DataFrameReader

Loads CSV files and returns the result as a DataFrame.

This function will go through the input once to determine the input schema if inferSchema is enabled. To avoid going through the entire data once, disable inferSchema option or specify the schema explicitly using schema.

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

• sep (default ,): sets a single character as a separator for each field and value.
• 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 java.text.SimpleDateFormat. This applies to date type.
• timestampFormat (default yyyy-MM-dd'T'HH:mm:ss.SSSXXX): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. 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.
• PERMISSIVE : when it meets a corrupted record, puts the malformed string into a field configured by columnNameOfCorruptRecord, and sets other 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.

Loads an Dataset[String] storing CSV rows and returns the result as a DataFrame.

If the schema is not specified using schema function and inferSchema option is enabled, this function goes through the input once to determine the input schema.

If the schema is not specified using schema function and inferSchema option is disabled, it determines the columns as string types and it reads only the first line to determine the names and the number of fields.

If the enforceSchema is set to false, only the CSV header in the first line is checked to conform specified or inferred schema.

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

Specifies the input data source format.

Construct a DataFrame representing the database table accessible via JDBC URL url named table using connection properties. The predicates parameter gives a list expressions suitable for inclusion in WHERE clauses; each one defines one partition of the DataFrame.

Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.

Construct a DataFrame representing the database table accessible via JDBC URL url named table. Partitions of the table will be retrieved in parallel based on the parameters passed to this function.

Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.

Construct a DataFrame representing the database table accessible via JDBC URL url named table and connection properties.

Loads a Dataset[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

Unless the schema is specified using schema function, this function goes through the input once to determine the input schema.

Loads JSON files and returns the results as a DataFrame.

JSON Lines (newline-delimited JSON) is supported by default. For JSON (one record per file), set the multiLine option to true.

This function goes through the input once to determine the input schema. If you know the schema in advance, use the version that specifies the schema to avoid the extra scan.

You can set the following JSON-specific options to deal with non-standard JSON files:

• primitivesAsString (default false): infers all primitive values as a string type
• prefersDecimal (default false): infers all floating-point values as a decimal type. If the values do not fit in decimal, then it infers them as doubles.
• allowComments (default false): ignores Java/C++ style comment in JSON records
• allowUnquotedFieldNames (default false): allows unquoted JSON field names
• allowSingleQuotes (default true): allows single quotes in addition to double quotes
• allowNumericLeadingZeros (default false): allows leading zeros in numbers (e.g. 00012)
• allowBackslashEscapingAnyCharacter (default false): allows accepting quoting of all character using backslash quoting mechanism
• allowUnquotedControlChars (default false): allows JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters) or not.
• mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
• PERMISSIVE : when it meets a corrupted record, puts the malformed string into a field configured by columnNameOfCorruptRecord, and sets other 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. When inferring a schema, it implicitly adds a columnNameOfCorruptRecord field in an output schema.
• 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.
• dateFormat (default yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to date type.
• timestampFormat (default yyyy-MM-dd'T'HH:mm:ss.SSSXXX): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.
• multiLine (default false): parse one record, which may span multiple lines, per file
• encoding (by default it is not set): allows to forcibly set one of standard basic or extended encoding for the JSON files. For example UTF-16BE, UTF-32LE. If the encoding is not specified and multiLine is set to true, it will be detected automatically.
• lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing.
• samplingRatio (default is 1.0): defines fraction of input JSON objects used for schema inferring.
• dropFieldIfAllNull (default false): whether to ignore column of all null values or empty array/struct during schema inference.

Loads a JSON file and returns the results as a DataFrame.

See the documentation on the overloaded json() method with varargs for more details.

Loads input in as a DataFrame, for data sources that support multiple paths. Only works if the source is a HadoopFsRelationProvider.

Loads input in as a DataFrame, for data sources that require a path (e.g. data backed by a local or distributed file system).

Loads input in as a DataFrame, for data sources that don't require a path (e.g. external key-value stores).

Adds an input option for the underlying data source.

Adds an input option for the underlying data source.

Adds an input option for the underlying data source.

Adds an input option for the underlying data source.

You can set the following option(s):

• timeZone (default session local timezone): sets the string that indicates a timezone to be used to parse timestamps in the JSON/CSV datasources or partition values.

Adds input options for the underlying data source.

You can set the following option(s):

• timeZone (default session local timezone): sets the string that indicates a timezone to be used to parse timestamps in the JSON/CSV datasources or partition values.

(Scala-specific) Adds input options for the underlying data source.

You can set the following option(s):

• timeZone (default session local timezone): sets the string that indicates a timezone to be used to parse timestamps in the JSON/CSV datasources or partition values.

Loads ORC files and returns the result as a DataFrame.

Loads an ORC file and returns the result as a DataFrame.

Loads a Parquet file, returning the result as a DataFrame.

You can set the following Parquet-specific option(s) for reading Parquet files:

• mergeSchema (default is the value specified in spark.sql.parquet.mergeSchema): sets whether we should merge schemas collected from all Parquet part-files. This will override spark.sql.parquet.mergeSchema.

Loads a Parquet file, returning the result as a DataFrame. See the documentation on the other overloaded parquet() method for more details.

Specifies the schema by using the input DDL-formatted string. Some data sources (e.g. JSON) can infer the input schema automatically from data. By specifying the schema here, the underlying data source can skip the schema inference step, and thus speed up data loading.

spark.read.schema("a INT, b STRING, c DOUBLE").csv("test.csv")

Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema automatically from data. By specifying the schema here, the underlying data source can skip the schema inference step, and thus speed up data loading.

Returns the specified table as a DataFrame.

Loads text files and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any.

By default, each line in the text files is a new row in the resulting DataFrame. For example:

// Scala:
spark.read.text("/path/to/spark/README.md")

// Java:
spark.read().text("/path/to/spark/README.md")

You can set the following text-specific option(s) for reading text files:

• wholetext (default false): If true, read a file as a single row and not split by "\n".
• lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing.

Loads text files and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any. See the documentation on the other overloaded text() method for more details.

Loads text files and returns a Dataset of String. The underlying schema of the Dataset contains a single string column named "value".

If the directory structure of the text files contains partitioning information, those are ignored in the resulting Dataset. To include partitioning information as columns, use text.

By default, each line in the text files is a new row in the resulting DataFrame. For example:

// Scala:
spark.read.textFile("/path/to/spark/README.md")

// Java:
spark.read().textFile("/path/to/spark/README.md")

You can set the following textFile-specific option(s) for reading text files:

• wholetext (default false): If true, read a file as a single row and not split by "\n".
• lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing.

Loads text files and returns a Dataset of String. See the documentation on the other overloaded textFile() method for more details.

Loads an RDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

Unless the schema is specified using schema function, this function goes through the input once to determine the input schema.

Loads a JavaRDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

Unless the schema is specified using schema function, this function goes through the input once to determine the input schema.