Backup and Restore Google Cloud Storage Source Connector for Confluent Platform¶

The Kafka Connect Backup and Restore Google Cloud Storage (GCS) Source connector provides the capability to read data exported to GCS by the Kafka Connect GCS Sink connector and publish it back to a Kafka topic. Depending on the format and partitioner used to write the data to GCS, this connector can write to the destination topic using the same partitions as the original messages exported to GCS and maintain the same message order. The connector selects folders based on the partitioner configuration and reads each folders GCS objects in alphabetical order. Each record is read based on the format selected. Configuration is designed to mirror the Kafka Connect GCS Sink connector and should be possible to create source connector configs with only minor changes to the original sink configuration.

Important

The recommended practice is to create topics manually in the destination Kafka cluster with the correct number of partitions before running the source connector. If the topics do not exist, Connect relies on configuring auto topic creation for source connectors and the number of partitions are based upon the Kafka broker defaults. If there are more partitions in the destination cluster, the extra partitions are not used. If there are fewer partitions in the destination cluster, the connector task continuously checks for the presence of the required partition and does not produce records but remains in the running state.

Be aware of the following connector actions:

The connector ignores any GCS object with a name that does not start with the configured topics directory. This name is /topics/ by default.
The connector ignores any GCS object that is below the topics directory but has an extension that does not match the configured format. For example, a JSON file is ignored when format.class is set for Avro files.
The connector stops and fails if the GCS object’s name does not match the expected format or is in an unexpected location.
The connector ignores any GCS object that is below the topics directory but has a partitioner structure that does not match the configured class. For example, DefaultPartitioner structure is ignored when partitioner.class is set for TimeBasedPartitioner.

Avoid the following configuration issues:

A file with the correct extension and a valid name format e.g. <topic>+<partition>+<offset>.<extension>, placed in a folder of a different topic will be read normally and written to whatever topic as defined by its filename.
Do not configure a field partitioner to match the expected folder. Doing so can break the ordering that was reflected when a GCS sink exported the records using a deterministic sink partitioner.

Features¶

The Backup and Restore GCS Source connector includes a variety of features:

At least once delivery
Multiple tasks
Pluggable data format with or without schema
Matching source partitioning
Source partition ordering
Pluggable partitioner

At least once delivery¶

In the event of a task failure the connector guarantees no messages are lost, although the last few messages may be processed again.

Multiple tasks¶

The Backup and Restore GCS Source connector supports running one or more tasks. You can specify the number of tasks in the tasks.max configuration parameter. This can lead to performance gains when multiple files need to be parsed.

Pluggable data format with or without schema¶

Out of the box, the connector supports reading data from GCS in Avro and JSON format. Besides records with schema, the connector supports importing plain JSON records without schema in text files, one record per line. In general, the connector may accept any format that provides an implementation of the Format interface.

Matching source partitioning¶

Messages will be put back on to the same Kafka partition for that topic when it was written.

Source partition ordering¶

The connector will read records back in time order in each topic-source partition if the DefaultPartitioner or a TimeBasedPartitioner is used. If a FieldPartitioner is used it isn’t possible to guarantee the order of these messages.

Pluggable partitioner¶

The connector comes out of the box with partitioners that support default partitioning based on Kafka partitions, field partitioning, and time-based partitioning in days or hours. You may implement your own partitioners by extending the Partitioner class. Additionally, you can customize time based partitioning by extending the TimeBasedPartitioner class.

All partitioners will notice new topic folders with the inbuilt task reconfiguration thread. The DefaultPartitioner detects new partition folders. The FieldPartitioner notices new folders for the fields specified. However, the TimeBasedPartititoner does not currently detect new files for a new time period.

Be careful when both the Connect GCS Sink connector and the GCS Source connector use the same Kafka cluster, since this results in the source connector writing to the same topic being consumed by the sink connector. This causes a continuous feedback loop that creates an ever-increasing number of duplicate Kafka records and GCS objects. It is possible to avoid this feedback loop by writing to a different topic than the one being consumed by the sink connector. Use the RegexRouter with the source connector to change the names of the topics where the records are written. Or, use the Extract topic SMT with the source connector to change the topic name based upon a field in each message.

Tip

By default, connectors inherit the partitioner used for the Kafka topic. You can create a custom partitioner for a connector which you must place in the connector’s /lib folder.

You can also put partitioners in a common location of choice. If you choose this option, you must add a symlink to the location from each connector’s /lib folder. For example, you would place a custom partitioner in the path share/confluent-hub-components/partitioners and then add the symlink share/confluent-hub-components/kafka-connect-s3/lib/partitioners -> ../../partitioners.

Limitations¶

For the TimeBasedPartitioner, the capacity to scale the connector across various time ranges is limited in Backup and Restore mode. Currently, the connector does not support processing data that spans several years.

License¶

You can use this connector for a 30-day trial period without a license key.

After 30 days, you must purchase a connector subscription which includes Confluent enterprise license keys to subscribers, along with enterprise-level support for Confluent Platform and your connectors. If you are a subscriber, you can contact Confluent Support at support@confluent.io for more information.

For license properties, see Confluent Platform license. For information about the license topic, see Confluent License Properties.

Configuration Properties¶

For a complete list of configuration properties for this connector, see Configuration Reference for Google Cloud Storage Source Connector for Confluent Platform.

For an example of how to get Kafka Connect connected to Confluent Cloud, see Connect Self-Managed Kafka Connect to Confluent Cloud.

Install the Backup and Restore GCS Source Connector¶

You can install this connector by using the confluent connect plugin install command, or by manually downloading the ZIP file.

Prerequisites¶

You must install the connector on every machine where Connect will run.
Kafka Broker: Confluent Platform 3.3.0 or later, or Kafka 0.11.0 or later.
Connect: Confluent Platform 4.0.0 or later, or Kafka 1.0.0 or later.
Java 1.8.
An installation of the latest (latest) connector version.

To install the latest connector version, navigate to your Confluent Platform installation directory and run the following command:
```
confluent connect plugin install confluentinc/kafka-connect-gcs-source:latest
```
You can install a specific version by replacing latest with a version number as shown in the following example:
```
confluent connect plugin install confluentinc/kafka-connect-gcs-source:2.6.0
```

Install the connector manually¶

Download and extract the ZIP file for your connector and then follow the manual connector installation instructions.

Quick Start¶

The following uses the GcsSinkConnector to write a file from the Kafka topic named gcs_topic to GCS. Then, the GcsSourceConnector loads that Avro file from GCS to the Kafka topic named copy_of_gcs_topic.

Follow the instructions from the GCS Sink connector quick start to set up the data to use below.
Install the connector through the Confluent Hub Client.
```
# run from your Confluent Platform installation directory
confluent connect plugin install confluentinc/kafka-connect-gcs-source:latest
```
Tip

By default, the connector will install the plugin into the share/confluent-hub-components directory and add the directory to the plugin path. For the plugin path change to take effect, you must restart the Connect worker.

Property-based example¶

Create a gcs-source-connector.properties file with the following contents. This file is included with the connector in etc/kafka-connect-gcs/gcs-source-connector.properties. This configuration is used typically along with standalone workers.:
```
name=gcs-source
tasks.max=1
connector.class=io.confluent.connect.gcs.GcsSourceConnector

# enter the bucket name and GCS credentials here
gcs.bucket.name=<bucket-name>
gcs.credentials.path=</full/path/to/credentials/keys.json>

format.class=io.confluent.connect.gcs.format.avro.AvroFormat

confluent.topic.bootstrap.servers=localhost:9092
confluent.topic.replication.factor=1

# for production environments, enter the Confluent license here
# confluent.license=
```
Tip

The following define the Confluent license stored in Kafka, so we need the Kafka bootstrap addresses. replication.factor may not be larger than the number of Kafka brokers in the destination cluster, so here we set this to ‘1’ for demonstration purposes. Always use at least ‘3’ in production configurations.
Edit the gcs-source-connector.properties to add the following properties:
```
transforms=AddPrefix
transforms.AddPrefix.type=org.apache.kafka.connect.transforms.RegexRouter
transforms.AddPrefix.regex=.*
transforms.AddPrefix.replacement=copy_of_$0
```
Important

Adding this renames the output of topic of the messages to copy_of_gcs_topic. This prevents a continuous feedback loop of messages.
Load the Backup and Restore GCS Source connector.

Caution

You must include a double dash (--) between the connector name and your flag. For more information, see this post.
```
confluent local services connect connector load gcs-source --config gcs-source-connector.properties
```
Important

Don’t use the Confluent CLI in production environments.

Confirm that the connector is in a RUNNING state.

confluent local services connect connector status gcs-source

Confirm that the messages are being sent to Kafka.

kafka-avro-console-consumer \
    --bootstrap-server localhost:9092 \
    --property schema.registry.url=http://localhost:8081 \
    --topic copy_of_gcs_topic \
    --from-beginning | jq '.'

The response should be 9 records as follows.

{"f1": "value1"}
{"f1": "value2"}
{"f1": "value3"}
{"f1": "value4"}
{"f1": "value5"}
{"f1": "value6"}
{"f1": "value7"}
{"f1": "value8"}
{"f1": "value9"}

REST-based example¶

Use this setting with distributed workers. Write the following JSON to config.json, configure all of the required values, and use the following command to post the configuration to one of the distributed connect workers. Check here for more information about the Kafka Connect REST API.

{
  "name" : "GCSSourceConnector",
  "config" : {
    "format.class": "io.confluent.connect.gcs.format.avro.AvroFormat",
    "connector.class" : "io.confluent.connect.gcs.GcsSourceConnector",
    "gcs.bucket.name" : "confluent-kafka-connect-gcs-testing",
    "gcs.credentials.path" : "#/path/to/credentials/keys.json",
    "tasks.max" : "1",
    "confluent.topic.bootstrap.servers" : "localhost:9092",
    "confluent.topic.replication.factor" : "1",
    "confluent.license" : " Omit to enable trial mode ",
    "transforms" : "AddPrefix",
    "transforms.AddPrefix.type" : "org.apache.kafka.connect.transforms.RegexRouter",
    "transforms.AddPrefix.regex" : ".*",
    "transforms.AddPrefix.replacement" : "copy_of_$0"
  }
}

Note

Change the confluent.topic.bootstrap.servers property to include your broker address(es), and change the confluent.topic.replication.factor to 3 for staging or production use.

Use curl to post a configuration to one of the Kafka Connect Workers. Change http://localhost:8083/ to the endpoint of one of your Kafka Connect worker(s).
```
curl -s -X POST -H 'Content-Type: application/json' --data @config.json http://localhost:8083/connectors
```

Use the following command to update the configuration of existing connector.

curl -s -X PUT -H 'Content-Type: application/json' --data @config.json http://localhost:8083/connectors/GCSSourceConnector/config

To consume records written by connector to the configured Kafka topic, run the following command:

kafka-avro-console-consumer --bootstrap-server localhost:9092 --property schema.registry.url=http://localhost:8081  --topic copy_of_gcs_topic --from-beginning

Backup and Restore GCS Source Connector Partitions¶

The Backup and Restore GCS Source connector supports a variety of partitions. The connector’s partitioner determines how records that are read from blob items are partitioned into Kafka topics. Messages will be put back on to the same Kafka partition for that topic when it was written.

The partitioner is specified in the connector configuration with the partitioner.class configuration property. The Backup and Restore GCS Source connector comes with the following partitioners:

DefaultPartitioner: To use the DefaultPartitioner, you must set the partitioner.class property to io.confluent.connect.storage.partitioner.DefaultPartitioner.
FieldPartitioner: A partitioner that uses record values of the configured partition.field.name to determine partitions. To use the FieldPartitioner, you must set the partitioner.class property to io.confluent.connect.storage.partitioner.FieldPartitioner.
TimeBasedPartitioner: To use the TimeBasedPartitioner, you must set the partitioner.class property to io.confluent.connect.storage.partitioner.TimeBasedPartitioner.
DailyPartitioner: A subclass of the TimeBasedPartitioner. To use the DailyPartitioner, you must set the partitioner.class property to io.confluent.connect.storage.partitioner.DailyPartitioner.
HourlyPartitioner: A subclass of the TimeBasedPartitioner. To use the HourlyPartitioner, you must set the partitioner.class property to io.confluent.connect.storage.partitioner.HourlyPartitioner.

Backup and Restore GCS Source Connector Data Formats¶

The Backup and Restore GCS Source connector supports several data formats:

Avro: For supporting Avro format. To use Avro format, you must set the format.class property to io.confluent.connect.gcs.format.avro.AvroFormat.
JSON: For supporting JSON format. To use JSON format, you must set the format.class property to io.confluent.connect.gcs.format.json.JsonFormat.
Raw Bytes: For supporting Raw Bytes format. To use the Raw Bytes format, you must set the format.class property to io.confluent.connect.gcs.format.bytearray.ByteArrayFormat.

Custom Credentials Provider¶

You can configure the GCS connector to use a custom credentials provider, instead of the default one provided by the GCS connector. To do this, you implement a custom credentials provider, build it as a JAR file, and deploy the JAR file to use the custom provider.

Complete the following steps to use a custom credentials provider:

Set a custom credentials provider class: Set the gcs.credentials.provider.class property to a class that implements the com.google.api.gax.core.CredentialsProvider interface. Configure the class to the fully qualified name of your custom credentials provider class.
Configure additional settings (Optional): For additional configuration, prefix the configuration keys with gcs.credentials.provider. If your custom credentials provider needs to accept additional configuration, implement the org.apache.kafka.common.Configurable interface that lets the connector receive configurations that are prefixed with gcs.credentials.provider..
Ensure a public no-args constructor: Your custom credentials provider class must have a public no-argument constructor. This is necessary because the connector creates an instance of the provider using this constructor.
Package your provider: Once your custom credentials provider class is implemented, package it into a JAR file.
Copy the JAR file to Connect Worker: Copy the built JAR file to the share/java/kafka-connect-gcs directory on all Connect workers. This step ensures that the GCS connector can access and use your custom credentials provider.

Troubleshooting Connector and Task Failures¶

Stack Trace¶

You can use the Connect REST API to check the status of the connectors and tasks. If a task or connector has failed, the trace field will include a reason and a stack trace.

Error Handling¶

The following behavior.on.error configuration properties set how the connector handles errors.

fail : The connector stops processing when an error occurs. The full batch of records will not be sent to Kafka if any record in the batch is corrupted.
ignore : The corrupted record is ignored. The connector continues processing the next record. For Avro, the connector ignores the file containing a corrupted record and continues processing records for the next file.
log : Logs an error message and continues processing the next record. For Avro, the connector ignores the file containing a corrupted record and continues processing records for the next file.

Note

The connector always ignores a file which is not in <topic>+<partition>+<offset>.<extension> format.