org.apache.spark.sql

SQLContext

class SQLContext extends Logging with Serializable

The entry point for working with structured data (rows and columns) in Spark. Allows the creation of DataFrame objects as well as the execution of SQL queries.

Self Type
SQLContext
Source
SQLContext.scala
Since

1.0.0

Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
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  1. SQLContext
  2. Serializable
  3. Serializable
  4. Logging
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Instance Constructors

  1. new SQLContext(sparkContext: JavaSparkContext)

  2. new SQLContext(sparkContext: SparkContext)

Type Members

  1. class QueryExecution extends execution.QueryExecution

    Attributes
    protected[org.apache.spark.sql]
    Annotations
    @deprecated
    Deprecated

    (Since version 1.6.0) use org.apache.spark.sql.QueryExecution

  2. class SparkPlanner extends execution.SparkPlanner

    Attributes
    protected[org.apache.spark.sql]
    Annotations
    @deprecated
    Deprecated

    (Since version 1.6.0) use org.apache.spark.sql.SparkPlanner

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

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

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

    Definition Classes
    Any
  6. def addJar(path: String): Unit

    Add a jar to SQLContext

    Add a jar to SQLContext

    Attributes
    protected[org.apache.spark.sql]
  7. lazy val analyzer: Analyzer

    Attributes
    protected[org.apache.spark.sql]
  8. def applySchemaToPythonRDD(rdd: RDD[Array[Any]], schema: StructType): DataFrame

    Apply a schema defined by the schema to an RDD.

    Apply a schema defined by the schema to an RDD. It is only used by PySpark.

    Attributes
    protected[org.apache.spark.sql]
  9. def applySchemaToPythonRDD(rdd: RDD[Array[Any]], schemaString: String): DataFrame

    Apply a schema defined by the schemaString to an RDD.

    Apply a schema defined by the schemaString to an RDD. It is only used by PySpark.

    Attributes
    protected[org.apache.spark.sql]
  10. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  11. def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame

    Convert a BaseRelation created for external data sources into a DataFrame.

    Convert a BaseRelation created for external data sources into a DataFrame.

    Since

    1.3.0

  12. val cacheManager: execution.CacheManager

    Attributes
    protected[org.apache.spark.sql]
  13. def cacheTable(tableName: String): Unit

    Caches the specified table in-memory.

    Caches the specified table in-memory.

    Since

    1.3.0

  14. lazy val catalog: Catalog

    Attributes
    protected[org.apache.spark.sql]
  15. 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

  16. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  17. lazy val conf: SQLConf

    returns

    Spark SQL configuration

    Attributes
    protected[org.apache.spark.sql]
  18. def createDataFrame(data: List[_], beanClass: Class[_]): DataFrame

    Applies a schema to an List of Java Beans.

    Applies a schema to an 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

  19. 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

  20. 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

  21. def createDataFrame(rows: List[Row], schema: StructType): DataFrame

    :: DeveloperApi :: Creates a DataFrame from an java.util.List containing Rows using the given schema.

    :: DeveloperApi :: Creates a DataFrame from an java.util.List containing Rows using the given schema. It is important to make sure that the structure of every Row of the provided List matches the provided schema. Otherwise, there will be runtime exception.

    Annotations
    @DeveloperApi()
    Since

    1.6.0

  22. def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame

    :: DeveloperApi :: Creates a DataFrame from an JavaRDD containing Rows using the given schema.

    :: DeveloperApi :: Creates a DataFrame from an JavaRDD 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.

    Annotations
    @DeveloperApi()
    Since

    1.3.0

  23. def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame

    :: DeveloperApi :: Creates a DataFrame from an RDD containing Rows using the given schema.

    :: DeveloperApi :: Creates a DataFrame from an RDD 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.registerTempTable("people")
    sqlContext.sql("select name from people").collect.foreach(println)
    Annotations
    @DeveloperApi()
    Since

    1.3.0

  24. def createDataFrame[A <: Product](data: Seq[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

    :: Experimental :: Creates a DataFrame from a local Seq of Product.

    :: Experimental :: Creates a DataFrame from a local Seq of Product.

    Annotations
    @Experimental()
    Since

    1.3.0

  25. def createDataFrame[A <: Product](rdd: RDD[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

    :: Experimental :: Creates a DataFrame from an RDD of Product (e.

    :: Experimental :: Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).

    Annotations
    @Experimental()
    Since

    1.3.0

  26. def createDataset[T](data: List[T])(implicit arg0: Encoder[T]): Dataset[T]

  27. def createDataset[T](data: RDD[T])(implicit arg0: Encoder[T]): Dataset[T]

  28. def createDataset[T](data: Seq[T])(implicit arg0: Encoder[T]): Dataset[T]

  29. def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

    :: Experimental :: (Scala-specific) Create an external table from the given path based on a data source, a schema and a set of options.

    :: Experimental :: (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
    @Experimental()
    Since

    1.3.0

  30. def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

    :: Experimental :: Create an external table from the given path based on a data source, a schema and a set of options.

    :: Experimental :: 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
    @Experimental()
    Since

    1.3.0

  31. def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

    :: Experimental :: (Scala-specific) Creates an external table from the given path based on a data source and a set of options.

    :: Experimental :: (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
    @Experimental()
    Since

    1.3.0

  32. def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

    :: Experimental :: Creates an external table from the given path based on a data source and a set of options.

    :: Experimental :: Creates an external table from the given path based on a data source and a set of options. Then, returns the corresponding DataFrame.

    Annotations
    @Experimental()
    Since

    1.3.0

  33. def createExternalTable(tableName: String, path: String, source: String): DataFrame

    :: Experimental :: Creates an external table from the given path based on a data source and returns the corresponding DataFrame.

    :: Experimental :: Creates an external table from the given path based on a data source and returns the corresponding DataFrame.

    Annotations
    @Experimental()
    Since

    1.3.0

  34. def createExternalTable(tableName: String, path: String): DataFrame

    :: Experimental :: Creates an external table from the given path and returns the corresponding DataFrame.

    :: Experimental :: 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
    @Experimental()
    Since

    1.3.0

  35. val ddlParser: DDLParser

    Attributes
    protected[org.apache.spark.sql]
  36. def dialectClassName: String

    Attributes
    protected[org.apache.spark.sql]
  37. 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

  38. lazy val emptyDataFrame: DataFrame

    :: Experimental :: Returns a DataFrame with no rows or columns.

    :: Experimental :: Returns a DataFrame with no rows or columns.

    Since

    1.3.0

  39. lazy val emptyResult: RDD[InternalRow]

    Attributes
    protected[org.apache.spark.sql]
  40. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  42. def executePlan(plan: LogicalPlan): execution.QueryExecution

    Attributes
    protected[org.apache.spark.sql]
  43. def executeSql(sql: String): execution.QueryExecution

    Attributes
    protected[org.apache.spark.sql]
  44. val 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.

    Since

    1.3.0

  45. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  46. lazy val functionRegistry: FunctionRegistry

    Attributes
    protected[org.apache.spark.sql]
  47. def getAllConfs: Map[String, String]

    Return all the configuration properties that have been set (i.

    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

  48. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  49. 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

  50. 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

  51. def getSQLDialect(): ParserDialect

    Attributes
    protected[org.apache.spark.sql]
  52. def getSchema(beanClass: Class[_]): Seq[AttributeReference]

    Returns a Catalyst Schema for the given java bean class.

    Returns a Catalyst Schema for the given java bean class.

    Attributes
    protected
  53. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  54. object implicits extends SQLImplicits with Serializable

    :: Experimental :: (Scala-specific) Implicit methods available in Scala for converting common Scala objects into DataFrames.

  55. 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

  56. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  57. val isRootContext: Boolean

  58. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  59. lazy val listenerManager: ExecutionListenerManager

  60. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  67. def logName: String

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

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

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

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

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

    Definition Classes
    AnyRef
  73. def newSession(): SQLContext

    Returns a SQLContext as new session, with separated SQL configurations, temporary tables, registered functions, but sharing the same SparkContext, CacheManager, SQLListener and SQLTab.

    Returns a SQLContext as new session, with separated SQL configurations, temporary tables, registered functions, but sharing the same SparkContext, CacheManager, SQLListener and SQLTab.

    Since

    1.6.0

  74. final def notify(): Unit

    Definition Classes
    AnyRef
  75. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  76. lazy val optimizer: Optimizer

    Attributes
    protected[org.apache.spark.sql]
  77. def parseDataType(dataTypeString: String): DataType

    Parses the data type in our internal string representation.

    Parses the data type in our internal string representation. The data type string should have the same format as the one generated by toString in scala. It is only used by PySpark.

    Attributes
    protected[org.apache.spark.sql]
  78. def parseSql(sql: String): LogicalPlan

    Attributes
    protected[org.apache.spark.sql]
  79. val planner: execution.SparkPlanner

    Attributes
    protected[org.apache.spark.sql]
  80. val prepareForExecution: RuleExecutor[SparkPlan]

    Prepares a planned SparkPlan for execution by inserting shuffle operations and internal row format conversions as needed.

    Prepares a planned SparkPlan for execution by inserting shuffle operations and internal row format conversions as needed.

    Attributes
    protected[org.apache.spark.sql]
  81. def range(start: Long, end: Long, step: Long, numPartitions: Int): DataFrame

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from start to end (exclusive) with an step value, with partition number specified.

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from start to end (exclusive) with an step value, with partition number specified.

    Annotations
    @Experimental()
    Since

    1.4.0

  82. def range(start: Long, end: Long): DataFrame

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from start to end (exclusive) with step value 1.

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from start to end (exclusive) with step value 1.

    Annotations
    @Experimental()
    Since

    1.4.0

  83. def range(end: Long): DataFrame

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from 0 to end (exclusive) with step value 1.

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from 0 to end (exclusive) with step value 1.

    Annotations
    @Experimental()
    Since

    1.4.1

  84. def read: DataFrameReader

    :: Experimental :: Returns a DataFrameReader that can be used to read data in as a DataFrame.

    :: Experimental :: Returns a DataFrameReader that can be used to read data in as a DataFrame.

    sqlContext.read.parquet("/path/to/file.parquet")
    sqlContext.read.schema(schema).json("/path/to/file.json")
    Annotations
    @Experimental()
    Since

    1.4.0

  85. 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

  86. def setConf(props: Properties): Unit

    Set Spark SQL configuration properties.

    Set Spark SQL configuration properties.

    Since

    1.0.0

  87. val sparkContext: SparkContext

  88. 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. The dialect that is used for SQL parsing can be configured with 'spark.sql.dialect'.

    Since

    1.3.0

  89. val sqlParser: SparkSQLParser

    Attributes
    protected[org.apache.spark.sql]
  90. final def synchronized[T0](arg0: ⇒ T0): T0

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

    Returns the specified table as a DataFrame.

    Returns the specified table as a DataFrame.

    Since

    1.3.0

  92. 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

  93. 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

  94. 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 two columns, tableName and isTemporary (a Boolean indicating if a table is a temporary one or not).

    Since

    1.3.0

  95. 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 two columns, tableName and isTemporary (a Boolean indicating if a table is a temporary one or not).

    Since

    1.3.0

  96. def toString(): String

    Definition Classes
    AnyRef → Any
  97. val 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",
     new UDF2<Integer, String, String>() {
         @Override
         public String call(Integer arg1, String arg2) {
             return arg2 + arg1;
         }
    }, DataTypes.StringType);

    Or, to use Java 8 lambda syntax:

    sqlContext.udf().register("myUDF",
    (Integer arg1, String arg2) -> arg2 + arg1,
    DataTypes.StringType);
    Since

    1.3.0 TODO move to SQLSession?

  98. 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

  99. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame

    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame. This will be removed in Spark 2.0.

  2. def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame

    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame. This will be removed in Spark 2.0.

  3. def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame

    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame. This will be removed in Spark 2.0.

  4. def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame

    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame. This will be removed in Spark 2.0.

  5. 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 the DataFrame.

    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.jdbc(). This will be removed in Spark 2.0.

  6. 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(). This will be removed in Spark 2.0.

  7. 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(). This will be removed in Spark 2.0.

  8. def jsonFile(path: String, samplingRatio: Double): DataFrame

    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json(). This will be removed in Spark 2.0.

  9. 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(). This will be removed in Spark 2.0.

  10. 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(). This will be removed in Spark 2.0.

  11. 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(). This will be removed in Spark 2.0.

  12. 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(). This will be removed in Spark 2.0.

  13. 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(). This will be removed in Spark 2.0.

  14. 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(). This will be removed in Spark 2.0.

  15. 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(). This will be removed in Spark 2.0.

  16. 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(). This will be removed in Spark 2.0.

  17. 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)

  18. 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)

  19. 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)

  20. 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)

  21. 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). This will be removed in Spark 2.0.

  22. def load(path: String): DataFrame

    Returns the dataset stored at path as a DataFrame, using the default data source configured by spark.

    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). This will be removed in Spark 2.0.

  23. 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 empty DataFrame if no paths are passed in.

    Annotations
    @deprecated @varargs()
    Deprecated

    (Since version 1.4.0) Use read.parquet(). This will be removed in Spark 2.0.

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Basic Operations

Cached Table Management

Configuration

dataframe

Custom DataFrame Creation

Persistent Catalog DDL

Generic Data Sources

Specific Data Sources

Support functions for language integrated queries