Processing log¶

ksqlDB emits a log of record processing events, named the "processing log", to help you with debugging your SQL queries. As ksqlDB executes a query, it writes records to the processing log that detail how it processes each row, including any errors it encounters along the way.

Log entries are written with a hierarchical name that you can use to track back to the query execution plan. Log entries are structured events, so in addition to using them to help you debug, they should be easy to consume from downstream applications and from ksqlDB itself. In fact, ksqlDB supports writing the processing log to Kafka and consuming it as ksqlDB stream.

Important

The processing log is not for server logging, but rather for per-record logging on ksqlDB applications. If you want to configure an Kafka appender for the server logs, assign the log4j.appender.kafka_appender.Topic and log4j.logger.io.confluent.ksql configuration settings in the ksqlDB Server config file. For more information, see ksqlDB Server Log Settings.

Logger Names¶

The logger name identifies the logger that emits a log record. Logger names are hierarchical. The logger name always has the prefix processing.<query-id>, where query-id refers to the SQL query ID, which you can see with statements like LIST QUERIES;.

Loggers for a given query are organized into a hierarchy according to the step in the execution plan that uses the logger. You can configure the log level by using a prefix of the logger name to set the level for all loggers under that prefix. The logger name for a given step is included in the execution plan:

Execution plan
--------------
> [ SINK ] | Schema: [VIEWTIME : BIGINT, KSQL_COL_1 : VARCHAR, KSQL_COL_2 : VARCHAR] | Logger: processing.CSAS_PAGEVIEWS_UPPER_0.PAGEVIEWS_UPPER
     > [ PROJECT ] | Schema: [VIEWTIME : BIGINT, KSQL_COL_1 : VARCHAR, KSQL_COL_2 : VARCHAR] | Logger: processing.CSAS_PAGEVIEWS_UPPER_0.Project
         > [ SOURCE ] | Schema: [PAGEVIEWS_ORIGINAL.ROWTIME : BIGINT, PAGEVIEWS_ORIGINAL.VIEWTIME : BIGINT, PAGEVIEWS_ORIGINAL.USERID : VARCHAR, PAGEVIEWS_ORIGINAL.PAGEID : VARCHAR] | Logger: processing.CSAS_PAGEVIEWS_UPPER_0.KsqlTopic

Configuration Using Log4J¶

Internally, the log uses Log4J to write entries, so you can configure it just like you configure the normal ksqlDB log.

For local deployments, edit the log4j.properties config file to assign Log4J properties.
For Docker deployments, set the corresponding environment variables. For more information, see Configure ksqlDB with Docker and Configure Docker Logging.

All entries are written under the processing logger hierarchy.

Restart the ksqlDB Server for your configuration changes to take effect.

The following example shows how to configure the processing log to emit all events at ERROR level or higher to an appender that writes to stdout:

log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c:%L)%n
log4j.logger.processing=ERROR, stdout
log4j.additivity.processing=false

If you're using a Docker deployment, set the following environment variables in your docker-compose.yml:

environment:
    # --- ksqlDB Server log config ---
    KSQL_LOG4J_ROOT_LOGLEVEL: "ERROR"
    KSQL_LOG4J_LOGGERS: "org.apache.kafka.connect.runtime.rest=WARN,org.reflections=ERROR"
    # --- ksqlDB processing log config ---
    KSQL_LOG4J_PROCESSING_LOG_BROKERLIST: kafka:29092
    KSQL_LOG4J_PROCESSING_LOG_TOPIC: <ksql-processing-log-topic-name>
    KSQL_KSQL_LOGGING_PROCESSING_TOPIC_NAME: <ksql-processing-log-topic-name>
    KSQL_KSQL_LOGGING_PROCESSING_TOPIC_AUTO_CREATE: "true"
    KSQL_KSQL_LOGGING_PROCESSING_STREAM_AUTO_CREATE: "true"

For the full Docker example configuration, see the Multi-node ksqlDB and Kafka Connect clusters demo.

Processing Log Security¶

By default, the record-processing log doesn't log any actual row data. To help you debug, you can enable including row data in log records by setting the ksqlDB property ksql.logging.processing.rows.include to true.

If you do this, ensure that the log is configured to write to a destination where it is safe to write the data being processed. It's also important to set log4j.additivity.processing=false as shown in the previous example, to ensure that processing log events are not forwarded to appenders configured for the other ksqlDB loggers.

You can disable the log completely by setting the level to OFF in the log4j.properties file:

log4j.logger.processing=OFF

Note

To enable security for the ksqlDB Processing Log, assign Log4J properties as shown in log4j-secure.properties.

Log Schema¶

Log entries are structured and have the following schema:

logger (STRING): The name of the logger that wrote the log entry.
level (STRING): The log level that the entry was logged with.
time (LONG): The time that the entry was logged.
message (STRUCT): The log entry itself.
message.type (INT): An int that describes the type of the log message. Currently, the following types are defined: 0 (DESERIALIZATION_ERROR), 1 (RECORD_PROCESSING_ERROR), 2 (PRODUCTION_ERROR), 3 (SERIALIZATION_ERROR).
message.deserializationError (STRUCT): The contents of a message with type 0 (DESERIALIZATION_ERROR). Logged when a deserializer fails to deserialize an Kafka record.
message.deserializationError.target (STRING): Either "key" or "value" representing the target component of the row that failed to deserialize.
message.deserializationError.errorMessage (STRING): A string containing a human-readable error message detailing the error encountered.
message.deserializationError.recordB64 (STRING): The Kafka record, encoded in Base64.
message.deserializationError.cause (LIST): A list of strings containing human-readable error messages for the chain of exceptions that caused the main error.
message.deserializationError.topic (STRING): The Kafka topic of the record for which deserialization failed.
message.recordProcessingError (STRUCT): The contents of a record with type 1 (RECORD_PROCESSING_ERROR). Logged when ksqlDB hits an error when processing a record, for example, an unexpected null value when evaluating an operator in a SELECT clause.
message.recordProcessingError.errorMessage (STRING): A string containing a human-readable error message detailing the error encountered.
message.recordProcessingError.record (STRING): The SQL record, serialized as a JSON string.
message.recordProcessingError.cause (LIST): A list of strings containing human-readable error messages for the chain of exceptions that caused the main error.
message.productionError (STRUCT): The contents of a message with type 2 (PRODUCTION_ERROR). Logged when a producer fails to publish an Kafka record.
message.productionError.errorMessage (STRING): A string containing a human-readable error message detailing the error encountered.
message.serializationError (STRUCT): The contents of a message with type 3 (SERIALIZATION_ERROR). Logged when a serializer fails to serialize a ksqlDB row.
message.serializationError.target (STRING): Either "key" or "value" representing the target component of the row that failed to serialize.
message.serializationError.errorMessage (STRING): A string containing a human-readable error message detailing the error encountered.
message.serializationError.record (STRING): The ksqlDB row, as a human-readable string.
message.serializationError.cause (LIST): A list of strings containing human-readable error messages for the chain of exceptions that caused the main error.
message.serializationError.topic (STRING): The Kafka topic to which the ksqlDB row that failed to serialize would have been produced.
message.kafkaStreamsThreadError.message (STRING): A string containing a human-readable error message detailing the error encountered.
message.kafkaStreamsThreadError.name (STRING): A string containing the thread name.
message.kafkaStreamsThreadError.cause (LIST): A list of strings containing human-readable error messages for the chain of exceptions that caused the main error.

Log Stream¶

We recommend configuring the query processing log to write entries back to Kafka. This way, you can configure ksqlDB to set up a stream over the topic automatically.

To log to Kafka, set up a Kafka appender and a special layout for formatting the log entries as JSON:

log4j.appender.kafka_appender=org.apache.kafka.log4jappender.KafkaLog4jAppender
log4j.appender.kafka_appender.layout=io.confluent.common.logging.log4j.StructuredJsonLayout
log4j.appender.kafka_appender.BrokerList=<list of kafka brokers>
log4j.appender.kafka_appender.Topic=<kafka topic>
log4j.logger.processing=ERROR, kafka_appender

The list of kafka brokers setting is a comma-separated list of brokers in the Kafka cluster, and kafka topic is the name of the Kafka topic to log to.

To have ksqlDB set up the log topic automatically at startup, include the following in your ksqlDB properties file:

ksql.logging.processing.topic.auto.create=true
ksql.logging.processing.topic.name=<kafka topic>  # defaults to <ksql service id>ksql_processing_log

The replication factor and partition count are configurable using the ksql.logging.processing.topic.replication.factor and ksql.logging.processing.topic.partitions properties, respectively.

If ksql.logging.processing.topic.auto.create is set to true, the created topic will be deleted as part of cluster termination.

If the ksql.logging.processing.topic.name property is not specified, the processing log topic name will default to <ksql service id>processing_log, where ksql service id is the value from the ksql.service.id property. This ensures each ksqlDB cluster gets its own processing log topic by default.

If you are bringing up a new interactive mode ksqlDB cluster, you can configure ksqlDB to set up a log stream automatically by including the following in your ksqlDB properties file:

ksql.logging.processing.stream.auto.create=true
ksql.logging.processing.stream.name=<stream name>  # defaults to PROCESSING_LOG

When you start ksqlDB, you should see the stream in your list of streams:

ksql> list streams;

 Stream Name    | Kafka Topic      | Key Format | Value Format | Windowed
-------------------------------------------------------------------------
 PROCESSING_LOG | processing_log   | KAFKA      | JSON         |
-------------------------------------------------------------------------

ksql> describe PROCESSING_LOG;

Name                 : PROCESSING_LOG
Field   | Type
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 LOGGER  | VARCHAR(STRING)
 LEVEL   | VARCHAR(STRING)
 TIME    | BIGINT
 MESSAGE | STRUCT<type INTEGER, deserializationError STRUCT<target VARCHAR(STRING), errorMessage VARCHAR(STRING), recordB64 VARCHAR(STRING), cause ARRAY<VARCHAR(STRING)>, topic VARCHAR(STRING)>, ...> 
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

You can query the stream just like you would any other ksqlDB stream.

You can also create the stream yourself by issuing the following DDL statement:

CREATE STREAM PROCESSING_LOG_STREAM (
   LOGGER STRING,
   LEVEL STRING,
   `TIME` BIGINT,
   MESSAGE STRUCT<
       `TYPE` INTEGER,
       deserializationError STRUCT<
           target STRING,
           errorMessage STRING,
           recordB64 STRING,
           cause ARRAY<STRING>,
          `topic` STRING>,
       recordProcessingError STRUCT<
           errorMessage STRING,
           record STRING,
           cause ARRAY<STRING>>,
       productionError STRUCT<
           errorMessage STRING>,
       serializationError STRUCT<
           target STRING,
           errorMessage STRING,
           record STRING,
           cause ARRAY<STRING>,
          `topic` STRING>>,
       kafkaStreamsError STRUCT<
           threadName STRING,
           errorMessage STRING,
           cause ARRAY<STRING>>)
   WITH (KAFKA_TOPIC='processing_log_topic', VALUE_FORMAT='JSON');

Note

Processing log stream auto-creation is supported for interactive mode only. Enabling this setting in headless mode causes a warning to be printed to the server log.

Last update: 2022-02-22