org.apache.spark.sql

Class DataFrameReader

Object

org.apache.spark.sql.DataFrameReader

public class DataFrameReader
extends Object

Interface used to load a Dataset from external storage systems (e.g. file systems, key-value stores, etc). Use SparkSession.read to access this.

Since:

1.4.0

Method Summary

Methods

Modifier and Type	Method and Description
Dataset<Row>	csv(scala.collection.Seq<String> paths) Loads a CSV file and returns the result as a DataFrame.
Dataset<Row>	csv(String... paths) Loads a CSV file and returns the result as a DataFrame.
Dataset<Row>	csv(String path) Loads a CSV file and returns the result as a DataFrame.
DataFrameReader	format(String source) Specifies the input data source format.
Dataset<Row>	jdbc(String url, String table, java.util.Properties properties) Construct a DataFrame representing the database table accessible via JDBC URL url named table and connection properties.
Dataset<Row>	jdbc(String url, String table, String[] predicates, java.util.Properties connectionProperties) Construct a DataFrame representing the database table accessible via JDBC URL url named table using connection properties.
Dataset<Row>	jdbc(String url, String table, String columnName, long lowerBound, long upperBound, int numPartitions, java.util.Properties connectionProperties) Construct a DataFrame representing the database table accessible via JDBC URL url named table.
Dataset<Row>	json(JavaRDD<String> jsonRDD) Loads a JavaRDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
Dataset<Row>	json(RDD<String> jsonRDD) Loads an RDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
Dataset<Row>	json(scala.collection.Seq<String> paths) Loads a JSON file (one object per line) and returns the result as a DataFrame.
Dataset<Row>	json(String... paths) Loads a JSON file (one object per line) and returns the result as a DataFrame.
Dataset<Row>	json(String path) Loads a JSON file (one object per line) and returns the result as a DataFrame.
Dataset<Row>	load() Loads input in as a DataFrame, for data sources that don't require a path (e.g.
Dataset<Row>	load(scala.collection.Seq<String> paths) Loads input in as a DataFrame, for data sources that support multiple paths.
Dataset<Row>	load(String... paths) Loads input in as a DataFrame, for data sources that support multiple paths.
Dataset<Row>	load(String path) Loads input in as a DataFrame, for data sources that require a path (e.g.
DataFrameReader	option(String key, boolean value) Adds an input option for the underlying data source.
DataFrameReader	option(String key, double value) Adds an input option for the underlying data source.
DataFrameReader	option(String key, long value) Adds an input option for the underlying data source.
DataFrameReader	option(String key, String value) Adds an input option for the underlying data source.
DataFrameReader	options(scala.collection.Map<String,String> options) (Scala-specific) Adds input options for the underlying data source.
DataFrameReader	options(java.util.Map<String,String> options) Adds input options for the underlying data source.
Dataset<Row>	orc(scala.collection.Seq<String> paths) Loads an ORC file and returns the result as a DataFrame.
Dataset<Row>	orc(String... paths) Loads an ORC file and returns the result as a DataFrame.
Dataset<Row>	orc(String path) Loads an ORC file and returns the result as a DataFrame.
Dataset<Row>	parquet(scala.collection.Seq<String> paths) Loads a Parquet file, returning the result as a DataFrame.
Dataset<Row>	parquet(String... paths) Loads a Parquet file, returning the result as a DataFrame.
Dataset<Row>	parquet(String path) Loads a Parquet file, returning the result as a DataFrame.
DataFrameReader	schema(StructType schema) Specifies the input schema.
Dataset<Row>	table(String tableName) Returns the specified table as a DataFrame.
Dataset<Row>	text(scala.collection.Seq<String> paths) 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.
Dataset<Row>	text(String... paths) 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.
Dataset<Row>	text(String path) 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.
Dataset<String>	textFile(scala.collection.Seq<String> paths) Loads text files and returns a Dataset of String.
Dataset<String>	textFile(String... paths) Loads text files and returns a Dataset of String.
Dataset<String>	textFile(String path) Loads text files and returns a Dataset of String.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

load

public Dataset<Row> load(String... paths)

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

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

json

public Dataset<Row> json(String... paths)

Loads a JSON file (one object per line) and returns the result as a DataFrame.

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
• mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
  • PERMISSIVE : sets other fields to null when it meets a corrupted record, and puts the malformed string into a new field configured by columnNameOfCorruptRecord. When a schema is set by user, it sets null for extra fields.
  • 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.SSSZZ): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

csv

public Dataset<Row> csv(String... paths)

Loads a CSV file 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 the 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 the 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 form com.databricks.spark.csv.
• escape (default \): sets the single character used for escaping quotes inside an already quoted value.
• comment (default empty string): sets the 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.
• inferSchema (default false): infers the input schema automatically from data. It requires one extra pass over the data.
• ignoreLeadingWhiteSpace (default false): defines whether or not leading whitespaces from values being read should be skipped.
• ignoreTrailingWhiteSpace (default false): defines 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.
• 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.SSSZZ): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.
java.sql.Timestamp.valueOf() and java.sql.Date.valueOf() or ISO 8601 format.
• maxColumns (default 20480): defines a hard limit of how many columns a record can have.
• maxCharsPerColumn (default 1000000): defines the maximum number of characters allowed for any given value being read.
• maxMalformedLogPerPartition (default 10): sets the maximum number of malformed rows Spark will log for each partition. Malformed records beyond this number will be ignored.
• mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
  • PERMISSIVE : sets other fields to null when it meets a corrupted record. When a schema is set by user, it sets null for extra fields.
  • DROPMALFORMED : ignores the whole corrupted records.
  • FAILFAST : throws an exception when it meets corrupted records.

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

parquet

public Dataset<Row> parquet(String... paths)

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.

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

orc

public Dataset<Row> orc(String... paths)

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

Parameters:

paths - input paths

Returns:

(undocumented)

Since:

2.0.0

text

public Dataset<Row> text(String... paths)

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.

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")
 

Parameters:

paths - input paths

Returns:

(undocumented)

Since:

1.6.0

textFile

public Dataset<String> textFile(String... paths)

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.

Each line in the text files is a new element in the resulting Dataset. For example:

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

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

Parameters:

paths - input path

Returns:

(undocumented)

Since:

2.0.0

format

public DataFrameReader format(String source)

Specifies the input data source format.

Parameters:

source - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

schema

public DataFrameReader schema(StructType schema)

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.

Parameters:

schema - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

option

public DataFrameReader option(String key,
                     String value)

Adds an input option for the underlying data source.

Parameters:

key - (undocumented)

value - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

option

public DataFrameReader option(String key,
                     boolean value)

Adds an input option for the underlying data source.

Parameters:

key - (undocumented)

value - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

option

public DataFrameReader option(String key,
                     long value)

Adds an input option for the underlying data source.

Parameters:

key - (undocumented)

value - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

option

public DataFrameReader option(String key,
                     double value)

Adds an input option for the underlying data source.

Parameters:

key - (undocumented)

value - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

options

public DataFrameReader options(scala.collection.Map<String,String> options)

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

Parameters:

options - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

options

public DataFrameReader options(java.util.Map<String,String> options)

Adds input options for the underlying data source.

Parameters:

options - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

load

public Dataset<Row> load()

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

Returns:

(undocumented)

Since:

1.4.0

load

public Dataset<Row> load(String path)

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

Parameters:

path - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

load

public Dataset<Row> load(scala.collection.Seq<String> paths)

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

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

jdbc

public Dataset<Row> jdbc(String url,
                String table,
                java.util.Properties properties)

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

Parameters:

url - (undocumented)

table - (undocumented)

properties - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

jdbc

public Dataset<Row> jdbc(String url,
                String table,
                String columnName,
                long lowerBound,
                long upperBound,
                int numPartitions,
                java.util.Properties connectionProperties)

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.

Parameters:

url - JDBC database url of the form jdbc:subprotocol:subname.

table - Name of the table in the external database.

columnName - the name of a column of integral type that will be used for partitioning.

lowerBound - the minimum value of columnName used to decide partition stride.

upperBound - the maximum value of columnName used to decide partition stride.

numPartitions - the number of partitions. This, along with lowerBound (inclusive), upperBound (exclusive), form partition strides for generated WHERE clause expressions used to split the column columnName evenly.

connectionProperties - JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included. "fetchsize" can be used to control the number of rows per fetch.

Returns:

(undocumented)

Since:

1.4.0

jdbc

public Dataset<Row> jdbc(String url,
                String table,
                String[] predicates,
                java.util.Properties connectionProperties)

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.

Parameters:

url - JDBC database url of the form jdbc:subprotocol:subname

table - Name of the table in the external database.

predicates - Condition in the where clause for each partition.

connectionProperties - JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included. "fetchsize" can be used to control the number of rows per fetch.

Returns:

(undocumented)

Since:

1.4.0

json

public Dataset<Row> json(String path)

Loads a JSON file (one object per line) and returns the result as a DataFrame. See the documentation on the overloaded json() method with varargs for more details.

Parameters:

path - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

json

public Dataset<Row> json(scala.collection.Seq<String> paths)

Loads a JSON file (one object per line) and returns the result as a DataFrame.

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
• mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
  • PERMISSIVE : sets other fields to null when it meets a corrupted record, and puts the malformed string into a new field configured by columnNameOfCorruptRecord. When a schema is set by user, it sets null for extra fields.
  • 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.SSSZZ): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

json

public Dataset<Row> json(JavaRDD<String> jsonRDD)

Loads a JavaRDD[String] storing JSON objects (one object per record) 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.

Parameters:

jsonRDD - input RDD with one JSON object per record

Returns:

(undocumented)

Since:

1.4.0

json

public Dataset<Row> json(RDD<String> jsonRDD)

Loads an RDD[String] storing JSON objects (one object per record) 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.

Parameters:

jsonRDD - input RDD with one JSON object per record

Returns:

(undocumented)

Since:

1.4.0

csv

public Dataset<Row> csv(String path)

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

Parameters:

path - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

csv

public Dataset<Row> csv(scala.collection.Seq<String> paths)

Loads a CSV file 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 the 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 the 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 form com.databricks.spark.csv.
• escape (default \): sets the single character used for escaping quotes inside an already quoted value.
• comment (default empty string): sets the 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.
• inferSchema (default false): infers the input schema automatically from data. It requires one extra pass over the data.
• ignoreLeadingWhiteSpace (default false): defines whether or not leading whitespaces from values being read should be skipped.
• ignoreTrailingWhiteSpace (default false): defines 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.
• 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.SSSZZ): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.
java.sql.Timestamp.valueOf() and java.sql.Date.valueOf() or ISO 8601 format.
• maxColumns (default 20480): defines a hard limit of how many columns a record can have.
• maxCharsPerColumn (default 1000000): defines the maximum number of characters allowed for any given value being read.
• maxMalformedLogPerPartition (default 10): sets the maximum number of malformed rows Spark will log for each partition. Malformed records beyond this number will be ignored.
• mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
  • PERMISSIVE : sets other fields to null when it meets a corrupted record. When a schema is set by user, it sets null for extra fields.
  • DROPMALFORMED : ignores the whole corrupted records.
  • FAILFAST : throws an exception when it meets corrupted records.

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

parquet

public Dataset<Row> parquet(String path)

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

Parameters:

path - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

parquet

public Dataset<Row> parquet(scala.collection.Seq<String> paths)

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.

Parameters:

paths - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

orc

public Dataset<Row> orc(String path)

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

Parameters:

path - input path

Returns:

(undocumented)

Since:

1.5.0

orc

public Dataset<Row> orc(scala.collection.Seq<String> paths)

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

Parameters:

paths - input paths

Returns:

(undocumented)

Since:

2.0.0

table

public Dataset<Row> table(String tableName)

Returns the specified table as a DataFrame.

Parameters:

tableName - (undocumented)

Returns:

(undocumented)

Since:

1.4.0

text

public Dataset<Row> text(String path)

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.

Parameters:

path - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

text

public Dataset<Row> text(scala.collection.Seq<String> paths)

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.

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")
 

Parameters:

paths - input paths

Returns:

(undocumented)

Since:

1.6.0

textFile

public Dataset<String> textFile(String path)

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

Parameters:

path - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

textFile

public Dataset<String> textFile(scala.collection.Seq<String> paths)

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.

Each line in the text files is a new element in the resulting Dataset. For example:

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

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

Parameters:

paths - input path

Returns:

(undocumented)

Since:

2.0.0