Kafka and MongoDB Integration
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Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Input and output integration overview
The Kafka plugin reads from Kafka and creates metrics using one of the supported input data formats.
The MongoDB Plugin allows you to send metrics to a MongoDB instance.
Integration details
Kafka
The Kafka plugin allows you to read messages from Kafka topics and create metrics. It supports various features, including SASL authentication, message headers as tags, and different message consumption strategies.
MongoDB
This plugin sends metrics to MongoDB, automatically creating time series collections where they don’t already exist. Time series collections require MongoDB 5.0+.
Configuration
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"
MongoDB
[[outputs.mongodb]]
# connection string examples for mongodb
dsn = "mongodb://localhost:27017"
# dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"
# overrides serverSelectionTimeoutMS in dsn if set
# timeout = "30s"
# default authentication, optional
# authentication = "NONE"
# for SCRAM-SHA-256 authentication
# authentication = "SCRAM"
# username = "root"
# password = "***"
# for x509 certificate authentication
# authentication = "X509"
# tls_ca = "ca.pem"
# tls_key = "client.pem"
# # tls_key_pwd = "changeme" # required for encrypted tls_key
# insecure_skip_verify = false
# database to store measurements and time series collections
# database = "telegraf"
# granularity can be seconds, minutes, or hours.
# configuring this value will be based on your input collection frequency.
# see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
# granularity = "seconds"
# optionally set a TTL to automatically expire documents from the measurement collections.
# ttl = "360h"
Input and output integration examples
Kafka
- Real-Time Data Processing: Use the Kafka Consumer Input Plugin to read data from Kafka topics in real-time, allowing for immediate metrics generation and processing.
- SASL Authentication: Configure the plugin with SASL authentication to securely connect to Kafka brokers, ensuring that only authorized users can access the data.
- Multiple Topic Consumption: Set up the plugin to consume from multiple Kafka topics by specifying them in the configuration. This allows you to gather metrics from various data sources simultaneously.
- Message Transformation: Leverage the plugin’s ability to parse and transform messages into metrics based on the specified data_format, enabling tailored data handling for your specific use case.
MongoDB
-
Log Management: Integrate this plugin to send application logs directly to MongoDB for structured storage and flexible querying. You can analyze logs as time series data, aggregating logs by hour, day, or month.
-
Metric Capture: Use the plugin to capture system metrics (CPU, memory usage) in real-time and store them in MongoDB. The time-series collections will allow for efficient queries over time ranges.
-
Monitoring Solutions: Combine this output plugin with inputs from various sources, such as disk usage metrics, network statistics, or application performance data. It allows for consolidated monitoring dashboards with historical trends saved in MongoDB.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Related Integrations
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View Integration