Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://api-docs.databricks.com/scala/spark/latest/org/apache/spark/sql/Dataset.html below:

Databricks Scala Spark API - org.apache.spark.sql.Dataset

 

1. Grouped
2. Alphabetic
3. By Inheritance

1. Dataset
2. Serializable
3. Serializable
4. AnyRef
5. Any

1.  final def !=(arg0: Any): Boolean
2.  final def ##(): Int
3.  final def ==(arg0: Any): Boolean
4.  def agg(expr: Column, exprs: Column*): DataFrame
5.  def agg(exprs: Map[String, String]): DataFrame
6.  def agg(exprs: Map[String, String]): DataFrame
7.  def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame
8.  def alias(alias: Symbol): Dataset[T]
9.  def alias(alias: String): Dataset[T]
10.  def apply(colName: String): Column
11.  def as(alias: Symbol): Dataset[T]
12.  def as(alias: String): Dataset[T]
13.  def as(schema: StructType): DataFrame
14.  def as[U](implicit arg0: Encoder[U]): Dataset[U]
15.  final def asInstanceOf[T0]: T0
16.  def cache(): Dataset.this.type
17.  def checkpoint(eager: Boolean): Dataset[T]
18.  def checkpoint(): Dataset[T]
19.  def clone(): AnyRef
20.  def coalesce(numPartitions: Int): Dataset[T]
21.  def col(colName: String): Column
22.  def colRegex(colName: String): Column
23.  def collect(): Array[T]
24.  def collectAsList(): List[T]
25.  def columns: Array[String]
26.  def count(): Long
27.  def createGlobalTempView(viewName: String): Unit
28.  def createOrReplaceGlobalTempView(viewName: String): Unit
29.  def createOrReplaceTempView(viewName: String): Unit
30.  def createTempView(viewName: String): Unit
31.  def crossJoin(right: Dataset[_]): DataFrame
32.  def cube(col1: String, cols: String*): RelationalGroupedDataset
33.  def cube(cols: Column*): RelationalGroupedDataset
34.  def describe(cols: String*): DataFrame
35.  def distinct(): Dataset[T]
36.  def drop(col: Column): DataFrame
37.  def drop(colNames: String*): DataFrame
38.  def drop(colName: String): DataFrame
39.  def dropDuplicates(col1: String, cols: String*): Dataset[T]
40.  def dropDuplicates(colNames: Array[String]): Dataset[T]
41.  def dropDuplicates(colNames: Seq[String]): Dataset[T]
42.  def dropDuplicates(): Dataset[T]
43.  def dtypes: Array[(String, String)]
44.  val encoder: Encoder[T]
45.  final def eq(arg0: AnyRef): Boolean
46.  def equals(arg0: Any): Boolean
47.  def except(other: Dataset[T]): Dataset[T]
48.  def exceptAll(other: Dataset[T]): Dataset[T]
49.  def explain(): Unit
50.  def explain(extended: Boolean): Unit
51.  def explain(mode: String): Unit
52.  def filter(func: FilterFunction[T]): Dataset[T]
53.  def filter(func: (T) ⇒ Boolean): Dataset[T]
54.  def filter(conditionExpr: String): Dataset[T]
55.  def filter(condition: Column): Dataset[T]
56.  def finalize(): Unit
57.  def first(): T
58.  def flatMap[U](f: FlatMapFunction[T, U], encoder: Encoder[U]): Dataset[U]
59.  def flatMap[U](func: (T) ⇒ TraversableOnce[U])(implicit arg0: Encoder[U]): Dataset[U]
60.  def foreach(func: ForeachFunction[T]): Unit
61.  def foreach(f: (T) ⇒ Unit): Unit
62.  def foreachPartition(func: ForeachPartitionFunction[T]): Unit
63.  def foreachPartition(f: (Iterator[T]) ⇒ Unit): Unit
64.  final def getClass(): Class[_]
65.  def groupBy(col1: String, cols: String*): RelationalGroupedDataset
66.  def groupBy(cols: Column*): RelationalGroupedDataset
67.  def groupByKey[K](func: MapFunction[T, K], encoder: Encoder[K]): KeyValueGroupedDataset[K, T]
68.  def groupByKey[K](func: (T) ⇒ K)(implicit arg0: Encoder[K]): KeyValueGroupedDataset[K, T]
69.  def hashCode(): Int
70.  def head(): T
71.  def head(n: Int): Array[T]
72.  def hint(name: String, parameters: Any*): Dataset[T]
73.  def inputFiles: Array[String]
74.  def intersect(other: Dataset[T]): Dataset[T]
75.  def intersectAll(other: Dataset[T]): Dataset[T]
76.  def isEmpty: Boolean
77.  final def isInstanceOf[T0]: Boolean
78.  def isLocal: Boolean
79.  def isStreaming: Boolean
80.  def javaRDD: JavaRDD[T]
81.  def join(right: Dataset[_], joinExprs: Column, joinType: String): DataFrame
82.  def join(right: Dataset[_], joinExprs: Column): DataFrame
83.  def join(right: Dataset[_], usingColumns: Seq[String], joinType: String): DataFrame
84.  def join(right: Dataset[_], usingColumns: Array[String], joinType: String): DataFrame
85.  def join(right: Dataset[_], usingColumn: String, joinType: String): DataFrame
86.  def join(right: Dataset[_], usingColumns: Seq[String]): DataFrame
87.  def join(right: Dataset[_], usingColumns: Array[String]): DataFrame
88.  def join(right: Dataset[_], usingColumn: String): DataFrame
89.  def join(right: Dataset[_]): DataFrame
90.  def joinWith[U](other: Dataset[U], condition: Column): Dataset[(T, U)]
91.  def joinWith[U](other: Dataset[U], condition: Column, joinType: String): Dataset[(T, U)]
92.  def limit(n: Int): Dataset[T]
93.  def localCheckpoint(eager: Boolean): Dataset[T]
94.  def localCheckpoint(): Dataset[T]
95.  def map[U](func: MapFunction[T, U], encoder: Encoder[U]): Dataset[U]
96.  def map[U](func: (T) ⇒ U)(implicit arg0: Encoder[U]): Dataset[U]
97.  def mapPartitions[U](f: MapPartitionsFunction[T, U], encoder: Encoder[U]): Dataset[U]
98.  def mapPartitions[U](func: (Iterator[T]) ⇒ Iterator[U])(implicit arg0: Encoder[U]): Dataset[U]
99.  def na: DataFrameNaFunctions
100.  final def ne(arg0: AnyRef): Boolean
101.  final def notify(): Unit
102.  final def notifyAll(): Unit
103.  def observe(observation: Observation, expr: Column, exprs: Column*): Dataset[T]
104.  def observe(name: String, expr: Column, exprs: Column*): Dataset[T]
105.  def offset(n: Int): Dataset[T]
106.  def orderBy(sortExprs: Column*): Dataset[T]
107.  def orderBy(sortCol: String, sortCols: String*): Dataset[T]
108.  def persist(newLevel: StorageLevel): Dataset.this.type
109.  def persist(): Dataset.this.type
110.  def printSchema(level: Int): Unit
111.  def printSchema(): Unit
112.  val queryExecution: QueryExecution
113.  def randomSplit(weights: Array[Double]): Array[Dataset[T]]
114.  def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]]
115.  def randomSplitAsList(weights: Array[Double], seed: Long): List[Dataset[T]]
116.  lazy val rdd: RDD[T]
117.  def reduce(func: ReduceFunction[T]): T
118.  def reduce(func: (T, T) ⇒ T): T
119.  def repartition(partitionExprs: Column*): Dataset[T]
120.  def repartition(numPartitions: Int, partitionExprs: Column*): Dataset[T]
121.  def repartition(numPartitions: Int): Dataset[T]
122.  def repartitionByRange(partitionExprs: Column*): Dataset[T]
123.  def repartitionByRange(numPartitions: Int, partitionExprs: Column*): Dataset[T]
124.  def rollup(col1: String, cols: String*): RelationalGroupedDataset
125.  def rollup(cols: Column*): RelationalGroupedDataset
126.  def sameSemantics(other: Dataset[T]): Boolean
127.  def sample(withReplacement: Boolean, fraction: Double): Dataset[T]
128.  def sample(withReplacement: Boolean, fraction: Double, seed: Long): Dataset[T]
129.  def sample(fraction: Double): Dataset[T]
130.  def sample(fraction: Double, seed: Long): Dataset[T]
131.  def schema: StructType
132.  def select[U1, U2, U3, U4, U5](c1: TypedColumn[T, U1], c2: TypedColumn[T, U2], c3: TypedColumn[T, U3], c4: TypedColumn[T, U4], c5: TypedColumn[T, U5]): Dataset[(U1, U2, U3, U4, U5)]
133.  def select[U1, U2, U3, U4](c1: TypedColumn[T, U1], c2: TypedColumn[T, U2], c3: TypedColumn[T, U3], c4: TypedColumn[T, U4]): Dataset[(U1, U2, U3, U4)]
134.  def select[U1, U2, U3](c1: TypedColumn[T, U1], c2: TypedColumn[T, U2], c3: TypedColumn[T, U3]): Dataset[(U1, U2, U3)]
135.  def select[U1, U2](c1: TypedColumn[T, U1], c2: TypedColumn[T, U2]): Dataset[(U1, U2)]
136.  def select[U1](c1: TypedColumn[T, U1]): Dataset[U1]
137.  def select(col: String, cols: String*): DataFrame
138.  def select(cols: Column*): DataFrame
139.  def selectExpr(exprs: String*): DataFrame
140.  def selectUntyped(columns: TypedColumn[_, _]*): Dataset[_]
141.  def semanticHash(): Int
142.  def show(numRows: Int, truncate: Int, vertical: Boolean): Unit
143.  def show(numRows: Int, truncate: Int): Unit
144.  def show(numRows: Int, truncate: Boolean): Unit
145.  def show(truncate: Boolean): Unit
146.  def show(): Unit
147.  def show(numRows: Int): Unit
148.  def sort(sortExprs: Column*): Dataset[T]
149.  def sort(sortCol: String, sortCols: String*): Dataset[T]
150.  def sortWithinPartitions(sortExprs: Column*): Dataset[T]
151.  def sortWithinPartitions(sortCol: String, sortCols: String*): Dataset[T]
152.  lazy val sparkSession: SparkSession
153.  lazy val sqlContext: SQLContext
154.  def stat: DataFrameStatFunctions
155.  def storageLevel: StorageLevel
156.  def summary(statistics: String*): DataFrame
157.  final def synchronized[T0](arg0: ⇒ T0): T0
158.  def tail(n: Int): Array[T]
159.  def take(n: Int): Array[T]
160.  def takeAsList(n: Int): List[T]
161.  def toDF(colNames: String*): DataFrame
162.  def toDF(): DataFrame
163.  def toJSON: Dataset[String]
164.  def toJavaRDD: JavaRDD[T]
165.  def toLocalIterator(): Iterator[T]
166.  def toString(): String
167.  def transform[U](t: (Dataset[T]) ⇒ Dataset[U]): Dataset[U]
168.  def union(other: Dataset[T]): Dataset[T]
169.  def unionAll(other: Dataset[T]): Dataset[T]
170.  def unionByName(other: Dataset[T], allowMissingColumns: Boolean): Dataset[T]
171.  def unionByName(other: Dataset[T]): Dataset[T]
172.  def unpersist(): Dataset.this.type
173.  def unpersist(blocking: Boolean): Dataset.this.type
174.  final def wait(): Unit
175.  final def wait(arg0: Long, arg1: Int): Unit
176.  final def wait(arg0: Long): Unit
177.  def where(conditionExpr: String): Dataset[T]
178.  def where(condition: Column): Dataset[T]
179.  def withColumn(colName: String, col: Column): DataFrame
180.  def withColumnRenamed(existingName: String, newName: String): DataFrame
181.  def withColumns(colsMap: Map[String, Column]): DataFrame
182.  def withColumns(colsMap: Map[String, Column]): DataFrame
183.  def withMetadata(columnName: String, metadata: Metadata): DataFrame
184.  def withWatermark(eventTime: String, delayThreshold: String): Dataset[T]
185.  def write: DataFrameWriter[T]
186.  def writeStream: DataStreamWriter[T]
187.  def writeTo(table: String): DataFrameWriterV2[T]

1.  def explode[A, B](inputColumn: String, outputColumn: String)(f: (A) ⇒ TraversableOnce[B])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[B]): DataFrame
2.  def explode[A <: Product](input: Column*)(f: (Row) ⇒ TraversableOnce[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame
3.  def registerTempTable(tableName: String): Unit

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4