# Read metrics from Kafka topics
[[inputs.kafka_consumer]]
  ## Kafka brokers.
  brokers = ["localhost:9092"]

  ## 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 = [ ]

  ## Topic regexp refresh interval.  If enabled, and if regular expressions
  ## are enabled, available topics will be rescanned at this interval to
  ## determine whether new ones are present.
  ## Exmaple: topic_refresh_interval = "5m"
  # topic_refresh_interval = ""

  ## When set this tag will be added to all metrics with the topic as the value.
  # topic_tag = ""

  ## Optional Client id
  # client_id = "Telegraf"

  ## Set the minimal supported Kafka version.  Setting this enables the use of new
  ## Kafka features and APIs.  Must be 0.10.2.0 or greater.
  ##   ex: version = "1.1.0"
  # version = ""

  ## 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

  ## 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"