目录
输入和输出集成概览
Kinesis 插件使您能够从 Kinesis 数据流中读取数据,支持各种数据格式和配置。
Prometheus 输出插件使 Telegraf 能够在 HTTP 端点公开指标,以供 Prometheus 服务器抓取。此集成允许用户以 Prometheus 可以有效处理的格式从各种来源收集和聚合指标。
集成详情
Kinesis
Kinesis Telegraf 插件旨在从 Amazon Kinesis 数据流中读取数据,使用户能够实时收集指标。作为服务输入插件,它通过监听传入数据而不是定期轮询来运行。配置指定了各种选项,包括 AWS 区域、流名称、身份验证凭据和数据格式。它支持跟踪未送达的消息以防止数据丢失,用户可以利用 DynamoDB 来维护上次处理记录的检查点。此插件特别适用于需要可靠且可扩展的流处理以及其他监控需求的应用程序。
Prometheus
此插件有助于与 Prometheus 集成,Prometheus 是一种著名的开源监控和警报工具包,专为大规模环境中的可靠性和效率而设计。通过充当 Prometheus 客户端,它允许用户通过 HTTP 服务器公开定义的指标集,Prometheus 可以按指定的间隔抓取这些指标。此插件在监控各种系统中起着至关重要的作用,它允许这些系统以标准化格式发布性能指标,从而可以广泛了解系统健康状况和行为。主要功能包括支持配置各种端点、启用 TLS 以进行安全通信以及 HTTP 基本身份验证选项。该插件还与全局 Telegraf 配置设置无缝集成,支持广泛的自定义以适应特定的监控需求。这促进了不同系统必须有效通信性能数据的环境中的互操作性。利用 Prometheus 的指标格式,它可以通过高级配置(例如指标过期和收集器控制)实现灵活的指标管理,为监控和警报工作流程提供了一种复杂的解决方案。
配置
Kinesis
# Configuration for the AWS Kinesis input.
[[inputs.kinesis_consumer]]
## Amazon REGION of kinesis endpoint.
region = "ap-southeast-2"
## 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 = ""
## Kinesis StreamName must exist prior to starting telegraf.
streamname = "StreamName"
## Shard iterator type (only 'TRIM_HORIZON' and 'LATEST' currently supported)
# shard_iterator_type = "TRIM_HORIZON"
## Max undelivered messages
## This plugin uses tracking metrics, which ensure messages are read to
## outputs before acknowledging them to the original broker to ensure data
## is not lost. This option sets the maximum messages to read from the
## broker that have not been written by an output.
##
## This value needs to be picked with awareness of the agent's
## metric_batch_size value as well. Setting max undelivered messages too high
## can result in a constant stream of data batches to the output. While
## setting it too low may never flush the broker's messages.
# max_undelivered_messages = 1000
## 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"
##
## The content encoding of the data from kinesis
## If you are processing a cloudwatch logs kinesis stream then set this to "gzip"
## as AWS compresses cloudwatch log data before it is sent to kinesis (aws
## also base64 encodes the zip byte data before pushing to the stream. The base64 decoding
## is done automatically by the golang sdk, as data is read from kinesis)
##
# content_encoding = "identity"
## Optional
## Configuration for a dynamodb checkpoint
[inputs.kinesis_consumer.checkpoint_dynamodb]
## unique name for this consumer
app_name = "default"
table_name = "default"
Prometheus
[[outputs.prometheus_client]]
## Address to listen on.
## ex:
## listen = ":9273"
## listen = "vsock://:9273"
listen = ":9273"
## Maximum duration before timing out read of the request
# read_timeout = "10s"
## Maximum duration before timing out write of the response
# write_timeout = "10s"
## Metric version controls the mapping from Prometheus metrics into Telegraf metrics.
## See "Metric Format Configuration" in plugins/inputs/prometheus/README.md for details.
## Valid options: 1, 2
# metric_version = 1
## Use HTTP Basic Authentication.
# basic_username = "Foo"
# basic_password = "Bar"
## If set, the IP Ranges which are allowed to access metrics.
## ex: ip_range = ["192.168.0.0/24", "192.168.1.0/30"]
# ip_range = []
## Path to publish the metrics on.
# path = "/metrics"
## Expiration interval for each metric. 0 == no expiration
# expiration_interval = "60s"
## Collectors to enable, valid entries are "gocollector" and "process".
## If unset, both are enabled.
# collectors_exclude = ["gocollector", "process"]
## Send string metrics as Prometheus labels.
## Unless set to false all string metrics will be sent as labels.
# string_as_label = true
## If set, enable TLS with the given certificate.
# tls_cert = "/etc/ssl/telegraf.crt"
# tls_key = "/etc/ssl/telegraf.key"
## Set one or more allowed client CA certificate file names to
## enable mutually authenticated TLS connections
# tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
## Export metric collection time.
# export_timestamp = false
## Specify the metric type explicitly.
## This overrides the metric-type of the Telegraf metric. Globbing is allowed.
# [outputs.prometheus_client.metric_types]
# counter = []
# gauge = []
输入和输出集成示例
Kinesis
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使用 Kinesis 进行实时数据处理:此用例涉及将 Kinesis 插件与监控仪表板集成,以实时分析传入的数据指标。例如,应用程序可以从多个服务使用日志并在视觉上呈现它们,从而使运营团队能够快速识别趋势并对异常情况做出反应。
-
无服务器日志聚合:在无服务器架构中使用此插件,其中 Kinesis 流聚合来自各种微服务的日志。该插件可以创建指标来帮助检测系统中的问题,通过第三方集成自动化警报流程,使团队能够最大限度地减少停机时间并提高可靠性。
-
基于流指标的动态扩展:实施一种解决方案,其中 Kinesis 插件使用的流指标可用于动态调整资源。例如,如果处理的记录数激增,则可以触发相应的纵向扩展操作来处理增加的负载,从而确保最佳的资源分配和性能。
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使用检查点的数据管道到 S3:创建一个强大的数据管道,其中 Kinesis 流数据通过 Telegraf Kinesis 插件处理,检查点存储在 DynamoDB 中。这种方法可以确保数据一致性和可靠性,因为它管理已处理数据的状态,从而可以与下游数据湖或存储解决方案无缝集成。
Prometheus
-
监控多云部署:利用 Prometheus 插件从跨多个云提供商运行的应用程序收集指标。此方案允许团队通过单个 Prometheus 实例集中监控,该实例从不同环境抓取指标,从而提供跨混合基础设施的性能指标的统一视图。它简化了报告和警报,提高了运营效率,而无需复杂的集成。
-
增强微服务可见性:实施该插件以公开 Kubernetes 集群中各种微服务的指标。使用 Prometheus,团队可以实时可视化服务指标、识别瓶颈并维护系统健康检查。此设置支持基于从收集的指标生成的见解进行自适应扩展和资源利用率优化。它增强了对服务交互进行故障排除的能力,从而显着提高了微服务架构的弹性。
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电子商务中的实时异常检测:通过将此插件与 Prometheus 一起使用,电子商务平台可以监控关键绩效指标,例如响应时间和错误率。将异常检测算法与抓取的指标集成在一起,可以识别指示潜在问题的意外模式,例如突发流量高峰或后端服务故障。这种主动监控增强了业务连续性和运营效率,最大限度地减少了潜在的停机时间,同时确保了服务可靠性。
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API 的性能指标报告:利用 Prometheus 输出插件收集和报告 API 性能指标,然后可以在 Grafana 仪表板中可视化这些指标。此用例可以详细分析 API 响应时间、吞吐量和错误率,从而促进 API 服务的持续改进。通过密切监控这些指标,团队可以快速响应性能下降,确保最佳的 API 性能并保持高水平的服务可用性。
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