org.apache.spark.sql.hive

HiveContext

class HiveContext extends SQLContext

An instance of the Spark SQL execution engine that integrates with data stored in Hive. Configuration for Hive is read from hive-site.xml on the classpath.

Self Type
HiveContext
Linear Supertypes
SQLContext, Serializable, Serializable, Logging, AnyRef, Any
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  1. HiveContext
  2. SQLContext
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
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Instance Constructors

  1. new HiveContext(sc: SparkContext)

Type Members

  1. class QueryExecution extends HiveContext.QueryExecution

    Extends QueryExecution with hive specific features.

  2. class SparkPlanner extends SparkStrategies

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext

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 analyze(tableName: String): Unit

    Analyzes the given table in the current database to generate statistics, which will be used in query optimizations.

    Analyzes the given table in the current database to generate statistics, which will be used in query optimizations.

    Right now, it only supports Hive tables and it only updates the size of a Hive table in the Hive metastore.

    Annotations
    @Experimental()
  7. lazy val analyzer: Analyzer { val extendedResolutionRules: List[org.apache.spark.sql.catalyst.rules.Rule[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]] }

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    HiveContextSQLContext
  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]
    Definition Classes
    SQLContext
  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]
    Definition Classes
    SQLContext
  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.

    Definition Classes
    SQLContext
  12. val cacheManager: CacheManager

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

    Caches the specified table in-memory.

    Caches the specified table in-memory.

    Definition Classes
    SQLContext
  14. lazy val catalog: HiveMetastoreCatalog with OverrideCatalog

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    HiveContextSQLContext
  15. def clearCache(): Unit

    Removes all cached tables from the in-memory cache.

    Removes all cached tables from the in-memory cache.

    Definition Classes
    SQLContext
  16. def clone(): AnyRef

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

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    HiveContextSQLContext
  18. def convertCTAS: Boolean

    When true, a table created by a Hive CTAS statement (no USING clause) will be converted to a data source table, using the data source set by spark.

    When true, a table created by a Hive CTAS statement (no USING clause) will be converted to a data source table, using the data source set by spark.sql.sources.default. The table in CTAS statement will be converted when it meets any of the following conditions:

    • The CTAS does not specify any of a SerDe (ROW FORMAT SERDE), a File Format (STORED AS), or a Storage Hanlder (STORED BY), and the value of hive.default.fileformat in hive-site.xml is either TextFile or SequenceFile.
    • The CTAS statement specifies TextFile (STORED AS TEXTFILE) as the file format and no SerDe is specified (no ROW FORMAT SERDE clause).
    • The CTAS statement specifies SequenceFile (STORED AS SEQUENCEFILE) as the file format and no SerDe is specified (no ROW FORMAT SERDE clause).
    Attributes
    protected[org.apache.spark.sql]
  19. def convertMetastoreParquet: Boolean

    When true, enables an experimental feature where metastore tables that use the parquet SerDe are automatically converted to use the Spark SQL parquet table scan, instead of the Hive SerDe.

    When true, enables an experimental feature where metastore tables that use the parquet SerDe are automatically converted to use the Spark SQL parquet table scan, instead of the Hive SerDe.

    Attributes
    protected[org.apache.spark.sql]
  20. def convertMetastoreParquetWithSchemaMerging: Boolean

    When true, also tries to merge possibly different but compatible Parquet schemas in different Parquet data files.

    When true, also tries to merge possibly different but compatible Parquet schemas in different Parquet data files.

    This configuration is only effective when "spark.sql.hive.convertMetastoreParquet" is true.

    Attributes
    protected[org.apache.spark.sql]
  21. 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.

    Definition Classes
    SQLContext
  22. 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.

    Definition Classes
    SQLContext
  23. def createDataFrame(rowRDD: JavaRDD[Row], columns: List[String]): DataFrame

    Creates a DataFrame from an JavaRDD containing Rows by applying a seq of names of columns to this RDD, the data type for each column will be inferred by the first row.

    Creates a DataFrame from an JavaRDD containing Rows by applying a seq of names of columns to this RDD, the data type for each column will be inferred by the first row.

    rowRDD

    an JavaRDD of Row

    columns

    names for each column

    returns

    DataFrame

    Definition Classes
    SQLContext
  24. 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.

    Definition Classes
    SQLContext
    Annotations
    @DeveloperApi()
  25. 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)
    Definition Classes
    SQLContext
    Annotations
    @DeveloperApi()
  26. 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  27. def createDataFrame[A <: Product](rdd: RDD[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

    :: Experimental :: Creates a DataFrame from an RDD of case classes.

    :: Experimental :: Creates a DataFrame from an RDD of case classes.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  28. 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  29. 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  30. 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  31. 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  32. 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  33. 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  34. val ddlParser: DDLParser

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext
  35. val ddlParserWithHiveQL: DDLParser

    Attributes
    protected[org.apache.spark.sql]
  36. 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.

    Definition Classes
    SQLContext
  37. lazy val emptyDataFrame: DataFrame

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

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

    Definition Classes
    SQLContext
  38. lazy val emptyResult: RDD[Row]

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext
  39. final def eq(arg0: AnyRef): Boolean

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

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

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    HiveContextSQLContext
  42. def executeSql(sql: String): HiveContext.QueryExecution

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext
  43. 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.

    Definition Classes
    SQLContext
  44. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  45. lazy val functionRegistry: HiveFunctionRegistry with OverrideFunctionRegistry

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    HiveContextSQLContext
  46. 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.

    Definition Classes
    SQLContext
  47. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  48. 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.

    Definition Classes
    SQLContext
  49. 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.

    Definition Classes
    SQLContext
  50. 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
    Definition Classes
    SQLContext
  51. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  52. lazy val hiveconf: HiveConf

    Attributes
    protected[org.apache.spark.sql.hive]
  53. object implicits extends Serializable

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

  54. def invalidateTable(tableName: String): Unit

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

    Definition Classes
    SQLContext
  56. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  57. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  58. def jdbc(url: String, table: String, theParts: Array[String]): DataFrame

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

    :: Experimental :: 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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  59. def jdbc(url: String, table: String, columnName: String, lowerBound: Long, upperBound: Long, numPartitions: Int): DataFrame

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

    :: Experimental :: 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 to retrieve

    upperBound

    the maximum value of columnName to retrieve

    numPartitions

    the number of partitions. the range minValue-maxValue will be split evenly into this many partitions

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  60. def jdbc(url: String, table: String): DataFrame

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

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

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  61. def jsonFile(path: String, samplingRatio: Double): DataFrame

    :: Experimental ::

    :: Experimental ::

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  62. def jsonFile(path: String, schema: StructType): DataFrame

    :: Experimental :: Loads a JSON file (one object per line) and applies the given schema, returning the result as a DataFrame.

    :: Experimental :: Loads a JSON file (one object per line) and applies the given schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  63. 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.

    Definition Classes
    SQLContext
  64. def jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame

    :: Experimental :: Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    :: Experimental :: Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  65. def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame

    :: Experimental :: Loads an RDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    :: Experimental :: Loads an RDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  66. def jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame

    :: Experimental :: Loads an JavaRDD<String> storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    :: Experimental :: Loads an JavaRDD<String> storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  67. def jsonRDD(json: RDD[String], schema: StructType): DataFrame

    :: Experimental :: Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    :: Experimental :: Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  68. 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.

    Definition Classes
    SQLContext
  69. 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.

    Definition Classes
    SQLContext
  70. def load(source: String, schema: StructType, options: Map[String, String]): DataFrame

    :: Experimental :: (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.

    :: Experimental :: (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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  71. def load(source: String, schema: StructType, options: Map[String, String]): DataFrame

    :: Experimental :: (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.

    :: Experimental :: (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.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  72. def load(source: String, options: Map[String, String]): DataFrame

    :: Experimental :: (Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    :: Experimental :: (Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  73. def load(source: String, options: Map[String, String]): DataFrame

    :: Experimental :: (Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    :: Experimental :: (Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  74. def load(path: String, source: String): DataFrame

    :: Experimental :: Returns the dataset stored at path as a DataFrame, using the given data source.

    :: Experimental :: Returns the dataset stored at path as a DataFrame, using the given data source.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  75. def load(path: String): DataFrame

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

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

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
  76. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  83. def logName: String

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

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

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

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

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

    Definition Classes
    AnyRef
  89. final def notify(): Unit

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

    Definition Classes
    AnyRef
  91. lazy val optimizer: Optimizer

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext
  92. lazy val outputBuffer: OutputStream { ... /* 4 definitions in type refinement */ }

    Attributes
    protected
  93. 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.

    Definition Classes
    SQLContext
    Annotations
    @varargs()
  94. 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]
    Definition Classes
    SQLContext
  95. def parseSql(sql: String): LogicalPlan

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext
  96. val planner: SparkPlanner with HiveStrategies

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    HiveContextSQLContext
  97. val prepareForExecution: RuleExecutor[SparkPlan] { val batches: List[this.Batch] }

    Prepares a planned SparkPlan for execution by inserting shuffle operations as needed.

    Prepares a planned SparkPlan for execution by inserting shuffle operations as needed.

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext
  98. def refreshTable(tableName: String): Unit

    Invalidate and refresh all the cached the metadata of the given table.

    Invalidate and refresh all the cached the metadata of the given table. For performance reasons, Spark SQL or the external data source library it uses might cache certain metadata about a table, such as the location of blocks. When those change outside of Spark SQL, users should call this function to invalidate the cache.

  99. def runHive(cmd: String, maxRows: Int = 1000): Seq[String]

    Execute the command using Hive and return the results as a sequence.

    Execute the command using Hive and return the results as a sequence. Each element in the sequence is one row.

    Attributes
    protected
  100. def runSqlHive(sql: String): Seq[String]

    Runs the specified SQL query using Hive.

    Runs the specified SQL query using Hive.

    Attributes
    protected[org.apache.spark.sql]
  101. lazy val sessionState: SessionState

    SQLConf and HiveConf contracts:

    SQLConf and HiveConf contracts:

    1. reuse existing started SessionState if any 2. when the Hive session is first initialized, params in HiveConf will get picked up by the SQLConf. Additionally, any properties set by set() or a SET command inside sql() will be set in the SQLConf *as well as* in the HiveConf.

    Attributes
    protected[org.apache.spark.sql.hive]
  102. def setConf(key: String, value: String): Unit

    Set the given Spark SQL configuration property.

    Set the given Spark SQL configuration property.

    Definition Classes
    HiveContextSQLContext
  103. def setConf(props: Properties): Unit

    Set Spark SQL configuration properties.

    Set Spark SQL configuration properties.

    Definition Classes
    SQLContext
  104. val sparkContext: SparkContext

    Definition Classes
    SQLContext
  105. 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'.

    Definition Classes
    HiveContextSQLContext
  106. val sqlParser: SparkSQLParser

    Attributes
    protected[org.apache.spark.sql]
    Definition Classes
    SQLContext
  107. final def synchronized[T0](arg0: ⇒ T0): T0

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

    Returns the specified table as a DataFrame.

    Returns the specified table as a DataFrame.

    Definition Classes
    SQLContext
  109. 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.

    Definition Classes
    SQLContext
  110. 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.

    Definition Classes
    SQLContext
  111. 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).

    Definition Classes
    SQLContext
  112. 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).

    Definition Classes
    SQLContext
  113. def toString(): String

    Definition Classes
    AnyRef → Any
  114. 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);
    Definition Classes
    SQLContext
  115. def uncacheTable(tableName: String): Unit

    Removes the specified table from the in-memory cache.

    Removes the specified table from the in-memory cache.

    Definition Classes
    SQLContext
  116. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def applySchema(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.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) use createDataFrame

  2. def applySchema(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.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) use createDataFrame

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

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) use createDataFrame

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

    :: DeveloperApi :: Creates a DataFrame from an RDD containing Rows by applying a schema to this RDD.

    :: DeveloperApi :: Creates a DataFrame from an RDD containing Rows by applying a schema to this RDD. 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. applySchema(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)
    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) use createDataFrame

Inherited from SQLContext

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Basic Operations

Cached Table Management

Configuration

Custom DataFrame Creation

Persistent Catalog DDL

Generic Data Sources

Specific Data Sources

Ungrouped