#SQL TABLE PATH THAT IS COMPATIBLE FOR MAC ADN WINDOWS FULL#
# with the partitioning column appeared in the partition directory paths.įind full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo. printSchema () # The final schema consists of all 3 columns in the Parquet files together parquet ( "data/test_table/key=2" ) # Read the partitioned table map ( lambda i : Row ( single = i, triple = i ** 3 ))) cubesDF. # adding a new column and dropping an existing columnĬubesDF = spark. parquet ( "data/test_table/key=1" ) # Create another DataFrame in a new partition directory, map ( lambda i : Row ( single = i, double = i ** 2 ))) squaresDF. # Create a simple DataFrame, stored into a partition directory printSchema () // The final schema consists of all 3 columns in the Parquet files together // with the partitioning column appeared in the partition directory paths // root // |- value: int (nullable = true) // |- square: int (nullable = true) // |- cube: int (nullable = true) // |- key: int (nullable = true)įrom pyspark.sql import Row # spark is from the previous example. parquet ( "data/test_table/key=2" ) // Read the partitioned table Dataset mergedDF = spark. parquet ( "data/test_table/key=1" ) List cubes = new ArrayList () for ( int value = 6 value cubesDF = spark. Here we prefix all the names with "Name:" schema <- structType ( structField ( "name", "string" )) teenNames <- dapply ( df, function ( p ) List squares = new ArrayList () for ( int value = 1 value squaresDF = spark. show () // +-+ // | value| // +-+ // |Name: Justin| // +-+ĭf = 13 AND age <= 19" ) head ( teenagers ) # name # 1 Justin # We can also run custom R-UDFs on Spark DataFrames. map ( ( MapFunction ) row -> "Name: " + row. sql ( "SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19" ) Dataset namesDS = namesDF.
createOrReplaceTempView ( "parquetFile" ) Dataset namesDF = spark. parquet ( "people.parquet" ) // Parquet files can also be used to create a temporary view and then used in SQL statements parquetFileDF. Parquet files are self-describing so the schema is preserved // The result of loading a parquet file is also a DataFrame Dataset parquetFileDF = spark. parquet ( "people.parquet" ) // Read in the Parquet file created above. json ( "examples/src/main/resources/people.json" ) // DataFrames can be saved as Parquet files, maintaining the schema information peopleDF. Import. import .Encoders import .Dataset import .Row Dataset peopleDF = spark.
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