目录
输入和输出集成概述
Webhooks 插件允许 Telegraf 通过 webhooks 接收和处理来自各种外部服务的 HTTP 请求。此插件使用户能够收集实时指标和事件,并将它们集成到他们的监控解决方案中。
AWS Timestream Telegraf 插件使用户能够将指标直接发送到 Amazon 的 Timestream 服务,该服务专为时序数据管理而设计。此插件提供了多种配置选项,用于身份验证、数据组织和保留设置。
集成详情
Webhooks
此 Telegraf 插件旨在充当 webhook 监听器,通过启动一个 HTTP 服务器来注册多个 webhook 端点。它提供了一种通过捕获发送到定义路径的 HTTP 请求来收集来自各种服务事件的方法。每个服务都可以配置其特定的身份验证详细信息和请求处理选项。该插件的突出之处在于允许与任何 Telegraf 输出插件集成,使其在事件驱动的架构中非常通用。通过实现事件的高效接收,它为实时监控和响应系统开辟了可能性,这对于需要即时事件处理的现代应用程序至关重要。
AWS Timestream
此插件旨在高效地将指标写入 Amazon 的 Timestream 服务,这是一种专为物联网和运营应用程序优化的时序数据库。借助此插件,Telegraf 可以发送从各种来源收集的数据,并支持灵活的配置,用于身份验证、数据组织和保留管理。它利用凭证链进行身份验证,允许各种方法,如 Web 身份、承担角色和共享配置文件。用户可以定义指标在 Timestream 中的组织方式——无论是使用单表还是多表,以及对磁存储和内存存储的保留期等方面的控制。一个关键特性是它能够处理多指标记录,从而实现高效的数据摄取并帮助减少多次写入的开销。在错误处理方面,该插件包含用于解决数据写入期间与 AWS 错误相关的常见问题的机制,例如用于节流的重试逻辑和根据需要创建表的能力。
配置
Webhooks
[[inputs.webhooks]]
## Address and port to host Webhook listener on
service_address = ":1619"
## Maximum duration before timing out read of the request
# read_timeout = "10s"
## Maximum duration before timing out write of the response
# write_timeout = "10s"
[inputs.webhooks.filestack]
path = "/filestack"
## HTTP basic auth
#username = ""
#password = ""
[inputs.webhooks.github]
path = "/github"
# secret = ""
## HTTP basic auth
#username = ""
#password = ""
[inputs.webhooks.mandrill]
path = "/mandrill"
## HTTP basic auth
#username = ""
#password = ""
[inputs.webhooks.rollbar]
path = "/rollbar"
## HTTP basic auth
#username = ""
#password = ""
[inputs.webhooks.papertrail]
path = "/papertrail"
## HTTP basic auth
#username = ""
#password = ""
[inputs.webhooks.particle]
path = "/particle"
## HTTP basic auth
#username = ""
#password = ""
[inputs.webhooks.artifactory]
path = "/artifactory"
AWS Timestream
[[outputs.timestream]]
## Amazon Region
region = "us-east-1"
## Amazon Credentials
## Credentials are loaded in the following order:
## 1) Web identity provider credentials via STS if role_arn and web_identity_token_file are specified
## 2) Assumed credentials via STS if role_arn is specified
## 3) explicit credentials from 'access_key' and 'secret_key'
## 4) shared profile from 'profile'
## 5) environment variables
## 6) shared credentials file
## 7) EC2 Instance Profile
#access_key = ""
#secret_key = ""
#token = ""
#role_arn = ""
#web_identity_token_file = ""
#role_session_name = ""
#profile = ""
#shared_credential_file = ""
## Endpoint to make request against, the correct endpoint is automatically
## determined and this option should only be set if you wish to override the
## default.
## ex: endpoint_url = "http://localhost:8000"
# endpoint_url = ""
## Timestream database where the metrics will be inserted.
## The database must exist prior to starting Telegraf.
database_name = "yourDatabaseNameHere"
## Specifies if the plugin should describe the Timestream database upon starting
## to validate if it has access necessary permissions, connection, etc., as a safety check.
## If the describe operation fails, the plugin will not start
## and therefore the Telegraf agent will not start.
describe_database_on_start = false
## Specifies how the data is organized in Timestream.
## Valid values are: single-table, multi-table.
## When mapping_mode is set to single-table, all of the data is stored in a single table.
## When mapping_mode is set to multi-table, the data is organized and stored in multiple tables.
## The default is multi-table.
mapping_mode = "multi-table"
## Specifies if the plugin should create the table, if the table does not exist.
create_table_if_not_exists = true
## Specifies the Timestream table magnetic store retention period in days.
## Check Timestream documentation for more details.
## NOTE: This property is valid when create_table_if_not_exists = true.
create_table_magnetic_store_retention_period_in_days = 365
## Specifies the Timestream table memory store retention period in hours.
## Check Timestream documentation for more details.
## NOTE: This property is valid when create_table_if_not_exists = true.
create_table_memory_store_retention_period_in_hours = 24
## Specifies how the data is written into Timestream.
## Valid values are: true, false
## When use_multi_measure_records is set to true, all of the tags and fields are stored
## as a single row in a Timestream table.
## When use_multi_measure_record is set to false, Timestream stores each field in a
## separate table row, thereby storing the tags multiple times (once for each field).
## The recommended setting is true.
## The default is false.
use_multi_measure_records = "false"
## Specifies the measure_name to use when sending multi-measure records.
## NOTE: This property is valid when use_multi_measure_records=true and mapping_mode=multi-table
measure_name_for_multi_measure_records = "telegraf_measure"
## Specifies the name of the table to write data into
## NOTE: This property is valid when mapping_mode=single-table.
# single_table_name = ""
## Specifies the name of dimension when all of the data is being stored in a single table
## and the measurement name is transformed into the dimension value
## (see Mapping data from Influx to Timestream for details)
## NOTE: This property is valid when mapping_mode=single-table.
# single_table_dimension_name_for_telegraf_measurement_name = "namespace"
## Only valid and optional if create_table_if_not_exists = true
## Specifies the Timestream table tags.
## Check Timestream documentation for more details
# create_table_tags = { "foo" = "bar", "environment" = "dev"}
## Specify the maximum number of parallel go routines to ingest/write data
## If not specified, defaulted to 1 go routines
max_write_go_routines = 25
## Please see README.md to know how line protocol data is mapped to Timestream
##
输入和输出集成示例
Webhooks
-
来自 Github 的实时通知:将 Webhooks 输入插件与 Github 集成,以接收有关事件的实时通知,例如拉取请求、提交和问题。这使开发团队能够立即监控其存储库中的关键更改和更新,从而改善协作和响应时间。
-
使用 Rollbar 进行自动化警报:使用此插件监听从 Rollbar 报告的错误,使团队能够快速响应生产中的错误和问题。通过将这些警报转发到集中式监控系统,团队可以根据严重程度优先处理他们的响应,并防止停机时间升级。
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来自 Filestack 的性能监控:捕获来自 Filestack 的事件,以跟踪文件上传、转换和错误。此设置有助于企业了解用户与文件管理流程的交互,优化工作流程,并确保文件服务的高可用性。
-
使用 Papertrail 进行集中日志记录:通过 webhooks 关联发送到 Papertrail 的所有日志,使您可以整合您的日志记录策略。借助实时日志转发,团队可以高效地分析趋势和异常,确保他们保持对关键操作的可见性。
AWS Timestream
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物联网数据指标:使用 Timestream 插件将来自物联网设备的实时指标发送到 Timestream,从而可以快速分析和可视化传感器数据。通过将设备读数组织成时序格式,用户可以跟踪趋势、识别异常,并根据设备性能简化运营决策。
-
应用程序性能监控:利用 Timestream 以及应用程序监控工具来发送有关服务性能随时间变化的指标。此集成使工程师能够执行应用程序性能的历史分析,将其与业务指标相关联,并根据随时间推移的使用模式优化资源分配。
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自动化数据归档:配置 Timestream 插件以将数据写入 Timestream,同时管理保留期。此设置可以自动化归档策略,确保根据预定义的标准保留旧数据。这对于合规性和历史分析尤其有用,使企业能够以最少的人工干预来维护其数据生命周期。
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多应用程序指标聚合:利用 Timestream 插件将来自多个应用程序的指标聚合到 Timestream 中。通过创建统一的性能指标数据库,组织可以获得跨各种服务的整体洞察力,从而提高对系统范围性能的可见性并促进跨应用程序故障排除。
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