Spark - load CSV file as DataFrame

Question

I would like to read a CSV in spark and convert it as DataFrame and store it in HDFS with df registerTempTable  table name     I have tried   scala gt  val df   sqlContext load  hdfs    csv file dir file csv     Error which I got   java lang RuntimeException  hdfs    csv file dir file csv is not a Parquet file  expected magic number at tail  80  65  82  49  but found  49  59  54  10      at parquet hadoop ParquetFileReader readFooter ParquetFileReader java 418      at org apache spark sql parquet ParquetRelation2 MetadataCache  anonfun refresh 6 apply newParquet scala 277      at org apache spark sql parquet ParquetRelation2 MetadataCache  anonfun refresh 6 apply newParquet scala 276      at scala collection parallel mutable ParArray Map leaf ParArray scala 658      at scala collection parallel Task  anonfun tryLeaf 1 apply mcV sp Tasks scala 54      at scala collection parallel Task  anonfun tryLeaf 1 apply Tasks scala 53      at scala collection parallel Task  anonfun tryLeaf 1 apply Tasks scala 53      at scala collection parallel Task class tryLeaf Tasks scala 56      at scala collection parallel mutable ParArray Map tryLeaf ParArray scala 650      at scala collection parallel AdaptiveWorkStealingTasks WrappedTask class compute Tasks scala 165      at scala collection parallel AdaptiveWorkStealingForkJoinTasks WrappedTask compute Tasks scala 514      at scala concurrent forkjoin RecursiveAction exec RecursiveAction java 160      at scala concurrent forkjoin ForkJoinTask doExec ForkJoinTask java 260      at scala concurrent forkjoin ForkJoinPool WorkQueue runTask ForkJoinPool java 1339      at scala concurrent forkjoin ForkJoinPool runWorker ForkJoinPool java 1979      at scala concurrent forkjoin ForkJoinWorkerThread run ForkJoinWorkerThread java 107    What is the right command to load CSV file as DataFrame in Apache Spark

User · Answer

To read from relative path on the system use System.getProperty method to get current directory and further uses to load the file using relative path.

scala> val path = System.getProperty("user.dir").concat("/../2015-summary.csv")
scala> val csvDf = spark.read.option("inferSchema","true").option("header", "true").csv(path)
scala> csvDf.take(3)

spark:2.4.4 scala:2.11.12

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With in-built Spark csv  you can get it done easily with new SparkSession object for Spark   2 0   val df   spark          read          option  inferSchema    false            option  header   true            option  mode   DROPMALFORMED            option  delimiter                 schema dataSchema           csv   csv file dir file csv   df show   df printSchema     There are various options you can set    header  whether your file includes header line at the top inferSchema  whether you want to infer schema automatically or not  Default is true  I always prefer to provide schema to ensure proper datatypes  mode  parsing mode  PERMISSIVE  DROPMALFORMED or FAILFAST delimiter  to specify delimiter  default is comma

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spark-csv is part of core Spark functionality and doesn t require a separate library   So you could just do for example   df   spark read format  csv   option  header    true   load  csvfile csv     In scala  this works for any format-in delimiter mention     for csv    t  for tsv etc    val df   sqlContext read format  com databricks spark csv        option  delimiter             load  csvfile csv

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It s for whose Hadoop is 2 6 and Spark is 1 6 and without  databricks  package   import org apache spark sql types  StructType StructField StringType IntegerType   import org apache spark sql Row   val csv   sc textFile   path to file csv   val rows   csv map line   gt  line split      map   trim   val header   rows first val data   rows filter   0     header 0   val rdd   data map row   gt  Row row 0  row 1  toInt    val schema   new StructType        add StructField  id   StringType  true        add StructField  val   IntegerType  true    val df   sqlContext createDataFrame rdd  schema

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Add following Spark dependencies to POM file     lt dependency gt       lt groupId gt org apache spark lt  groupId gt       lt artifactId gt spark-core 2 11 lt  artifactId gt       lt version gt 2 2 0 lt  version gt   lt  dependency gt   lt dependency gt       lt groupId gt org apache spark lt  groupId gt       lt artifactId gt spark-sql 2 11 lt  artifactId gt       lt version gt 2 2 0 lt  version gt   lt  dependency gt      Spark configuration   val spark   SparkSession builder   master  local   appName  Sample App   getOrCreate      Read csv file   val df   spark read option  header    true   csv  FILE PATH       Display output  df show

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In case you are building a jar with scala 2 11 and Apache 2 0 or higher   There is no need to create a sqlContext or sparkContext object  Just a SparkSession object suffices the requirement for all needs   Following is mycode which works fine   import org apache spark sql  DataFrame  Row  SQLContext  SparkSession  import org apache log4j  Level  LogManager  Logger   object driver      def main args  Array String          val log   LogManager getRootLogger      log info            JAR EXECUTION STARTED                  val spark   SparkSession builder   master  local   appName  ValidationFrameWork   getOrCreate       val df   spark read format  csv          option  header    true          option  delimiter              option  inferSchema   true          load  d  small projects spark test pos       df show           In case you are running in cluster just change  master  local   to  master  yarn   while defining the sparkBuilder object  The Spark Doc covers this  https   spark apache org docs 2 2 0 sql-programming-guide html

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With Spark 2 4   if you want to load a csv from a local directory  then you can use 2 sessions and load that into hive  The first session should be created with master   config as  local     and the second session with  yarn  and Hive enabled   The below one worked for me   import org apache log4j  Level  Logger  import org apache spark   import org apache spark rdd   import org apache spark sql    object testCSV       def main args  Array String         Logger getLogger  org   setLevel Level ERROR      val spark local   SparkSession builder   appName  CSV local files reader   master  local      getOrCreate        import spark local implicits       spark local sql  SET   show 100 false      val local path   tmp data spend diversity csv      Local file     val df local   spark local read format  csv   option  inferSchema   true   load  file     local path      file     is mandatory     df local show false       val spark   SparkSession builder   appName  CSV HDFS   config  spark sql warehouse dir     apps hive warehouse   enableHiveSupport   getOrCreate        import spark implicits       spark sql  SET   show 100 false      val df   df local     df createOrReplaceTempView  lcsv       spark sql   drop table if exists work local csv        spark sql   create table work local csv as select   from lcsv            When ran with spark2-submit  --master  yarn  --conf spark ui enabled false testCSV jar it went fine and created the table in hive

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There are a lot of challenges to parsing a CSV file  it keeps adding up if the file size is bigger  if there are non-english escape separator other characters in the column values  that could cause parsing errors   The magic then is in the options that are used  The ones that worked for me and hope should cover most of the edge cases are in code below       Create a Spark Session spark   SparkSession builder master  local   appName  Classify Urls   getOrCreate        Note the options that are used  You may have to tweak these in case of error html df   spark read csv html csv file path                            header True                            multiLine True                            ignoreLeadingWhiteSpace True                            ignoreTrailingWhiteSpace True                            encoding  UTF-8                            sep                               quote                                escape                               maxColumns 2                           inferSchema True    Hope that helps  For more refer  Using PySpark 2 to read CSV having HTML source code  Note  The code above is from Spark 2 API  where the CSV file reading API comes bundled with built-in packages of Spark installable   Note  PySpark is a Python wrapper for Spark and shares the same API as Scala Java

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Penny s Spark 2 example is the way to do it in spark2  There s one more trick  have that header generated for you by doing an initial scan of the data  by setting the option inferSchema to true  Here  then  assumming that spark is a spark session you have set up  is the operation to load in the CSV index file of all the Landsat images which amazon host on S3             Licensed to the Apache Software Foundation  ASF  under one or more      contributor license agreements   See the NOTICE file distributed with      this work for additional information regarding copyright ownership       The ASF licenses this file to You under the Apache License  Version 2 0       the  License    you may not use this file except in compliance with      the License   You may obtain a copy of the License at              http   www apache org licenses LICENSE-2 0           Unless required by applicable law or agreed to in writing  software      distributed under the License is distributed on an  AS IS  BASIS       WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND  either express or implied       See the License for the specific language governing permissions and      limitations under the License         val csvdata   spark read options Map       header  - gt   true        ignoreLeadingWhiteSpace  - gt   true        ignoreTrailingWhiteSpace  - gt   true        timestampFormat  - gt   yyyy-MM-dd HH mm ss SSSZZZ        inferSchema  - gt   true        mode  - gt   FAILFAST       csv  s3a   landsat-pds scene list gz     The bad news is  this triggers a scan through the file  for something large like this 20 MB zipped CSV file  that can take 30s over a long haul connection  Bear that in mind  you are better off manually coding up the schema once you ve got it coming in    code snippet Apache Software License 2 0 licensed to avoid all ambiguity  something I ve done as a demo integration test of S3 integration

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Try this if using spark 2 0   For non-hdfs file  df   spark read csv  file    csvfile csv     For hdfs file  df   spark read csv  hdfs    csvfile csv    For hdfs file  with different delimiter than comma  df   spark read option  delimiter      csv  hdfs    csvfile csv     Note - this work for any delimited file  Just use option    delimiter      to change value   Hope this is helpful

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In Java 1 8 This code snippet perfectly working to read CSV files   POM xml    lt dependency gt       lt groupId gt org apache spark lt  groupId gt       lt artifactId gt spark-core 2 11 lt  artifactId gt       lt version gt 2 0 0 lt  version gt   lt  dependency gt   lt  -- https   mvnrepository com artifact org apache spark spark-sql 2 10 -- gt   lt dependency gt       lt groupId gt org apache spark lt  groupId gt       lt artifactId gt spark-sql 2 10 lt  artifactId gt       lt version gt 2 0 0 lt  version gt   lt  dependency gt    lt  -- https   mvnrepository com artifact org scala-lang scala-library -- gt   lt dependency gt       lt groupId gt org scala-lang lt  groupId gt       lt artifactId gt scala-library lt  artifactId gt       lt version gt 2 11 8 lt  version gt   lt  dependency gt   lt dependency gt       lt groupId gt com databricks lt  groupId gt       lt artifactId gt spark-csv 2 10 lt  artifactId gt       lt version gt 1 4 0 lt  version gt   lt  dependency gt    Java   SparkConf conf   new SparkConf   setAppName  JavaWordCount   setMaster  local       create Spark Context SparkContext context   new SparkContext conf      create spark Session SparkSession sparkSession   new SparkSession context    Dataset lt Row gt  df   sparkSession read   format  com databricks spark csv   option  header   true  option  inferSchema   true  load  hdfs   localhost 9000 usr local hadoop data loan 100 csv                 hdfs   localhost 9000 usr local hadoop data loan 100 csv    System out println             Print Schema                 df printSchema    System out println             Print Data                   df show    System out println             Print title                   df select  title   show

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With Spark 2 0  following is how you can read CSV  val conf   new SparkConf   setMaster  local 2    setAppName  my app   val sc   new SparkContext conf  val sparkSession   SparkSession builder    config conf   conf     appName  spark session example      getOrCreate    val path     Users xxx Downloads usermsg csv  val base df   sparkSession read option  header   true      csv path

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Parse CSV and load as DataFrame DataSet with Spark 2 x First  initialize SparkSession object by default it will available in shells as spark val spark   org apache spark sql SparkSession builder          master  quot local quot     Change it as per your cluster          appName  quot Spark CSV Reader quot            getOrCreate    Use any one of the following ways to load CSV as DataFrame DataSet  1  Do it in a programmatic way  val df   spark read           format  quot csv quot             option  quot header quot    quot true quot     first line in file has headers           option  quot mode quot    quot DROPMALFORMED quot             load  quot hdfs    csv file dir file csv quot    Update  Adding all options from here in case the link will be broken in future  path  location of files  Similar to Spark can accept standard Hadoop globbing expressions  header  when set to true the first line of files will be used to name columns and will not be included in data  All types will be assumed string  The default value is false  delimiter  by default columns are delimited using  but delimiter can be set to any character quote  by default the quote character is  quot   but can be set to any character  Delimiters inside quotes are ignored escape  by default  the escape character is   but can be set to any character  Escaped quote characters are ignored parserLib  by default  it is  quot commons quot  that can be set to  quot univocity quot  to use that library for CSV parsing  mode  determines the parsing mode  By default it is PERMISSIVE  Possible values are   PERMISSIVE  tries to parse all lines  nulls are inserted for missing tokens and extra tokens are ignored  DROPMALFORMED  drops lines which have fewer or more tokens than expected or tokens which do not match the schema FAILFAST  aborts with a RuntimeException if encounters any malformed line charset  defaults to  UTF-8  but can be set to other valid charset names   inferSchema  automatically infers column types  It requires one extra pass over the data and is false by default comment  skip lines beginning with this character  Default is  quot   quot   Disable comments by setting this to null  nullValue  specifies a string that indicates a null value  any fields matching this string will be set as nulls in the DataFrame dateFormat  specifies a string that indicates the date format to use when reading dates or timestamps  Custom date formats follow the formats at java text SimpleDateFormat  This applies to both DateType and TimestampType  By default  it is null which means trying to parse times and date by java sql Timestamp valueOf   and java sql Date valueOf     2  You can do this SQL way as well  val df   spark sql  quot SELECT   FROM csv  hdfs    csv file dir file csv  quot    Dependencies    quot org apache spark quot     quot spark-core 2 11 quot    2 0 0    quot org apache spark quot     quot spark-sql 2 11 quot    2 0 0    Spark version  lt  2 0 val df   sqlContext read      format  quot com databricks spark csv quot        option  quot header quot    quot true quot         option  quot mode quot    quot DROPMALFORMED quot        load  quot csv file path quot      Dependencies   quot org apache spark quot     quot spark-sql 2 10 quot    1 6 0   quot com databricks quot     quot spark-csv 2 10 quot    1 6 0   quot com univocity quot     quot univocity-parsers quot    LATEST

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Default file format is Parquet with spark read   and file reading csv that why you are getting the exception  Specify csv format with api you are trying to use

[scala] Spark - load CSV file as DataFrame?

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