Kafka and Azure Data Explorer Integration

<pre><code class="language-toml"> [[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 &amp; 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" </code></pre>

<pre><code class="language-toml">[[outputs.azure_data_explorer]] ## The URI property of the Azure Data Explorer resource on Azure ## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net endpoint_url = "" ## The Azure Data Explorer database that the metrics will be ingested into. ## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion. ## ex: "exampledatabase" database = "" ## Timeout for Azure Data Explorer operations # timeout = "20s" ## Type of metrics grouping used when pushing to Azure Data Explorer. ## Default is "TablePerMetric" for one table per different metric. ## For more information, please check the plugin README. # metrics_grouping_type = "TablePerMetric" ## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable"). # table_name = "" ## Creates tables and relevant mapping if set to true(default). ## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role. # create_tables = true ## Ingestion method to use. ## Available options are ## - managed -- streaming ingestion with fallback to batched ingestion or the "queued" method below ## - queued -- queue up metrics data and process sequentially # ingestion_type = "queued" </code></pre>