You simply cannot. DataFrames, same as other distributed data structures, are not iterable and can be accessed using only dedicated higher order function and / or SQL methods. You can of course collect for row in df.rdd.collect(): do_something(row) or convert toLocalIterator for row in df.rdd.toLocalIterator(): do_something(row) and iterate locally as shown above, but it beats … Read more
Writing data Include applicable JDBC driver when you submit the application or start shell. You can use for example –packages: bin/pyspark –packages group:name:version or combining driver-class-path and jars bin/pyspark –driver-class-path $PATH_TO_DRIVER_JAR –jars $PATH_TO_DRIVER_JAR These properties can be also set using PYSPARK_SUBMIT_ARGS environment variable before JVM instance has been started or using conf/spark-defaults.conf to set spark.jars.packages … Read more
Try the below code, you need not specify the schema. When you give inferSchema as true it should take it from your csv file. val pagecount = sqlContext.read.format(“csv”) .option(“delimiter”,” “).option(“quote”,””) .option(“header”, “true”) .option(“inferSchema”, “true”) .load(“dbfs:/databricks-datasets/wikipedia-datasets/data-001/pagecounts/sample/pagecounts-20151124-170000”) If you want to manually specify the schema, you can do it as below: import org.apache.spark.sql.types._ val customSchema = StructType(Array( … Read more
According to documentation, isin takes a vararg, not a list. List is actually a confusing name here. You can try converting your List to vararg like this: val items = List(“a”, “b”, “c”) sqlContext.sql(“select c1 from table”) .filter($”c1″.isin(items:_*)) .collect .foreach(println) Your variant with mkString compiles, because one single String is also a vararg (with number … Read more
Provide small sample data, that can be easily recreated. At the very least, posters should provide a couple of rows and columns on their dataframe and code that can be used to easily create it. By easy, I mean cut and paste. Make it as small as possible to demonstrate your problem. I have the … Read more
According to spark documentation “where() is an alias for filter()“ filter(condition) Filters rows using the given condition. where() is an alias for filter(). Parameters: condition – a Column of types.BooleanType or a string of SQL expression. >>> df.filter(df.age > 3).collect() [Row(age=5, name=u’Bob’)] >>> df.where(df.age == 2).collect() [Row(age=2, name=u’Alice’)] >>> df.filter(“age > 3”).collect() [Row(age=5, name=u’Bob’)] >>> … Read more
You should use & / | operators and be careful about operator precedence (== has lower precedence than bitwise AND and OR): df1 = sqlContext.createDataFrame( [(1, “a”, 2.0), (2, “b”, 3.0), (3, “c”, 3.0)], (“x1”, “x2”, “x3”)) df2 = sqlContext.createDataFrame( [(1, “f”, -1.0), (2, “b”, 0.0)], (“x1”, “x2”, “x3”)) df = df1.join(df2, (df1.x1 == df2.x1) … Read more
Let’s say you have this data setup (so that results are reproducible): // declaring data types case class Company(cName: String, cId: String, details: String) case class Employee(name: String, id: String, email: String, company: Company) // setting up example data val e1 = Employee(“n1”, null, “n1@c1.com”, Company(“c1”, “1”, “d1”)) val e2 = Employee(“n2”, “2”, “n2@c1.com”, Company(“c1”, … Read more
Spark >= 2.4 You can replace zip_ udf with arrays_zip function from pyspark.sql.functions import arrays_zip, col, explode (df .withColumn(“tmp”, arrays_zip(“b”, “c”)) .withColumn(“tmp”, explode(“tmp”)) .select(“a”, col(“tmp.b”), col(“tmp.c”), “d”)) Spark < 2.4 With DataFrames and UDF: from pyspark.sql.types import ArrayType, StructType, StructField, IntegerType from pyspark.sql.functions import col, udf, explode zip_ = udf( lambda x, y: list(zip(x, y)), … Read more
SparkSession.createDataFrame, which is used under the hood, requires an RDD / list of Row/tuple/list/dict* or pandas.DataFrame, unless schema with DataType is provided. Try to convert float to tuple like this: myFloatRdd.map(lambda x: (x, )).toDF() or even better: from pyspark.sql import Row row = Row(“val”) # Or some other column name myFloatRdd.map(row).toDF() To create a DataFrame … Read more