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Pre-processing for the Yelp dataset
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from pyspark.sql import SparkSession | |
from pyspark.sql.functions import explode, split, to_timestamp | |
TS_FORMAT = "yyyy-MM-dd HH:mm:ss" | |
checkins_path = ... | |
tips_path = ... | |
spark = SparkSession.builder.master("local").appName("preprocessing").getOrCreate() | |
checkins = spark.read.json(checkins_path) | |
tips = spark.read.json(tips_path) | |
checkins_exploded = checkins.select( | |
checkins["business_id"], explode(split(checkins["date"], ", ")).alias("date") | |
) | |
checkins_ts = ( | |
checkins_exploded.withColumn( | |
"ts", to_timestamp(checkins_exploded["date"], TS_FORMAT).cast("long") | |
) | |
.drop("date") | |
.orderBy("business_id", "ts") | |
) | |
tips_ts = ( | |
tips.withColumn("ts", to_timestamp(tips["date"], TS_FORMAT).cast("long")) | |
.drop("date") | |
.orderBy("business_id", "ts") | |
) | |
tips_ts.write.parquet("target/yelp_tips.parquet") | |
checkins_ts.write.parquet("target/yelp_checkins.parquet") |
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object Yelp { | |
final val TS_FORMAT = "yyyy-MM-dd HH:mm:ss" | |
def main(args: Array[String]): Unit = { | |
val spark: SparkSession = SparkSession | |
.builder() | |
.master("local") | |
.appName("Preprocessing") | |
.getOrCreate() | |
val checkIns = spark.read.json( | |
CHECK_IN_PATH | |
) | |
val tips = spark.read.json( | |
TIP_PATH | |
) | |
val checkInsExploded = | |
checkIns.select( | |
checkIns("business_id"), | |
explode(split(checkIns("date"), ", ")).as("date") | |
) | |
val checkInsTS = | |
checkInsExploded | |
.withColumn( | |
"ts", | |
to_timestamp(checkInsExploded("date"), TS_FORMAT).cast("long") | |
) | |
.drop("date") | |
.orderBy(col("business_id"), col("ts")) | |
val tipsTS = | |
tips | |
.withColumn("ts", to_timestamp(tips("date"), TS_FORMAT).cast("long")) | |
.drop("date") | |
.orderBy(col("business_id"), col("ts")) | |
tipsTS.write.parquet("target/yelp_tips.parquet") | |
checkInsTS.write.parquet("target/yelp_checkins.parquet") | |
} | |
} |
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