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,它会更有价值。 借助 InfluxDB,排名第一的时间序列平台,与 Telegraf 协同扩展。
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。
Sumo Logic 插件旨在方便地将指标从 Telegraf 发送到 Sumo Logic 的 HTTP 源。 通过使用此插件,用户可以在 Sumo Logic 平台上分析其指标数据,利用各种输出数据格式。
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,并且能够使用 SASL 等配置(例如安全凭据)以及使用消息偏移量和消费者组管理消息处理,从而从指定主题中消费消息。 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。
Sumo Logic
此插件有助于将指标传输到 Sumo Logic 的 HTTP 源,并为 HTTP 消息采用指定的数据格式。 Telegraf(必须是 1.16.0 或更高版本)可以发送以多种格式编码的指标,包括 graphite
、carbon2
和 prometheus
。 这些格式对应于 Sumo Logic 识别的不同内容类型,确保指标得到正确解释以进行分析。 与 Sumo Logic 集成使用户能够利用全面的分析平台,从而从其指标数据中获得丰富的可视化效果和见解。 该插件提供配置选项,例如设置 HTTP 指标源的 URL、选择数据格式以及指定超时和请求大小等附加参数,这些参数增强了数据监控工作流中的灵活性和控制力。
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"
Sumo Logic
[[outputs.sumologic]]
## Unique URL generated for your HTTP Metrics Source.
## This is the address to send metrics to.
# url = "https://events.sumologic.net/receiver/v1/http/"
## Data format to be used for sending metrics.
## This will set the "Content-Type" header accordingly.
## Currently supported formats:
## * graphite - for Content-Type of application/vnd.sumologic.graphite
## * carbon2 - for Content-Type of application/vnd.sumologic.carbon2
## * prometheus - for Content-Type of application/vnd.sumologic.prometheus
##
## More information can be found at:
## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#content-type-headers-for-metrics
##
## NOTE:
## When unset, telegraf will by default use the influx serializer which is currently unsupported
## in HTTP Source.
data_format = "carbon2"
## Timeout used for HTTP request
# timeout = "5s"
## Max HTTP request body size in bytes before compression (if applied).
## By default 1MB is recommended.
## NOTE:
## Bear in mind that in some serializer a metric even though serialized to multiple
## lines cannot be split any further so setting this very low might not work
## as expected.
# max_request_body_size = 1000000
## Additional, Sumo specific options.
## Full list can be found here:
## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#supported-http-headers
## Desired source name.
## Useful if you want to override the source name configured for the source.
# source_name = ""
## Desired host name.
## Useful if you want to override the source host configured for the source.
# source_host = ""
## Desired source category.
## Useful if you want to override the source category configured for the source.
# source_category = ""
## Comma-separated key=value list of dimensions to apply to every metric.
## Custom dimensions will allow you to query your metrics at a more granular level.
# dimensions = ""
</code></pre>
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察性和故障排除能力。
-
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。
Sumo Logic
-
实时系统监控仪表板:利用 Sumo Logic 插件将服务器的性能指标持续馈送到 Sumo Logic 仪表板。 此设置允许技术团队实时可视化系统运行状况和负载,从而通过详细的图表和指标更快地识别任何性能瓶颈或系统故障。
-
自动化警报系统:配置插件以发送在 Sumo Logic 中触发特定阈值(例如 CPU 使用率或内存消耗)警报的指标。 通过设置自动化警报,团队可以在问题升级为严重故障之前主动解决问题,从而显着缩短响应时间并提高整体系统可靠性。
-
跨系统指标聚合:集成跨不同环境(开发、测试、生产)的多个 Telegraf 实例,并使用此插件将所有指标汇集到中央 Sumo Logic 实例。 此聚合支持跨环境的全面分析,从而促进更好的监控和跨软件开发生命周期的知情决策。
-
带有维度跟踪的自定义指标:使用 Sumo Logic 插件发送自定义指标,其中包括标识基础设施各个方面的维度(例如,环境、服务类型)。 这种精细的跟踪允许进行更定制的分析,使您的团队能够剖析跨不同应用层或业务功能的性能。
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,它会更有价值。 借助 InfluxDB,排名第一的时间序列平台,与 Telegraf 协同扩展。
查看入门方法