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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-3871) Add Kafka TableSource with Avro serialization
Date Wed, 26 Apr 2017 12:27:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-3871?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15984700#comment-15984700
] 

ASF GitHub Bot commented on FLINK-3871:
---------------------------------------

Github user twalthr commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3663#discussion_r113437414
  
    --- Diff: flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/util/serialization/AvroRowDeserializationSchema.java
---
    @@ -0,0 +1,157 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *    http://www.apache.org/licenses/LICENSE-2.0
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +package org.apache.flink.streaming.util.serialization;
    +
    +import java.io.ByteArrayInputStream;
    +import java.io.IOException;
    +import java.util.List;
    +import org.apache.avro.Schema;
    +import org.apache.avro.generic.GenericData;
    +import org.apache.avro.generic.GenericRecord;
    +import org.apache.avro.io.DatumReader;
    +import org.apache.avro.io.Decoder;
    +import org.apache.avro.io.DecoderFactory;
    +import org.apache.avro.reflect.ReflectDatumReader;
    +import org.apache.avro.specific.SpecificData;
    +import org.apache.avro.specific.SpecificRecord;
    +import org.apache.avro.util.Utf8;
    +import org.apache.flink.types.Row;
    +import org.apache.flink.util.Preconditions;
    +
    +/**
    + * Deserialization schema from Avro bytes over {@link SpecificRecord} to {@link Row}.
    + *
    + * Deserializes the <code>byte[]</code> messages into (nested) Flink Rows.
    + *
    + * {@link Utf8} is converted to regular Java Strings.
    + */
    +public class AvroRowDeserializationSchema extends AbstractDeserializationSchema<Row>
{
    +
    +	/**
    +	 * Schema for deterministic field order.
    +	 */
    +	private final Schema schema;
    +
    +	/**
    +	 * Reader that deserializes byte array into a record.
    +	 */
    +	private final DatumReader<GenericRecord> datumReader;
    +
    +	/**
    +	 * Input stream to read message from.
    +	 */
    +	private final MutableByteArrayInputStream inputStream;
    +
    +	/**
    +	 * Avro decoder that decodes binary data
    +	 */
    +	private final Decoder decoder;
    +
    +	/**
    +	 * Record to deserialize byte array to.
    +	 */
    +	private GenericRecord record;
    +
    +	/**
    +	 * Creates a Avro deserialization schema for the given record.
    +	 *
    +	 * @param recordClazz Avro record class used to deserialize Avro's record to Flink's
row
    +	 */
    +	@SuppressWarnings("unchecked")
    +	public AvroRowDeserializationSchema(Class<? extends SpecificRecord> recordClazz)
{
    +		Preconditions.checkNotNull(recordClazz, "Avro record class must not be null.");
    +		this.schema = SpecificData.get().getSchema(recordClazz);
    +		this.datumReader = new ReflectDatumReader<>(schema);
    +		this.record = new GenericData.Record(schema);
    +		this.inputStream = new MutableByteArrayInputStream();
    +		this.decoder = DecoderFactory.get().binaryDecoder(inputStream, null);
    +	}
    +
    +	@Override
    +	public Row deserialize(byte[] message) throws IOException {
    +		// read record
    +		try {
    +			inputStream.setBuffer(message);
    +			this.record = datumReader.read(record, decoder);
    +		} catch (IOException e) {
    +			throw new RuntimeException("Failed to deserialize Row.", e);
    +		}
    +
    +		// convert to row
    +		final Object row = convertToRow(schema, record);
    +		return (Row) row;
    +	}
    +
    +	/**
    +	 * Converts a (nested) Avro {@link SpecificRecord} into Flink's Row type.
    +	 * Avro's {@link Utf8} fields are converted into regular Java strings.
    +	 */
    +	private static Object convertToRow(Schema schema, Object recordObj) {
    +		if (recordObj instanceof GenericRecord) {
    +			// records can be wrapped in a union
    +			if (schema.getType() == Schema.Type.UNION) {
    +				final List<Schema> types = schema.getTypes();
    +				if (types.size() == 2 && types.get(0).getType() == Schema.Type.NULL &&
types.get(1).getType() == Schema.Type.RECORD) {
    +					schema = types.get(1);
    +				}
    +				else {
    +					throw new RuntimeException("Currently we only support schemas of the following form:
UNION[null, RECORD]. Given: " + schema);
    +				}
    +			} else if (schema.getType() != Schema.Type.RECORD) {
    +				throw new RuntimeException("Record type for row type expected. But is: " + schema);
    +			}
    +			final List<Schema.Field> fields = schema.getFields();
    +			final Row row = new Row(fields.size());
    --- End diff --
    
    No, because `Row` can again be nested.


> Add Kafka TableSource with Avro serialization
> ---------------------------------------------
>
>                 Key: FLINK-3871
>                 URL: https://issues.apache.org/jira/browse/FLINK-3871
>             Project: Flink
>          Issue Type: New Feature
>          Components: Table API & SQL
>            Reporter: Fabian Hueske
>            Assignee: Ivan Mushketyk
>
> Add a Kafka TableSource which supports Avro serialized data.
> The KafkaAvroTableSource should support two modes:
> # SpecificRecord Mode: In this case the user specifies a class which was code-generated
by Avro depending on a schema. Flink treats these classes as regular POJOs. Hence, they are
also natively supported by the Table API and SQL. Classes generated by Avro contain their
Schema in a static field. The schema should be used to automatically derive field names and
types. Hence, there is no additional information required than the name of the class.
> # GenericRecord Mode: In this case the user specifies an Avro Schema. The schema is used
to deserialize the data into a GenericRecord which must be translated into possibly nested
{{Row}} based on the schema information. Again, the Avro Schema is used to automatically derive
the field names and types. This mode is less efficient than the SpecificRecord mode because
the {{GenericRecord}} needs to be converted into {{Row}}.
> This feature depends on FLINK-5280, i.e., support for nested data in {{TableSource}}.



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