See, why this way that you are doing is not working. First, you are trying to get integer from a Row Type, the output of your collect is like this: >>> mvv_list = mvv_count_df.select(‘mvv’).collect() >>> mvv_list[0] Out: Row(mvv=1) If you take something like this: >>> firstvalue = mvv_list[0].mvv Out: 1 You will get the mvv … Read more
You can use Column.isNull / Column.isNotNull: df.where(col(“dt_mvmt”).isNull()) df.where(col(“dt_mvmt”).isNotNull()) If you want to simply drop NULL values you can use na.drop with subset argument: df.na.drop(subset=[“dt_mvmt”]) Equality based comparisons with NULL won’t work because in SQL NULL is undefined so any attempt to compare it with another value returns NULL: sqlContext.sql(“SELECT NULL = NULL”).show() ## +————-+ ## … Read more
This should help to get distinct values of a column: df.select(‘column1′).distinct().collect() Note that .collect() doesn’t have any built-in limit on how many values can return so this might be slow — use .show() instead or add .limit(20) before .collect() to manage this.
Just execute this command in the spark directory: cp conf/log4j.properties.template conf/log4j.properties Edit log4j.properties: # Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose log4j.logger.org.eclipse.jetty=WARN log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO Replace at the first line: log4j.rootCategory=INFO, console by: … Read more
You cannot add an arbitrary column to a DataFrame in Spark. New columns can be created only by using literals (other literal types are described in How to add a constant column in a Spark DataFrame?) from pyspark.sql.functions import lit df = sqlContext.createDataFrame( [(1, “a”, 23.0), (3, “B”, -23.0)], (“x1”, “x2”, “x3”)) df_with_x4 = df.withColumn(“x4”, … Read more
Spark 2.2+ Spark 2.2 introduces typedLit to support Seq, Map, and Tuples (SPARK-19254) and following calls should be supported (Scala): import org.apache.spark.sql.functions.typedLit df.withColumn(“some_array”, typedLit(Seq(1, 2, 3))) df.withColumn(“some_struct”, typedLit((“foo”, 1, 0.3))) df.withColumn(“some_map”, typedLit(Map(“key1” -> 1, “key2” -> 2))) Spark 1.3+ (lit), 1.4+ (array, struct), 2.0+ (map): The second argument for DataFrame.withColumn should be a Column so … Read more
The original answer discussing the code can be found below. First of all, you have to distinguish between different types of API, each with its own performance considerations. RDD API (pure Python structures with JVM based orchestration) This is the component which will be most affected by the performance of the Python code and the … Read more
There are many ways to do that: Option 1. Using selectExpr. data = sqlContext.createDataFrame([(“Alberto”, 2), (“Dakota”, 2)], [“Name”, “askdaosdka”]) data.show() data.printSchema() # Output #+——-+———-+ #| Name|askdaosdka| #+——-+———-+ #|Alberto| 2| #| Dakota| 2| #+——-+———-+ #root # |– Name: string (nullable = true) # |– askdaosdka: long (nullable = true) df = data.selectExpr(“Name as name”, “askdaosdka as … Read more