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SparkCSVDataPOC
Commit 2b2b8338 authored by Potharaju Peddi's avatar Potharaju Peddi
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for spark csv read and write POC

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="CompilerConfiguration">
<annotationProcessing>
<profile default="true" name="Default" enabled="true" />
<profile name="Maven default annotation processors profile" enabled="true">
<sourceOutputDir name="target/generated-sources/annotations" />
<sourceTestOutputDir name="target/generated-test-sources/test-annotations" />
<outputRelativeToContentRoot value="true" />
<module name="SparkStreamingPOC" />
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<option name="name" value="Central Repository" />
<option name="url" value="https://repo.maven.apache.org/maven2" />
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<option name="id" value="central" />
<option name="name" value="Maven Central repository" />
<option name="url" value="https://repo1.maven.org/maven2" />
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<option name="id" value="jboss.community" />
<option name="name" value="JBoss Community repository" />
<option name="url" value="https://repository.jboss.org/nexus/content/repositories/public/" />
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<component name="ExternalStorageConfigurationManager" enabled="true" />
<component name="MavenProjectsManager">
<option name="originalFiles">
<list>
<option value="$PROJECT_DIR$/pom.xml" />
</list>
</option>
</component>
<component name="ProjectRootManager" version="2" languageLevel="JDK_1_8" default="true" project-jdk-name="1.8" project-jdk-type="JavaSDK">
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{"batchWatermarkMs":0,"batchTimestampMs":1685079604942,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":30}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685082497616,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":40}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685099275385,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":220}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685099530009,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":240}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685103227472,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":260}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685104243503,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":300}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685084864674,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":60}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685092846391,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":80}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685093160018,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":100}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685093650004,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":120}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685096736482,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":140}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685096808089,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":160}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685096920004,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":180}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685096990006,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":200}}
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{"batchWatermarkMs":0,"batchTimestampMs":1685097607478,"conf":{"spark.sql.streaming.stateStore.providerClass":"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider","spark.sql.streaming.join.stateFormatVersion":"2","spark.sql.streaming.stateStore.compression.codec":"lz4","spark.sql.streaming.stateStore.rocksdb.formatVersion":"5","spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion":"2","spark.sql.streaming.multipleWatermarkPolicy":"min","spark.sql.streaming.aggregation.stateFormatVersion":"2","spark.sql.shuffle.partitions":"200"}}
{"MyStreamingTopic":{"0":200}}
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>SparkStreamingPOC</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<spark.version>3.2.1</spark.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql-kafka-0-10_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.26</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>com.opencsv</groupId>
<artifactId>opencsv</artifactId>
<version>5.5.2</version>
</dependency>
</dependencies>
</project>
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import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
@Data
@AllArgsConstructor
@NoArgsConstructor
public class Product {
private String productId;
private String productName;
private String productPrice;
private String deliveryStatus;
private String date;
}
src/main/java/SparkSamples.java 0 → 100644
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import org.apache.spark.sql.*;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.apache.spark.storage.StorageLevel;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.TimeoutException;
public class SparkSamples {
public static void main(String[] args) throws StreamingQueryException, TimeoutException {
SparkSession sparkSession = SparkSession
.builder().master("local[*]").appName("Spark Streaming Example").getOrCreate();
Dataset<Row> dataset = sparkSession.read().
format("csv").option("header",true).
load("src/main/resources/productData.csv");
dataset.show();
publishCsvRecordsToKafkaTopic(dataset);
}
private static void publishCsvRecordsToKafkaTopic(Dataset<Row> rowDataset) throws TimeoutException {
if (null != rowDataset) {
Dataset<Row> KafkaPublishJson = rowDataset
.withColumn("value", functions.to_json(functions.struct(functions.col("productId"),
functions.col("productName"), functions.col("productPrice"),
functions.col("deliveryStatus"), functions.col("date"))))
.alias("value").select("value")
.persist(StorageLevel.MEMORY_AND_DISK());
KafkaPublishJson
.write().format("kafka").options(KafkaCsvConfig()).save();
}
}
private static Map<String,String> KafkaCsvConfig(){
Map<String, String> kafkaConfigMap = new HashMap<>();
kafkaConfigMap.put("kafka.bootstrap.servers","127.0.0.1:9092");
kafkaConfigMap.put("topic", "MyStreamingTopic");
return kafkaConfigMap;
}
}
src/main/java/SparkStreamingData.java 0 → 100644
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import org.apache.spark.sql.*;
import org.apache.spark.sql.streaming.OutputMode;
import org.apache.spark.sql.streaming.StreamingQuery;
import org.apache.spark.sql.types.StructType;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.TimeoutException;
public class SparkStreamingData {
public static void main(String[] args) throws TimeoutException {
SparkSession sparkSession = SparkSession
.builder().master("local[*]").appName("Spark Streaming Example 2").getOrCreate();
Map<String, String> kafkaConfigMap = new HashMap<>();
kafkaConfigMap.put("kafka.bootstrap.servers", "localhost:9092");
kafkaConfigMap.put("subscribe", "MyStreamingTopic");
kafkaConfigMap.put("startingOffsets", "earliest");
kafkaConfigMap.put("failOnDataLoss", "false");
kafkaConfigMap.put("maxOffsetsPerTrigger","100");
// kafkaConfigMap.put("kafka.con.group.id", "localStream");
Dataset<Row> df = sparkSession
.readStream()
.format("kafka")
.options(kafkaConfigMap)
.load();
if(df != null){
StructType structType = Encoders.bean(Product.class).schema();
Dataset<Row> df1 = df
.selectExpr("CAST(value as String) as message")
.select(functions.from_json(functions.col("message"), structType).as("data"))
.select("data.*");
Dataset<Row> filteredDataFrame = df1.filter(functions.col("deliveryStatus").equalTo("shipped"))
.filter(functions.col("date").equalTo("25-05-2023"));
Dataset<Product> ds = filteredDataFrame.as(Encoders.bean(Product.class));
StreamingQuery streamingQuery = writeStreamingData(ds);
try{
streamingQuery.awaitTermination();
}catch (Exception e){
e.printStackTrace();
streamingQuery.stop();
sparkSession.stop();
}
}
}
private static StreamingQuery writeStreamingData(Dataset<Product> ds) throws TimeoutException {
return ds
.writeStream()
.outputMode("append")
.format("csv")
//.option("checkpointLocation","checkpointLocation/jobs")
.trigger(org.apache.spark.sql.streaming.Trigger.ProcessingTime("10 seconds"))
.foreachBatch((batchDF, batchId) -> {
batchDF.write().format("csv").mode("append").save("src/main/resources/productDataOutPutCSV");
})
.start();
}
private static Dataset<Row> readKafkaCsvData(SparkSession sparkSession) {
Map<String, String> kafkaConfigData = new HashMap<>();
kafkaConfigData.put("kafka.bootstrap.servers","127.0.0.1:9092");
kafkaConfigData.put("subscribe","MyStreamingTopic");
kafkaConfigData.put("startingOffsets","earliest");
kafkaConfigData.put("failOnDataLoss","false");
kafkaConfigData.put("kafka.con.group.id","localStream");
//sparkSession.streams().addListener((StreamingQueryListener) kafkaConfigData);
Dataset<Row> csvData = sparkSession.readStream().format("kafka")
.options(kafkaConfigData).load();
return csvData.selectExpr("cast(value as String) as message");
}
}
src/main/resources/productData.csv 0 → 100644
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productId,productName,productPrice,deliveryStatus,date
101,mobile,15000,ordered,10-05-2023
102,charger,25000,delivered,15-05-2023
103,cooker,35000,shipped,25-05-2023
104,tv,45000,shipped,25-05-2023
105,laptop,55000,delivered,25-05-2023
106,bottle,1500,ordered,12-05-2023
107,mouse,1000,delivered,15-05-2023
108,keyboard,500,shipped,25-05-2023
109,table,10000,shipped,22-05-2023
110,phone,12000,delivered,23-05-2023
111,shop,150000,ordered,16-05-2023
112,byke,75000,delivered,18-05-2023
113,car,20000,shipped,27-05-2023
114,cycle,33000,shipped,28-05-2023
115,bed,15000,delivered,26-05-2023
116,chairs,12000,ordered,15-05-2023
117,server,19555,delivered,18-05-2023
118,van,89200,shipped,25-05-2023
119,designing,14000,shipped,25-05-2023
120,interier,13000,delivered,25-05-2023
src/main/resources/productDataOutPutCSV/part-00000-7130a558-3ece-4d93-a61d-045cd384c95a-c000.csv 0 → 100644
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25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
src/main/resources/productDataOutPutCSV/part-00000-b19ffadd-ca80-4358-9d8e-5d4ee5cdbcb5-c000.csv 0 → 100644
View file @ 2b2b8338
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
src/main/resources/productDataOutPutCSV/part-00000-ca1eb03a-8aa8-4a4f-9011-312738e36f2a-c000.csv 0 → 100644
View file @ 2b2b8338
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
25-05-2023,shipped,103,cooker,35000
25-05-2023,shipped,104,tv,45000
25-05-2023,shipped,108,keyboard,500
25-05-2023,shipped,118,van,89200
25-05-2023,shipped,119,designing,14000
target/classes/productData.csv 0 → 100644
View file @ 2b2b8338
productId,productName,productPrice,deliveryStatus,date
101,mobile,15000,ordered,10-05-2023
102,charger,25000,delivered,15-05-2023
103,cooker,35000,shipped,25-05-2023
104,tv,45000,shipped,25-05-2023
105,laptop,55000,delivered,25-05-2023
106,bottle,1500,ordered,12-05-2023
107,mouse,1000,delivered,15-05-2023
108,keyboard,500,shipped,25-05-2023
109,table,10000,shipped,22-05-2023
110,phone,12000,delivered,23-05-2023
111,shop,150000,ordered,16-05-2023
112,byke,75000,delivered,18-05-2023
113,car,20000,shipped,27-05-2023
114,cycle,33000,shipped,28-05-2023
115,bed,15000,delivered,26-05-2023
116,chairs,12000,ordered,15-05-2023
117,server,19555,delivered,18-05-2023
118,van,89200,shipped,25-05-2023
119,designing,14000,shipped,25-05-2023
120,interier,13000,delivered,25-05-2023
target/classes/productDataOutPutCSV/part-00000-2c05c665-25e0-42ff-9ac1-5d4e74e60487-c000.json 0 → 100644
View file @ 2b2b8338
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
target/classes/productDataOutPutCSV/part-00000-4a654b59-c660-4c3e-9ad9-979b88559e01-c000.json 0 → 100644
View file @ 2b2b8338
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
target/classes/productDataOutPutCSV/part-00000-a3438257-79e2-4990-a3ed-4ebf54bbd997-c000.json 0 → 100644
View file @ 2b2b8338
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"103","productName":"cooker","productPrice":"35000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"104","productName":"tv","productPrice":"45000"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"108","productName":"keyboard","productPrice":"500"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"118","productName":"van","productPrice":"89200"}
{"date":"25-05-2023","deliveryStatus":"shipped","productId":"119","productName":"designing","productPrice":"14000"}
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