Spark指南

向右看齐 2022-12-27 14:15 77阅读 0赞

## 入门 ##

### 依赖 ###

<dependency>
 <groupId>org.apache.spark</groupId>
 <artifactId>spark-sql_2.12</artifactId>
 <version>3.0.1</version>
 <scope>provided</scope>
 </dependency>
 <dependency>
 <groupId>org.codehaus.janino</groupId>
 <artifactId>janino</artifactId>
 <version>3.0.8</version>
 </dependency>

### 编码 ###

import org.apache.spark.api.java.function.FilterFunction;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.SparkSession;
 
 public class SimpleApp {
 public static void main(String[] args) {
 String logFile = "/Users/fanshaorong/Desktop/Project/Moses/moses-framework/README.md"; // Should be some file on your system
 SparkSession spark = SparkSession.builder().appName("Simple Application").master("local").getOrCreate();
 Dataset<String> logData = spark.read().textFile(logFile).cache();
 long numAs = logData.filter((FilterFunction<String>) s -> s.contains("a")).count();
 long numBs = logData.filter((FilterFunction<String>) s -> s.contains("b")).count();
 System.out.println("Lines with a: " + numAs + ", lines with b: " + numBs);
 spark.stop();
 }
 }

### 查看 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70][]

## Spark RDDs ##

### 并行集合 ###

import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.sql.SparkSession;
 
 import java.util.ArrayList;
 import java.util.List;
 
 /**
 * Computes an approximation to pi
 * Usage: JavaSparkPi [partitions]
 */
 public final class JavaSparkPi {
 public static void main(String[] args) throws Exception {
 SparkSession spark = SparkSession
 .builder()
 .appName("JavaSparkPi")
 .master("local")
 .getOrCreate();
 JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());
 //切片数，正常情况下，Spark 会试着基于你的集群状况自动地设置切片的数目
 int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2;
 int n = 100000 * slices;
 List<Integer> l = new ArrayList<>(n);
 for (int i = 0; i < n; i++) {
 l.add(i);
 }
 //集合中的元素被复制到一个可并行操作的分布式数据集中
 JavaRDD<Integer> dataSet = jsc.parallelize(l, slices);
 //将这个数组中的元素相加
 int count = dataSet.map(integer -> {
 double x = Math.random() * 2 - 1;
 double y = Math.random() * 2 - 1;
 return (x * x + y * y <= 1) ? 1 : 0;
 }).reduce((integer, integer2) -> integer + integer2);
 System.out.println("Pi is roughly " + 4.0 * count / n);
 spark.stop();
 }
 }

### ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 1][] ###

### RDD 操作 ###

import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.sql.SparkSession;
 import scala.Tuple2;
 
 import java.util.Arrays;
 import java.util.List;
 import java.util.regex.Pattern;
 
 public final class JavaWordCount {
 private static final Pattern SPACE = Pattern.compile(" ");
 
 public static void main(String[] args) throws Exception {
 if (args.length < 1) {
 System.err.println("Usage: JavaWordCount <file>");
 System.exit(1);
 }
 SparkSession spark = SparkSession
 .builder()
 .appName("JavaWordCount")
 .master("local")
 .getOrCreate();
 JavaRDD<String> lines = spark.read().textFile(args[0]).javaRDD();
 JavaRDD<String> words = lines.flatMap(s -> Arrays.asList(SPACE.split(s)).iterator());
 JavaPairRDD<String, Integer> ones = words.mapToPair(s -> new Tuple2<>(s, 1));
 JavaPairRDD<String, Integer> counts = ones.reduceByKey((i1, i2) -> i1 + i2);
 List<Tuple2<String, Integer>> output = counts.collect();
 for (Tuple2<?, ?> tuple : output) {
 System.out.println(tuple._1() + ": " + tuple._2());
 }
 spark.stop();
 }
 }

## ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 2][] ##

## Web框架 ##

### 依赖 ###

<dependency>
 <groupId>com.sparkjava</groupId>
 <artifactId>spark-core</artifactId>
 <version>2.9.3</version>
 </dependency>

### 编码 ###

import static spark.Spark.get;
    
    public class HelloWorld {
        public static void main(String[] args) {
            get("/hello", (req, res) -> "Hello World");
        }
    }

### 查看 ###

[http://localhost:4567/hello][http_localhost_4567_hello]

![20201211194328176.png][]

## 视图和模版 ##

### 使用Thymeleaf模板引擎渲染HTML ###

<dependency>
 <groupId>com.sparkjava</groupId>
 <artifactId>spark-template-thymeleaf</artifactId>
 <version>2.7.1</version>
 </dependency>

### 编码 ###

import spark.ModelAndView;
 import spark.template.thymeleaf.ThymeleafTemplateEngine;
 
 import java.util.HashMap;
 import java.util.Map;
 
 import static spark.Spark.get;
 
 /**
 * ThymeleafTemplateRoute example.
 */
 public final class ThymeleafExample {
 public static void main(final String[] args) {
 get("/hello", (request, response) -> {
 Map<String, Object> model = new HashMap<>();
 model.put("message", "Hello Thymeleaf!");
 return new ModelAndView(model, "hello"); // located in resources/templates
 }, new ThymeleafTemplateEngine());
 }
 }

### hello.html ###

<h1 th:text="${message}" xmlns:th="http://www.w3.org/1999/xhtml">Text inside tag will be replaced by ${message}</h1>

### ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 3][] ###

### 查看 ###

[http://localhost:4567/hello][http_localhost_4567_hello]

![20201211201939552.png][]

## Spark Streaming ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 4][]

Spark Streaming接收实时输入数据流，并将数据分成批次，然后由Spark引擎进行处理，以生成批次的最终结果流。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 5][]

### pom.xml ###

<dependency>
 <groupId>org.apache.spark</groupId>
 <artifactId>spark-streaming_2.12</artifactId>
 <version>3.0.1</version>
 <scope>provided</scope>
 </dependency>

### JavaQueueStream ###

import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 import scala.Tuple2;
 
 import java.util.ArrayList;
 import java.util.LinkedList;
 import java.util.List;
 import java.util.Queue;
 
 public final class JavaQueueStream {
 public static void main(String[] args) throws Exception {
 // StreamingExamples.setStreamingLogLevels();
 SparkConf sparkConf = new SparkConf().setMaster("local[2]").setAppName("JavaQueueStream");
 // Create the context
 JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));
 // Create the queue through which RDDs can be pushed to
 // a QueueInputDStream
 // Create and push some RDDs into the queue
 List<Integer> list = new ArrayList<>();
 for (int i = 0; i < 1000; i++) {
 list.add(i);
 }
 Queue<JavaRDD<Integer>> rddQueue = new LinkedList<>();
 for (int i = 0; i < 30; i++) {
 rddQueue.add(ssc.sparkContext().parallelize(list));
 }
 // Create the QueueInputDStream and use it do some processing
 JavaDStream<Integer> inputStream = ssc.queueStream(rddQueue);
 JavaPairDStream<Integer, Integer> mappedStream = inputStream.mapToPair(
 i -> new Tuple2<>(i % 10, 1));
 JavaPairDStream<Integer, Integer> reducedStream = mappedStream.reduceByKey(
 (i1, i2) -> i1 + i2);
 reducedStream.print();
 ssc.start();
 ssc.awaitTermination();
 }
 }

### ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 6][] ###

### JavaNetworkWordCount ###

import org.apache.spark.SparkConf;
 import org.apache.spark.streaming.Durations;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 import scala.Tuple2;
 
 import java.util.Arrays;
 import java.util.List;
 import java.util.regex.Pattern;
 
 /**
 * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
 * 
 * Usage: JavaNetworkWordCount <hostname> <port>
 * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
 * 
 * To run this on your local machine, you need to first run a Netcat server
 * `$ nc -lk 9999`
 * and then run the example
 * `$ bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999`
 */
 public final class JavaNetworkWordCount {
 private static final Pattern SPACE = Pattern.compile(" ");
 
 public static void main(String[] args) throws Exception {
 // Create the context with a 1 second batch size
 SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount");
 JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(10));
 // Create a DStream that will connect to hostname:port, like localhost:9999
 JavaReceiverInputDStream<String> lines = jssc.socketTextStream("localhost", 9999);
 // Split each line into words
 JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(x.split(" ")).iterator());
 // Count each word in each batch
 JavaPairDStream<String, Integer> pairs = words.mapToPair(s -> new Tuple2<>(s, 1));
 JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey((i1, i2) -> i1 + i2);
 // Print the first ten elements of each RDD generated in this DStream to the console
 // wordCounts.print();
 wordCounts.foreachRDD(record -> {
 List<Tuple2<String, Integer>> collect = record.collect();
 if (collect.size() > 0) {
 System.out.println(collect.toString());
 }
 });
 jssc.start(); // Start the computation
 jssc.awaitTermination(); // Wait for the computation to terminate
 }
 }

### 运行Netcat ###

> nc -lk 9999

![20201227212554735.png][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 7][]

### **DStreams上的输出操作** ###

<table> 
 <tbody> 
 <tr> 
 <th style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;width:256px;">输出操作</th> 
 <th style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">含义</th> 
 </tr> 
 <tr> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">print()</td> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">在运行流应用程序的驱动程序节点上，打印DStream中每批数据的前十个元素。这对于开发和调试很有用。 在Python API中称为&nbsp;pprint（）。</td> 
 </tr> 
 <tr> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">saveAsTextFiles(prefix, [suffix])</td> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">将此DStream的内容另存为文本文件。基于产生在每批间隔的文件名的前缀和后缀：“前缀TIME_IN_MS [.suffix]”。</td> 
 </tr> 
 <tr> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">saveAsObjectFiles(prefix, [suffix])</td> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">将此DStream的内容保存为<code>SequenceFiles</code>序列化Java对象的内容。基于产生在每批间隔的文件名的前缀和&nbsp;后缀：“前缀TIME_IN_MS [.suffix]”。 Python API中不可用。</td> 
 </tr> 
 <tr> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">saveAsHadoopFiles(prefix, [suffix])</td> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">将此DStream的内容另存为Hadoop文件。基于产生在每批间隔的文件名的前缀和后缀：“前缀TIME_IN_MS [.suffix]”。 Python API中不可用。</td> 
 </tr> 
 <tr> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">foreachRDD(func)</td> 
 <td style="border-color:#dddddd #cccccc #cccccc;text-align:left;vertical-align:top;">最通用的输出运算符，将函数func应用于从流生成的每个RDD。此功能应将每个RDD中的数据推送到外部系统，例如将RDD保存到文件或通过网络将其写入数据库。请注意，函数func是在运行流应用程序的驱动程序进程中执行的，并且通常在其中具有RDD操作，这将强制计算流RDD。</td> 
 </tr> 
 </tbody> 
</table>

## Kafka集成 ##

### pom.xml ###

<dependency>
 <groupId>org.apache.spark</groupId>
 <artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
 <version>3.0.1</version>
 <scope>provided</scope>
 </dependency>

### JavaDirectKafkaWordCount ###

import org.apache.kafka.clients.consumer.ConsumerConfig;
 import org.apache.kafka.clients.consumer.ConsumerRecord;
 import org.apache.kafka.common.serialization.StringDeserializer;
 import org.apache.spark.SparkConf;
 import org.apache.spark.streaming.Durations;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaInputDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 import org.apache.spark.streaming.kafka010.ConsumerStrategies;
 import org.apache.spark.streaming.kafka010.KafkaUtils;
 import org.apache.spark.streaming.kafka010.LocationStrategies;
 import scala.Tuple2;
 
 import java.util.*;
 import java.util.regex.Pattern;
 
 /**
 * Consumes messages from one or more topics in Kafka and does wordcount.
 * Usage: JavaDirectKafkaWordCount <brokers> <groupId> <topics>
 * <brokers> is a list of one or more Kafka brokers
 * <groupId> is a consumer group name to consume from topics
 * <topics> is a list of one or more kafka topics to consume from
 * 
 * Example:
 * $ bin/run-example streaming.JavaDirectKafkaWordCount broker1-host:port,broker2-host:port \
 * consumer-group topic1,topic2
 */
 public final class JavaDirectKafkaWordCount {
 private static final Pattern SPACE = Pattern.compile(" ");
 
 public static void main(String[] args) throws Exception {
 if (args.length < 3) {
 System.err.println("Usage: JavaDirectKafkaWordCount <brokers> <groupId> <topics>\n" +
 " <brokers> is a list of one or more Kafka brokers\n" +
 " <groupId> is a consumer group name to consume from topics\n" +
 " <topics> is a list of one or more kafka topics to consume from\n\n");
 System.exit(1);
 }
 // StreamingExamples.setStreamingLogLevels();
 String brokers = args[0];
 String groupId = args[1];
 String topics = args[2];
 // Create context with a 2 seconds batch interval
 SparkConf sparkConf = new SparkConf().setMaster("local[2]").setAppName("JavaDirectKafkaWordCount");
 JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2));
 Set<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
 Map<String, Object> kafkaParams = new HashMap<>();
 kafkaParams.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers);
 kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
 kafkaParams.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
 kafkaParams.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
 // Create direct kafka stream with brokers and topics
 JavaInputDStream<ConsumerRecord<String, String>> messages = KafkaUtils.createDirectStream(
 jssc,
 LocationStrategies.PreferConsistent(),
 ConsumerStrategies.Subscribe(topicsSet, kafkaParams));
 // Get the lines, split them into words, count the words and print
 JavaDStream<String> lines = messages.map(ConsumerRecord::value);
 JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());
 JavaPairDStream<String, Integer> wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1))
 .reduceByKey((i1, i2) -> i1 + i2);
 // wordCounts.print();
 wordCounts.foreachRDD(record -> {
 List<Tuple2<String, Integer>> collect = record.collect();
 if (collect.size() > 0) {
 System.out.println(collect.toString());
 }
 });
 // Start the computation
 jssc.start();
 jssc.awaitTermination();
 }
 }

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 8][]

## ES集成 ##

### pom.xml ###

<properties>
 <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
 <maven.compiler.source>1.8</maven.compiler.source>
 <maven.compiler.target>1.8</maven.compiler.target>
 <java.version>1.8</java.version>
 <spark.version>2.3.0</spark.version>
 <elasticsearch.version>7.9.0</elasticsearch.version>
 </properties>
 <dependencies>
 
 <dependency>
 <groupId>org.apache.spark</groupId>
 <artifactId>spark-core_2.11</artifactId>
 <version>${spark.version}</version>
 </dependency>
 <dependency>
 <groupId>org.apache.spark</groupId>
 <artifactId>spark-streaming_2.11</artifactId>
 <version>${spark.version}</version>
 </dependency>
 <dependency>
 <groupId>org.elasticsearch</groupId>
 <artifactId>elasticsearch-spark-20_2.11</artifactId>
 <version>${elasticsearch.version}</version>
 </dependency>
 </dependencies>

### EsSparkTest ###

import com.google.common.collect.ImmutableList;
 import com.google.common.collect.ImmutableMap;
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.sql.SparkSession;
 import org.elasticsearch.spark.rdd.api.java.JavaEsSpark;
 
 import java.util.Map;
 
 /**
 * @author Moses
 * @date 2020/12/29
 */
 public class EsSparkTest {
 String ELASTIC_SEARCH_IP = "192.168.31.102:9200";
 
 public void writeEs() {
 String elasticIndex = "spark/docs";
 //https://www.elastic.co/guide/en/elasticsearch/hadoop/current/spark.html#spark-native
 SparkConf sparkConf = new SparkConf().setAppName("writeEs").setMaster("local[*]").set("es.index.auto.create", "true")
 .set("es.nodes", ELASTIC_SEARCH_IP).set("es.port", "9200").set("es.nodes.wan.only", "true");
 SparkSession sparkSession = SparkSession.builder().config(sparkConf).getOrCreate();
 JavaSparkContext jsc = new JavaSparkContext(sparkSession.sparkContext());//adapter
 Map<String, ?> numbers = ImmutableMap.of("one", 1, "two", 2);
 Map<String, ?> airports = ImmutableMap.of("OTP", "Otopeni", "SFO", "San Fran");
 JavaRDD<Map<String, ?>> javaRDD = jsc.parallelize(ImmutableList.of(numbers, airports));
 JavaEsSpark.saveToEs(javaRDD, elasticIndex);
 }
 
 public void readEs() {
 SparkConf sparkConf = new SparkConf().setAppName("writeEs").setMaster("local[*]").set("es.index.auto.create", "true")
 .set("es.nodes", ELASTIC_SEARCH_IP).set("es.port", "9200").set("es.nodes.wan.only", "true")
 .set("es.index.read.missing.as.empty", "yes");
 SparkSession sparkSession = SparkSession.builder().config(sparkConf).getOrCreate();
 JavaSparkContext jsc = new JavaSparkContext(sparkSession.sparkContext());//adapter
 JavaRDD<Map<String, Object>> searchRdd = JavaEsSpark.esRDD(jsc, "spark/docs", "?q=Otopeni").values();
 for (Map<String, Object> item : searchRdd.collect()) {
 item.forEach((key, value) -> {
 System.out.println("search key:" + key + ", search value:" + value);
 });
 }
 sparkSession.stop();
 }
 
 public static void main(String[] args) {
 new EsSparkTest().writeEs();
 // new EsSparkTest().readEs();
 }
 }

### 写ES ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 9][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 10][]

### 读ES ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 11][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70]: /images/20221120/55b08b96182e4485b7c3ff79fb525a4e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 1]: /images/20221120/f70c64abb0454ee5b0875a170716350d.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 2]: /images/20221120/d20a9e91fc8047788f48c74b56bcbeb5.png
[http_localhost_4567_hello]: http://localhost:4567/hello
[20201211194328176.png]: /images/20221120/ef943460e3f54f5da6d62adb2ac1a844.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0ZXZlX2ZyYW5r_size_16_color_FFFFFF_t_70 3]: /images/20221120/0327b220db084aeba203ad85ed4231a7.png
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