S3 Connector

The S3 connector, currently available as a sink, allows you to export data from Kafka topics to S3 objects in either Avro or JSON formats. In addition, for certain data layouts, S3 connector exports data by guaranteeing exactly-once delivery semantics to consumers of the S3 objects it produces.

Being a sink, the S3 connector periodically polls data from Kafka and in turn uploads it to S3. A partitioner is used to split the data of every Kafka partition into chunks. Each chunk of data is represented as an S3 object, whose key name encodes the topic, the Kafka partition and the start offset of this data chunk. If no partitioner is specified in the configuration, the default partitioner which preserves Kafka partitioning is used. The size of each data chunk is determined by the number of records written to S3 and by schema compatibility.

Features

The S3 connector offers a variety of features:

  • Exactly Once Delivery: Records that are exported using a deterministic partitioner are delivered with exactly-once semantics regardless of the eventual consistency of S3.
  • Pluggable Data Format with or without Schema: Out of the box, the connector supports writing data to S3 in Avro and JSON format. Besides records with schema, the connector supports exporting 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.
  • Schema Evolution: When schemas are used, the connector supports schema evolution based on schema compatibility modes. The available modes are: NONE, BACKWARD, FORWARD and FULL and a selection can be made by setting the property schema.compatibility in the connector’s configuration. When the connector observes a schema change, it decides whether to roll the file or project the record to the proper schema according to the schema.compatibility configuration in use.
  • 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.

Exactly-once delivery on top of eventual consistency

The S3 connector is able to provide exactly-once semantics to consumers of the objects it exports to S3, under the condition that the connector is supplied with a deterministic partitioner.

Currently, out of the available partitioners, the default and field partitioners are deterministic. This implies that, when any of these partitioners is used, splitting of files always happens at the same offsets for a given set of Kafka records. These partitioners take into account flush.size and schema.compatibility to decide when to roll and save a new file to S3. The connector always delivers files in S3 that contain the same records, even under the presence of failures. If a connector task fails before an upload completes, the file does not become visible to S3. If, on the other hand, a failure occurs after the upload has completed but before the corresponding offset is committed to Kafka by the connector, then a re-upload will take place. However, such a re-upload is transparent to the user of the S3 bucket, who at any time will have access to the same records made eventually available by successful uploads to S3.

In the current version, time-based partitioners, as opposed to default and field partitioners, depend on wall-clock time to partition data. A version of time-based partitioners based only on record timestamps that will guarantee exactly-once delivery to S3 will become soon available.

Schema Evolution

The S3 connector supports schema evolution and reacts to schema changes of data according to the schema.compatibility configuration. In this section, we will explain how the connector reacts to schema evolution under different values of schema.compatibility. The schema.compatibility can be set to NONE, BACKWARD, FORWARD and FULL, which means NO compatibility, BACKWARD compatibility, FORWARD compatibility and FULL compatibility respectively.

  • NO Compatibility: By default, the schema.compatibility is set to NONE. In this case, the connector ensures that each file written to S3 has the proper schema. When the connector observes a schema change in data, it commits the current set of files for the affected topic partitions and writes the data with new schema in new files.

  • BACKWARD Compatibility: If a schema is evolved in a backward compatible way, we can always use the latest schema to query all the data uniformly. For example, removing fields is backward compatible change to a schema, since when we encounter records written with the old schema that contain these fields we can just ignore them. Adding a field with a default value is also backward compatible.

    If BACKWARD is specified in the schema.compatibility, the connector keeps track of the latest schema used in writing data to S3, and if a data record with a schema version larger than current latest schema arrives, the connector commits the current set of files and writes the data record with new schema to new files. For data records arriving at a later time with schema of an earlier version, the connector projects the data record to the latest schema before writing to the same set of files in S3.

  • FORWARD Compatibility: If a schema is evolved in a forward compatible way, we can always use the oldest schema to query all the data uniformly. Removing a field that had a default value is forward compatible, since the old schema will use the default value when the field is missing.

    If FORWARD is specified in the schema.compatibility, the connector projects the data to the oldest schema before writing to the same set of files in S3.

  • Full Compatibility: Full compatibility means that old data can be read with the new schema and new data can also be read with the old schema.

    If FULL is specified in the schema.compatibility, the connector performs the same action as BACKWARD.

Schema evolution in the S3 connector works in the same way as in the HDFS connector.

Quickstart

In this Quickstart, we use the S3 connector to export data produced by the Avro console producer to S3.

Before you begin, you will need to create an S3 destination bucket in advance and grant the user or IAM role running the connector write access to it.

Next, start the services with one command using Confluent CLI:

Tip

If not already in your PATH, add Confluent’s bin directory by running: export PATH=<path-to-confluent>/bin:$PATH

$ confluent start

Every service will start in order, printing a message with its status:

Starting zookeeper
zookeeper is [UP]
Starting kafka
kafka is [UP]
Starting schema-registry
schema-registry is [UP]
Starting kafka-rest
kafka-rest is [UP]
Starting connect
connect is [UP]

Note

You need to make sure the connector user has write access to the S3 bucket specified in s3.bucket.name and has deployed credentials appropriately.

To import a few records with a simple schema in Kafka, start the Avro console producer as follows:

$ ./bin/kafka-avro-console-producer --broker-list localhost:9092 --topic s3_topic \
--property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'

Then, in the console producer, type in:

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

The nine records entered are published to the Kafka topic s3_topic in Avro format.

Before starting the connector, please make sure that the configurations in etc/kafka-connect-s3/quickstart-s3.properties are properly set to your configurations of S3, e.g. s3.bucket.name points to your bucket, s3.region directs to your S3 region and flush.size=3 for this example. Then start the S3 connector by loading its configuration with the following command:

$ confluent load s3-sink
{
  "name": "s3-sink",
  "config": {
    "connector.class": "io.confluent.connect.s3.S3SinkConnector",
    "tasks.max": "1",
    "topics": "s3_topic",
    "s3.region": "us-west-2",
    "s3.bucket.name": "confluent-kafka-connect-s3-testing",
    "s3.part.size": "5242880",
    "flush.size": "3",
    "storage.class": "io.confluent.connect.s3.storage.S3Storage",
    "format.class": "io.confluent.connect.s3.format.avro.AvroFormat",
    "schema.generator.class": "io.confluent.connect.storage.hive.schema.DefaultSchemaGenerator",
    "partitioner.class": "io.confluent.connect.storage.partitioner.DefaultPartitioner",
    "schema.compatibility": "NONE",
    "name": "s3-sink"
  },
  "tasks": []
}

To check that the connector started successfully view the Connect worker’s log by running:

$ confluent log connect

Towards the end of the log you should see that the connector starts, logs a few messages, and then uploads data from Kafka to S3. Once the connector has ingested some records check that the data is available in S3, for instance by using AWS CLI:

$ aws s3api list-objects --bucket "your-bucket-name"

You should see three objects with keys:

topics/s3_topic/partition=0/s3_topic+0+0000000000.avro
topics/s3_topic/partition=0/s3_topic+0+0000000003.avro
topics/s3_topic/partition=0/s3_topic+0+0000000006.avro

Each file is encoded as <topic>+<kafkaPartition>+<startOffset>.<format>.

To verify the contents, first copy each file from S3 to your local filesystem, for instance by running:

$ aws s3 cp s3://<your-bucket>/topics/s3_topic/partition=0/s3_topic+0+0000000000.avro

and use avro-tools-1.8.2.jar (available in Apache mirrors) to print the records:

$ java -jar avro-tools-1.8.2.jar tojson s3_topic+0+0000000000.avro

For the file above, you should see the following output:

{"f1":"value1"}
{"f1":"value2"}
{"f1":"value3"}

with the rest of the records contained in the other two files.

Finally, stop the Connect worker as well as all the rest of the Confluent services by running:

$ confluent stop
Stopping connect
connect is [DOWN]
Stopping kafka-rest
kafka-rest is [DOWN]
Stopping schema-registry
schema-registry is [DOWN]
Stopping kafka
kafka is [DOWN]
Stopping zookeeper
zookeeper is [DOWN]

or stop all the services and additionally wipe out any data generated during this quickstart by running:

$ confluent destroy
Stopping connect
connect is [DOWN]
Stopping kafka-rest
kafka-rest is [DOWN]
Stopping schema-registry
schema-registry is [DOWN]
Stopping kafka
kafka is [DOWN]
Stopping zookeeper
zookeeper is [DOWN]
Deleting: /tmp/confluent.w1CpYsaI

Configuration

This section gives example configurations that cover common scenarios. For detailed description of all the available configuration options of the S3 connector go to Configuration Options

Basic Example

The example settings are contained in etc/kafka-connect-s3/quickstart-s3.properties as follows:

name=s3-sink
connector.class=io.confluent.connect.s3.S3SinkConnector
tasks.max=1
topics=s3_topic
flush.size=3

The first few settings are common to most connectors. topics specifies the topics we want to export data from, in this case s3_topic. The property flush.size specifies the number of records per partition the connector needs to write before completing a multipart upload to S3.

s3.bucket.name=confluent-kafka-connect-s3-testing
s3.part.size=5242880

The next settings are specific to Amazon S3. A mandatory setting is the name of your S3 bucket to host the exported Kafka records. Other useful settings are s3.region, which you should set if you use a region other than the default, and s3.part.size to control the size of each part in the multipart uploads that will be used to upload a single chunk of Kafka records.

storage.class=io.confluent.connect.s3.storage.S3Storage
format.class=io.confluent.connect.s3.format.avro.AvroFormat
schema.generator.class=io.confluent.connect.storage.hive.schema.DefaultSchemaGenerator
partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner

These class settings are required to specify the storage interface (here S3), the output file format, currently io.confluent.connect.s3.format.avro.AvroFormat or io.confluent.connect.s3.format.json.JsonFormat and the partitioner class along with its schema generator class. When using a format with no schema definition, it is sufficient to set the schema generator class to its default value.

schema.compatibility=NONE

Finally, schema evolution is disabled in this example by setting schema.compatibility to NONE, as explained above.

Write raw message values into S3

It is possible to use the S3 connector to write out the unmodified original message values into newline-separated files in S3. We accomplish this by telling Connect to not deserialize any of the messages, and by configuring the S3 connector to store the message values in a binary format in S3.

The first part of our S3 connector is similar to other examples:

name=s3-raw-sink
connector.class=io.confluent.connect.s3.S3SinkConnector
tasks.max=1
topics=s3_topic
flush.size=3

The topics setting specifies the topics we want to export data from, in this case s3_topic. The property flush.size specifies the number of records per partition the connector needs to write before completing a multipart upload to S3.

Next we need to configure the particulars of Amazon S3:

s3.bucket.name=confluent-kafka-connect-s3-testing
s3.region=us-west-2
s3.part.size=5242880

The s3.bucket.name is mandatory and names your S3 bucket where the exported Kafka records should be written. Another useful setting is s3.region that you should set if you use a region other than the default. And since the S3 connector uses multi-part uploads, you can use the s3.part.size to control the size of each of these continuous parts used to upload Kafka records into a single S3 object. The part size affects throughput and latency, as an S3 object is visible/available only after all parts are uploaded.

So far this example configuration is relatively typical of most S3 connectors. Now lets define that we should read the raw message values and write them in binary format:

value.converter=org.apache.kafka.connect.converters.ByteArrayConverter
format.class=io.confluent.connect.s3.format.bytearray.ByteArrayFormat
storage.class=io.confluent.connect.s3.storage.S3Storage
schema.compatibility=NONE

The value.converter setting overrides for our connector the default that is in the Connect worker configuration, and we use the ByteArrayConverter to instruct Connect to skip deserializing the message values and instead give the connector the message values in their raw binary form. We use the format.class setting to instruct the S3 connector to write these binary message values as-is into S3 objects. By default the message values written to the same S3 object will be separated by a newline character sequence, but you can control this with the format.bytearray.separator setting. Also, by default the files written to S3 will have an extension of .bin, or you can use the format.bytearray.extension setting to change this.

Next we need to decide how we want to partition the consumed messages in S3 objects. We have a few options, including the default partitioner that preserves the same partitions as in Kafka:

partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner

Or, we could instead partition by the timestamp of the Kafka messages:

partitioner.class=io.confluent.connect.storage.partitioner.TimeBasedPartitioner
timestamp.extract=Record

or the timestamp that the S3 connector processes each message:

partitioner.class=io.confluent.connect.storage.partitioner.TimeBasedPartitioner
timestamp.extract=Wallclock

Custom partitioners are always an option, too. Just be aware that since the record value is an opaque binary value, we cannot extract timestamps from fields using the RecordField option.