Scala: Read CSV File as Spark DataFrame

access_time 29 days ago visibility49 comment 0

This article shows about how read CSV or TSV file as Spark DataFrame using Scala. The CSV file can be a local file or a file in HDFS (Hadoop Distributed File System). 

Read CSV Spark API can be used to read CSV files. 

def csv(path: String): DataFrame
Loads a CSV file and returns the result as a DataFrame. See the documentation on the other overloaded csv() method for more details.

This function is only available for Spark version 2.0. For Spark 1.x, you need to user SparkContext to convert the data to RDD and then convert it to Spark DataFrame

Read CSV file

The following code snippet reads from a local CSV file named test.csv with the following content:


Code snippet

|   1|   2|
|   3|   4|
|   5|   6|
|   7|   8|

Alternatively, you can also use the generic load APIs with format and options specified:

|   1|   2|
|   3|   4|
|   5|   6|
|   7|   8|
infoIn the path parameter, file:/// is specified to indicate the input is from a local source file. If you only specify path like /path/to/your/file, Spark will assume the data is available in HDFS by default.

CSV format options

There are a number of CSV options can be specified. The following options are cited from Spark 3.0.1 Scala API documentation for reference:

You can set the following CSV-specific options to deal with CSV files:

  • sep (default ,): sets a separator for each field and value. This separator can be one or more characters.
  • encoding (default UTF-8): decodes the CSV files by the given encoding type.
  • quote (default "): sets a single character used for escaping quoted values where the separator can be part of the value. If you would like to turn off quotations, you need to set not null but an empty string. This behaviour is different from com.databricks.spark.csv.
  • escape (default \): sets a single character used for escaping quotes inside an already quoted value.
  • charToEscapeQuoteEscaping (default escape or \0): sets a single character used for escaping the escape for the quote character. The default value is escape character when escape and quote characters are different, \0 otherwise.
  • comment (default empty string): sets a single character used for skipping lines beginning with this character. By default, it is disabled.
  • header (default false): uses the first line as names of columns.
  • enforceSchema (default true): If it is set to true, the specified or inferred schema will be forcibly applied to datasource files, and headers in CSV files will be ignored. If the option is set to false, the schema will be validated against all headers in CSV files in the case when the header option is set to true. Field names in the schema and column names in CSV headers are checked by their positions taking into account spark.sql.caseSensitive. Though the default value is true, it is recommended to disable the enforceSchema option to avoid incorrect results.
  • inferSchema (default false): infers the input schema automatically from data. It requires one extra pass over the data.
  • samplingRatio (default is 1.0): defines fraction of rows used for schema inferring.
  • ignoreLeadingWhiteSpace (default false): a flag indicating whether or not leading whitespaces from values being read should be skipped.
  • ignoreTrailingWhiteSpace (default false): a flag indicating whether or not trailing whitespaces from values being read should be skipped.
  • nullValue (default empty string): sets the string representation of a null value. Since 2.0.1, this applies to all supported types including the string type.
  • emptyValue (default empty string): sets the string representation of an empty value.
  • nanValue (default NaN): sets the string representation of a non-number" value.
  • positiveInf (default Inf): sets the string representation of a positive infinity value.
  • negativeInf (default -Inf): sets the string representation of a negative infinity value.
  • dateFormat (default yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at Datetime Patterns. This applies to date type.
  • timestampFormat (default yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]): sets the string that indicates a timestamp format. Custom date formats follow the formats at Datetime Patterns. This applies to timestamp type.
  • maxColumns (default 20480): defines a hard limit of how many columns a record can have.
  • maxCharsPerColumn (default -1): defines the maximum number of characters allowed for any given value being read. By default, it is -1 meaning unlimited length
  • mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing. It supports the following case-insensitive modes. Note that Spark tries to parse only required columns in CSV under column pruning. Therefore, corrupt records can be different based on required set of fields. This behavior can be controlled by spark.sql.csv.parser.columnPruning.enabled (enabled by default).
  • PERMISSIVE : when it meets a corrupted record, puts the malformed string into a field configured by columnNameOfCorruptRecord, and sets malformed fields to null. To keep corrupt records, an user can set a string type field named columnNameOfCorruptRecord in an user-defined schema. If a schema does not have the field, it drops corrupt records during parsing. A record with less/more tokens than schema is not a corrupted record to CSV. When it meets a record having fewer tokens than the length of the schema, sets null to extra fields. When the record has more tokens than the length of the schema, it drops extra tokens.
  • DROPMALFORMED : ignores the whole corrupted records.
  • FAILFAST : throws an exception when it meets corrupted records.
  • columnNameOfCorruptRecord (default is the value specified in spark.sql.columnNameOfCorruptRecord): allows renaming the new field having malformed string created by PERMISSIVE mode. This overrides spark.sql.columnNameOfCorruptRecord.
  • multiLine (default false): parse one record, which may span multiple lines.
  • locale (default is en-US): sets a locale as language tag in IETF BCP 47 format. For instance, this is used while parsing dates and timestamps.
  • lineSep (default covers all \r\r\n and \n): defines the line separator that should be used for parsing. Maximum length is 1 character.
  • pathGlobFilter: an optional glob pattern to only include files with paths matching the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter. It does not change the behavior of partition discovery.
  • recursiveFileLookup: recursively scan a directory for files. Using this option disables partition discovery

Load TSV file

Option sep can be used to specify input file as TSV (tab separated values) or any other character delimited files. By default, the value is , (comma)."csv").option("header","true").option("sep","\t").load("file:///F:\\big-data/test.csv").show()


Refer to Spark Scala official documentation for more details about the APIs:

Spark 3.0.1 ScalaDoc - org.apache.spark.sql.DataFrameReader

info Last modified by Raymond 29 days ago copyright This page is subject to Site terms.
Like this article?
Share on

Please log in or register to comment.

account_circle Log in person_add Register

Log in with external accounts

Follow Kontext

Get our latest updates on LinkedIn or Twitter.

Want to publish your article on Kontext?

Learn more

More from Kontext

visibility 95
thumb_up 1
access_time 29 days ago

This article shows how to convert a JSON string to a Spark DataFrame using Scala. It can be used for processing small in memory JSON string.  The following sample JSON string will be used. It is a simple JSON array with three items in the array. For each item, there are two attributes named ...

visibility 20
thumb_up 0
access_time 19 days ago

When installing a vanilla Spark on Windows or Linux, you may encounter the following error to invoke spark-sql command: Error: Failed to load class org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver This error usually occurs when installing a Spark version without built-in Hadoop ...

visibility 1067
thumb_up 0
access_time 2 years ago

Sometime it is necessary to pass environment variables to Spark executors. To pass environment variable to executors, use setExecutorEnv function of SparkConf class. In the following code snippet, an environment variable name ENV_NAME is set up with value as 'ENV_Value'. from pyspark import ...