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 a 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.ROWKEY : VARCHAR, 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 would the normal ksqlDB log. All entries are written under the processing logger hierarchy. 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

Restart the ksqlDB Server for the configuration change to take effect.

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 messages 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:

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).

message.deserializationError (STRUCT)

The contents of a message with type 0 (DESERIALIZATION_ERROR). Logged when a deserializer fails to deserialize a Kafka record.

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.recordProcessingError (STRUCT)

The contents of a message 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.productionError (STRUCT)

The contents of a message with type 2 (PRODUCTION_ERROR). Logged when a producer fails to publish a Kafka record.

message.productionError.errorMessage (STRING)

A string containing a human-readable error message detailing the error encountered.

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            | Format
------------------------------------------------------
 PROCESSING_LOG     | processing_log         | JSON
------------------------------------------------------

ksql> describe PROCESSING_LOG;

Name                 : PROCESSING_LOG
Field   | Type
---------------------------------------------------------------------------------------------------------------------------
 ROWTIME | BIGINT           (system)
 ROWKEY  | VARCHAR(STRING)  (system)
 LOGGER  | VARCHAR(STRING)
 LEVEL   | VARCHAR(STRING)
 TIME    | BIGINT
 MESSAGE | STRUCT<type INTEGER, deserializationError STRUCT<errorMessage VARCHAR(STRING), recordB64 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:

ksql> CREATE STREAM PROCESSING_LOG_STREAM (
         LOGGER STRING,
         LEVEL STRING,
         `TIME` BIGINT,
         MESSAGE STRUCT<
             `TYPE` INTEGER,
             deserializationError STRUCT<
                 errorMessage STRING,
                 cause ARRAY<STRING>,
                 recordB64 STRING>,
             recordProcessingError STRUCT<
                 errorMessage STRING,
                 cause ARRAY<STRING>,
                 record STRING>,
             productionError STRUCT<
                 errorMessage 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.

Page last revised on: 2020-06-19

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Last update: 2020-06-19