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,它会更有价值。借助 InfluxDB,这个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
此插件从 RabbitMQ 服务器读取指标,提供对消息传递系统性能和状态的基本洞察。
AWS Timestream Telegraf 插件使用户能够将指标直接发送到 Amazon 的 Timestream 服务,该服务专为时间序列数据管理而设计。此插件为身份验证、数据组织和保留设置提供了各种配置选项。
集成详情
RabbitMQ
用于 Telegraf 的 RabbitMQ 插件允许用户通过 RabbitMQ 管理插件从 RabbitMQ 服务器收集指标。此功能对于监控 RabbitMQ 实例的性能和健康状况至关重要,RabbitMQ 实例广泛用于各种应用程序中的消息队列和处理。该插件提供了对关键 RabbitMQ 指标的全面洞察,包括消息速率、队列深度和节点健康统计信息,从而使操作员能够维护其消息传递基础设施的最佳性能和稳健性。此外,它还支持用于安全管理敏感凭据的密钥存储,从而使与现有系统的集成更加顺畅。配置选项允许灵活指定要监控的节点、队列和交换机,为各种部署场景提供有价值的适应性。
AWS Timestream
此插件旨在高效地将指标写入 Amazon 的 Timestream 服务,Timestream 服务是一个针对物联网和运营应用程序优化的时间序列数据库。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。它使用凭证链进行身份验证,允许各种方法,例如 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 - 是使用单个表还是多个表,以及控制磁存储和内存存储的保留期等方面。一个关键特性是它能够处理多度量记录,从而实现高效的数据摄取并有助于减少多次写入的开销。在错误处理方面,该插件包括用于解决数据写入期间与 AWS 错误相关的常见问题的机制,例如用于限制的重试逻辑以及根据需要创建表的能力。
配置
RabbitMQ
[[inputs.rabbitmq]]
## Management Plugin url. (default: http://localhost:15672)
# url = "http://localhost:15672"
## Tag added to rabbitmq_overview series; deprecated: use tags
# name = "rmq-server-1"
## Credentials
# username = "guest"
# password = "guest"
## Optional TLS Config
# 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
## Optional request timeouts
## ResponseHeaderTimeout, if non-zero, specifies the amount of time to wait
## for a server's response headers after fully writing the request.
# header_timeout = "3s"
##
## client_timeout specifies a time limit for requests made by this client.
## Includes connection time, any redirects, and reading the response body.
# client_timeout = "4s"
## A list of nodes to gather as the rabbitmq_node measurement. If not
## specified, metrics for all nodes are gathered.
# nodes = ["rabbit@node1", "rabbit@node2"]
## A list of queues to gather as the rabbitmq_queue measurement. If not
## specified, metrics for all queues are gathered.
## Deprecated in 1.6: Use queue_name_include instead.
# queues = ["telegraf"]
## A list of exchanges to gather as the rabbitmq_exchange measurement. If not
## specified, metrics for all exchanges are gathered.
# exchanges = ["telegraf"]
## Metrics to include and exclude. Globs accepted.
## Note that an empty array for both will include all metrics
## Currently the following metrics are supported: "exchange", "federation", "node", "overview", "queue"
# metric_include = []
# metric_exclude = []
## Queues to include and exclude. Globs accepted.
## Note that an empty array for both will include all queues
# queue_name_include = []
# queue_name_exclude = []
## Federation upstreams to include and exclude specified as an array of glob
## pattern strings. Federation links can also be limited by the queue and
## exchange filters.
# federation_upstream_include = []
# federation_upstream_exclude = []
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
RabbitMQ
-
监控队列性能指标:使用 RabbitMQ 插件来跟踪队列性能随时间的变化。这包括设置监控仪表板,以可视化关键队列指标,例如消息速率、消费者数量和消息传递速率。借助这些信息,团队可以通过分析趋势并根据数据做出关于扩展或优化其 RabbitMQ 配置的明智决策,从而主动解决任何瓶颈或性能问题。
-
系统健康状况警报:将 RabbitMQ 插件与警报系统集成,以通知运营团队 RabbitMQ 实例中可能存在的问题。例如,如果未确认消息的数量达到临界阈值,或者队列变得不堪重负,则可以触发警报,从而可以立即进行调查并采取快速补救措施来维护消息流的健康状况。
-
分析消息处理指标:使用该插件收集关于消息处理性能的详细指标,例如已发布、已确认和已重新传递的消息速率。通过分析这些指标,团队可以评估其消息使用者应用程序的效率,并在必要时调整配置或代码,从而提高整体系统吞吐量和弹性。
-
跨系统数据集成:利用 RabbitMQ 插件收集的指标来集成 RabbitMQ 和其他系统或服务之间的数据流。例如,使用收集的指标来驱动自动化工作流或分析管道,这些管道利用 RabbitMQ 中处理的消息,使组织能够优化工作流并提高其生态系统中的数据敏捷性。
AWS Timestream
-
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间序列格式,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:将 Timestream 与应用程序监控工具结合使用,以随时间推移发送关于服务性能的指标。这种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,并根据随时间推移查看的使用模式优化资源分配。
-
自动化数据存档:配置 Timestream 插件以将数据写入 Timestream,同时管理保留期。此设置可以自动化存档策略,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特别有用,使企业能够以最少的人工干预来维护其数据生命周期。
-
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,提高对系统范围性能的可见性,并促进跨应用程序的故障排除。
反馈
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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,它会更有价值。借助 InfluxDB,这个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