目录
输入和输出集成概述
此插件允许您实时从 Kafka 主题收集指标,从而增强 Telegraf 设置中的数据监控和收集功能。
此插件使 Telegraf 能够使用 InfluxDB 行协议将指标有效地直接写入 VictoriaMetrics,从而利用 VictoriaMetrics 的性能和可扩展性特性来处理大规模时间序列数据。
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,并且能够使用来自指定主题的消息,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。
VictoriaMetrics
VictoriaMetrics 支持直接摄取 InfluxDB 行协议中的指标,这使得此插件成为高效实时指标存储和检索的理想选择。该集成结合了 Telegraf 广泛的指标收集功能与 VictoriaMetrics 优化的存储和查询功能,包括压缩、快速摄取速率和高效的磁盘利用率。此插件非常适合云原生和大规模监控场景,它提供了简单性、强大的性能和高可靠性,为大量指标实现了高级操作洞察和长期存储解决方案。
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"
VictoriaMetrics
[[outputs.influxdb]]
## URL of the VictoriaMetrics write endpoint
urls = ["http://localhost:8428"]
## VictoriaMetrics accepts InfluxDB line protocol directly
database = "db_name"
## Optional authentication
# username = "username"
# password = "password"
# skip_database_creation = true
# exclude_retention_policy_tag = true
# content_encoding = "gzip"
## Timeout for HTTP requests
timeout = "5s"
## 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
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 插件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。
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异常检测系统:将 Kafka 与机器学习算法结合使用,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。
VictoriaMetrics
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云原生应用程序监控:将来自 Kubernetes 上部署的微服务的指标直接流式传输到 VictoriaMetrics。通过集中指标,组织可以在动态演变的云环境中执行实时监控、快速异常检测和无缝可扩展性。
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可扩展的物联网数据管理:使用此插件将来自物联网部署的传感器数据摄取到 VictoriaMetrics 中。此方法有助于实时分析、预测性维护和高效管理海量传感器数据,同时最大限度地减少存储开销。
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金融系统性能跟踪:通过此插件利用 VictoriaMetrics 存储和分析来自金融系统的指标,捕获延迟、交易量和错误率。组织可以快速识别和解决性能瓶颈,确保高可用性和法规遵从性。
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跨环境性能仪表板:将来自不同基础设施组件(例如云实例、容器和物理服务器)的指标集成到 VictoriaMetrics 中。使用可视化工具,团队可以构建全面的仪表板,以实现端到端的性能可见性、主动故障排除和基础设施优化。
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