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。使用 InfluxDB,排名第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法
输入和输出集成概述
此插件能够通过 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。
Telegraf PostgreSQL 插件允许您高效地将指标写入 PostgreSQL 数据库,同时自动管理数据库模式。
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 提供了关于在 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性能统计数据做出数据驱动的决策。
PostgreSQL
PostgreSQL 插件使用户能够将指标写入 PostgreSQL 数据库或兼容数据库,通过自动更新缺失的列来提供对模式管理的强大支持。该插件旨在促进与监控解决方案的集成,允许用户高效地存储和管理时间序列数据。它为连接设置、并发和错误处理提供可配置选项,并支持 JSONB 存储标签和字段、外键标记、模板化模式修改以及通过 pguint 扩展支持无符号整数数据类型等高级功能。
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>
PostgreSQL
# Publishes metrics to a postgresql database
[[outputs.postgresql]]
## Specify connection address via the standard libpq connection string:
## host=... user=... password=... sslmode=... dbname=...
## Or a URL:
## postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
## See https://postgresql.ac.cn/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
##
## All connection parameters are optional. Environment vars are also supported.
## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
## All supported vars can be found here:
## https://postgresql.ac.cn/docs/current/libpq-envars.html
##
## Non-standard parameters:
## pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
## pool_min_conns (default: 0) - Minimum size of connection pool.
## pool_max_conn_lifetime (default: 0s) - Maximum age of a connection before closing.
## pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
## pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
# connection = ""
## Postgres schema to use.
# schema = "public"
## Store tags as foreign keys in the metrics table. Default is false.
# tags_as_foreign_keys = false
## Suffix to append to table name (measurement name) for the foreign tag table.
# tag_table_suffix = "_tag"
## Deny inserting metrics if the foreign tag can't be inserted.
# foreign_tag_constraint = false
## Store all tags as a JSONB object in a single 'tags' column.
# tags_as_jsonb = false
## Store all fields as a JSONB object in a single 'fields' column.
# fields_as_jsonb = false
## Name of the timestamp column
## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
# timestamp_column_name = "time"
## Type of the timestamp column
## Currently, "timestamp without time zone" and "timestamp with time zone"
## are supported
# timestamp_column_type = "timestamp without time zone"
## Templated statements to execute when creating a new table.
# create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }})''',
# ]
## Templated statements to execute when adding columns to a table.
## Set to an empty list to disable. Points containing tags for which there is no column will be skipped. Points
## containing fields for which there is no column will have the field omitted.
# add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## Templated statements to execute when creating a new tag table.
# tag_table_create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
# ]
## Templated statements to execute when adding columns to a tag table.
## Set to an empty list to disable. Points containing tags for which there is no column will be skipped.
# tag_table_add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## The postgres data type to use for storing unsigned 64-bit integer values (Postgres does not have a native
## unsigned 64-bit integer type).
## The value can be one of:
## numeric - Uses the PostgreSQL "numeric" data type.
## uint8 - Requires pguint extension (https://github.com/petere/pguint)
# uint64_type = "numeric"
## When using pool_max_conns>1, and a temporary error occurs, the query is retried with an incremental backoff. This
## controls the maximum backoff duration.
# retry_max_backoff = "15s"
## Approximate number of tag IDs to store in in-memory cache (when using tags_as_foreign_keys).
## This is an optimization to skip inserting known tag IDs.
## Each entry consumes approximately 34 bytes of memory.
# tag_cache_size = 100000
## Enable & set the log level for the Postgres driver.
# log_level = "warn" # trace, debug, info, warn, error, none
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,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:该插件可用于提取来自各种 Google Cloud 服务的指标,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。
PostgreSQL
-
使用复杂查询进行实时分析:利用 PostgreSQL 插件将来自各种来源的指标存储在 PostgreSQL 数据库中,从而可以使用复杂查询进行实时分析。这种设置可以帮助数据科学家和分析师发现模式和趋势,因为他们可以在利用 PostgreSQL 强大的查询优化功能的同时,跨多个表操作关系数据。具体来说,用户可以使用跨不同指标表的 JOIN 操作创建复杂的报告,从而揭示通常隐藏在嵌入式系统中的见解。
-
与 TimescaleDB 集成以进行时间序列数据处理:在 TimescaleDB 实例中使用 PostgreSQL 插件,以高效地处理和分析时间序列数据。通过实施超表,用户可以在时间维度上实现更高的性能和主题分区。这种集成允许用户在处理大量时间序列数据的同时运行分析查询,同时保留 PostgreSQL 的 SQL 查询的全部功能,从而确保指标分析的可靠性和效率。
-
数据版本控制和历史分析:实施使用 PostgreSQL 插件的策略,以维护指标的不同版本。用户可以设置不可变的数据表结构,其中保留旧版本的表,从而可以轻松进行历史分析。这种方法不仅提供了对数据演变的见解,还有助于遵守数据保留策略,确保数据集的历史完整性保持不变。
-
用于不断发展的指标的动态模式管理:使用插件的模板功能来创建动态变化的模式,以响应指标变化。此用例允许组织随着指标的发展调整其数据结构,添加必要的字段并确保遵守数据完整性策略。通过利用模板化的 SQL 命令,用户无需手动干预即可扩展其数据库,从而促进敏捷数据管理实践。
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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。使用 InfluxDB,排名第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法