目录
输入和输出集成概述
HTTP 插件允许从指定的 HTTP 端点收集指标,处理各种数据格式和身份验证方法。
Azure 数据资源管理器插件允许将指标收集与 Azure 数据资源管理器集成,使用户能够高效地分析和查询他们的遥测数据。借助此插件,用户可以配置摄取设置以满足他们的需求,并利用 Azure 强大的分析功能。
集成详情
HTTP
HTTP 插件从一个或多个 HTTP(S) 端点收集指标,这些端点应具有以支持的输入数据格式之一格式化的指标。它还支持来自 secret-stores 的密钥,用于各种身份验证选项,并包括全局支持的配置设置。
Azure 数据资源管理器
Azure 数据资源管理器插件允许用户将从各种 Telegraf 输入插件收集的指标、日志和时序数据写入 Azure 数据资源管理器、Azure Synapse 和 Fabric 中的实时分析。此集成充当桥梁,使应用程序和服务能够有效地监控其性能指标或日志。Azure 数据资源管理器针对大量不同数据类型的分析进行了优化,使其成为云环境中实时分析和监控解决方案的绝佳选择。该插件使用户能够根据其需求配置指标摄取,动态定义表架构,并设置各种摄取方法,同时保留数据库操作所需的角色和权限方面的灵活性。这支持利用云服务的现代应用程序的可扩展和安全的监控设置。
配置
HTTP
[[inputs.http]]
## One or more URLs from which to read formatted metrics.
urls = [
"http://localhost/metrics",
"http+unix:///run/user/420/podman/podman.sock:/d/v4.0.0/libpod/pods/json"
]
## HTTP method
# method = "GET"
## Optional HTTP headers
# headers = {"X-Special-Header" = "Special-Value"}
## HTTP entity-body to send with POST/PUT requests.
# body = ""
## HTTP Content-Encoding for write request body, can be set to "gzip" to
## compress body or "identity" to apply no encoding.
# content_encoding = "identity"
## Optional Bearer token settings to use for the API calls.
## Use either the token itself or the token file if you need a token.
# token = "eyJhbGc...Qssw5c"
# token_file = "/path/to/file"
## Optional HTTP Basic Auth Credentials
# username = "username"
# password = "pa$$word"
## OAuth2 Client Credentials. The options 'client_id', 'client_secret', and 'token_url' are required to use OAuth2.
# client_id = "clientid"
# client_secret = "secret"
# token_url = "https://indentityprovider/oauth2/v1/token"
# scopes = ["urn:opc:idm:__myscopes__"]
## HTTP Proxy support
# use_system_proxy = false
# http_proxy_url = ""
## Optional TLS Config
## Set to true/false to enforce TLS being enabled/disabled. If not set,
## enable TLS only if any of the other options are specified.
# tls_enable =
## Trusted root certificates for server
# tls_ca = "/path/to/cafile"
## Used for TLS client certificate authentication
# tls_cert = "/path/to/certfile"
## Used for TLS client certificate authentication
# tls_key = "/path/to/keyfile"
## Password for the key file if it is encrypted
# tls_key_pwd = ""
## Send the specified TLS server name via SNI
# tls_server_name = "kubernetes.example.com"
## Minimal TLS version to accept by the client
# tls_min_version = "TLS12"
## List of ciphers to accept, by default all secure ciphers will be accepted
## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values.
## Use "all", "secure" and "insecure" to add all support ciphers, secure
## suites or insecure suites respectively.
# tls_cipher_suites = ["secure"]
## Renegotiation method, "never", "once" or "freely"
# tls_renegotiation_method = "never"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Optional Cookie authentication
# cookie_auth_url = "https://localhost/authMe"
# cookie_auth_method = "POST"
# cookie_auth_username = "username"
# cookie_auth_password = "pa$$word"
# cookie_auth_headers = { Content-Type = "application/json", X-MY-HEADER = "hello" }
# cookie_auth_body = '{"username": "user", "password": "pa$$word", "authenticate": "me"}'
## cookie_auth_renewal not set or set to "0" will auth once and never renew the cookie
# cookie_auth_renewal = "5m"
## Amount of time allowed to complete the HTTP request
# timeout = "5s"
## List of success status codes
# success_status_codes = [200]
## Data format to consume.
## Each data format has its own unique set of configuration options, read
## more about them here:
## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
# data_format = "influx"
Azure 数据资源管理器
[[outputs.azure_data_explorer]]
## The URI property of the Azure Data Explorer resource on Azure
## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
endpoint_url = ""
## The Azure Data Explorer database that the metrics will be ingested into.
## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
## ex: "exampledatabase"
database = ""
## Timeout for Azure Data Explorer operations
# timeout = "20s"
## Type of metrics grouping used when pushing to Azure Data Explorer.
## Default is "TablePerMetric" for one table per different metric.
## For more information, please check the plugin README.
# metrics_grouping_type = "TablePerMetric"
## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
# table_name = ""
## Creates tables and relevant mapping if set to true(default).
## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
# create_tables = true
## Ingestion method to use.
## Available options are
## - managed -- streaming ingestion with fallback to batched ingestion or the "queued" method below
## - queued -- queue up metrics data and process sequentially
# ingestion_type = "queued"
输入和输出集成示例
HTTP
- 从本地主机收集指标: 该插件可以从 HTTP 端点(如
http://localhost/metrics
)获取指标,从而实现轻松的本地监控。 - 使用 Unix 域套接字: 您可以通过使用 http+unix 方案来指定从 Unix 域套接字上的服务收集指标,例如
http+unix:///path/to/service.sock:/api/endpoint
。
Azure 数据资源管理器
-
实时监控仪表板:通过使用此插件将来自各种服务的指标集成到 Azure 数据资源管理器中,组织可以构建反映实时性能指标的综合仪表板。这使团队能够主动响应性能问题并立即优化系统健康状况。
-
集中式日志管理:利用 Azure 数据资源管理器来整合来自多个应用程序和服务的日志。通过使用此插件,组织可以简化其日志分析流程,从而更轻松地搜索、过滤和从随着时间积累的历史数据中获取见解。
-
数据驱动的警报系统:通过基于通过此插件发送的指标配置警报来增强监控功能。组织可以设置阈值并自动化事件响应,从而显着减少停机时间并提高关键操作的可靠性。
-
机器学习模型训练:通过利用发送到 Azure 数据资源管理器的数据,组织可以执行大规模分析并准备数据以供输入机器学习模型。此插件支持构建数据结构,这些数据结构随后可用于预测分析,从而提高决策能力。
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