FlinkSQL解析Json格式数据的方法
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：
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org.apache.flink
flink-table_2.11
1.7.2
org.apache.flink
flink-json
1.7.2
com.fasterxml.jackson.core
jackson-databind
2.9.8
joda-time
joda-time
2.10.1
3. Google Protobuf消息定义
3.1 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 Producer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 producer = new KafkaProducer<>(props);
int count = 0;
while (count++ < recordsNumber){
producer.send(new ProducerRecord(topic,
String.valueOf(Instant.now().toEpochMilli()),
publisher.randomMessage(10)));
TimeUnit.MILLISECONDS.sleep(100);
}
// producer.flush();
producer.close();
}
}
4. 源代码Java:
4.1 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;4.2 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");4.3 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, //数组对象子字段,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.
4.3 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.
4.4 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);
}
}
}4.5 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.
5. 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.isInstanceOf1583857352) {
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"
}文章标题：FlinkSQL解析Json格式数据的方法
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