Stream processing with Apache Kafka and Azure Databricks

This article describes how you can use Apache Kafka as either a source or a sink when running Structured Streaming workloads on Azure Databricks.

For more Kafka, see the Kafka documentation.

Read data from Kafka

The following is an example for a streaming read from Kafka:

df = (spark.readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "<server:ip>")
  .option("subscribe", "<topic>")
  .option("startingOffsets", "latest")
  .load()
)

Azure Databricks also supports batch read semantics for Kafka data sources, as shown in the following example:

df = (spark
  .read
  .format("kafka")
  .option("kafka.bootstrap.servers", "<server:ip>")
  .option("subscribe", "<topic>")
  .option("startingOffsets", "earliest")
  .option("endingOffsets", "latest")
  .load()
)

For incremental batch loading, Databricks recommends using Kafka with Trigger.AvailableNow. See Configuring incremental batch processing.

In Databricks Runtime 13.3 LTS and above, Azure Databricks provides a SQL function for reading Kafka data. Streaming with SQL is supported only in Delta Live Tables or with streaming tables in Databricks SQL. See read_kafka table-valued function.

Configure Kafka Structured Streaming reader

Azure Databricks provides the kafka keyword as a data format to configure connections to Kafka 0.10+.

The following are the most common configurations for Kafka:

There are multiple ways of specifying which topics to subscribe to. You should provide only one of these parameters:

Option	Value	Description
subscribe	A comma-separated list of topics.	The topic list to subscribe to.
subscribePattern	Java regex string.	The pattern used to subscribe to topic(s).
assign	JSON string {"topicA":[0,1],"topic":[2,4]}.	Specific topicPartitions to consume.

Other notable configurations:

Option	Value	Default Value	Description
kafka.bootstrap.servers	Comma-separated list of host:port.	empty	[Required] The Kafka bootstrap.servers configuration. If you find there is no data from Kafka, check the broker address list first. If the broker address list is incorrect, there might not be any errors. This is because Kafka client assumes the brokers will become available eventually and in the event of network errors retry forever.
failOnDataLoss	true or false.	true	[Optional] Whether to fail the query when it's possible that data was lost. Queries can permanently fail to read data from Kafka due to many scenarios such as deleted topics, topic truncation before processing, and so on. We try to estimate conservatively whether data was possibly lost or not. Sometimes this can cause false alarms. Set this option to false if it does not work as expected, or you want the query to continue processing despite data loss.
minPartitions	Integer >= 0, 0 = disabled.	0 (disabled)	[Optional] Minimum number of partitions to read from Kafka. You can configure Spark to use an arbitrary minimum of partitions to read from Kafka using the minPartitions option. Normally Spark has a 1-1 mapping of Kafka topicPartitions to Spark partitions consuming from Kafka. If you set the minPartitions option to a value greater than your Kafka topicPartitions, Spark will divvy up large Kafka partitions to smaller pieces. This option can be set at times of peak loads, data skew, and as your stream is falling behind to increase processing rate. It comes at a cost of initializing Kafka consumers at each trigger, which may impact performance if you use SSL when connecting to Kafka.
kafka.group.id	A Kafka consumer group ID.	not set	[Optional] Group ID to use while reading from Kafka. Use this with caution. By default, each query generates a unique group ID for reading data. This ensures that each query has its own consumer group that does not face interference from any other consumer, and therefore can read all of the partitions of its subscribed topics. In some scenarios (for example, Kafka group-based authorization), you may want to use specific authorized group IDs to read data. You can optionally set the group ID. However, do this with extreme caution as it can cause unexpected behavior. * Concurrently running queries (both, batch and streaming) with the same group ID are likely interfere with each other causing each query to read only part of the data. * This may also occur when queries are started/restarted in quick succession. To minimize such issues, set the Kafka consumer configuration session.timeout.ms to be very small.
startingOffsets	earliest , latest	latest	[Optional] The start point when a query is started, either "earliest" which is from the earliest offsets, or a json string specifying a starting offset for each TopicPartition. In the json, -2 as an offset can be used to refer to earliest, -1 to latest. Note: For batch queries, latest (either implicitly or by using -1 in json) is not allowed. For streaming queries, this only applies when a new query is started, and that resuming will always pick up from where the query left off. Newly discovered partitions during a query will start at earliest.

See Structured Streaming Kafka Integration Guide for other optional configurations.

Schema for Kafka records

The schema of Kafka records is:

Column	Type
key	binary
value	binary
topic	string
partition	int
offset	long
timestamp	long
timestampType	int

The key and the value are always deserialized as byte arrays with the ByteArrayDeserializer. Use DataFrame operations (such as cast("string")) to explicitly deserialize the keys and values.

Write data to Kafka

The following is an example for a streaming write to Kafka:

(df
  .writeStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "<server:ip>")
  .option("topic", "<topic>")
  .start()
)

Azure Databricks also supports batch write semantics to Kafka data sinks, as shown in the following example:

(df
  .write
  .format("kafka")
  .option("kafka.bootstrap.servers", "<server:ip>")
  .option("topic", "<topic>")
  .save()
)

Configure Kafka Structured Streaming writer

The schema provided to the DataStreamWriter interacts with the Kafka sink. You can use the following fields:

Column name	Required or optional	Type
key	optional	STRING or BINARY
value	required	STRING or BINARY
headers	optional	ARRAY
topic	optional (ignored if topic is set as writer option)	STRING
partition	optional	INT

The following are common options set while writing to Kafka:

Option	Value	Default value	Description
kafka.boostrap.servers	A comma-separated list of <host:port>	none	[Required] The Kafka bootstrap.servers configuration.
topic	STRING	not set	[Optional] Sets the topic for all rows to be written. This option overrides any topic column that exists in the data.
includeHeaders	BOOLEAN	false	[Optional] Whether to include the Kafka headers in the row.

See Structured Streaming Kafka Integration Guide for other optional configurations.

Retrieve Kafka metrics

You can get the average, min, and max of the number of offsets that the streaming query is behind the latest available offset among all the subscribed topics with the avgOffsetsBehindLatest, maxOffsetsBehindLatest, and minOffsetsBehindLatest metrics. See Reading Metrics Interactively.

Get the estimated total number of bytes that the query process has not consumed from the subscribed topics by examining the value of estimatedTotalBytesBehindLatest. This estimate is based on the batches that were processed in the last 300 seconds. The timeframe that the estimate is based on can be changed by setting the option bytesEstimateWindowLength to a different value. For example, to set it to 10 minutes:

df = (spark.readStream
  .format("kafka")
  .option("bytesEstimateWindowLength", "10m") # m for minutes, you can also use "600s" for 600 seconds
)

If you are running the stream in a notebook, you can see these metrics under the Raw Data tab in the streaming query progress dashboard:

{
  "sources" : [ {
    "description" : "KafkaV2[Subscribe[topic]]",
    "metrics" : {
      "avgOffsetsBehindLatest" : "4.0",
      "maxOffsetsBehindLatest" : "4",
      "minOffsetsBehindLatest" : "4",
      "estimatedTotalBytesBehindLatest" : "80.0"
    },
  } ]
}

Use SSL to connect Azure Databricks to Kafka

To enable SSL connections to Kafka, follow the instructions in the Confluent documentation Encryption and Authentication with SSL. You can provide the configurations described there, prefixed with kafka., as options. For example, you specify the trust store location in the property kafka.ssl.truststore.location.

Databricks recommends that you:

• Store your certificates in cloud object storage. You can restrict access to the certificates only to clusters that can access Kafka. See Data governance with Unity Catalog.
• Store your certificate passwords as secrets in a secret scope.

The following example uses object storage locations and Databricks secrets to enable an SSL connection:

df = (spark.readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", ...)
  .option("kafka.security.protocol", "SASL_SSL")
  .option("kafka.ssl.truststore.location", <truststore-location>)
  .option("kafka.ssl.keystore.location", <keystore-location>)
  .option("kafka.ssl.keystore.password", dbutils.secrets.get(scope=<certificate-scope-name>,key=<keystore-password-key-name>))
  .option("kafka.ssl.truststore.password", dbutils.secrets.get(scope=<certificate-scope-name>,key=<truststore-password-key-name>))
)

Connect Kafka on HDInsight to Azure Databricks

1. Create an HDInsight Kafka cluster.

   See Connect to Kafka on HDInsight through an Azure Virtual Network for instructions.

2. Configure the Kafka brokers to advertise the correct address.

   Follow the instructions in Configure Kafka for IP advertising. If you manage Kafka yourself on Azure Virtual Machines, make sure that the advertised.listeners configuration of the brokers is set to the internal IP of the hosts.

3. Create an Azure Databricks cluster.

4. Peer the Kafka cluster to the Azure Databricks cluster.

   Follow the instructions in Peer virtual networks.

Service Principal authentication with Microsoft Entra ID and Azure Event Hubs

Azure Databricks supports the authentication of Spark jobs with Event Hubs services. This authentication is done via OAuth with Microsoft Entra ID.

Azure Databricks supports Microsoft Entra ID authentication with a client ID and secret in the following compute environments:

• Databricks Runtime 12.2 LTS and above on compute configured with single user access mode.
• Databricks Runtime 14.3 LTS and above on compute configured with shared access mode.
• Delta Live Tables pipelines configured without Unity Catalog.

Azure Databricks does not support Microsoft Entra ID authentication with a certificate in any compute environment, or in Delta Live Tables pipelines configured with Unity Catalog.

This authentication does not work on shared clusters or on Unity Catalog Delta Live Tables.

Configuring the Structured Streaming Kafka Connector

To perform authentication with Microsoft Entra ID, you'll need the following values:

• A tenant ID. You can find this in the Microsoft Entra ID services tab.

• A clientID (also known as Application ID).

• A client secret. Once you have this, you should add it as a secret to your Databricks Workspace. To add this secret, see Secret management.

• An EventHubs topic. You can find a list of topics in the Event Hubs section under the Entities section on a specific Event Hubs Namespace page. To work with multiple topics, you can set the IAM role at the Event Hubs level.

• An EventHubs server. You can find this on the overview page of your specific Event Hubs namespace:

  

Additionally, to use Entra ID, we need to tell Kafka to use the OAuth SASL mechanism (SASL is a generic protocol, and OAuth is a type of SASL "mechanism"):

• kafka.security.protocol should be SASL_SSL
• kafka.sasl.mechanism should be OAUTHBEARER
• kafka.sasl.login.callback.handler.class should be a fully qualified name of the Java class with a value of kafkashaded to the login callback handler of our shaded Kafka class. See the following example for the exact class.

Example

Next, let's look at a running example:

Python

# This is the only section you need to modify for auth purposes!
# ------------------------------
tenant_id = "..."
client_id = "..."
client_secret = dbutils.secrets.get("your-scope", "your-secret-name")

event_hubs_server = "..."
event_hubs_topic = "..."
# -------------------------------

sasl_config = f'kafkashaded.org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required clientId="{client_id}" clientSecret="{client_secret}" scope="https://{event_hubs_server}/.default" ssl.protocol="SSL";'

kafka_options = {
# Port 9093 is the EventHubs Kafka port
"kafka.bootstrap.servers": f"{event_hubs_server}:9093",
"kafka.sasl.jaas.config": sasl_config,
"kafka.sasl.oauthbearer.token.endpoint.url": f"https://login.partner.microsoftonline.cn/{tenant_id}/oauth2/v2.0/token",
"subscribe": event_hubs_topic,

# You should not need to modify these
"kafka.security.protocol": "SASL_SSL",
"kafka.sasl.mechanism": "OAUTHBEARER",
"kafka.sasl.login.callback.handler.class": "kafkashaded.org.apache.kafka.common.security.oauthbearer.secured.OAuthBearerLoginCallbackHandler"
}

df = spark.readStream.format("kafka").options(**kafka_options)

display(df)

Scala

// This is the only section you need to modify for auth purposes!
// -------------------------------
val tenantId = "..."
val clientId = "..."
val clientSecret = dbutils.secrets.get("your-scope", "your-secret-name")

val eventHubsServer = "..."
val eventHubsTopic = "..."
// -------------------------------

val saslConfig = s"""kafkashaded.org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required clientId="$clientId" clientSecret="$clientSecret" scope="https://$eventHubsServer/.default" ssl.protocol="SSL";"""

val kafkaOptions = Map(
// Port 9093 is the EventHubs Kafka port
"kafka.bootstrap.servers" -> s"$eventHubsServer:9093",
"kafka.sasl.jaas.config" -> saslConfig,
"kafka.sasl.oauthbearer.token.endpoint.url" -> s"https://login.partner.microsoftonline.cn/$tenantId/oauth2/v2.0/token",
"subscribe" -> eventHubsTopic,

// You should not need to modify these
"kafka.security.protocol" -> "SASL_SSL",
"kafka.sasl.mechanism" -> "OAUTHBEARER",
"kafka.sasl.login.callback.handler.class" -> "kafkashaded.org.apache.kafka.common.security.oauthbearer.secured.OAuthBearerLoginCallbackHandler"
)

val scalaDF = spark.readStream
  .format("kafka")
  .options(kafkaOptions)
  .load()

display(scalaDF)

Handling potential errors

• Streaming options are not supported.

  If you try to use this authentication mechanism in a Delta Live Tables pipeline configured with Unity Catalog you might receive the following error:

  

  To resolve this error, use a supported compute configuration. See Service Principal authentication with Microsoft Entra ID and Azure Event Hubs.

• Failed to create a new KafkaAdminClient.

  This is an internal error that Kafka throws if any of the following authentication options are incorrect:

  • Client ID (also known as Application ID)
  • Tenant ID
  • EventHubs server

  To resolve the error, verify that the values are correct for these options.

  Additionally, you might see this error if you modify the configuration options provided by default in the example (that you were asked not to modify), such as kafka.security.protocol.

• There are no records being returned

  If you are trying to display or process your DataFrame but aren't getting results, you will see the following in the UI.

  

  This message means that authentication was successful, but EventHubs didn't return any data. Some possible (though by no means exhaustive) reasons are:

  • You specified the wrong EventHubs topic.
  • The default Kafka configuration option for startingOffsets is latest, and you're not currently receiving any data through the topic yet. You can set startingOffsetstoearliest to start reading data starting from Kafka's earliest offsets.