## Write consistency (clusters only), can be: "any", "one", "quorum", "all".
## Only takes effect when using HTTP.
# write_consistency = "any"
## Timeout for HTTP messages.
# timeout = "5s"
## HTTP Basic Auth
# username = "telegraf"
# password = "metricsmetricsmetricsmetrics"
## HTTP User-Agent
# user_agent = "telegraf"
## UDP payload size is the maximum packet size to send.
# udp_payload = "512B"
## Optional TLS Config for use on HTTP connections.
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## HTTP Proxy override, if unset values the standard proxy environment
## variables are consulted to determine which proxy, if any, should be used.
# http_proxy = "http://corporate.proxy:3128"
## Additional HTTP headers
# http_headers = {"X-Special-Header" = "Special-Value"}
## HTTP Content-Encoding for write request body, can be set to "gzip" to
## compress body or "identity" to apply no encoding.
# content_encoding = "gzip"
## When true, Telegraf will output unsigned integers as unsigned values,
## i.e.: "42u". You will need a version of InfluxDB supporting unsigned
## integer values. Enabling this option will result in field type errors if
## existing data has been written.
# influx_uint_support = false
## When true, Telegraf will omit the timestamp on data to allow InfluxDB
## to set the timestamp of the data during ingestion. This is generally NOT
## what you want as it can lead to data points captured at different times
## getting omitted due to similar data.
# influx_omit_timestamp = false
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 Google Cloud 中的指标导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。
-
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。了解过去绩效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。
InfluxDB
-
实时系统监控:利用 InfluxDB 插件捕获和存储来自各种系统组件的指标,例如 CPU 使用率、内存消耗和磁盘 I/O。通过将这些指标推送到 InfluxDB 中,您可以创建一个实时仪表板,以可视化系统性能。此设置不仅有助于识别性能瓶颈,还可以通过分析随时间推移的趋势来协助主动容量规划。
-
Web 应用程序的性能跟踪:自动收集与 Web 应用程序性能相关的指标(例如请求持续时间、错误率和用户交互),并将其推送到 InfluxDB。通过在您的监控堆栈中使用此插件,您可以使用存储的指标生成报告和分析,以帮助了解用户行为和应用程序效率,从而指导开发和优化工作。
-
物联网数据聚合:利用 InfluxDB Telegraf 插件从各种物联网设备收集传感器数据,并将其存储在集中的 InfluxDB 实例中。此用例使您可以分析环境或机器数据随时间推移的趋势和模式,从而促进更智能的决策和预测性维护策略。通过将物联网数据集成到 InfluxDB 中,组织可以利用历史数据分析的力量来推动创新和运营效率。
-
分析用于预测的历史指标:设置 InfluxDB 插件以将历史指标数据发送到 InfluxDB,并使用它来驱动预测模型。通过分析过去的性能指标,您可以创建预测未来趋势和需求的预测模型。此应用程序对于商业智能目的特别有用,可帮助组织根据历史使用模式为资源需求的波动做好准备。
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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间序列数据时,它都更有价值。借助 InfluxDB,排名第一的、旨在与 Telegraf 一起扩展的时间序列平台。
查看入门方法