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间序列数据时,它都更有价值。借助 InfluxDB,这个排名第一的时间序列平台旨在通过 Telegraf 进行扩展。
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,从而增强 Telegraf 设置中的数据监控和收集能力。
此输出插件通过 HTTP 将指标从 Telegraf 直接流式传输到 ServiceNow MID Server,利用 nowmetric
序列化器与 ServiceNow 的 Operational Intelligence 和 Event Management 高效集成。
集成详情
Kafka
Kafka Telegraf 插件旨在从 Kafka 主题读取数据,并使用受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,这与按固定间隔运行的标准输入插件不同。此特定插件可以使用来自各种 Kafka 版本的功能,并且能够使用 SASL 等配置的安全性凭据,以及使用消息偏移量和消费者组选项管理消息处理,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。
ServiceNow
Telegraf 可用于将指标数据直接发送到 ServiceNow MID Server REST 端点。指标使用 ServiceNow 的 Operational Intelligence (OI) 格式或 JSONv2 格式进行格式化,从而实现与 ServiceNow 的 Event Management 和 Operational Intelligence 平台的无缝集成。序列化器有效地批量处理指标,通过最大限度地减少 HTTP POST 请求的数量来降低网络开销。此集成允许用户在 ServiceNow 中快速利用指标,以增强可观测性、主动事件管理和性能监控以及 ServiceNow 的 Operational Intelligence 功能。
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"
ServiceNow
[[outputs.http]]
## ServiceNow MID Server metrics endpoint
url = "http://mid-server.example.com:9082/api/mid/sa/metrics"
## HTTP request method
method = "POST"
## Basic Authentication credentials
username = "evt.integration"
password = "P@$$w0rd!"
## Data serialization format for ServiceNow
data_format = "nowmetric"
## Metric format type: "oi" (default) or "jsonv2"
nowmetric_format = "oi"
## HTTP Headers
[outputs.http.headers]
Content-Type = "application/json"
Accept = "application/json"
## Optional timeout
# timeout = "5s"
## TLS configuration options
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
# insecure_skip_verify = false
输入和输出集成示例
Kafka
-
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。
-
动态指标收集:利用此插件根据 Kafka 内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。
-
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测性和故障排除能力。
-
异常检测系统:将 Kafka 与机器学习算法结合用于实时异常检测。通过不断分析流式数据,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。
ServiceNow
-
主动事件管理:利用 Telegraf 和 ServiceNow 集成将基础设施和应用程序指标实时流式传输到 ServiceNow Event Management。根据阈值自动触发事件或补救工作流程,从而显著缩短事件检测和响应时间。
-
端到端应用程序监控:在应用程序堆栈的多个层部署 Telegraf 代理,将性能指标直接发送到 ServiceNow。利用 ServiceNow 的 Operational Intelligence,团队可以将跨组件的指标关联起来,快速识别性能瓶颈。
-
动态 CI 性能跟踪:通过使用此插件推送性能数据,将 Telegraf 指标与 ServiceNow 的 CMDB 集成,从而允许根据实时指标自动更新配置项 (CI) 健康状态。这确保了 ServiceNow 中基础设施健康状态的准确和最新状态。
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云资源优化:使用 Telegraf 从混合云和多云基础设施收集指标,直接流式传输到 ServiceNow。利用这些指标进行实时分析、预测性容量规划和资源优化,从而实现主动管理并降低运营成本。
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间序列数据时,它都更有价值。借助 InfluxDB,这个排名第一的时间序列平台旨在通过 Telegraf 进行扩展。
查看入门方法