Kafka 和 MySQL 集成

强大的性能和简单的集成,由 InfluxData 构建的开源数据连接器 Telegraf 提供支持。

info

对于大规模实时查询,这不是推荐的配置。 为了查询和压缩优化、高速摄取和高可用性,您可能需要考虑Kafka 和 InfluxDB

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时间序列数据库
来源:DB Engines

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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可以与 Telegraf 一起扩展。

查看入门方法

输入和输出集成概述

此插件允许您实时从 Kafka 主题收集指标,增强 Telegraf 设置中的数据监控和收集能力。

Telegraf SQL 插件允许您将 Telegraf 的指标直接存储到 MySQL 数据库中,从而更轻松地分析和可视化收集的指标。

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,以及使用消息偏移和消费者组的选项管理消息处理。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。

MySQL

Telegraf 的 SQL 输出插件旨在通过基于传入指标动态创建表和列,将指标数据无缝写入 SQL 数据库。当配置为 MySQL 时,该插件利用 go-sql-driver/mysql,这需要启用 ANSI_QUOTES SQL 模式以确保正确处理带引号的标识符。这种动态模式创建方法确保每个指标都存储在自己的表中,其结构源自其字段和标签,从而提供系统性能的详细时间戳记录。该插件的灵活性使其能够处理高吞吐量环境,使其成为需要强大、精细的指标日志记录和历史数据分析的场景的理想选择。

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"

MySQL

[[outputs.sql]]
  ## Database driver
  ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
  ##  sqlite (SQLite3), snowflake (snowflake.com) clickhouse (ClickHouse)
  driver = "mysql"

  ## Data source name
  ## The format of the data source name is different for each database driver.
  ## See the plugin readme for details.
  data_source_name = "username:password@tcp(host:port)/dbname"

  ## Timestamp column name
  timestamp_column = "timestamp"

  ## Table creation template
  ## Available template variables:
  ##  {TABLE} - table name as a quoted identifier
  ##  {TABLELITERAL} - table name as a quoted string literal
  ##  {COLUMNS} - column definitions (list of quoted identifiers and types)
  table_template = "CREATE TABLE {TABLE}({COLUMNS})"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - tablename as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL
  init_sql = "SET sql_mode='ANSI_QUOTES';"

  ## Maximum amount of time a connection may be idle. "0s" means connections are
  ## never closed due to idle time.
  connection_max_idle_time = "0s"

  ## Maximum amount of time a connection may be reused. "0s" means connections
  ## are never closed due to age.
  connection_max_lifetime = "0s"

  ## Maximum number of connections in the idle connection pool. 0 means unlimited.
  connection_max_idle = 2

  ## Maximum number of open connections to the database. 0 means unlimited.
  connection_max_open = 0

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of the
  ## table

  ## Metric type to SQL type conversion
  ## The values on the left are the data types Telegraf has and the values on
  ## the right are the data types Telegraf will use when sending to a database.
  ##
  ## The database values used must be data types the destination database
  ## understands. It is up to the user to ensure that the selected data type is
  ## available in the database they are using. Refer to your database
  ## documentation for what data types are available and supported.
  #[outputs.sql.convert]
  #  integer              = "INT"
  #  real                 = "DOUBLE"
  #  text                 = "TEXT"
  #  timestamp            = "TIMESTAMP"
  #  defaultvalue         = "TEXT"
  #  unsigned             = "UNSIGNED"
  #  bool                 = "BOOL"
  #  ## This setting controls the behavior of the unsigned value. By default the
  #  ## setting will take the integer value and append the unsigned value to it. The other
  #  ## option is "literal", which will use the actual value the user provides to
  #  ## the unsigned option. This is useful for a database like ClickHouse where
  #  ## the unsigned value should use a value like "uint64".
  #  # conversion_style = "unsigned_suffix"

输入和输出集成示例

Kafka

  1. 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程序尤其有用,从而使企业能够更快地对其环境中的变化条件做出反应。

  2. 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。

  3. 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。

  4. 异常检测系统:将 Kafka 与机器学习算法相结合进行实时异常检测。 通过不断分析流数据,此设置可以自动识别异常模式,从而更有效地触发警报和缓解潜在问题。

MySQL

  1. 实时 Web 分析存储:利用该插件捕获网站性能指标并将其存储在 MySQL 中。 此设置使团队能够监控用户交互,分析流量模式,并根据实时数据洞察动态调整站点功能。

  2. 物联网设备监控:利用该插件从物联网传感器网络收集指标,并将它们记录到 MySQL 数据库中。 此用例支持对设备健康和性能的持续监控,从而实现预测性维护和对异常的即时响应。

  3. 金融交易日志记录:记录具有精确时间戳的高频金融交易数据。 这种方法支持强大的审计跟踪、实时欺诈检测以及用于合规性和报告目的的全面历史分析。

  4. 应用程序性能基准测试:将该插件与应用程序性能监控系统集成,以将指标记录到 MySQL 中。 这有助于随时间推移进行详细的基准测试和趋势分析,使组织能够识别性能瓶颈并有效地优化资源分配。

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可以与 Telegraf 一起扩展。

查看入门方法

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