All operations preserve the order, except those that explicitly do not. Ordering is always “meaningful”, not just after a sortBy. For example, if you read a file (sc.textFile) the lines of the RDD will be in the order that they were in the file. Without trying to give a complete list, map, filter and flatMap … Read more
Using join (it will result in more than one row in group in case of ties): import pyspark.sql.functions as F from pyspark.sql.functions import count, col cnts = df.groupBy(“id_sa”, “id_sb”).agg(count(“*”).alias(“cnt”)).alias(“cnts”) maxs = cnts.groupBy(“id_sa”).agg(F.max(“cnt”).alias(“mx”)).alias(“maxs”) cnts.join(maxs, (col(“cnt”) == col(“mx”)) & (col(“cnts.id_sa”) == col(“maxs.id_sa”)) ).select(col(“cnts.id_sa”), col(“cnts.id_sb”)) Using window functions (will drop ties): from pyspark.sql.functions import row_number from pyspark.sql.window import … Read more
You can do it the same way SQLContext.createDataFrame does it: import org.apache.spark.sql.catalyst.ScalaReflection val schema = ScalaReflection.schemaFor[TestCase].dataType.asInstanceOf[StructType]
By default Spark uses the /tmp directory to store intermediate data. If you actually do have space left on some device — you can alter this by creating the file SPARK_HOME/conf/spark-defaults.conf and adding the line. Here SPARK_HOME is wherever you root directory for the spark install is. spark.local.dir SOME/DIR/WHERE/YOU/HAVE/SPACE
Use df.select(cols.head, cols.tail: _*) Let me know if it works 🙂 Explanation from @Ben: The key is the method signature of select: select(col: String, cols: String*) The cols:String* entry takes a variable number of arguments. :_* unpacks arguments so that they can be handled by this argument. Very similar to unpacking in python with *args. … Read more
Use wildcard, e.g. replace 2008 with *: df = sqlContext.read .format(“com.databricks.spark.csv”) .option(“header”, “true”) .load(“../Downloads/*.csv”) // <– note the star (*) Spark 2.0 // these lines are equivalent in Spark 2.0 spark.read.format(“csv”).option(“header”, “true”).load(“../Downloads/*.csv”) spark.read.option(“header”, “true”).csv(“../Downloads/*.csv”) Notes: Replace format(“com.databricks.spark.csv”) by using format(“csv”) or csv method instead. com.databricks.spark.csv format has been integrated to 2.0. Use spark not sqlContext
When Spark reads a file from HDFS, it creates a single partition for a single input split. Input split is set by the Hadoop InputFormat used to read this file. For instance, if you use textFile() it would be TextInputFormat in Hadoop, which would return you a single partition for a single block of HDFS … Read more
There are a few efficient ways to implement this. Let’s start with required imports: from pyspark.sql.functions import col, expr, when You can use Hive IF function inside expr: new_column_1 = expr( “””IF(fruit1 IS NULL OR fruit2 IS NULL, 3, IF(fruit1 = fruit2, 1, 0))””” ) or when + otherwise: new_column_2 = when( col(“fruit1”).isNull() | col(“fruit2”).isNull(), … Read more
Spark >= 3.4 In Spark 3.4 or later you can use built-in melt method (sdf .melt( ids=[‘A’], values=[‘B’, ‘C’], variableColumnName=”variable”, valueColumnName=”value”) .show()) +—+——–+—–+ | A|variable|value| +—+——–+—–+ | a| B| 1| | a| C| 2| | b| B| 3| | b| C| 4| | c| B| 5| | c| C| 6| +—+——–+—–+ This method is available … Read more
Addition to @Binary Nerd If you are using Spark, use the following to get the Spark version: spark-submit –version or Login to the Cloudera Manager and goto Hosts page then run inspect hosts in cluster