目录
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功能。
此插件使用 Prometheus 远程写入协议通过 HTTP 将指标从 Telegraf 发送到 Thanos,从而实现高效且可扩展地摄取到 Thanos Receive 组件中。
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来自各种 Kafka 版本的功能,并且能够从指定主题消费消息,应用诸如使用 SASL 的安全凭证之类的配置,以及使用消息偏移量和消费者组的选项来管理消息处理。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。
Thanos
Telegraf 的 HTTP 插件可以通过其远程写入兼容的接收组件将指标直接发送到 Thanos。通过将数据格式设置为 prometheusremotewrite
,Telegraf 可以将指标序列化为本机 Prometheus 客户端使用的相同基于 protobuf 的格式。此设置实现了高吞吐量、低延迟的指标摄取到 Thanos 中,从而促进了大规模的集中式可观测性。这在混合环境中尤其有用,在这些环境中,Telegraf 从 Prometheus 本机范围之外的系统(例如 SNMP 设备、Windows 主机或自定义应用程序)收集指标,并将它们直接流式传输到 Thanos 以进行长期存储和全局查询。
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"
Thanos
[[outputs.http]]
## Thanos Receive endpoint for remote write
url = "http://thanos-receive.example.com/api/v1/receive"
## HTTP method
method = "POST"
## Data format set to Prometheus remote write
data_format = "prometheusremotewrite"
## Optional headers (authorization, etc.)
# [outputs.http.headers]
# Authorization = "Bearer YOUR_TOKEN"
## Optional TLS configuration
# tls_ca = "/path/to/ca.pem"
# tls_cert = "/path/to/cert.pem"
# tls_key = "/path/to/key.pem"
# insecure_skip_verify = false
## Request timeout
timeout = "10s"
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 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,从而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:使用 Kafka Consumer Plugin 实施集中式日志记录系统,以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跨不同服务的问题,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 与机器学习算法相结合以进行实时异常检测。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。
Thanos
-
无代理云监控:在云虚拟机上部署 Telegraf 代理以收集系统和应用程序指标,然后使用远程写入将它们直接流式传输到 Thanos。这提供了集中式可观测性,而无需在每个位置都部署 Prometheus 节点。
-
可扩展的 Windows 主机监控:在 Windows 机器上使用 Telegraf 收集操作系统级别的指标,并通过远程写入将它们发送到 Thanos Receive。这实现了跨异构环境的可观测性,而本机 Prometheus 仅在 Linux 上受支持。
-
跨区域指标联合:多个地理区域中的 Telegraf 代理可以使用此插件将数据推送到区域本地的 Thanos 接收器。从那里,Thanos 可以去重并全局查询指标,从而减少延迟和网络出口成本。
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将第三方数据集成到 Thanos:使用 Telegraf 输入从自定义遥测源(例如 REST API 或专有日志)收集指标,并通过远程写入将它们转发到 Thanos。这会将非原生数据带入与 Prometheus 兼容的长期分析管道中。
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