1.概述

转载：Flink SQL 自定义 Source format

1.背景

由于 kafka 中的 json 属于嵌套，又不想二次序列化再把它展开，故自定义 format。

2.步骤

1.自定义 Factory 实现 DeserializationFormatFactory

2.自定义 DeserializationSchema 实现 DeserializationSchema

自定义 Factory 中 createDecodingFormat 方法返回 createDecodingFormat

3.自定义 Format

为了简单起见，我们自定义一个 NullFormat ，也就是无论 kafka 中的消息是什么都返回 null，相当于 kafka 中没有消息

自定义 Factory

import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.serialization.SerializationSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.configuration.ConfigOption;
import org.apache.flink.configuration.ReadableConfig;
import org.apache.flink.formats.json.JsonOptions;
import org.apache.flink.formats.json.TimestampFormat;
import org.apache.flink.table.api.ValidationException;
import org.apache.flink.table.connector.ChangelogMode;
import org.apache.flink.table.connector.format.DecodingFormat;
import org.apache.flink.table.connector.source.DynamicTableSource;
import org.apache.flink.table.data.RowData;
import org.apache.flink.table.factories.DeserializationFormatFactory;
import org.apache.flink.table.factories.DynamicTableFactory;
import org.apache.flink.table.factories.FactoryUtil;
import org.apache.flink.table.types.DataType;
import org.apache.flink.table.types.logical.RowType;

import java.util.Collections;
import java.util.HashSet;
import java.util.Set;

import static org.apache.flink.formats.json.JsonOptions.*;

/** * Table format factory for providing configured instances of JSON to RowData * {@link SerializationSchema} and {@link DeserializationSchema}. */
public class NullFormatFactory implements
		DeserializationFormatFactory {
	// Factory 的唯一标识
	public static final String IDENTIFIER = "null";
	
	@SuppressWarnings("unchecked")
	@Override
	// 解码的入口方法 基本上属于固定写法
	public DecodingFormat<DeserializationSchema<RowData>> createDecodingFormat(
			DynamicTableFactory.Context context,
			ReadableConfig formatOptions) {
		FactoryUtil.validateFactoryOptions(this, formatOptions);
		validateFormatOptions(formatOptions);
		
		final boolean failOnMissingField = formatOptions.get(FAIL_ON_MISSING_FIELD);
		final boolean ignoreParseErrors = formatOptions.get(IGNORE_PARSE_ERRORS);
		TimestampFormat timestampOption = JsonOptions.getTimestampFormat(formatOptions);
		
		return new DecodingFormat<DeserializationSchema<RowData>>() {
			@Override
			public DeserializationSchema<RowData> createRuntimeDecoder(
					DynamicTableSource.Context context,//ScanRuntimeProviderContext
					DataType producedDataType) { // 表的字段名和数据类型
				final RowType rowType = (RowType) producedDataType.getLogicalType();
				final TypeInformation<RowData> rowDataTypeInfo =
						(TypeInformation<RowData>) context.createTypeInformation(producedDataType);
				return new NullRowDataDeserializationSchema(
						rowType,
						rowDataTypeInfo,
						failOnMissingField,
						ignoreParseErrors,
						timestampOption
				);
			}
			
			@Override
			public ChangelogMode getChangelogMode() {
				return ChangelogMode.insertOnly();
			}
		};
	}
	
	
	@Override
	public String factoryIdentifier() {
		return IDENTIFIER;
	}
	
	@Override
	public Set<ConfigOption<?>> requiredOptions() {
		return Collections.emptySet();
	}
	
	@Override
	public Set<ConfigOption<?>> optionalOptions() {
		Set<ConfigOption<?>> options = new HashSet<>();
		options.add(FAIL_ON_MISSING_FIELD);
		options.add(IGNORE_PARSE_ERRORS);
		options.add(TIMESTAMP_FORMAT);
		return options;
	}
	
	// ------------------------------------------------------------------------
	// Validation
	// ------------------------------------------------------------------------
	
	static void validateFormatOptions(ReadableConfig tableOptions) {
		boolean failOnMissingField = tableOptions.get(FAIL_ON_MISSING_FIELD);
		boolean ignoreParseErrors = tableOptions.get(IGNORE_PARSE_ERRORS);
		String timestampFormat = tableOptions.get(TIMESTAMP_FORMAT);
		if (ignoreParseErrors && failOnMissingField) {
			throw new ValidationException(FAIL_ON_MISSING_FIELD.key()
					+ " and "
					+ IGNORE_PARSE_ERRORS.key()
					+ " shouldn't both be true.");
		}
		if (!TIMESTAMP_FORMAT_ENUM.contains(timestampFormat)) {
			throw new ValidationException(String.format("Unsupported value '%s' for %s. Supported values are [SQL, ISO-8601].",
					timestampFormat, TIMESTAMP_FORMAT.key()));
		}
	}
}

自定义 DeserializationSchema

import org.apache.flink.annotation.Internal;
import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.formats.json.TimestampFormat;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.JsonNode;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.flink.table.data.RowData;
import org.apache.flink.table.types.logical.RowType;

import java.io.IOException;
import java.util.Objects;

import static org.apache.flink.util.Preconditions.checkNotNull;

@Internal
public class NullRowDataDeserializationSchema implements DeserializationSchema<RowData> {
	private static final long serialVersionUID = 1L;
	
	/** * Flag indicating whether to fail if a field is missing. */
	private final boolean failOnMissingField;
	
	/** * Flag indicating whether to ignore invalid fields/rows (default: throw an exception). */
	private final boolean ignoreParseErrors;
	
	/** * TypeInformation of the produced {@link RowData}. **/
	private final TypeInformation<RowData> resultTypeInfo;
	
	/** * Runtime converter that converts {@link JsonNode}s into * objects of Flink SQL internal data structures. **/
	
	/** * Object mapper for parsing the JSON. */
	private final ObjectMapper objectMapper = new ObjectMapper();
	
	/** * Timestamp format specification which is used to parse timestamp. */
	private final TimestampFormat timestampFormat;
	
	public NullRowDataDeserializationSchema(
			RowType rowType,
			TypeInformation<RowData> resultTypeInfo,
			boolean failOnMissingField,
			boolean ignoreParseErrors,
			TimestampFormat timestampFormat) {
		if (ignoreParseErrors && failOnMissingField) {
			throw new IllegalArgumentException(
					"JSON format doesn't support failOnMissingField and ignoreParseErrors are both enabled.");
		}
		this.resultTypeInfo = checkNotNull(resultTypeInfo);
		this.failOnMissingField = failOnMissingField;
		this.ignoreParseErrors = ignoreParseErrors;
		this.timestampFormat = timestampFormat;
	}
	
	@Override
	// 这里其实是真正的反序列化逻辑，比如说将 json 拍平 (多层嵌套转化为一层嵌套 )
	// 这里是重点，记得关注重点
	public RowData deserialize(byte[] message) throws IOException {
		return null;
	}
	
	@Override
	public boolean isEndOfStream(RowData nextElement) {
		return false;
	}
	
	@Override
	public TypeInformation<RowData> getProducedType() {
		return resultTypeInfo;
	}
	
	@Override
	public boolean equals(Object o) {
		if (this == o) {
			return true;
		}
		if (o == null || getClass() != o.getClass()) {
			return false;
		}
		NullRowDataDeserializationSchema that = (NullRowDataDeserializationSchema) o;
		return failOnMissingField == that.failOnMissingField &&
				ignoreParseErrors == that.ignoreParseErrors &&
				resultTypeInfo.equals(that.resultTypeInfo) &&
				timestampFormat.equals(that.timestampFormat);
	}
	
	@Override
	public int hashCode() {
		return Objects.hash(failOnMissingField, ignoreParseErrors, resultTypeInfo, timestampFormat);
	}
}

4.使用自定义 Format

public class SqlKafka {
	public static void main(String[] args) throws Exception {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		
		EnvironmentSettings environmentSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
		StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, environmentSettings);
		// enable checkpointing
		Configuration configuration = tableEnv.getConfig().getConfiguration();
		configuration.set(
				ExecutionCheckpointingOptions.CHECKPOINTING_MODE, CheckpointingMode.EXACTLY_ONCE);
		configuration.set(
				ExecutionCheckpointingOptions.CHECKPOINTING_INTERVAL, Duration.ofSeconds(10));
		
		String sql = "CREATE TABLE sourcedata (`id` bigint,`status` int,`city_id` bigint,`courier_id` bigint,info_index int,order_id bigint,tableName String" +
				") WITH (" +
				"'connector' = 'kafka','topic' = 'canal_monitor_order'," +
				"'properties.bootstrap.servers' = 'bigdata-dev-mq:9092','properties.group.id' = 'testGroup'," +
				"'format' = 'null','scan.startup.mode' = 'earliest-offset')";
		tableEnv.executeSql(sql);
		......

‘format’ = ‘null’ Factory 的唯一标识

然后就可以直接执行了