Kafka and IoTDB Integration
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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.
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Input and output integration overview
This plugin allows you to gather metrics from Kafka topics in real-time, enhancing data monitoring and collection capabilities within your Telegraf setup.
This plugin saves Telegraf metrics to an Apache IoTDB backend, supporting session connection and data insertion.
Integration details
Kafka
The Kafka Telegraf plugin is designed to read data from Kafka topics and create metrics using supported input data formats. As a service input plugin, it listens continuously for incoming metrics and events, differing from standard input plugins that operate at fixed intervals. This particular plugin can utilize features from various Kafka versions and is capable of consuming messages from specified topics, applying configurations such as security credentials using SASL, and managing message processing with options for message offsets and consumer groups. The flexibility of this plugin allows it to handle a wide array of message formats and use cases, making it a valuable asset for applications relying on Kafka for data ingestion.
IoTDB
Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Its light-weight architecture, high performance, and rich feature set create a perfect fit for massive data storage, high-speed data ingestion, and complex analytics in the IoT industrial fields. IoTDB deeply integrates with Apache Hadoop, Spark, and Flink, which further enhances its capabilities in handling large scale data and sophisticated processing tasks.
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"
IoTDB
[[outputs.iotdb]]
## Configuration of IoTDB server connection
host = "127.0.0.1"
# port = "6667"
## Configuration of authentication
# user = "root"
# password = "root"
## Timeout to open a new session.
## A value of zero means no timeout.
# timeout = "5s"
## Configuration of type conversion for 64-bit unsigned int
## IoTDB currently DOES NOT support unsigned integers (version 13.x).
## 32-bit unsigned integers are safely converted into 64-bit signed integers by the plugin,
## however, this is not true for 64-bit values in general as overflows may occur.
## The following setting allows to specify the handling of 64-bit unsigned integers.
## Available values are:
## - "int64" -- convert to 64-bit signed integers and accept overflows
## - "int64_clip" -- convert to 64-bit signed integers and clip the values on overflow to 9,223,372,036,854,775,807
## - "text" -- convert to the string representation of the value
# uint64_conversion = "int64_clip"
## Configuration of TimeStamp
## TimeStamp is always saved in 64bits int. timestamp_precision specifies the unit of timestamp.
## Available value:
## "second", "millisecond", "microsecond", "nanosecond"(default)
# timestamp_precision = "nanosecond"
## Handling of tags
## Tags are not fully supported by IoTDB.
## A guide with suggestions on how to handle tags can be found here:
## https://iotdb.apache.org/UserGuide/Master/API/InfluxDB-Protocol.html
##
## Available values are:
## - "fields" -- convert tags to fields in the measurement
## - "device_id" -- attach tags to the device ID
##
## For Example, a metric named "root.sg.device" with the tags `tag1: "private"` and `tag2: "working"` and
## fields `s1: 100` and `s2: "hello"` will result in the following representations in IoTDB
## - "fields" -- root.sg.device, s1=100, s2="hello", tag1="private", tag2="working"
## - "device_id" -- root.sg.device.private.working, s1=100, s2="hello"
# convert_tags_to = "device_id"
## Handling of unsupported characters
## Some characters in different versions of IoTDB are not supported in path name
## A guide with suggetions on valid paths can be found here:
## for iotdb 0.13.x -> https://iotdb.apache.org/UserGuide/V0.13.x/Reference/Syntax-Conventions.html#identifiers
## for iotdb 1.x.x and above -> https://iotdb.apache.org/UserGuide/V1.3.x/User-Manual/Syntax-Rule.html#identifier
##
## Available values are:
## - "1.0", "1.1", "1.2", "1.3" -- enclose in `` the world having forbidden character
## such as @ $ # : [ ] { } ( ) space
## - "0.13" -- enclose in `` the world having forbidden character
## such as space
##
## Keep this section commented if you don't want to sanitize the path
# sanitize_tag = "1.3"
Input and output integration examples
Kafka
-
Real-Time Data Processing: Use the Kafka plugin to feed live data from a Kafka topic into a monitoring system. This can be particularly useful for applications that require instant feedback on performance metrics or user activity, allowing businesses to react more swiftly to changing conditions in their environments.
-
Dynamic Metrics Collection: Leverage this plugin to dynamically adjust the metrics being captured based on events occurring within Kafka. For instance, by integrating with other services, users can have the plugin reconfigure itself on-the-fly, ensuring relevant metrics are always collected according to the needs of the business or application.
-
Centralized Logging and Monitoring: Implement a centralized logging system using the Kafka Consumer Plugin to aggregate logs from multiple services into a unified monitoring dashboard. This setup can help identify issues across different services and improve overall system observability and troubleshooting capabilities.
-
Anomaly Detection System: Combine Kafka with machine learning algorithms for real-time anomaly detection. By constantly analyzing streaming data, this setup can automatically identify unusual patterns, triggering alerts and mitigating potential issues more effectively.
IoTDB
-
Real-Time IoT Monitoring: Utilize the IoTDB plugin to gather sensor data from various IoT devices and save it in an Apache IoTDB backend, facilitating real-time monitoring of environmental conditions such as temperature and humidity. This use case enables organizations to analyze trends over time and make informed decisions based on historical data, while also utilizing IoTDB’s efficient storage and querying capabilities.
-
Smart Agriculture Data Collection: Use the IoTDB plugin to collect metrics from smart agriculture sensors deployed in fields. By transmitting moisture levels, nutrient content, and atmospheric conditions to IoTDB, farmers can access detailed insights into optimal planting and watering schedules, thus improving crop yields and resource management.
-
Energy Consumption Analytics: Leverage the IoTDB plugin to track energy consumption metrics from smart meters across a utility network. This integration enables analytics to identify peaks in usage and predict future consumption patterns, ultimately supporting energy conservation initiatives and improved utility management.
-
Automated Industrial Equipment Monitoring: Use this plugin to gather operational metrics from machinery in a manufacturing plant and store them in IoTDB for analysis. This setup can help identify inefficiencies, predictive maintenance needs, and operational anomalies, ensuring optimal performance and minimizing unexpected downtimes.
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
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