CH9.2 Reading and Writing csv files

CSV files:   CSV stands for comma-separated values.  CSV reader has a large number of options. These options give you the ability to work around issues like certain characters needing to be escaped—for example, commas inside of columns when the file is also comma-delimited or null values labeled in an unconventional way. Following is the list of important options for CSV reader: Read/write Key Potential values Default Description Both sep Any single string character , The single character that is used as separator for each field and value. Both header true, false false A Boolean flag that declares whether the first line in the file(s) are the names of the columns. Read escape Any string character \ The character Spark should use to escape other characters in the file. Read inferSchema true, false false Specifies whether Spark should infer column types when reading the file. Read ignoreLeadingWhiteSpace true, false false Declares whether leading spaces from values being read should be skipped. Read ignoreTrailingWhiteSpace true, false false Declares whether trailing spaces from values being read should be skipped. Both nullValue Any string character “” Declares what character represents a null value in the file. Both nanValue Any string character NaN Declares what character represents a NaN or missing character in the CSV file. Both positiveInf Any string or character Inf Declares what character(s) represent a positive infinite value. Both negativeInf Any string or character -Inf Declares what character(s) represent a negative infinite value. Both compression or codec None, uncompressed, bzip2, deflate, gzip, lz4, or snappy none Declares what compression codec Spark should use to read or write the file. Both dateFormat Any string or character that conforms to java’s SimpleDataFormat. yyyy-MM-dd Declares the date format for any columns that are date type. Both timestampFormat Any string or character that conforms to java’s SimpleDataFormat. yyyy-MM-dd’T’HH:mm​:ss.SSSZZ Declares the timestamp format for any columns that are timestamp type. Read maxColumns Any integer 20480 Declares the maximum number of columns in the file. Read maxCharsPerColumn Any integer 1000000 Declares the maximum number of characters in a column. Read escapeQuotes true, false true Declares whether Spark should escape quotes that are found in lines. Read maxMalformedLogPerPartition Any integer 10 Sets the maximum number of malformed rows Spark will log for each partition. Malformed records beyond this number will be ignored. Write quoteAll true, false false Specifies whether all values should be enclosed in quotes, as opposed to just escaping values that have a quote character. Read multiLine true, false false This option allows you to read multiline CSV files where each logical row in the CSV file might span multiple rows in the file itself. Note we can specify the compression codec to read/write to compressed files. Following shows how to read from csv files. First we first create a DataFrameReader for that specific format. In below example, we specify that 1) the csv files contains "header". 2) Schema will be inferred from the files 3) set the read mode to FAILFAST which will fail the job if the data is malformed. Next we also show how to specify the schema explicitly. 1    import org.apache.spark.sql._ 2    import org.apache.spark.sql.types._ 3    4    object SparkDefinitiveTesting { 5    6      def main(args: Array[String]): Unit = { 7        val spark = SparkSession.builder().master("local[*]").appName("Test App").getOrCreate() 8        spark.sparkContext.setLogLevel("FATAL") 9    10       val df_csv = spark.read.format("csv").option("header", "true").option("mode", "FAILFAST").option("inferSchema", "true").load("C:\\Users\\sukulma\\Downloads\\Spark-Data\\Spark-data\\data\\retail-data\\by-day") 11       println("Count of Dataframe df_csv:" + df_csv.count()) 12   13       val manualSchema = StructType(Seq(StructField("InvoiceNo", StringType, true), 14         StructField("StockCode", StringType, true), 15         StructField("Description", StringType, true), 16         StructField("Quantity", IntegerType, true), 17         StructField("InvoiceDate", TimestampType, true), 18         StructField("UnitPrice", DoubleType, true), 19         StructField("CustomerID", DoubleType, true), 20         StructField("Country", StringType, true))) 21   22       val df_csv_manual = spark.read.format("csv").option("header", "true").option("mode", "FAILFAST").option("inferSchema", "true").schema(manualSchema).load("C:\\Users\\sukulma\\Downloads\\Spark-Data\\Spark-data\\data\\retail-data\\by-day") 23       println("Count of Dataframe df_csv_manual:" + df_csv_manual.count()) 24     } 25   } 2 Count of Dataframe df_csv:541909 Count of Dataframe df_csv_manual:541909 If the data that we read does not match the schema, the job will fail when Spark actually reads the data.(Not when the code is written). As soon as we start our Spark job, it will immediately fail (after we execute a job) due to the data not conforming to the specified schema. In general, Spark will fail only at job execution time rather than DataFrame definition time—even if, for example, we point to a file that does not exist. This is due to lazy evaluation Following shows how to write data to tab separated files in gzip compressed format. Note that the write method is not available on SparkSession. It's available on the dataframe itself. SparkDefinitiveTesting.scala 1    import org.apache.spark.sql._ 2    import org.apache.spark.sql.types._ 3    4    object SparkDefinitiveTesting { 5    6      def main(args: Array[String]): Unit = { 7        val spark = SparkSession.builder().master("local[*]").appName("Test App").getOrCreate() 8        spark.sparkContext.setLogLevel("FATAL") 9    10       val manualSchema = StructType(Seq(StructField("InvoiceNo", StringType, true), 11         StructField("StockCode", StringType, true), 12         StructField("Description", StringType, true), 13         StructField("Quantity", IntegerType, true), 14         StructField("InvoiceDate", TimestampType, true), 15         StructField("UnitPrice", DoubleType, true), 16         StructField("CustomerID", DoubleType, true), 17         StructField("Country", StringType, true))) 18   19       val df_csv_manual = spark.read.format("csv").option("header", "true").option("mode", "FAILFAST").option("inferSchema", "true").schema(manualSchema).load("C:\\Users\\sukulma\\Downloads\\Spark-Data\\Spark-data\\data\\retail-data\\by-day").repartition(5) 20       println("Count of Dataframe df_csv_manual:" + df_csv_manual.count()) 21       df_csv_manual.write.format("csv").mode(SaveMode.Overwrite).option("sep", "\t").option("codec", "gzip").save("C:\\Users\\sukulma\\Downloads\\Spark-Data\\Spark-data\\data\\retail-data\\tsv") 22   23   24     } 25   } 26   C:\Users\sukulma>cd C:\Users\sukulma\Downloads\Spark-Data\Spark-data\data\retail-data\tsv C:\Users\sukulma\Downloads\Spark-Data\Spark-data\data\retail-data\tsv>dir  Volume in drive C is OSDisk  Volume Serial Number is C05C-4437  Directory of C:\Users\sukulma\Downloads\Spark-Data\Spark-data\data\retail-data\tsv 04/14/2019  02:00 PM    <DIR>          . 04/14/2019  02:00 PM    <DIR>          .. 04/14/2019  01:59 PM            15,328 .part-00000-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz.crc 04/14/2019  01:59 PM            15,308 .part-00001-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz.crc 04/14/2019  01:59 PM            15,308 .part-00002-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz.crc 04/14/2019  01:59 PM            15,328 .part-00003-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz.crc 04/14/2019  02:00 PM            15,332 .part-00004-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz.crc 04/14/2019  02:00 PM                 8 ._SUCCESS.crc 04/14/2019  01:59 PM         1,960,940 part-00000-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz 04/14/2019  01:59 PM         1,958,236 part-00001-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz 04/14/2019  01:59 PM         1,958,342 part-00002-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz 04/14/2019  01:59 PM         1,960,794 part-00003-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz 04/14/2019  02:00 PM         1,961,057 part-00004-b190aff7-124c-4991-aed8-f3529d982c33.csv.gz 04/14/2019  02:00 PM                 0 _SUCCESS               12 File(s)      9,875,981 bytes                2 Dir(s)  77,774,761,984 bytes free Note that 5 .gz files are created inside the directory. This actually reflects the number of partitions in our DataFrame at the time we write it out.