THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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Status
Current state: "Under Discussion"Accepted
Discussion thread: TBD here
JIRA: KAFKA-6738 here
Released: 2.0.0
The proposal discussed in this KIP is implemented in this pull request.
Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
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There are several places in Connect during which failures may occur. Any failure to deserialize, convert, process, or read/write a record in Kafka Connect can cause a task to fail. Although some errors can be addressed with SMTs or transformations or custom converters that check for malformed data, in general it is difficult to ensure correct and valid data or to tell Connect to skip problematic records.
Connect should allow users to configure how failures should be handled during all phases of processing records. Some failures such as the lack of availability of some external components may be resolved by simply retrying, while other errors should be recorded and the problem records simply skipped. Where possible, Connect should be able to record the error and optionally include the problematic records and configuration states of the connector, transform and converter. Since no single solution works for everyone, all of this error handling behavior should be configurable.
This proposal aims to change the Connect framework to allow it to automatically deal with errors while processing records in a Connector. By default, Connect will fail immediately when an error occurs, which is the previous Connect behavior. Therefore, all new behaviors must be explicitly enabled.
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Several new behaviors for handling and reporting errors are introduced, and all must be configured in the individual connector configurations.
Retry on Failure
Retry Connect will attempt to retry the failed operation for a configurable number of times, with total duration, starting with a fixed duration (value of 300ms) and with exponential backoff between each retry. The number of retries and backoff can be configured using the following new properties:
| Configuration Name | Description | Default Value | Domain |
|---|---|---|---|
| errors. |
| retry. |
| timeout | The total duration a failed operation will be retried for. | 0 | [-1, 0, 1, ... Long.MAX_VALUE], where -1 means infinite |
| duration. |
| errors. |
| retry.delay.max.ms | The maximum |
| delay between two consecutive retries (in milliseconds). Jitter will be added to the delay once this limit is reached to prevent any thundering herd issues. | 60000 | [1, ... Long.MAX_VALUE] |
Task Tolerance Limits
Tolerate up to a configurable number of failures errors in a task. A failed operation is declared to be an error only if Connect has exhausted all retry options. If the task fails to successfully perform an operation on a record within tolerance limit, the record is skipped. Once the tolerance limit (overall or rate) is reached, the task will fail. Tolerance limits can be configured using the following new properties:
| Config Option | Description | Default Value | Domain |
|---|
| errors.tolerance |
| Fail the task if we exceed specified number of errors in the observed duration. |
| none | [ |
| none, all]. |
Log Error Context
The error context and processing information can be logged along with the standard application logs using the following configuration properties:
| Config Option | Description | Default Value | Domain |
|---|---|---|---|
| errors.log.enable | Log the error context along with the other application logs |
| . This context includes details about the failed operation, and the record which caused the failure. | false | Boolean |
| errors.log.include.messages |
| Whether to include the Connect Record in every log. This is useful if users do not want records to be written to log files because they contain sensitive information, or are simply very large. If this property is disabled, Connect will still log some minimal information about the record (for example, the source partition and offset if it is a SourceRecord, and Kafka topic and offset if it is a SinkRecord). | false | Boolean |
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Dead Letter Queue (
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for Sink Connectors only)
For sink connectors, we will write the original record (from the Kafka topic the sink connector is consuming from) that failed in the converter or transformation step into a configurable Kafka topic. Produce a message which contains the processing context and error information to a Kafka topic. By default, the bootstrap.servers property in the worker config will be used to locate the Kafka cluster and the producer.* properties in the worker config will be used to configure the producer. This can be overridden using the errors.dlq.producer.* configs as shown below:
| Config Option | Description | Default Value | Domain |
|---|---|---|---|
| errors. |
| deadletterqueue.topic.name | The name of the |
| dead letter queue topic. If not set, this feature will be disabled. | "" | A valid Kafka topic name |
| errors. |
| deadletterqueue.topic.replication. |
| factor | Replication factor used to create the dead letter queue topic when it doesn't already exist. | 3 | [1 ... Short.MAX_VALUE] |
| errors. |
| deadletterqueue. |
| context. |
| headers. |
errors.dlq.producer.bootstrap.servers will set the bootstrap servers of the Kafka cluster).connect.json.JsonConverter
| enable | If true, multiple headers will be added to annotate the record with the error context | false | Boolean |
If the property errors.deadletterqueue.context.headers.enable is set to true, the following headers will be added to the produced raw message (only if they don't already exist in the message). All values will be serialized as UTF-8 strings.
| Header Name | Description |
|---|---|
| __connect.errors.topic | Name of the topic that contained the message. |
| __connect.errors.partition | The numeric ID of the partition in the original topic that contained the message (encoded as a UTF-8 string). |
| __connect.errors.offset | The numeric value of the message offset in the original topic (encoded as a UTF-8 string). |
| __connect.errors.connector.name | The name of the connector which encountered the error. |
| __connect.errors.task.id | The numeric ID of the task that encountered the error (encoded as a UTF-8 string). |
| __connect.errors.stage | The name of the stage where the error occurred. |
| __connect.errors.class.name | The fully qualified name of the class that caused the error. |
| __connect.errors.exception.class.name | The fully qualified classname of the exception that was thrown during the execution. |
| __connect.errors.exception.message | The message in the exception. |
| __connect.errors.exception.stacktrace | The stacktrace of the exception. |
Metrics
The following new metrics will monitor the number of failures, and the behavior of the response handler. Specifically, the following set of counters:
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MBean name: kafka.connect:type=task-error-metrics,connector=([-.\w]+),task=([-.\w]+)
Metric/Attribute Name | Description | Implemented |
|---|---|---|
| total-record-failures | Total number of failures seen by this task. | 2.0.0 |
| total-record-errors | Total number of errors seen by this task. | 2.0.0 |
| total-records-skipped | Total number of records skipped by this task. | 2.0.0 |
| total-retries | Total number of retries made by this task. | 2.0.0 |
| total-errors-logged | The number of messages that was logged into either the dead letter queue or with Log4j. | 2.0.0 |
| deadletterqueue- |
| produce- |
| requests | Number of |
| produce requests to the dead letter queue. | 2.0.0 |
| deadletterqueue-produce-failures | Number of records which failed to produce correctly to the dead letter queue. | 2.0.0 |
| last- |
| error-timestamp | The timestamp when the last |
| error occurred in this task. | 2.0.0 |
Proposed Changes
A connector consists of multiple stages. For source connectors, Connect retrieves the records from the connector, applies zero or more transformations, uses the converters to serialize each record’s key, value, and headers, and finally writes each record to Kafka. For sink connectors, Connect reads the topic(s), uses the converters to deserialize each record’s key, value, and headers, and for each record applies zero or more transformations and delivers the records to the sink connector. In this proposal, we will specifically deal with the following failure scenarios which can occur during these stages:
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| Operation | Will Retry? | Tolerated Exceptions |
|---|---|---|
| Transformation | only on org.apache.kafka.connect.errors.RetriableException | java.lang.Exception |
Key, Value and Header Converter | only on org.apache.kafka.connect.errors.RetriableException | java.lang.Exception |
Kafka Produce and Consume | only on org.apache.kafka.common.errors.RetriableException | only on org.apache.kafka.connect.errors.RetriableException, fail task otherwise. |
put() in SinkTask and poll() in SourceTask | only on org.apache.kafka.connect.errors.RetriableException | only on org.apache.kafka.connect.errors.RetriableException, fail task otherwise |
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There are two behavioral changes introduced by this KIP. First, a failure in any stage will be reattempted, thereby “blocking” the connector. This helps in situations where time is needed to manually update an external system, such as manually correcting a schema in the Schema Registry. More complex causes, such as requiring code changes or corruptions in the data that can’t be fixed externally, will require the worker to be stopped, data to be fixed and then the Connector to be restarted. In the case of data corruption, the topic might need to be cleaned up too. If the retry limit for a failure is reached, then the tolerance limit is used to determine if this record should be skipped, or if the task is to be killed. The second behavioral change is introduced in how we log report these failures. Currently, only the exception which kills the task is written with the application logs. With the additions presented in this KIP, more context will be logged:
- The record which failed to process (if available).
- The Exception along with the stack trace.
- Information about the stage which failed to process the record (for example: classname of the transform and its configs).
- The various stages in the connector, along with their configurations and order of processing.
- The number of attempts, and time taken for these attempts.
Logging errors with application logs is convenient and requires no additional setup. The log messages are informative but not made easily actionable. For example, it is hard to collect log files from various machines, parse them and take appropriate actions. By introducing a dead letter queue, we can overcome these problems. For sink connectors, a developer can consume bad records from this topic, correct them and write the corrected records back to the original Kafka topics. Similarly, for source connectors, the developer can write the corrected records back to the original source.
While logging the error context, it might be worthwhile to take precautions to hide sensitive content. For example, some of the configs might contain sensitive information such as usernames or passwords. To prevent logging critical information, we provide configuration options to disable logging the messages (errors.dlq.include.messages) and configs (errors.dlq.include.configs).
we are logging details about the failed operation along with the bad record. We are also introducing an option to write bad records into a dead letter queue for Sink Connectors. This would write the original key, value and headers of failed records into a configured Kafka topic.
Example 1: Fail Fast
To maintain backward compatibility, by default a Connector task will fail immediately upon an error or exception. This reflects the same behavior as earlier releases, ensuring that existing installations work the same way. Although it is not necessary to add extra configuration properties, the following properties may be added to a connector configuration to achieve this older behavior:
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# disable retries on failure errors.retriesretry.limittimeout=0 # do not log the error and their contexts errors.log.enable=false # do not record errors in a dead letter queue topic errors.dlqdeadletterqueue.topic.enablename=false # Fail on first failureerror errors.tolerance.limit=0none |
Example 2: Record and Skip
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# retry for at most 10010 minutes times waiting up to 530 minutesseconds between consecutive failures errors.retriesretry.limittimeout=100600000 errors.retriesretry.delay.max.ms=30000030000 # log error context along with application logs, but do not include configs and messages errors.log.enable=true errors.log.include.configs=false errors.log.include.messages=false # produce error context into the Kafka topic errors.dlq.enable=true errors.dlq.deadletterqueue.topic.name=my-connector-errors errors.dlq.topic.partitions=25 errors.dlq.topic.replication.factor=3 errors.dlq.include.configs=true errors.dlq.include.messages=true # Tolerate all errors. errors.tolerance.limit=-1 errors.tolerance.rate.limit=-1 errors.tolerance.rate.duration.ms=60000 |
Example 3: Record to separate Kafka cluster
In the previous example, errors are recorded in the log and in a separate "dead letter queue" (DLQ) Kafka topic in the same broker cluster that Connect is using for its internal topics. It is possible to record the errors in a DLQ on a separate Kafka cluster by defining extra errors.dlq.producer.* configuration properties. Here is the same set of connector configuration properties as in Example 2, except with the additional errors.dlq.producer.* properties:
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# retry up to 100 times waiting up to 5 minutes between consecutive failures errors.retries.limit=100 errors.retries.delay.max.ms=300000 # log error context along with application logs, but do not include configs and messages errors.log.enable=true errors.log.include.configs=false errors.log.include.messages=false # produce error context into a secure Kafka topic errors.dlq.enable=true errors.dlq.topic.name=my-connector-errors errors.dlq.topic.partitions=25 errors.dlq.topic.replication.factor=3 errors.dlq.include.configs=true errors.dlq.include.messages=true errors.dlq.producer.bootstrap.servers=secure-broker:9093 errors.dlq.producer.acks = 0 errors.dlq.producer.security.protocol=SSL errors.dlq.producer.ssl.truststore.location=/var/private/ssl/kafka.client.truststore.jks errors.dlq.producer.ssl.truststore.password=test1234 errors.dlq.producer.ssl.keystore.location=/var/private/ssl/kafka.client.keystore.jks errors.dlq.producer.ssl.keystore.password=test1234 errors.dlq.producer.ssl.key.password=test1234 # Tolerate all errors errors.tolerance.limit=-1 errors.tolerance.rate.limit=-1 errors.tolerance.rate.duration.ms=60000 |
Compatibility, Deprecation, and Migration Plan
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Defining and configuring properties in the worker config: Firstly, it is out of place to specify error handling properties in a worker config when it will never be used by a worker (since all the failures are handled at the Connector level). Secondly, adding inheritance in configurations adds a level of complexity which can be avoided at this stage of development.
Write records that fail in the put() step of a sink connector to the dead letter queue: since sink connectors can chose to batch records in a put() method, it is not clear what errors are caused by what records (they might be because of records that were immediately written to put(), or by some previous records that were processed later). Also, there might be connection issues that are not handled by the connector, and simply bubbled up as IOException (for example). Effectively, errors sent back to the framework from the put() method currently do not have sufficient context to determine the problematic records (if any). Addressing these issues would need a separate KIP.