目录
强大的性能,无限的扩展
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,它都会变得更有价值。借助 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
此插件允许您从 Kafka 主题实时收集指标,增强 Telegraf 设置中的数据监控和收集能力。
此插件允许使用 Metrics API 将指标发送到 New Relic Insights,从而有效监控和分析应用程序性能。
集成详情
Kafka
Kafka Telegraf 插件旨在从 Kafka 主题读取数据,并使用支持的输入数据格式创建指标。作为服务输入插件,它持续监听传入的指标和事件,这与以固定间隔运行的标准输入插件不同。此特定插件可以使用各种 Kafka 版本的功能,并能够使用 SASL 等配置(例如安全凭证)以及使用消息偏移量和消费者组管理消息处理,从而使用来自指定主题的消息。此插件的灵活性使其能够处理各种消息格式和用例,使其成为依赖 Kafka 进行数据摄取的应用程序的宝贵资产。
New Relic
此插件使用 Metrics API 将指标写入 New Relic Insights,Metrics API 提供了一种将时间序列数据发送到 New Relic 平台的强大机制。用户必须首先获取 Insights API 密钥才能验证和授权其数据提交。该插件旨在促进与 New Relic 的监控和分析功能轻松集成,支持各种指标类型并允许高效的数据处理。核心功能包括能够为指标添加前缀以更好地识别、API 请求的可自定义超时以及对代理设置的支持以增强连接性。用户必须根据自己的要求配置这些选项,以实现数据无缝流入 New Relic,从而进行全面的实时分析和洞察。
配置
Kafka
[[inputs.kafka_consumer]]
## Kafka brokers.
brokers = ["localhost:9092"]
## Set the minimal supported Kafka version. Should be a string contains
## 4 digits in case if it is 0 version and 3 digits for versions starting
## from 1.0.0 separated by dot. This setting enables the use of new
## Kafka features and APIs. Must be 0.10.2.0(used as default) or greater.
## Please, check the list of supported versions at
## https://pkg.go.dev/github.com/Shopify/sarama#SupportedVersions
## ex: kafka_version = "2.6.0"
## ex: kafka_version = "0.10.2.0"
# kafka_version = "0.10.2.0"
## Topics to consume.
topics = ["telegraf"]
## Topic regular expressions to consume. Matches will be added to topics.
## Example: topic_regexps = [ "*test", "metric[0-9A-z]*" ]
# topic_regexps = [ ]
## When set this tag will be added to all metrics with the topic as the value.
# topic_tag = ""
## The list of Kafka message headers that should be pass as metric tags
## works only for Kafka version 0.11+, on lower versions the message headers
## are not available
# msg_headers_as_tags = []
## The name of kafka message header which value should override the metric name.
## In case when the same header specified in current option and in msg_headers_as_tags
## option, it will be excluded from the msg_headers_as_tags list.
# msg_header_as_metric_name = ""
## Set metric(s) timestamp using the given source.
## Available options are:
## metric -- do not modify the metric timestamp
## inner -- use the inner message timestamp (Kafka v0.10+)
## outer -- use the outer (compressed) block timestamp (Kafka v0.10+)
# timestamp_source = "metric"
## Optional Client id
# client_id = "Telegraf"
## Optional TLS Config
# enable_tls = false
# 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
## Period between keep alive probes.
## Defaults to the OS configuration if not specified or zero.
# keep_alive_period = "15s"
## SASL authentication credentials. These settings should typically be used
## with TLS encryption enabled
# sasl_username = "kafka"
# sasl_password = "secret"
## Optional SASL:
## one of: OAUTHBEARER, PLAIN, SCRAM-SHA-256, SCRAM-SHA-512, GSSAPI
## (defaults to PLAIN)
# sasl_mechanism = ""
## used if sasl_mechanism is GSSAPI
# sasl_gssapi_service_name = ""
# ## One of: KRB5_USER_AUTH and KRB5_KEYTAB_AUTH
# sasl_gssapi_auth_type = "KRB5_USER_AUTH"
# sasl_gssapi_kerberos_config_path = "/"
# sasl_gssapi_realm = "realm"
# sasl_gssapi_key_tab_path = ""
# sasl_gssapi_disable_pafxfast = false
## used if sasl_mechanism is OAUTHBEARER
# sasl_access_token = ""
## SASL protocol version. When connecting to Azure EventHub set to 0.
# sasl_version = 1
# Disable Kafka metadata full fetch
# metadata_full = false
## Name of the consumer group.
# consumer_group = "telegraf_metrics_consumers"
## Compression codec represents the various compression codecs recognized by
## Kafka in messages.
## 0 : None
## 1 : Gzip
## 2 : Snappy
## 3 : LZ4
## 4 : ZSTD
# compression_codec = 0
## Initial offset position; one of "oldest" or "newest".
# offset = "oldest"
## Consumer group partition assignment strategy; one of "range", "roundrobin" or "sticky".
# balance_strategy = "range"
## Maximum number of retries for metadata operations including
## connecting. Sets Sarama library's Metadata.Retry.Max config value. If 0 or
## unset, use the Sarama default of 3,
# metadata_retry_max = 0
## Type of retry backoff. Valid options: "constant", "exponential"
# metadata_retry_type = "constant"
## Amount of time to wait before retrying. When metadata_retry_type is
## "constant", each retry is delayed this amount. When "exponential", the
## first retry is delayed this amount, and subsequent delays are doubled. If 0
## or unset, use the Sarama default of 250 ms
# metadata_retry_backoff = 0
## Maximum amount of time to wait before retrying when metadata_retry_type is
## "exponential". Ignored for other retry types. If 0, there is no backoff
## limit.
# metadata_retry_max_duration = 0
## When set to true, this turns each bootstrap broker address into a set of
## IPs, then does a reverse lookup on each one to get its canonical hostname.
## This list of hostnames then replaces the original address list.
## resolve_canonical_bootstrap_servers_only = false
## Strategy for making connection to kafka brokers. Valid options: "startup",
## "defer". If set to "defer" the plugin is allowed to start before making a
## connection. This is useful if the broker may be down when telegraf is
## started, but if there are any typos in the broker setting, they will cause
## connection failures without warning at startup
# connection_strategy = "startup"
## Maximum length of a message to consume, in bytes (default 0/unlimited);
## larger messages are dropped
max_message_len = 1000000
## 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
## Maximum amount of time the consumer should take to process messages. If
## the debug log prints messages from sarama about 'abandoning subscription
## to [topic] because consuming was taking too long', increase this value to
## longer than the time taken by the output plugin(s).
##
## Note that the effective timeout could be between 'max_processing_time' and
## '2 * max_processing_time'.
# max_processing_time = "100ms"
## The default number of message bytes to fetch from the broker in each
## request (default 1MB). This should be larger than the majority of
## your messages, or else the consumer will spend a lot of time
## negotiating sizes and not actually consuming. Similar to the JVM's
## `fetch.message.max.bytes`.
# consumer_fetch_default = "1MB"
## 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"
New Relic
[[outputs.newrelic]]
## The 'insights_key' parameter requires a NR license key.
## New Relic recommends you create one
## with a convenient name such as TELEGRAF_INSERT_KEY.
## reference: https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#ingest-license-key
# insights_key = "New Relic License Key Here"
## Prefix to add to add to metric name for easy identification.
## This is very useful if your metric names are ambiguous.
# metric_prefix = ""
## Timeout for writes to the New Relic API.
# timeout = "15s"
## HTTP Proxy override. If unset use values from the standard
## proxy environment variables to determine proxy, if any.
# http_proxy = "http://corporate.proxy:3128"
## Metric URL override to enable geographic location endpoints.
# If not set use values from the standard
# metric_url = "https://metric-api.newrelic.com/metric/v1"
输入和输出集成示例
Kafka
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实时数据处理:使用 Kafka 插件将来自 Kafka 主题的实时数据馈送到监控系统。这对于需要即时反馈性能指标或用户活动的应用尤其有用,使企业能够更快地对其环境中的变化条件做出反应。
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动态指标收集:利用此插件根据 Kafka 中发生的事件动态调整正在捕获的指标。例如,通过与其他服务集成,用户可以让插件即时重新配置自身,确保始终根据业务或应用程序的需求收集相关指标。
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集中式日志记录和监控:实施集中式日志记录系统,使用 Kafka Consumer Plugin 将来自多个服务的日志聚合到统一的监控仪表板中。此设置可以帮助识别不同服务之间的问题,并提高整体系统可观察性和故障排除能力。
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异常检测系统:将 Kafka 与机器学习算法结合使用进行实时异常检测。通过不断分析流数据,此设置可以自动识别异常模式,触发警报并更有效地缓解潜在问题。
New Relic
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应用程序性能监控:使用 New Relic Telegraf 插件将来自 Web 服务的应用程序性能指标发送到 New Relic Insights。通过集成此插件,开发人员可以收集响应时间、错误率和吞吐量等数据,使团队能够实时监控应用程序运行状况,并在问题影响用户之前快速解决问题。此设置促进了应用程序性能和用户体验的积极主动管理。
-
基础设施指标聚合:利用此插件聚合来自各种服务器的系统级指标(CPU 使用率、内存消耗等)并将其发送到 New Relic。这有助于系统管理员维护基础设施性能的全面视图,从而促进容量规划并识别潜在瓶颈。通过将指标集中在 New Relic 中,团队可以可视化随时间变化的趋势,并就资源分配做出明智的决策。
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多租户应用程序的动态指标命名:使用 metric_prefix 选项实现动态前缀,以区分多租户应用程序中的不同租户。通过配置插件以在指标名称中包含每个租户的唯一标识符,团队可以分析每个租户的使用模式和性能指标。这为租户行为提供了有价值的见解,支持定制优化并提高不同客户群的服务质量。
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实时异常检测:将 New Relic 插件与警报机制相结合,以根据异常指标模式触发通知。通过发送请求计数和响应时间等指标,团队可以在 New Relic 中设置阈值,当阈值被违反时,将自动提醒责任方。这种用户驱动的方法支持对潜在问题做出即时响应,防止其升级为更大的事件。
反馈
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强大的性能,无限的扩展
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,它都会变得更有价值。借助 InfluxDB,第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法