AMQP 和 InfluxDB 集成

强大的性能和简易的集成,由 InfluxData 构建的开源数据连接器 Telegraf 提供支持。

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时序数据库
来源:DB Engines

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InfluxDB 下载量

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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序数据时,它都更有价值。InfluxDB 是排名第一的时序平台,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)摄取数据,从而为监控和分析目的实现无缝数据收集。

InfluxDB 插件将指标写入 InfluxDB HTTP 服务,从而可以高效地存储和检索时序数据。

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,最初是为了实现网络中不同系统之间可靠、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来从兼容 AMQP 的消息传递系统收集数据。这使用户能够利用现有的 RabbitMQ 实现来有效地监控其应用程序,通过捕获用于分析和警报的详细指标。

InfluxDB

InfluxDB Telegraf 插件用于将指标发送到 InfluxDB HTTP API,从而以结构化方式促进时序数据的存储和查询。该插件与 InfluxDB 无缝集成,提供了诸如基于令牌的身份验证和对多个 InfluxDB 集群节点的支持等基本功能,从而确保了可靠且可扩展的数据摄取。通过其可配置性,用户可以指定诸如组织、目标存储桶和 HTTP 特定设置等选项,从而灵活地定制数据的发送和存储方式。该插件还支持敏感数据的秘密管理,从而增强了生产环境中的安全性。此插件在现代可观测性堆栈中尤其有益,在这些堆栈中,实时分析和时序数据的存储至关重要。

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"

InfluxDB

[[outputs.influxdb]]
  ## The full HTTP or UDP URL for your InfluxDB instance.
  ##
  ## Multiple URLs can be specified for a single cluster, only ONE of the
  ## urls will be written to each interval.
  # urls = ["unix:///var/run/influxdb.sock"]
  # urls = ["udp://127.0.0.1:8089"]
  # urls = ["http://127.0.0.1:8086"]

  ## Local address to bind when connecting to the server
  ## If empty or not set, the local address is automatically chosen.
  # local_address = ""

  ## The target database for metrics; will be created as needed.
  ## For UDP url endpoint database needs to be configured on server side.
  # database = "telegraf"

  ## The value of this tag will be used to determine the database.  If this
  ## tag is not set the 'database' option is used as the default.
  # database_tag = ""

  ## If true, the 'database_tag' will not be included in the written metric.
  # exclude_database_tag = false

  ## If true, no CREATE DATABASE queries will be sent.  Set to true when using
  ## Telegraf with a user without permissions to create databases or when the
  ## database already exists.
  # skip_database_creation = false

  ## Name of existing retention policy to write to.  Empty string writes to
  ## the default retention policy.  Only takes effect when using HTTP.
  # retention_policy = ""

  ## The value of this tag will be used to determine the retention policy.  If this
  ## tag is not set the 'retention_policy' option is used as the default.
  # retention_policy_tag = ""

  ## If true, the 'retention_policy_tag' will not be included in the written metric.
  # exclude_retention_policy_tag = false

  ## Write consistency (clusters only), can be: "any", "one", "quorum", "all".
  ## Only takes effect when using HTTP.
  # write_consistency = "any"

  ## Timeout for HTTP messages.
  # timeout = "5s"

  ## HTTP Basic Auth
  # username = "telegraf"
  # password = "metricsmetricsmetricsmetrics"

  ## HTTP User-Agent
  # user_agent = "telegraf"

  ## UDP payload size is the maximum packet size to send.
  # udp_payload = "512B"

  ## Optional TLS Config for use on HTTP connections.
  # 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

  ## HTTP Proxy override, if unset values the standard proxy environment
  ## variables are consulted to determine which proxy, if any, should be used.
  # http_proxy = "http://corporate.proxy:3128"

  ## Additional HTTP headers
  # http_headers = {"X-Special-Header" = "Special-Value"}

  ## HTTP Content-Encoding for write request body, can be set to "gzip" to
  ## compress body or "identity" to apply no encoding.
  # content_encoding = "gzip"

  ## When true, Telegraf will output unsigned integers as unsigned values,
  ## i.e.: "42u".  You will need a version of InfluxDB supporting unsigned
  ## integer values.  Enabling this option will result in field type errors if
  ## existing data has been written.
  # influx_uint_support = false

  ## When true, Telegraf will omit the timestamp on data to allow InfluxDB
  ## to set the timestamp of the data during ingestion. This is generally NOT
  ## what you want as it can lead to data points captured at different times
  ## getting omitted due to similar data.
  # influx_omit_timestamp = false

输入和输出集成示例

AMQP

  1. 集成应用程序指标与 AMQP:使用 AMQP Consumer 插件收集发布到 RabbitMQ 交换机的应用程序指标。通过配置插件以监听特定队列,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,还为容量规划和系统优化提供有价值的数据。

  2. 事件驱动的监控:每当应用程序中满足某些条件时,配置 AMQP Consumer 以触发特定的监控事件。例如,如果收到指示高错误率的消息,插件可以将此数据馈送到监控工具,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。

  3. 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。

  4. 实时日志处理:扩展 AMQP Consumer 的使用范围,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。此应用程序确保通过分析日志模式、趋势和异常情况(在发生时)来快速检测和解决操作问题。

InfluxDB

  1. 实时系统监控:利用 InfluxDB 插件捕获和存储来自各种系统组件的指标,例如 CPU 使用率、内存消耗和磁盘 I/O。通过将这些指标推送到 InfluxDB 中,您可以创建一个实时仪表板,以可视化系统性能。这种设置不仅有助于识别性能瓶颈,还可以通过分析随时间变化的趋势来协助主动容量规划。

  2. Web 应用程序的性能跟踪:自动收集并将与 Web 应用程序性能相关的指标(例如请求持续时间、错误率和用户交互)推送到 InfluxDB。通过在您的监控堆栈中使用此插件,您可以使用存储的指标生成报告和分析,以帮助了解用户行为和应用程序效率,从而指导开发和优化工作。

  3. 物联网数据聚合:利用 InfluxDB Telegraf 插件从各种物联网设备收集传感器数据,并将其存储在集中的 InfluxDB 实例中。此用例使您能够分析随时间变化的环境或机器数据中的趋势和模式,从而促进更明智的决策和预测性维护策略。通过将物联网数据集成到 InfluxDB 中,组织可以利用历史数据分析的力量来推动创新和运营效率。

  4. 分析历史指标以进行预测:设置 InfluxDB 插件以将历史指标数据发送到 InfluxDB,并使用它来驱动预测模型。通过分析过去的性能指标,您可以创建预测未来趋势和需求的预测模型。此应用程序对于商业智能目的特别有用,可帮助组织根据历史使用模式为资源需求的波动做好准备。

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序数据时,它都更有价值。InfluxDB 是排名第一的时序平台,旨在与 Telegraf 一起扩展。

查看入门方法

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