目录
强大的性能,无限的扩展能力
收集、组织和处理大量高速数据。当您将任何数据视为时间序列数据时,它都更有价值。 使用 InfluxDB,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
AMQP Consumer 输入插件允许您从兼容 AMQP 0-9-1 的消息代理(如 RabbitMQ)中提取数据,从而实现无缝数据收集,以用于监控和分析目的。
OpenSearch 输出插件允许用户使用 HTTP 将指标直接发送到 OpenSearch 实例,从而促进 OpenSearch 生态系统内有效的数据管理和分析。
集成详情
AMQP
此插件为 AMQP 0-9-1 提供了一个消费者,RabbitMQ 是其一个突出的实现。 AMQP,或高级消息队列协议,最初是为了在网络中不同的系统之间实现可靠的、可互操作的消息传递而开发的。 该插件使用配置的队列和绑定键从主题交换中读取指标,从而提供了一种灵活高效的方式,从兼容 AMQP 的消息传递系统中收集数据。 这使用户能够利用现有的 RabbitMQ 实施,通过捕获详细的指标进行分析和警报,从而有效地监控他们的应用程序。
OpenSearch
OpenSearch Telegraf 插件通过 HTTP 与 OpenSearch 数据库集成,从而可以简化指标的收集和存储。 作为一个专为 OpenSearch 2.x 及更高版本设计的强大工具,该插件提供了强大的功能,同时通过原始的 Elasticsearch 插件与 1.x 版本兼容。 此插件有助于在 OpenSearch 中创建和管理索引,自动管理模板并确保数据结构化以提高分析效率。 该插件支持各种配置选项,例如索引名称、身份验证、运行状况检查和值处理,使其可以根据不同的操作要求进行定制。 其功能使其对于希望利用 OpenSearch 的强大功能进行指标存储和查询的组织至关重要。
配置
AMQP
[[inputs.amqp_consumer]]
## Brokers to consume from. If multiple brokers are specified a random broker
## will be selected anytime a connection is established. This can be
## helpful for load balancing when not using a dedicated load balancer.
brokers = ["amqp://localhost:5672/influxdb"]
## Authentication credentials for the PLAIN auth_method.
# username = ""
# password = ""
## Name of the exchange to declare. If unset, no exchange will be declared.
exchange = "telegraf"
## Exchange type; common types are "direct", "fanout", "topic", "header", "x-consistent-hash".
# exchange_type = "topic"
## If true, exchange will be passively declared.
# exchange_passive = false
## Exchange durability can be either "transient" or "durable".
# exchange_durability = "durable"
## Additional exchange arguments.
# exchange_arguments = { }
# exchange_arguments = {"hash_property" = "timestamp"}
## AMQP queue name.
queue = "telegraf"
## AMQP queue durability can be "transient" or "durable".
queue_durability = "durable"
## If true, queue will be passively declared.
# queue_passive = false
## Additional arguments when consuming from Queue
# queue_consume_arguments = { }
# queue_consume_arguments = {"x-stream-offset" = "first"}
## A binding between the exchange and queue using this binding key is
## created. If unset, no binding is created.
binding_key = "#"
## Maximum number of messages server should give to the worker.
# prefetch_count = 50
## 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
## Timeout for establishing the connection to a broker
# timeout = "30s"
## Auth method. PLAIN and EXTERNAL are supported
## Using EXTERNAL requires enabling the rabbitmq_auth_mechanism_ssl plugin as
## described here: https://rabbitmq.cn/plugins.html
# auth_method = "PLAIN"
## 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
## Content encoding for message payloads, can be set to
## "gzip", "identity" or "auto"
## - Use "gzip" to decode gzip
## - Use "identity" to apply no encoding
## - Use "auto" determine the encoding using the ContentEncoding header
# content_encoding = "identity"
## Maximum size of decoded message.
## Acceptable units are B, KiB, KB, MiB, MB...
## Without quotes and units, interpreted as size in bytes.
# max_decompression_size = "500MB"
## 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"
OpenSearch
[[outputs.opensearch]]
## URLs
## The full HTTP endpoint URL for your OpenSearch instance. Multiple URLs can
## be specified as part of the same cluster, but only one URLs is used to
## write during each interval.
urls = ["http://node1.os.example.com:9200"]
## Index Name
## Target index name for metrics (OpenSearch will create if it not exists).
## This is a Golang template (see https://pkg.go.dev/text/template)
## You can also specify
## metric name (`{{.Name}}`), tag value (`{{.Tag "tag_name"}}`), field value (`{{.Field "field_name"}}`)
## If the tag does not exist, the default tag value will be empty string "".
## the timestamp (`{{.Time.Format "xxxxxxxxx"}}`).
## For example: "telegraf-{{.Time.Format \"2006-01-02\"}}-{{.Tag \"host\"}}" would set it to telegraf-2023-07-27-HostName
index_name = ""
## Timeout
## OpenSearch client timeout
# timeout = "5s"
## Sniffer
## Set to true to ask OpenSearch a list of all cluster nodes,
## thus it is not necessary to list all nodes in the urls config option
# enable_sniffer = false
## GZIP Compression
## Set to true to enable gzip compression
# enable_gzip = false
## Health Check Interval
## Set the interval to check if the OpenSearch nodes are available
## Setting to "0s" will disable the health check (not recommended in production)
# health_check_interval = "10s"
## Set the timeout for periodic health checks.
# health_check_timeout = "1s"
## HTTP basic authentication details.
# username = ""
# password = ""
## HTTP bearer token authentication details
# auth_bearer_token = ""
## 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"
## Send the specified TLS server name via SNI
# tls_server_name = "kubernetes.example.com"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Template Config
## Manage templates
## Set to true if you want telegraf to manage its index template.
## If enabled it will create a recommended index template for telegraf indexes
# manage_template = true
## Template Name
## The template name used for telegraf indexes
# template_name = "telegraf"
## Overwrite Templates
## Set to true if you want telegraf to overwrite an existing template
# overwrite_template = false
## Document ID
## If set to true a unique ID hash will be sent as
## sha256(concat(timestamp,measurement,series-hash)) string. It will enable
## data resend and update metric points avoiding duplicated metrics with
## different id's
# force_document_id = false
## Value Handling
## Specifies the handling of NaN and Inf values.
## This option can have the following values:
## none -- do not modify field-values (default); will produce an error
## if NaNs or infs are encountered
## drop -- drop fields containing NaNs or infs
## replace -- replace with the value in "float_replacement_value" (default: 0.0)
## NaNs and inf will be replaced with the given number, -inf with the negative of that number
# float_handling = "none"
# float_replacement_value = 0.0
## Pipeline Config
## To use a ingest pipeline, set this to the name of the pipeline you want to use.
# use_pipeline = "my_pipeline"
## Pipeline Name
## Additionally, you can specify a tag name using the notation (`{{.Tag "tag_name"}}`)
## which will be used as the pipeline name (e.g. "{{.Tag \"os_pipeline\"}}").
## If the tag does not exist, the default pipeline will be used as the pipeline.
## If no default pipeline is set, no pipeline is used for the metric.
# default_pipeline = ""
输入和输出集成示例
AMQP
-
将应用程序指标与 AMQP 集成:使用 AMQP Consumer 插件收集发布到 RabbitMQ 交换机的应用程序指标。 通过配置插件以侦听特定队列,团队可以深入了解应用程序性能、跟踪请求率、错误计数和延迟指标,所有这些都是实时的。 这种设置不仅有助于异常检测,而且还为容量规划和系统优化提供有价值的数据。
-
事件驱动的监控:配置 AMQP Consumer 以在应用程序中满足特定条件时触发特定的监控事件。 例如,如果收到指示错误率高的消息,插件可以将此数据馈送到监控工具中,生成警报或扩展事件。 这种集成可以提高对问题的响应速度并自动化部分操作工作流程。
-
跨平台数据聚合:利用 AMQP Consumer 插件整合来自分布在不同平台上的各种应用程序的指标。 通过使用 RabbitMQ 作为集中式消息代理,组织可以统一其监控数据,从而可以通过 Telegraf 进行全面的分析和仪表板显示,从而在异构环境中保持可见性。
-
实时日志处理:扩展 AMQP Consumer 的使用,以捕获发送到 RabbitMQ 交换机的日志数据,实时处理日志以进行监控和警报。 此应用程序通过分析日志模式、趋势和异常情况(在发生时),确保及时检测和解决操作问题。
OpenSearch
-
时间序列数据的动态索引:使用 OpenSearch Telegraf 插件为时间序列指标动态创建索引,确保数据以有组织的方式存储,从而有利于基于时间的查询。 通过使用 Go 模板定义索引模式,用户可以利用该插件创建每日或每月索引,这可以大大简化数据管理和长期检索,从而提高分析性能。
-
多租户应用程序的集中日志记录:在多租户应用程序中实施 OpenSearch 插件,其中每个租户的日志都发送到单独的索引。 这使得可以对每个租户进行有针对性的分析和监控,同时保持数据隔离。 通过利用索引名称模板功能,用户可以自动创建租户特定的索引,这不仅简化了流程,而且还增强了租户数据的安全性和可访问性。
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与机器学习集成以进行异常检测:将 OpenSearch 插件与机器学习工具结合使用,以自动检测指标数据中的异常。 通过配置插件以将实时指标发送到 OpenSearch,用户可以将机器学习模型应用于传入的数据流,以识别异常值或不寻常的模式,从而促进主动监控和快速补救措施。
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使用 OpenSearch 增强监控仪表板:使用从 OpenSearch 收集的指标创建实时仪表板,以深入了解系统性能。 通过将指标馈送到 OpenSearch,组织可以利用 OpenSearch Dashboards 可视化关键绩效指标,从而使运营团队能够快速评估运行状况和性能,并做出数据驱动的决策。
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强大的性能,无限的扩展能力
收集、组织和处理大量高速数据。当您将任何数据视为时间序列数据时,它都更有价值。 使用 InfluxDB,第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法