Flink SQL解析Json格式数据的方法

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1. Flink版本1.7.2

2. 引入依赖

使用maven构建工程，因此pom.xml添加如下依赖：

    <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-json -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-json</artifactId>
            <version>1.7.2</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
            <version>2.9.8</version>
        </dependency>
        <dependency>
            <groupId>joda-time</groupId>
            <artifactId>joda-time</artifactId>
            <version>2.10.1</version>
        </dependency>

3. Google Protobuf消息定义

response.proto文件

syntax = "proto3";

package com.google.protos;

//搜索响应
message SearchResponse {
    uint64 search_time = 1;
    uint32 code = 2;
    Result results = 3;
}

//搜索结果
message Result {
    string id = 1;
    repeated Item items = 2;
}

//搜索结果项
message Item{
    string id = 1;
    string name = 2;
    string title = 3;
    string url = 4;
    uint64 publish_time = 5;
    float score = 6;    //推荐或者相似加权分值
}

消息示例,包含嵌套对象results以及数组对象items:

{
    "search_time":1553650604,
    "code":200,
    "results":{
        "id":"449",
        "items":[
            {
                "id":"47",
                "name":"name47",
                "title":"标题47",
                "url":"https://www.google.com.hk/item-47",
                "publish_time":1552884870,
                "score":96.03
            },
            {
                "id":"2",
                "name":"name2",
                "title":"标题2",
                "url":"https://www.google.com.hk/item-2",
                "publish_time":1552978902,
                "score":16.06
            },
            {
                "id":"60",
                "name":"name60",
                "title":"标题60",
                "url":"https://www.google.com.hk/item-60",
                "publish_time":1553444982,
                "score":62.58
            },
            {
                "id":"67",
                "name":"name67",
                "title":"标题67",
                "url":"https://www.google.com.hk/item-67",
                "publish_time":1553522957,
                "score":12.17
            },
            {
                "id":"15",
                "name":"name15",
                "title":"标题15",
                "url":"https://www.google.com.hk/item-15",
                "publish_time":1553525421,
                "score":32.36
            },
            {
                "id":"53",
                "name":"name53",
                "title":"标题53",
                "url":"https://www.google.com.hk/item-53",
                "publish_time":1553109227,
                "score":52.13
            },
            {
                "id":"70",
                "name":"name70",
                "title":"标题70",
                "url":"https://www.google.com.hk/item-70",
                "publish_time":1552781921,
                "score":1.72
            },
            {
                "id":"53",
                "name":"name53",
                "title":"标题53",
                "url":"https://www.google.com.hk/item-53",
                "publish_time":1553229003,
                "score":5.31
            },
            {
                "id":"30",
                "name":"name30",
                "title":"标题30",
                "url":"https://www.google.com.hk/item-30",
                "publish_time":1553282629,
                "score":26.51
            },
            {
                "id":"36",
                "name":"name36",
                "title":"标题36",
                "url":"https://www.google.com.hk/item-36",
                "publish_time":1552665833,
                "score":48.76
            }
        ]
    }
}

3.2 Kakfa Produder发布随机响应Json串

import com.google.protos.GoogleProtobuf.*;
import com.googlecode.protobuf.format.JsonFormat;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.text.DecimalFormat;
import java.time.Instant;
import java.util.Properties;
import java.util.Random;
import java.util.concurrent.TimeUnit;

/**
 * @author lynn
 * @ClassName com.lynn.kafka.SearchResponsePublisher
 * @Description TODO
 * @Date 19-3-26 上午8:17
 * @Version 1.0
 **/
public class SearchResponsePublisher {

    private static final Logger LOG = LoggerFactory.getLogger(SearchResponsePublisher.class);

    public String randomMessage(int results){
        Random random = new Random();
        DecimalFormat fmt = new DecimalFormat("##0.00");

        SearchResponse.Builder response = SearchResponse.newBuilder();
        response.setSearchTime(Instant.now().getEpochSecond())
            .setCode(random.nextBoolean()?200:404);
        Result.Builder result = Result.newBuilder()
                .setId(""+random.nextInt(1000));
        for (int i = 0; i < results; i++) {
            int number = random.nextInt(100);
            Item.Builder builder = Item.newBuilder()
                    .setId(number+"")
                    .setName("name"+number)
                    .setTitle("标题"+number)
                    .setUrl("https://www.google.com.hk/item-"+number)
                    .setPublishTime(Instant.now().getEpochSecond() - random.nextInt(1000000))
                    .setScore(Float.parseFloat(fmt.format(random.nextInt(99) + random.nextFloat())));

            result.addItems(builder.build());
        }

        response.setResults(result.build());

        return new JsonFormat().printToString(response.build());
    }

    /**
     *
     * @param args
     */
    public static void main(String[] args) throws InterruptedException{
        if(args.length < 3){
            System.err.println("Please input broker.servers and topic and records number!");
            System.exit(-1);
        }
        String brokers = args[0];
        String topic = args[1];
        int recordsNumber = Integer.parseInt(args[2]);

        LOG.info("I will publish {} records...", recordsNumber);
        SearchResponsePublisher publisher = new SearchResponsePublisher();

//        System.out.println(publisher.randomMessage(10));
//        if(recordsNumber == 1000) return;

        Properties props = new Properties();
        props.put("bootstrap.servers", brokers);
        //all:-1
        props.put("acks", "all");
        props.put("retries", 0);
        props.put("batch.size", 16384);
        props.put("linger.ms", 1);
        props.put("buffer.memory", 33554432);
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<>(props);
        int count = 0;
        while (count++ < recordsNumber){
            producer.send(new ProducerRecord<String, String>(topic,
                    String.valueOf(Instant.now().toEpochMilli()),
                    publisher.randomMessage(10)));
            TimeUnit.MILLISECONDS.sleep(100);
        }

//        producer.flush();
        producer.close();
    }
}

3. 源代码Java:

3.1 引入pakcages

import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.typeutils.ObjectArrayTypeInfo;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.Types;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.table.descriptors.Json;
import org.apache.flink.table.descriptors.Kafka;
import org.apache.flink.table.descriptors.Schema;
import org.apache.flink.table.sinks.PrintTableSink;
import org.apache.flink.types.Row;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

3.2 源代码:

// set up the streaming execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//        env.setParallelism(1);
        StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

        Kafka kafka = new Kafka().version("0.11")
                .topic(sourceTopic)
                .startFromEarliest()
//                .startFromLatest()
                .property("bootstrap.servers", brokers)
                .property("group.id", "res")
                .property("session.timeout.ms", "30000")
                .sinkPartitionerFixed();

        tableEnv.connect(kafka)
                .withFormat(new Json()
                        .failOnMissingField(false)
                        .deriveSchema())
                .withSchema(new Schema()
                        .field("search_time", Types.LONG())
                        .field("code", Types.INT())
                        .field("results", Types.ROW(
                                new String[]{"id", "items"},
                                new TypeInformation[]{
                                        Types.STRING(),
                                        ObjectArrayTypeInfo.getInfoFor(Row[].class,  //Array.newInstance(Row.class, 10).getClass(),
                                                Types.ROW(
                                                        new String[]{"id", "name", "title", "url", "publish_time", "score"},
                                                        new TypeInformation[]{Types.STRING(),Types.STRING(),Types.STRING(),Types.STRING(),Types.LONG(),Types.FLOAT()}
                                                        ))})
                        )).inAppendMode().registerTableSource("tb_json");

//item[1] item[10] 数组下标从1开始
String sql4 = "select search_time, code, results.id as result_id, items[1].name as item_1_name, items[2].id as item_2_id\n"
                + "from tb_json";

        Table table4 = tableEnv.sqlQuery(sql4);
        tableEnv.registerTable("tb_item_2", table4);
        LOG.info("------------------print {} schema------------------", "tb_item_2");
        table4.printSchema();
        tableEnv.registerTableSink("console4",
                new String[]{"f0", "f1", "f2", "f3", "f4"},
                new TypeInformation[]{
                        Types.LONG(),Types.INT(),
                        Types.STRING(),
                        Types.STRING(),
                        Types.STRING()
                },
                new PrintTableSink());

        table4.insertInto("console4");

        // execute program
        env.execute("Flink Table Json Engine");

3.3 SQL语句

select 
    search_time, 
    code, 
    results.id as result_id, //嵌套json子字段
    items[1].name as item_1_name,  //数组对像子字段,数组下标从1开始
    items[2].id as item_2_id
from tb_json

??嵌套字段可以通过.连接符直接获取,而数组元素可以通过[下标]获取,下标从1开始,与Java中数组下标从0开始不同.

3.4 Schema定义

??按照Json对象的嵌套以及数组格式进行定义,即无需将每个字段展平进行定义,将嵌套字段定义为Row类型,数组类型定义为ObjectArrayTypeInfo或BasicArrayTypeInfo, ObjectArrayTypeInfo的第一个参数为数组类型,如示例中Row[].class 或Array.newInstance(Row.class, 10).getClass()方式获取class.

3.5 经测试发现flink-json*.jar中的代码问题:

convert方法中的类型判断使用==,可能时由于flink版本的原因引起的==运算符没有重载.因此将此运算符替换为.equals()方法.
JsonRowDeserializationSchema.java

private Object convert(JsonNode node, TypeInformation<?> info) {
        if (Types.VOID.equals(info) || node.isNull()) {
            return null;
        } else if (Types.BOOLEAN.equals(info)) {
            return node.asBoolean();
        } else if (Types.STRING.equals(info)) {
            return node.asText();
        } else if (Types.BIG_DEC.equals(info)) {
            return node.decimalValue();
        } else if (Types.BIG_INT.equals(info)) {
            return node.bigIntegerValue();
        } else if(Types.LONG.equals(info)){
            return node.longValue();
        } else if(Types.INT.equals(info)){
            return node.intValue();
        } else if(Types.FLOAT.equals(info)){
            return node.floatValue();
        } else if(Types.DOUBLE.equals(info)){
            return node.doubleValue();
        } else if (Types.SQL_DATE.equals(info)) {
            return Date.valueOf(node.asText());
        } else if (Types.SQL_TIME.equals(info)) {
            // according to RFC 3339 every full-time must have a timezone;
            // until we have full timezone support, we only support UTC;
            // users can parse their time as string as a workaround
            final String time = node.asText();
            if (time.indexOf(‘Z‘) < 0 || time.indexOf(‘.‘) >= 0) {
                throw new IllegalStateException(
                        "Invalid time format. Only a time in UTC timezone without milliseconds is supported yet. " +
                                "Format: HH:mm:ss‘Z‘");
            }
            return Time.valueOf(time.substring(0, time.length() - 1));
        } else if (Types.SQL_TIMESTAMP.equals(info)) {
            // according to RFC 3339 every date-time must have a timezone;
            // until we have full timezone support, we only support UTC;
            // users can parse their time as string as a workaround
            final String timestamp = node.asText();
            if (timestamp.indexOf(‘Z‘) < 0) {
                throw new IllegalStateException(
                        "Invalid timestamp format. Only a timestamp in UTC timezone is supported yet. " +
                                "Format: yyyy-MM-dd‘T‘HH:mm:ss.SSS‘Z‘");
            }
            return Timestamp.valueOf(timestamp.substring(0, timestamp.length() - 1).replace(‘T‘, ‘ ‘));
        } else if (info instanceof RowTypeInfo) {
            return convertRow(node, (RowTypeInfo) info);
        } else if (info instanceof ObjectArrayTypeInfo) {
            return convertObjectArray(node, ((ObjectArrayTypeInfo) info).getComponentInfo());
        } else if (info instanceof BasicArrayTypeInfo) {
            return convertObjectArray(node, ((BasicArrayTypeInfo) info).getComponentInfo());
        } else if (info instanceof PrimitiveArrayTypeInfo &&
                ((PrimitiveArrayTypeInfo) info).getComponentType() == Types.BYTE) {
            return convertByteArray(node);
        } else {
            // for types that were specified without JSON schema
            // e.g. POJOs
            try {
                return objectMapper.treeToValue(node, info.getTypeClass());
            } catch (JsonProcessingException e) {
                throw new IllegalStateException("Unsupported type information ‘" + info + "‘ for node: " + node);
            }
        }
    }

JsonRowSerializationSchema.java

private JsonNode convert(ContainerNode<?> container, JsonNode reuse, TypeInformation<?> info, Object object) {
        if (Types.VOID.equals(info) || object == null) {
            return container.nullNode();
        } else if (Types.BOOLEAN.equals(info)) {
            return container.booleanNode((Boolean) object);
        } else if (Types.STRING.equals(info)) {
            return container.textNode((String) object);
        } else if (Types.BIG_DEC.equals(info)) {
            // convert decimal if necessary
            if (object instanceof BigDecimal) {
                return container.numberNode((BigDecimal) object);
            }
            return container.numberNode(BigDecimal.valueOf(((Number) object).doubleValue()));
        } else if (Types.BIG_INT.equals(info)) {
            // convert integer if necessary
            if (object instanceof BigInteger) {
                return container.numberNode((BigInteger) object);
            }
            return container.numberNode(BigInteger.valueOf(((Number) object).longValue()));
        } else if(Types.LONG.equals(info)){
            if(object instanceof Long){
                return container.numberNode((Long) object);
            }
            return container.numberNode(Long.valueOf(((Number) object).longValue()));
        } else if(Types.INT.equals(info)){
            if(object instanceof Integer){
                return container.numberNode((Integer) object);
            }
            return container.numberNode(Integer.valueOf(((Number) object).intValue()));
        } else if(Types.FLOAT.equals(info)){
            if(object instanceof Float){
                return container.numberNode((Float) object);
            }
            return container.numberNode(Float.valueOf(((Number) object).floatValue()));
        } else if(Types.DOUBLE.equals(info)){
            if(object instanceof Double){
                return container.numberNode((Double) object);
            }
            return container.numberNode(Double.valueOf(((Number) object).doubleValue()));
        }  else if (Types.SQL_DATE.equals(info)) {
            return container.textNode(object.toString());
        } else if (Types.SQL_TIME.equals(info)) {
            final Time time = (Time) object;
            // strip milliseconds if possible
            if (time.getTime() % 1000 > 0) {
                return container.textNode(timeFormatWithMillis.format(time));
            }
            return container.textNode(timeFormat.format(time));
        } else if (Types.SQL_TIMESTAMP.equals(info)) {
            return container.textNode(timestampFormat.format((Timestamp) object));
        } else if (info instanceof RowTypeInfo) {
            if (reuse != null && reuse instanceof ObjectNode) {
                return convertRow((ObjectNode) reuse, (RowTypeInfo) info, (Row) object);
            } else {
                return convertRow(null, (RowTypeInfo) info, (Row) object);
            }
        } else if (info instanceof ObjectArrayTypeInfo) {
            if (reuse != null && reuse instanceof ArrayNode) {
                return convertObjectArray((ArrayNode) reuse, ((ObjectArrayTypeInfo) info).getComponentInfo(), (Object[]) object);
            } else {
                return convertObjectArray(null, ((ObjectArrayTypeInfo) info).getComponentInfo(), (Object[]) object);
            }
        } else if (info instanceof BasicArrayTypeInfo) {
            if (reuse != null && reuse instanceof ArrayNode) {
                return convertObjectArray((ArrayNode) reuse, ((BasicArrayTypeInfo) info).getComponentInfo(), (Object[]) object);
            } else {
                return convertObjectArray(null, ((BasicArrayTypeInfo) info).getComponentInfo(), (Object[]) object);
            }
        } else if (info instanceof PrimitiveArrayTypeInfo && ((PrimitiveArrayTypeInfo) info).getComponentType() == Types.BYTE) {
            return container.binaryNode((byte[]) object);
        } else {
            // for types that were specified without JSON schema
            // e.g. POJOs
            try {
                return mapper.valueToTree(object);
            } catch (IllegalArgumentException e) {
                throw new IllegalStateException("Unsupported type information ‘" + info + "‘ for object: " + object, e);
            }
        }
    }

3.6 提交jar包到集群运行

添加文件:
resources/META-INF/services/org.apache.flink.table.factories.TableFactory

org.apache.flink.formats.json.JsonRowFormatFactory
org.apache.flink.streaming.connectors.kafka.Kafka011TableSourceSinkFactory

由于打包后kafka-connector jar中与json jar中的同名文件会覆盖,需要将两个文件的内容保留.

4 附PrintTableSink源码

参考阿里巴巴blink分支
scala:
BatchCompatibleStreamTableSink.scala

/*
 * 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.table.sinks

import org.apache.flink.table.api._
import org.apache.flink.streaming.api.datastream.{DataStream, DataStreamSink}

/** Defines an external [[TableSink]] to emit a batch [[Table]] for
  * compatible with stream connect plugin.
  */
trait BatchCompatibleStreamTableSink[T] extends TableSink[T] {

  /** Emits the DataStream. */
  def emitBoundedStream(boundedStream: DataStream[T]): DataStreamSink[_]
}

PrintTableSink.scala

/*
 * 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.table.sinks

import java.lang.{Boolean => JBool}
import java.util.TimeZone
import java.util.{Date => JDate}
import java.sql.Date
import java.sql.Time
import java.sql.Timestamp

import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.api.java.tuple.{Tuple2 => JTuple2}
import org.apache.flink.api.java.typeutils.RowTypeInfo
import org.apache.flink.streaming.api.datastream.{DataStream, DataStreamSink}
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction
import org.apache.flink.streaming.api.operators.StreamingRuntimeContext
import org.apache.flink.types.Row
import org.apache.flink.configuration.Configuration
import org.apache.flink.table.runtime.functions.DateTimeFunctions
import org.apache.flink.util.StringUtils

/**
  * A simple [[TableSink]] to output data to console.
  *
  */
class PrintTableSink()
  extends TableSinkBase[JTuple2[JBool, Row]]
    with BatchCompatibleStreamTableSink[JTuple2[JBool, Row]]
    with UpsertStreamTableSink[Row] {

  override def emitDataStream(dataStream: DataStream[JTuple2[JBool, Row]]) = {
    val sink: PrintSinkFunction = new PrintSinkFunction()
    dataStream.addSink(sink).name(sink.toString)
  }

  override protected def copy: TableSinkBase[JTuple2[JBool, Row]] = new PrintTableSink()

  override def setKeyFields(keys: Array[String]): Unit = {}

  override def setIsAppendOnly(isAppendOnly: JBool): Unit = {}

//  override def getRecordType: DataType = DataTypes.createRowType(getFieldTypes, getFieldNames)

  override def getRecordType: TypeInformation[Row] = {
    new RowTypeInfo(getFieldTypes, getFieldNames)
  }

  /** Emits the DataStream. */
  override def emitBoundedStream(boundedStream: DataStream[JTuple2[JBool, Row]]) = {
    val sink: PrintSinkFunction = new PrintSinkFunction()
    boundedStream.addSink(sink).name(sink.toString)
  }
}

/**
  * Implementation of the SinkFunction writing every tuple to the standard output.
  *
  */
class PrintSinkFunction() extends RichSinkFunction[JTuple2[JBool, Row]] {
  private var prefix: String = _

  override def open(parameters: Configuration): Unit = {
    super.open(parameters)
    val context = getRuntimeContext.asInstanceOf[StreamingRuntimeContext]
    prefix = "task-" + (context.getIndexOfThisSubtask + 1) + "> "
  }

  override def invoke(in: JTuple2[JBool, Row]): Unit = {
    val sb = new StringBuilder
    val row = in.f1
    for (i <- 0 until row.getArity) {
      if (i > 0) sb.append(",")
      val f = row.getField(i)
      if (f.isInstanceOf[Date]) {
        sb.append(DateTimeFunctions.dateFormat(f.asInstanceOf[JDate].getTime, "yyyy-MM-dd"))
      } else if (f.isInstanceOf[Time]) {
        sb.append(DateTimeFunctions.dateFormat(f.asInstanceOf[JDate].getTime, "HH:mm:ss"))
      } else if (f.isInstanceOf1602915887) {
        sb.append(DateTimeFunctions.dateFormat(f.asInstanceOf[JDate].getTime,
          "yyyy-MM-dd HH:mm:ss.SSS"))
      } else {
        sb.append(StringUtils.arrayAwareToString(f))
      }
    }

    if (in.f0) {
      System.out.println(prefix + "(+)" + sb.toString())
    } else {
      System.out.println(prefix + "(-)" + sb.toString())
    }
  }

  override def close(): Unit = {
    this.prefix = ""
  }

  override def toString: String = "Print to System.out"
}

Flink SQL解析Json格式数据的方法

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本文系统来源：https://blog.51cto.com/1196740/2369735

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