Hive is case insensitive, while Parquet is not.There are two key differences between Hive and Parquet from the perspective of table schema This behavior is controlled by the Ĭonfiguration, and is turned on by default. Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe forīetter performance. ![]() When reading from Hive metastore Parquet tables and writing to non-partitioned Hive metastore 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)įind full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo. 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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