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hw0 | Mashchenko Bogdan #7

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20 changes: 20 additions & 0 deletions hw0/mashchenko/build.gradle
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plugins {
id 'java'
}

group 'org.example'
version '1.0-SNAPSHOT'

repositories {
mavenCentral()
}

dependencies {
testCompile group: 'junit', name: 'junit', version: '4.12'

compile group: 'org.apache.spark', name: 'spark-core_2.11', version: '2.3.3'
compile group: 'org.apache.spark', name: 'spark-sql_2.11', version: '2.3.3'
runtime group: 'org.apache.spark', name: 'spark-mllib_2.11', version: '2.3.3'

compile group: 'com.github.davidmoten', name: 'geo', version: '0.7.1'
}
133 changes: 133 additions & 0 deletions hw0/mashchenko/src/main/java/HomeWork0.java
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import com.github.davidmoten.geo.GeoHash;
import com.github.davidmoten.geo.LatLong;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.api.java.UDF3;
import org.apache.spark.sql.expressions.UserDefinedFunction;
import org.apache.spark.sql.expressions.Window;
import static org.apache.spark.sql.functions.avg;
import static org.apache.spark.sql.functions.callUDF;
import static org.apache.spark.sql.functions.col;
import static org.apache.spark.sql.functions.corr;
import static org.apache.spark.sql.functions.count;
import static org.apache.spark.sql.functions.desc;
import static org.apache.spark.sql.functions.first;
import static org.apache.spark.sql.functions.lit;
import static org.apache.spark.sql.functions.udf;
import static org.apache.spark.sql.functions.variance;
import org.apache.spark.sql.types.DataTypes;

public class HomeWork0 {

private static SparkSession sparkSession;

public static void main(String[] args) {
sparkSession = SparkSession
.builder()
.appName("Spakl hw 0")
.config("spark.master", "local")
.getOrCreate();

final Dataset<Row> dataset = sparkSession.read()
.option("header", "true")
.option("inferSchema", "true")
.option("mode", "DROPMALFORMED")
.option("escape", "\"")
.csv("/Users/bogdan.mashchenko/github/technopolis/bigData2020/hw0/mashchenko/src/main/resources/AB_NYC_2019.csv");

// median
dataset.groupBy("room_type")
.agg(callUDF("percentile_approx", col("price"), lit(0.5)).as("median"))
.show();
// +---------------+------+
// | room_type|median|
// +---------------+------+
// | Shared room| 45.0|
// |Entire home/apt| 160.0|
// | Private room| 70.0|
// +---------------+------+

//average
dataset.groupBy("room_type")
.agg(avg("price").as("avg"))
.show();
// +---------------+------------------+
// | room_type| avg|
// +---------------+------------------+
// | Shared room| 70.13298791018998|
// |Entire home/apt|211.88216032823104|
// | Private room| 89.51396823968689|
// +---------------+------------------+

// variance
dataset.groupBy("room_type")
.agg(variance("price").as("variance"))
.show();
// +---------------+------------------+
// | room_type| variance|
// +---------------+------------------+
// | Shared room|10365.890682680929|
// |Entire home/apt| 80852.24645965557|
// | Private room|23907.680804069663|
// +---------------+------------------+

// mode
dataset.withColumn("count", count("price").over(Window.partitionBy("room_type")))
.withColumn("price_mode", first("price").over(Window.orderBy("count").partitionBy("room_type")).as("mode"))
.groupBy("room_type")
.agg(first("price_mode").as("mode"))
.show();
// +---------------+----+
// | room_type|mode|
// +---------------+----+
// | Shared room| 40|
// |Entire home/apt| 225|
// | Private room| 149|
// +---------------+----+

// min price
dataset.orderBy("price")
.show(1);
// +--------+--------------------+-------+---------+-------------------+------------------+--------+---------+------------+-----+--------------+-----------------+-----------+-----------------+------------------------------+----------------+
// | id| name|host_id|host_name|neighbourhood_group| neighbourhood|latitude|longitude| room_type|price|minimum_nights|number_of_reviews|last_review|reviews_per_month|calculated_host_listings_count|availability_365|
// +--------+--------------------+-------+---------+-------------------+------------------+--------+---------+------------+-----+--------------+-----------------+-----------+-----------------+------------------------------+----------------+
// |18750597|Huge Brooklyn Bro...|8993084| Kimberly| Brooklyn|Bedford-Stuyvesant|40.69023|-73.95428|Private room| 0| 4| 1| 2018-01-06| 0.05| 4.0| 28|
// +--------+--------------------+-------+---------+-------------------+------------------+--------+---------+------------+-----+--------------+-----------------+-----------+-----------------+------------------------------+----------------+

// max price
dataset.orderBy(desc("price"))
.show(1);
// +-------+--------------------+-------+---------+-------------------+---------------+--------+---------+------------+-----+--------------+-----------------+-----------+-----------------+------------------------------+----------------+
// | id| name|host_id|host_name|neighbourhood_group| neighbourhood|latitude|longitude| room_type|price|minimum_nights|number_of_reviews|last_review|reviews_per_month|calculated_host_listings_count|availability_365|
// +-------+--------------------+-------+---------+-------------------+---------------+--------+---------+------------+-----+--------------+-----------------+-----------+-----------------+------------------------------+----------------+
// |9528920|Quiet, Clean, Lit...|3906464| Amy| Manhattan|Lower East Side|40.71355|-73.98507|Private room| 9999| 99| 6| 2016-01-01| 0.14| 1.0| 83|
// +-------+--------------------+-------+---------+-------------------+---------------+--------+---------+------------+-----+--------------+-----------------+-----------+-----------------+------------------------------+----------------+

// correlation
dataset.agg(
corr("price", "minimum_nights").as("correlation_between_price_and_minimum_nights"),
corr("price", "number_of_reviews").as("correlation_between_price_and_number_of_reviews")
).show();
// +--------------------------------------------+-----------------------------------------------+
// |correlation_between_price_and_minimum_nights|correlation_between_price_and_number_of_reviews|
// +--------------------------------------------+-----------------------------------------------+
// | 0.04238800501413225| -0.04806955416645...|
// +--------------------------------------------+-----------------------------------------------+


// max price for square 5km x 5km
UserDefinedFunction geoHash = udf((UDF3<Double, Double, Integer, String>) GeoHash::encodeHash, DataTypes.StringType);
sparkSession.sqlContext().udf().register("geoHash", geoHash);
final Row result = dataset.withColumn("geoHash", geoHash.apply(col("latitude").cast(DataTypes.DoubleType), col("longitude").cast(DataTypes.DoubleType), lit(5)))
.withColumn("price", col("price").cast(DataTypes.LongType))
.groupBy("geoHash")
.mean("price")
.orderBy(col("avg(price)").desc())
.first();

final LatLong latLong = GeoHash.decodeHash(result.getString(0));
System.out.printf("latitude : longitude = %f : %f, price = %f%n", latLong.getLat(), latLong.getLon(), result.getDouble(1));
// latitude : longitude = 40,583496 : -73,718262, price = 350,000000
}
}
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