目录
输入和输出集成概述
Tail Telegraf 插件通过跟踪指定的日志文件来收集指标,实时捕获新的日志条目以进行进一步分析。
此插件使用 HTTP 将 Telegraf 指标直接发送到 Grafana 的 Mimir 数据库,为 Prometheus 兼容指标提供可扩展且高效的长期存储和分析。
集成详情
Tail
Tail 插件旨在持续监控和解析日志文件,使其成为实时日志分析和监控的理想选择。它模仿 Unix `tail` 命令的功能,允许用户指定文件或模式,并在添加新行时开始读取。主要功能包括跟踪日志轮换文件、从文件末尾开始读取以及支持日志消息的各种解析格式。用户可以通过各种配置选项自定义插件,例如指定文件编码、监视文件更新的方法以及处理日志数据的过滤器设置。此插件在日志数据对于监控应用程序性能和诊断问题至关重要的环境中尤其有价值。
Mimir
Grafana Mimir 支持 Prometheus Remote Write 协议,使 Telegraf 收集的指标能够有效地摄取到 Mimir 集群中,以实现大规模、长期存储。此集成利用 Prometheus 成熟的标准,允许用户将 Telegraf 广泛的数据收集功能与 Mimir 的高级功能相结合,例如查询联邦、多租户、高可用性和经济高效的存储。Grafana Mimir 的架构经过优化,可处理大量指标数据并提供快速查询响应,使其成为复杂监控环境和分布式系统的理想选择。
配置
Tail
[[inputs.tail]]
## File names or a pattern to tail.
## These accept standard unix glob matching rules, but with the addition of
## ** as a "super asterisk". ie:
## "/var/log/**.log" -> recursively find all .log files in /var/log
## "/var/log/*/*.log" -> find all .log files with a parent dir in /var/log
## "/var/log/apache.log" -> just tail the apache log file
## "/var/log/log[!1-2]* -> tail files without 1-2
## "/var/log/log[^1-2]* -> identical behavior as above
## See https://github.com/gobwas/glob for more examples
##
files = ["/var/mymetrics.out"]
## Read file from beginning.
# from_beginning = false
## Whether file is a named pipe
# pipe = false
## Method used to watch for file updates. Can be either "inotify" or "poll".
## inotify is supported on linux, *bsd, and macOS, while Windows requires
## using poll. Poll checks for changes every 250ms.
# watch_method = "inotify"
## Maximum lines of the file to process that have not yet be written by the
## output. For best throughput set based on the number of metrics on each
## line and the size of the output's metric_batch_size.
# max_undelivered_lines = 1000
## Character encoding to use when interpreting the file contents. Invalid
## characters are replaced using the unicode replacement character. When set
## to the empty string the data is not decoded to text.
## ex: character_encoding = "utf-8"
## character_encoding = "utf-16le"
## character_encoding = "utf-16be"
## character_encoding = ""
# character_encoding = ""
## 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"
## Set the tag that will contain the path of the tailed file. If you don't want this tag, set it to an empty string.
# path_tag = "path"
## Filters to apply to files before generating metrics
## "ansi_color" removes ANSI colors
# filters = []
## multiline parser/codec
## https://elastic.ac.cn/guide/en/logstash/2.4/plugins-filters-multiline.html
#[inputs.tail.multiline]
## The pattern should be a regexp which matches what you believe to be an indicator that the field is part of an event consisting of multiple lines of log data.
#pattern = "^\s"
## The field's value must be previous or next and indicates the relation to the
## multi-line event.
#match_which_line = "previous"
## The invert_match can be true or false (defaults to false).
## If true, a message not matching the pattern will constitute a match of the multiline filter and the what will be applied. (vice-versa is also true)
#invert_match = false
## The handling method for quoted text (defaults to 'ignore').
## The following methods are available:
## ignore -- do not consider quotation (default)
## single-quotes -- consider text quoted by single quotes (')
## double-quotes -- consider text quoted by double quotes (")
## backticks -- consider text quoted by backticks (`)
## When handling quotes, escaped quotes (e.g. \") are handled correctly.
#quotation = "ignore"
## The preserve_newline option can be true or false (defaults to false).
## If true, the newline character is preserved for multiline elements,
## this is useful to preserve message-structure e.g. for logging outputs.
#preserve_newline = false
#After the specified timeout, this plugin sends the multiline event even if no new pattern is found to start a new event. The default is 5s.
#timeout = 5s
Mimir
[[outputs.http]]
url = "http://data-load-balancer-backend-1:9009/api/v1/push"
data_format = "prometheusremotewrite"
username = "*****"
password = "******"
[outputs.http.headers]
Content-Type = "application/x-protobuf"
Content-Encoding = "snappy"
X-Scope-OrgID = "****"
输入和输出集成示例
Tail
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实时服务器健康状况监控:实施 Tail 插件以实时解析 Web 服务器访问日志,从而立即了解用户活动、错误率和性能指标。通过可视化此日志数据,运维团队可以快速识别和响应流量或错误峰值,从而提高系统可靠性和用户体验。
-
集中式日志管理:利用 Tail 插件聚合来自分布式系统中多个来源的日志。通过配置每个服务以通过 Tail 插件将其日志发送到集中位置,团队可以简化日志分析,并确保从单个界面访问所有相关数据,从而简化故障排除流程。
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安全事件检测:使用此插件监控身份验证日志,以检测未经授权的访问尝试或可疑活动。通过在某些日志消息上设置警报,团队可以利用此插件增强安全态势,并及时响应潜在的安全威胁,从而降低漏洞风险并提高整体系统完整性。
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动态应用程序性能洞察:与分析工具集成,以创建实时仪表板,显示基于日志数据的应用程序性能指标。此设置不仅可以帮助开发人员诊断瓶颈和效率低下问题,还可以进行主动性能调优和资源分配,从而优化应用程序在不同负载下的行为。
Mimir
-
企业级 Kubernetes 监控:将 Telegraf 与 Grafana Mimir 集成,以企业级规模从 Kubernetes 集群流式传输指标。这实现了全面的可见性、改进的资源分配以及跨数百个集群的主动故障排除,从而利用 Mimir 的水平可扩展性和高可用性。
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多租户 SaaS 应用程序可观测性:使用此插件将来自不同 SaaS 租户的指标集中到 Grafana Mimir 中,从而实现租户隔离和基于资源使用情况的准确计费。这种方法提供了可靠的可观测性、高效的成本管理和安全的多租户支持。
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全球边缘网络性能跟踪:将来自全球分布式边缘服务器的延迟和可用性指标流式传输到 Grafana Mimir 中。组织可以快速识别性能下降或中断,利用 Mimir 的快速查询功能来确保最佳的服务可靠性和用户体验。
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高容量微服务实时分析:在高容量微服务架构中实施 Telegraf 指标收集,将数据馈送到 Grafana Mimir 中以进行实时分析和异常检测。Mimir 强大的查询功能使团队能够检测异常并快速响应,从而保持高服务可用性和性能。
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