DataFrameReader

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( ... )
def csv(paths: String*): DataFrame

Loads a CSV file and returns the result as a DataFrame.
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.
- 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 null): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to both date type and timestamp type. By default, it is null which means trying to parse times and date by java.sql.Timestamp.valueOf() and java.sql.Date.valueOf().
- 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.
Annotations
@varargs()
Since
2.0.0
def csv(path: String): DataFrame

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

Since
2.0.0
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( classOf[java.lang.Throwable] )
def format(source: String): DataFrameReader

Specifies the input data source format.
Specifies the input data source format.

Since
1.4.0
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def jdbc(url: String, table: String, predicates: Array[String], connectionProperties: Properties): DataFrame

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

Since
1.4.0
def jdbc(url: String, table: String, columnName: String, lowerBound: Long, upperBound: Long, numPartitions: Int, connectionProperties: Properties): DataFrame

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

Since
1.4.0
def jdbc(url: String, table: String, properties: Properties): DataFrame

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

Since
1.4.0
def json(jsonRDD: RDD[String]): DataFrame

Loads an RDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
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.
jsonRDD
input RDD with one JSON object per record

Since
1.4.0
def json(jsonRDD: JavaRDD[String]): DataFrame

Loads a JavaRDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
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.
jsonRDD
input RDD with one JSON object per record

Since
1.4.0
def json(paths: String*): DataFrame

Loads a JSON file (one object per line) and returns the result as a DataFrame.
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.
Annotations
@varargs()
Since
2.0.0
def json(path: String): DataFrame

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

Since
1.4.0
def load(paths: String*): DataFrame

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

Annotations
@varargs()
Since
1.6.0
def load(path: String): DataFrame

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

Since
1.4.0
def load(): DataFrame

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

Since
1.4.0
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def option(key: String, value: Double): DataFrameReader

Adds an input option for the underlying data source.
Adds an input option for the underlying data source.

Since
2.0.0
def option(key: String, value: Long): DataFrameReader

Adds an input option for the underlying data source.
Adds an input option for the underlying data source.

Since
2.0.0
def option(key: String, value: Boolean): DataFrameReader

Adds an input option for the underlying data source.
Adds an input option for the underlying data source.

Since
2.0.0
def option(key: String, value: String): DataFrameReader

Adds an input option for the underlying data source.
Adds an input option for the underlying data source.

Since
1.4.0
def options(options: Map[String, String]): DataFrameReader

Adds input options for the underlying data source.
Adds input options for the underlying data source.

Since
1.4.0
def options(options: Map[String, String]): DataFrameReader

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

Since
1.4.0
def orc(paths: String*): DataFrame

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

Annotations
@varargs()
Since
2.0.0
Note
Currently, this method can only be used after enabling Hive support.
def orc(path: String): DataFrame

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

Since
1.5.0
Note
Currently, this method can only be used after enabling Hive support.
def parquet(paths: String*): DataFrame

Loads a Parquet file, returning 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.
Annotations
@varargs()
Since
1.4.0
def parquet(path: String): DataFrame

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

Since
2.0.0
def schema(schema: StructType): DataFrameReader

Specifies the input 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.

Since
1.4.0
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def table(tableName: String): DataFrame

Returns the specified table as a DataFrame.
Returns the specified table as a DataFrame.

Since
1.4.0
def text(paths: String*): 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.
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")
```
paths
input paths

Annotations
@varargs()
Since
1.6.0
def text(path: String): 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.
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.

Since
2.0.0
def textFile(paths: String*): Dataset[String]

Loads text files and returns a Dataset of String.
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")
```
paths
input path

Annotations
@varargs()
Since
2.0.0
def textFile(path: String): Dataset[String]

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

Since
2.0.0
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package sql

class DataFrameReader extends Logging

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def csv(paths: String*): DataFrame

def csv(path: String): DataFrame

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def format(source: String): DataFrameReader

final def getClass(): Class[_]

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def jdbc(url: String, table: String, predicates: Array[String], connectionProperties: Properties): DataFrame

def jdbc(url: String, table: String, columnName: String, lowerBound: Long, upperBound: Long, numPartitions: Int, connectionProperties: Properties): DataFrame

def jdbc(url: String, table: String, properties: Properties): DataFrame

def json(jsonRDD: RDD[String]): DataFrame

def json(jsonRDD: JavaRDD[String]): DataFrame

def json(paths: String*): DataFrame

def json(path: String): DataFrame

def load(paths: String*): DataFrame

def load(path: String): DataFrame

def load(): DataFrame

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def option(key: String, value: Double): DataFrameReader

def option(key: String, value: Long): DataFrameReader

def option(key: String, value: Boolean): DataFrameReader

def option(key: String, value: String): DataFrameReader

def options(options: Map[String, String]): DataFrameReader

def options(options: Map[String, String]): DataFrameReader

def orc(paths: String*): DataFrame

def orc(path: String): DataFrame

def parquet(paths: String*): DataFrame

def parquet(path: String): DataFrame

def schema(schema: StructType): DataFrameReader

final def synchronized[T0](arg0: ⇒ T0): T0

def table(tableName: String): DataFrame

def text(paths: String*): DataFrame

def text(path: String): DataFrame

def textFile(paths: String*): Dataset[String]

def textFile(path: String): Dataset[String]

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped