class SQLContext extends Logging with Serializable
The entry point for working with structured data (rows and columns) in Spark 1.x.
As of Spark 2.0, this is replaced by SparkSession. However, we are keeping the class here for backward compatibility.
- Self Type
- SQLContext
- Annotations
- @Stable()
- Source
- SQLContext.scala
- Since
1.0.0
- Grouped
- Alphabetic
- By Inheritance
- SQLContext
- Serializable
- Serializable
- Logging
- AnyRef
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Instance Constructors
-
new
SQLContext(sparkContext: JavaSparkContext)
- Annotations
- @deprecated
- Deprecated
(Since version 2.0.0) Use SparkSession.builder instead
-
new
SQLContext(sc: SparkContext)
- Annotations
- @deprecated
- Deprecated
(Since version 2.0.0) Use SparkSession.builder instead
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
baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame
Convert a
BaseRelation
created for external data sources into aDataFrame
.Convert a
BaseRelation
created for external data sources into aDataFrame
.- Since
1.3.0
-
def
cacheTable(tableName: String): Unit
Caches the specified table in-memory.
Caches the specified table in-memory.
- Since
1.3.0
-
def
clearCache(): Unit
Removes all cached tables from the in-memory cache.
Removes all cached tables from the in-memory cache.
- Since
1.3.0
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
createDataFrame(data: List[_], beanClass: Class[_]): DataFrame
Applies a schema to a List of Java Beans.
Applies a schema to a List of Java Beans.
WARNING: Since there is no guaranteed ordering for fields in a Java Bean, SELECT * queries will return the columns in an undefined order.
- Since
1.6.0
-
def
createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame
Applies a schema to an RDD of Java Beans.
Applies a schema to an RDD of Java Beans.
WARNING: Since there is no guaranteed ordering for fields in a Java Bean, SELECT * queries will return the columns in an undefined order.
- Since
1.3.0
-
def
createDataFrame(rdd: RDD[_], beanClass: Class[_]): DataFrame
Applies a schema to an RDD of Java Beans.
Applies a schema to an RDD of Java Beans.
WARNING: Since there is no guaranteed ordering for fields in a Java Bean, SELECT * queries will return the columns in an undefined order.
- Since
1.3.0
-
def
createDataFrame(rows: List[Row], schema: StructType): DataFrame
:: DeveloperApi :: Creates a
DataFrame
from ajava.util.List
containing Rows using the given schema. -
def
createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame
:: DeveloperApi :: Creates a
DataFrame
from aJavaRDD
containing Rows using the given schema. -
def
createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame
:: DeveloperApi :: Creates a
DataFrame
from anRDD
containing Rows using the given schema.:: DeveloperApi :: Creates a
DataFrame
from anRDD
containing Rows using the given schema. It is important to make sure that the structure of every Row of the provided RDD matches the provided schema. Otherwise, there will be runtime exception. Example:import org.apache.spark.sql._ import org.apache.spark.sql.types._ val sqlContext = new org.apache.spark.sql.SQLContext(sc) val schema = StructType( StructField("name", StringType, false) :: StructField("age", IntegerType, true) :: Nil) val people = sc.textFile("examples/src/main/resources/people.txt").map( _.split(",")).map(p => Row(p(0), p(1).trim.toInt)) val dataFrame = sqlContext.createDataFrame(people, schema) dataFrame.printSchema // root // |-- name: string (nullable = false) // |-- age: integer (nullable = true) dataFrame.createOrReplaceTempView("people") sqlContext.sql("select name from people").collect.foreach(println)
- Annotations
- @DeveloperApi()
- Since
1.3.0
-
def
createDataFrame[A <: Product](data: Seq[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame
Creates a DataFrame from a local Seq of Product.
Creates a DataFrame from a local Seq of Product.
- Since
1.3.0
-
def
createDataFrame[A <: Product](rdd: RDD[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame
Creates a DataFrame from an RDD of Product (e.g.
Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
- Since
1.3.0
-
def
createDataset[T](data: List[T])(implicit arg0: Encoder[T]): Dataset[T]
Creates a Dataset from a
java.util.List
of a given type.Creates a Dataset from a
java.util.List
of a given type. This method requires an encoder (to convert a JVM object of typeT
to and from the internal Spark SQL representation) that is generally created automatically through implicits from aSparkSession
, or can be created explicitly by calling static methods on Encoders.Java Example
List<String> data = Arrays.asList("hello", "world"); Dataset<String> ds = spark.createDataset(data, Encoders.STRING());
- Since
2.0.0
-
def
createDataset[T](data: RDD[T])(implicit arg0: Encoder[T]): Dataset[T]
Creates a Dataset from an RDD of a given type.
Creates a Dataset from an RDD of a given type. This method requires an encoder (to convert a JVM object of type
T
to and from the internal Spark SQL representation) that is generally created automatically through implicits from aSparkSession
, or can be created explicitly by calling static methods on Encoders.- Since
2.0.0
-
def
createDataset[T](data: Seq[T])(implicit arg0: Encoder[T]): Dataset[T]
Creates a Dataset from a local Seq of data of a given type.
Creates a Dataset from a local Seq of data of a given type. This method requires an encoder (to convert a JVM object of type
T
to and from the internal Spark SQL representation) that is generally created automatically through implicits from aSparkSession
, or can be created explicitly by calling static methods on Encoders.Example
import spark.implicits._ case class Person(name: String, age: Long) val data = Seq(Person("Michael", 29), Person("Andy", 30), Person("Justin", 19)) val ds = spark.createDataset(data) ds.show() // +-------+---+ // | name|age| // +-------+---+ // |Michael| 29| // | Andy| 30| // | Justin| 19| // +-------+---+
- Since
2.0.0
-
def
dropTempTable(tableName: String): Unit
Drops the temporary table with the given table name in the catalog.
Drops the temporary table with the given table name in the catalog. If the table has been cached/persisted before, it's also unpersisted.
- tableName
the name of the table to be unregistered.
- Since
1.3.0
-
def
emptyDataFrame: DataFrame
Returns a
DataFrame
with no rows or columns.Returns a
DataFrame
with no rows or columns.- Since
1.3.0
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
experimental: ExperimentalMethods
:: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.
:: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.
- Annotations
- @Experimental() @transient() @Unstable()
- Since
1.3.0
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
getAllConfs: Map[String, String]
Return all the configuration properties that have been set (i.e.
Return all the configuration properties that have been set (i.e. not the default). This creates a new copy of the config properties in the form of a Map.
- Since
1.0.0
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
getConf(key: String, defaultValue: String): String
Return the value of Spark SQL configuration property for the given key.
Return the value of Spark SQL configuration property for the given key. If the key is not set yet, return
defaultValue
.- Since
1.0.0
-
def
getConf(key: String): String
Return the value of Spark SQL configuration property for the given key.
Return the value of Spark SQL configuration property for the given key.
- Since
1.0.0
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
isCached(tableName: String): Boolean
Returns true if the table is currently cached in-memory.
Returns true if the table is currently cached in-memory.
- Since
1.3.0
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
listenerManager: ExecutionListenerManager
An interface to register custom org.apache.spark.sql.util.QueryExecutionListeners that listen for execution metrics.
-
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
-
def
newSession(): SQLContext
Returns a SQLContext as new session, with separated SQL configurations, temporary tables, registered functions, but sharing the same
SparkContext
, cached data and other things.Returns a SQLContext as new session, with separated SQL configurations, temporary tables, registered functions, but sharing the same
SparkContext
, cached data and other things.- Since
1.6.0
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
range(start: Long, end: Long, step: Long, numPartitions: Int): DataFrame
Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in an range fromstart
toend
(exclusive) with an step value, with partition number specified.Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in an range fromstart
toend
(exclusive) with an step value, with partition number specified.- Since
1.4.0
-
def
range(start: Long, end: Long, step: Long): DataFrame
Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in a range fromstart
toend
(exclusive) with a step value.Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in a range fromstart
toend
(exclusive) with a step value.- Since
2.0.0
-
def
range(start: Long, end: Long): DataFrame
Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in a range fromstart
toend
(exclusive) with step value 1.Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in a range fromstart
toend
(exclusive) with step value 1.- Since
1.4.0
-
def
range(end: Long): DataFrame
Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in a range from 0 toend
(exclusive) with step value 1.Creates a
DataFrame
with a singleLongType
column namedid
, containing elements in a range from 0 toend
(exclusive) with step value 1.- Since
1.4.1
-
def
read: DataFrameReader
Returns a DataFrameReader that can be used to read non-streaming data in as a
DataFrame
.Returns a DataFrameReader that can be used to read non-streaming data in as a
DataFrame
.sqlContext.read.parquet("/path/to/file.parquet") sqlContext.read.schema(schema).json("/path/to/file.json")
- Since
1.4.0
-
def
readStream: DataStreamReader
Returns a
DataStreamReader
that can be used to read streaming data in as aDataFrame
.Returns a
DataStreamReader
that can be used to read streaming data in as aDataFrame
.sparkSession.readStream.parquet("/path/to/directory/of/parquet/files") sparkSession.readStream.schema(schema).json("/path/to/directory/of/json/files")
- Since
2.0.0
-
def
setConf(key: String, value: String): Unit
Set the given Spark SQL configuration property.
Set the given Spark SQL configuration property.
- Since
1.0.0
-
def
setConf(props: Properties): Unit
Set Spark SQL configuration properties.
Set Spark SQL configuration properties.
- Since
1.0.0
- def sparkContext: SparkContext
- val sparkSession: SparkSession
-
def
sql(sqlText: String): DataFrame
Executes a SQL query using Spark, returning the result as a
DataFrame
.Executes a SQL query using Spark, returning the result as a
DataFrame
. This API eagerly runs DDL/DML commands, but not for SELECT queries.- Since
1.3.0
-
def
streams: StreamingQueryManager
Returns a
StreamingQueryManager
that allows managing all the StreamingQueries active onthis
context.Returns a
StreamingQueryManager
that allows managing all the StreamingQueries active onthis
context.- Since
2.0.0
-
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
1.3.0
-
def
tableNames(databaseName: String): Array[String]
Returns the names of tables in the given database as an array.
Returns the names of tables in the given database as an array.
- Since
1.3.0
-
def
tableNames(): Array[String]
Returns the names of tables in the current database as an array.
Returns the names of tables in the current database as an array.
- Since
1.3.0
-
def
tables(databaseName: String): DataFrame
Returns a
DataFrame
containing names of existing tables in the given database.Returns a
DataFrame
containing names of existing tables in the given database. The returned DataFrame has three columns, database, tableName and isTemporary (a Boolean indicating if a table is a temporary one or not).- Since
1.3.0
-
def
tables(): DataFrame
Returns a
DataFrame
containing names of existing tables in the current database.Returns a
DataFrame
containing names of existing tables in the current database. The returned DataFrame has three columns, database, tableName and isTemporary (a Boolean indicating if a table is a temporary one or not).- Since
1.3.0
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
udf: UDFRegistration
A collection of methods for registering user-defined functions (UDF).
A collection of methods for registering user-defined functions (UDF).
The following example registers a Scala closure as UDF:
sqlContext.udf.register("myUDF", (arg1: Int, arg2: String) => arg2 + arg1)
The following example registers a UDF in Java:
sqlContext.udf().register("myUDF", (Integer arg1, String arg2) -> arg2 + arg1, DataTypes.StringType);
- Since
1.3.0
- Note
The user-defined functions must be deterministic. Due to optimization, duplicate invocations may be eliminated or the function may even be invoked more times than it is present in the query.
-
def
uncacheTable(tableName: String): Unit
Removes the specified table from the in-memory cache.
Removes the specified table from the in-memory cache.
- Since
1.3.0
-
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( ... ) @native()
-
object
implicits extends SQLImplicits with Serializable
(Scala-specific) Implicit methods available in Scala for converting common Scala objects into
DataFrame
s.(Scala-specific) Implicit methods available in Scala for converting common Scala objects into
DataFrame
s.val sqlContext = new SQLContext(sc) import sqlContext.implicits._
- Since
1.3.0
Deprecated Value Members
-
def
applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame
- Annotations
- @deprecated
- Deprecated
(Since version 1.3.0) Use createDataFrame instead.
-
def
applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame
- Annotations
- @deprecated
- Deprecated
(Since version 1.3.0) Use createDataFrame instead.
-
def
applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame
- Annotations
- @deprecated
- Deprecated
(Since version 1.3.0) Use createDataFrame instead.
-
def
applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame
- Annotations
- @deprecated
- Deprecated
(Since version 1.3.0) Use createDataFrame instead.
-
def
createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame
(Scala-specific) Create an external table from the given path based on a data source, a schema and a set of options.
(Scala-specific) Create an external table from the given path based on a data source, a schema and a set of options. Then, returns the corresponding DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 2.2.0) use sparkSession.catalog.createTable instead.
- Since
1.3.0
-
def
createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame
Create an external table from the given path based on a data source, a schema and a set of options.
Create an external table from the given path based on a data source, a schema and a set of options. Then, returns the corresponding DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 2.2.0) use sparkSession.catalog.createTable instead.
- Since
1.3.0
-
def
createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame
(Scala-specific) Creates an external table from the given path based on a data source and a set of options.
(Scala-specific) Creates an external table from the given path based on a data source and a set of options. Then, returns the corresponding DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 2.2.0) use sparkSession.catalog.createTable instead.
- Since
1.3.0
-
def
createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame
Creates an external table from the given path based on a data source and a set of options.
Creates an external table from the given path based on a data source and a set of options. Then, returns the corresponding DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 2.2.0) use sparkSession.catalog.createTable instead.
- Since
1.3.0
-
def
createExternalTable(tableName: String, path: String, source: String): DataFrame
Creates an external table from the given path based on a data source and returns the corresponding DataFrame.
Creates an external table from the given path based on a data source and returns the corresponding DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 2.2.0) use sparkSession.catalog.createTable instead.
- Since
1.3.0
-
def
createExternalTable(tableName: String, path: String): DataFrame
Creates an external table from the given path and returns the corresponding DataFrame.
Creates an external table from the given path and returns the corresponding DataFrame. It will use the default data source configured by spark.sql.sources.default.
- Annotations
- @deprecated
- Deprecated
(Since version 2.2.0) use sparkSession.catalog.createTable instead.
- Since
1.3.0
-
def
jdbc(url: String, table: String, theParts: Array[String]): 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. The theParts parameter gives a list expressions suitable for inclusion in WHERE clauses; each one defines one partition of theDataFrame
.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.jdbc() instead.
-
def
jdbc(url: String, table: String, columnName: String, lowerBound: Long, upperBound: Long, numPartitions: Int): 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.- 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. the range
minValue
-maxValue
will be split evenly into this many partitions
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.jdbc() instead.
-
def
jdbc(url: String, table: String): 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.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.jdbc() instead.
-
def
jsonFile(path: String, samplingRatio: Double): DataFrame
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonFile(path: String, schema: StructType): DataFrame
Loads a JSON file (one object per line) and applies the given schema, returning the result as a
DataFrame
.Loads a JSON file (one object per line) and applies the given schema, returning the result as a
DataFrame
.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonFile(path: String): DataFrame
Loads a JSON file (one object per line), returning the result as a
DataFrame
.Loads a JSON file (one object per line), returning the result as a
DataFrame
. It goes through the entire dataset once to determine the schema.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame
Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a
DataFrame
.Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a
DataFrame
.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame
Loads an RDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a
DataFrame
.Loads an RDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a
DataFrame
.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame
Loads an JavaRDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a
DataFrame
.Loads an JavaRDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a
DataFrame
.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonRDD(json: RDD[String], schema: StructType): DataFrame
Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a
DataFrame
.Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a
DataFrame
.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonRDD(json: JavaRDD[String]): DataFrame
Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
DataFrame
.Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
DataFrame
. It goes through the entire dataset once to determine the schema.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
jsonRDD(json: RDD[String]): DataFrame
Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
DataFrame
.Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
DataFrame
. It goes through the entire dataset once to determine the schema.- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.json() instead.
-
def
load(source: String, schema: StructType, options: Map[String, String]): DataFrame
(Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.
(Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.format(source).schema(schema).options(options).load() instead.
-
def
load(source: String, schema: StructType, options: Map[String, String]): DataFrame
(Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.
(Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.format(source).schema(schema).options(options).load() instead.
-
def
load(source: String, options: Map[String, String]): DataFrame
(Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.
(Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.format(source).options(options).load() instead.
-
def
load(source: String, options: Map[String, String]): DataFrame
(Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.
(Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.format(source).options(options).load() instead.
-
def
load(path: String, source: String): DataFrame
Returns the dataset stored at path as a DataFrame, using the given data source.
Returns the dataset stored at path as a DataFrame, using the given data source.
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.format(source).load(path) instead.
-
def
load(path: String): DataFrame
Returns the dataset stored at path as a DataFrame, using the default data source configured by spark.sql.sources.default.
Returns the dataset stored at path as a DataFrame, using the default data source configured by spark.sql.sources.default.
- Annotations
- @deprecated
- Deprecated
(Since version 1.4.0) Use read.load(path) instead.
-
def
parquetFile(paths: String*): DataFrame
Loads a Parquet file, returning the result as a
DataFrame
.Loads a Parquet file, returning the result as a
DataFrame
. This function returns an emptyDataFrame
if no paths are passed in.- Annotations
- @deprecated @varargs()
- Deprecated
(Since version 1.4.0) Use read.parquet() instead.