The Kafka consumer plugin reads from Kafka and creates metrics using one of the supported input data formats.
For old kafka version (< 0.8), please use the kafka_consumer_legacy input plugin and use the old zookeeper connection method.
In addition to the plugin-specific configuration settings, plugins support additional global and plugin configuration settings. These settings are used to modify metrics, tags, and field or create aliases and configure ordering, etc. See the CONFIGURATION.md for more details.
# Read metrics from Kafka topics
[[inputs.kafka_consumer]]
## Kafka brokers.
brokers = ["localhost:9092"]
## Topics to consume.
topics = ["telegraf"]
## 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 (experimental)
# 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 (experimental)
# 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
## Maximum messages to read from the broker that have not been written by an
## output. For best throughput set based on the number of metrics within
## each message and the size of the output's metric_batch_size.
##
## For example, if each message from the queue contains 10 metrics and the
## output metric_batch_size is 1000, setting this to 100 will ensure that a
## full batch is collected and the write is triggered immediately without
## waiting until the next flush_interval.
# 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"The plugin accepts arbitrary input and parses it according to the data_format
setting. There is no predefined metric format.
There is no predefined metric format, so output depends on plugin input.