Dataset (Spark 3.5.0 JavaDoc)

Object
- org.apache.spark.sql.Dataset<T>

All Implemented Interfaces:

java.io.Serializable
```
public class Dataset<T>
extends Object
implements scala.Serializable
```
A Dataset is a strongly typed collection of domain-specific objects that can be transformed in parallel using functional or relational operations. Each Dataset also has an untyped view called a DataFrame, which is a Dataset of Row.
Operations available on Datasets are divided into transformations and actions. Transformations are the ones that produce new Datasets, and actions are the ones that trigger computation and return results. Example transformations include map, filter, select, and aggregate (groupBy). Example actions count, show, or writing data out to file systems.
Datasets are "lazy", i.e. computations are only triggered when an action is invoked. Internally, a Dataset represents a logical plan that describes the computation required to produce the data. When an action is invoked, Spark's query optimizer optimizes the logical plan and generates a physical plan for efficient execution in a parallel and distributed manner. To explore the logical plan as well as optimized physical plan, use the explain function.
To efficiently support domain-specific objects, an Encoder is required. The encoder maps the domain specific type T to Spark's internal type system. For example, given a class Person with two fields, name (string) and age (int), an encoder is used to tell Spark to generate code at runtime to serialize the Person object into a binary structure. This binary structure often has much lower memory footprint as well as are optimized for efficiency in data processing (e.g. in a columnar format). To understand the internal binary representation for data, use the schema function.
There are typically two ways to create a Dataset. The most common way is by pointing Spark to some files on storage systems, using the read function available on a SparkSession.
```
 val people = spark.read.parquet("...").as[Person] // Scala
 Dataset<Person> people = spark.read().parquet("...").as(Encoders.bean(Person.class)); // Java
 
```
Datasets can also be created through transformations available on existing Datasets. For example, the following creates a new Dataset by applying a filter on the existing one:
```
 val names = people.map(_.name) // in Scala; names is a Dataset[String]
 Dataset<String> names = people.map((Person p) -> p.name, Encoders.STRING));
 
```
Dataset operations can also be untyped, through various domain-specific-language (DSL) functions defined in: Dataset (this class), Column, and functions. These operations are very similar to the operations available in the data frame abstraction in R or Python.
To select a column from the Dataset, use apply method in Scala and col in Java.
```
 val ageCol = people("age") // in Scala
 Column ageCol = people.col("age"); // in Java
 
```
Note that the Column type can also be manipulated through its various functions.
```
 // The following creates a new column that increases everybody's age by 10.
 people("age") + 10 // in Scala
 people.col("age").plus(10); // in Java
 
```
A more concrete example in Scala:
```
 // To create Dataset[Row] using SparkSession
 val people = spark.read.parquet("...")
 val department = spark.read.parquet("...")

 people.filter("age > 30")
 .join(department, people("deptId") === department("id"))
 .groupBy(department("name"), people("gender"))
 .agg(avg(people("salary")), max(people("age")))
 
```
and in Java:
```
 // To create Dataset<Row> using SparkSession
 Dataset<Row> people = spark.read().parquet("...");
 Dataset<Row> department = spark.read().parquet("...");

 people.filter(people.col("age").gt(30))
 .join(department, people.col("deptId").equalTo(department.col("id")))
 .groupBy(department.col("name"), people.col("gender"))
 .agg(avg(people.col("salary")), max(people.col("age")));
 
```
Since:

1.6.0

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`Dataset(SparkSession sparkSession, org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan, Encoder<T> encoder)`
`Dataset(SQLContext sqlContext, org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan, Encoder<T> encoder)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`Dataset<Row>`	`agg(Column expr, Column... exprs)` Aggregates on the entire Dataset without groups.
`Dataset<Row>`	`agg(Column expr, scala.collection.Seq<Column> exprs)` Aggregates on the entire Dataset without groups.
`Dataset<Row>`	`agg(scala.collection.immutable.Map<String,String> exprs)` (Scala-specific) Aggregates on the entire Dataset without groups.
`Dataset<Row>`	`agg(java.util.Map<String,String> exprs)` (Java-specific) Aggregates on the entire Dataset without groups.
`Dataset<Row>`	`agg(scala.Tuple2<String,String> aggExpr, scala.collection.Seq<scala.Tuple2<String,String>> aggExprs)` (Scala-specific) Aggregates on the entire Dataset without groups.
`Dataset<T>`	`alias(String alias)` Returns a new Dataset with an alias set.
`Dataset<T>`	`alias(scala.Symbol alias)` (Scala-specific) Returns a new Dataset with an alias set.
`Column`	`apply(String colName)` Selects column based on the column name and returns it as a `Column`.
`<U> Dataset<U>`	`as(Encoder<U> evidence$2)` Returns a new Dataset where each record has been mapped on to the specified type.
`Dataset<T>`	`as(String alias)` Returns a new Dataset with an alias set.
`Dataset<T>`	`as(scala.Symbol alias)` (Scala-specific) Returns a new Dataset with an alias set.
`Dataset<T>`	`cache()` Persist this Dataset with the default storage level (`MEMORY_AND_DISK`).
`Dataset<T>`	`checkpoint()` Eagerly checkpoint a Dataset and return the new Dataset.
`Dataset<T>`	`checkpoint(boolean eager)` Returns a checkpointed version of this Dataset.
`scala.reflect.ClassTag<T>`	`classTag()`
`Dataset<T>`	`coalesce(int numPartitions)` Returns a new Dataset that has exactly `numPartitions` partitions, when the fewer partitions are requested.
`static String`	`COL_POS_KEY()`
`Column`	`col(String colName)` Selects column based on the column name and returns it as a `Column`.
`Object`	`collect()` Returns an array that contains all rows in this Dataset.
`java.util.List<T>`	`collectAsList()` Returns a Java list that contains all rows in this Dataset.
`Column`	`colRegex(String colName)` Selects column based on the column name specified as a regex and returns it as `Column`.
`String[]`	`columns()` Returns all column names as an array.
`long`	`count()` Returns the number of rows in the Dataset.
`void`	`createGlobalTempView(String viewName)` Creates a global temporary view using the given name.
`void`	`createOrReplaceGlobalTempView(String viewName)` Creates or replaces a global temporary view using the given name.
`void`	`createOrReplaceTempView(String viewName)` Creates a local temporary view using the given name.
`void`	`createTempView(String viewName)` Creates a local temporary view using the given name.
`Dataset<Row>`	`crossJoin(Dataset<?> right)` Explicit cartesian join with another `DataFrame`.
`RelationalGroupedDataset`	`cube(Column... cols)` Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`cube(scala.collection.Seq<Column> cols)` Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`cube(String col1, scala.collection.Seq<String> cols)` Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`cube(String col1, String... cols)` Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them.
`static java.util.concurrent.atomic.AtomicLong`	`curId()`
`static String`	`DATASET_ID_KEY()`
`static org.apache.spark.sql.catalyst.trees.TreeNodeTag<scala.collection.mutable.HashSet<Object>>`	`DATASET_ID_TAG()`
`Dataset<Row>`	`describe(scala.collection.Seq<String> cols)` Computes basic statistics for numeric and string columns, including count, mean, stddev, min, and max.
`Dataset<Row>`	`describe(String... cols)` Computes basic statistics for numeric and string columns, including count, mean, stddev, min, and max.
`Dataset<T>`	`distinct()` Returns a new Dataset that contains only the unique rows from this Dataset.
`Dataset<Row>`	`drop(Column col)` Returns a new Dataset with column dropped.
`Dataset<Row>`	`drop(Column col, Column... cols)` Returns a new Dataset with columns dropped.
`Dataset<Row>`	`drop(Column col, scala.collection.Seq<Column> cols)` Returns a new Dataset with columns dropped.
`Dataset<Row>`	`drop(scala.collection.Seq<String> colNames)` Returns a new Dataset with columns dropped.
`Dataset<Row>`	`drop(String... colNames)` Returns a new Dataset with columns dropped.
`Dataset<Row>`	`drop(String colName)` Returns a new Dataset with a column dropped.
`Dataset<T>`	`dropDuplicates()` Returns a new Dataset that contains only the unique rows from this Dataset.
`Dataset<T>`	`dropDuplicates(scala.collection.Seq<String> colNames)` (Scala-specific) Returns a new Dataset with duplicate rows removed, considering only the subset of columns.
`Dataset<T>`	`dropDuplicates(String[] colNames)` Returns a new Dataset with duplicate rows removed, considering only the subset of columns.
`Dataset<T>`	`dropDuplicates(String col1, scala.collection.Seq<String> cols)` Returns a new `Dataset` with duplicate rows removed, considering only the subset of columns.
`Dataset<T>`	`dropDuplicates(String col1, String... cols)` Returns a new `Dataset` with duplicate rows removed, considering only the subset of columns.
`Dataset<T>`	`dropDuplicatesWithinWatermark()` Returns a new Dataset with duplicates rows removed, within watermark.
`Dataset<T>`	`dropDuplicatesWithinWatermark(scala.collection.Seq<String> colNames)` Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
`Dataset<T>`	`dropDuplicatesWithinWatermark(String[] colNames)` Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
`Dataset<T>`	`dropDuplicatesWithinWatermark(String col1, scala.collection.Seq<String> cols)` Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
`Dataset<T>`	`dropDuplicatesWithinWatermark(String col1, String... cols)` Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
`scala.Tuple2<String,String>[]`	`dtypes()` Returns all column names and their data types as an array.
`Encoder<T>`	`encoder()`
`Dataset<T>`	`except(Dataset<T> other)` Returns a new Dataset containing rows in this Dataset but not in another Dataset.
`Dataset<T>`	`exceptAll(Dataset<T> other)` Returns a new Dataset containing rows in this Dataset but not in another Dataset while preserving the duplicates.
`void`	`explain()` Prints the physical plan to the console for debugging purposes.
`void`	`explain(boolean extended)` Prints the plans (logical and physical) to the console for debugging purposes.
`void`	`explain(String mode)` Prints the plans (logical and physical) with a format specified by a given explain mode.
`<A extends scala.Product> Dataset<Row>`	`explode(scala.collection.Seq<Column> input, scala.Function1<Row,scala.collection.TraversableOnce<A>> f, scala.reflect.api.TypeTags.TypeTag<A> evidence$4)` Deprecated. use flatMap() or select() with functions.explode() instead. Since 2.0.0.
`<A,B> Dataset<Row>`	`explode(String inputColumn, String outputColumn, scala.Function1<A,scala.collection.TraversableOnce<B>> f, scala.reflect.api.TypeTags.TypeTag<B> evidence$5)` Deprecated. use flatMap() or select() with functions.explode() instead. Since 2.0.0.
`Dataset<T>`	`filter(Column condition)` Filters rows using the given condition.
`Dataset<T>`	`filter(FilterFunction<T> func)` (Java-specific) Returns a new Dataset that only contains elements where `func` returns `true`.
`Dataset<T>`	`filter(scala.Function1<T,Object> func)` (Scala-specific) Returns a new Dataset that only contains elements where `func` returns `true`.
`Dataset<T>`	`filter(String conditionExpr)` Filters rows using the given SQL expression.
`T`	`first()` Returns the first row.
`<U> Dataset<U>`	`flatMap(FlatMapFunction<T,U> f, Encoder<U> encoder)` (Java-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.
`<U> Dataset<U>`	`flatMap(scala.Function1<T,scala.collection.TraversableOnce<U>> func, Encoder<U> evidence$8)` (Scala-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.
`void`	`foreach(ForeachFunction<T> func)` (Java-specific) Runs `func` on each element of this Dataset.
`void`	`foreach(scala.Function1<T,scala.runtime.BoxedUnit> f)` Applies a function `f` to all rows.
`void`	`foreachPartition(ForeachPartitionFunction<T> func)` (Java-specific) Runs `func` on each partition of this Dataset.
`void`	`foreachPartition(scala.Function1<scala.collection.Iterator<T>,scala.runtime.BoxedUnit> f)` Applies a function `f` to each partition of this Dataset.
`RelationalGroupedDataset`	`groupBy(Column... cols)` Groups the Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`groupBy(scala.collection.Seq<Column> cols)` Groups the Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`groupBy(String col1, scala.collection.Seq<String> cols)` Groups the Dataset using the specified columns, so that we can run aggregation on them.
`RelationalGroupedDataset`	`groupBy(String col1, String... cols)` Groups the Dataset using the specified columns, so that we can run aggregation on them.
`<K> KeyValueGroupedDataset<K,T>`	`groupByKey(scala.Function1<T,K> func, Encoder<K> evidence$3)` (Scala-specific) Returns a `KeyValueGroupedDataset` where the data is grouped by the given key `func`.
`<K> KeyValueGroupedDataset<K,T>`	`groupByKey(MapFunction<T,K> func, Encoder<K> encoder)` (Java-specific) Returns a `KeyValueGroupedDataset` where the data is grouped by the given key `func`.
`T`	`head()` Returns the first row.
`Object`	`head(int n)` Returns the first `n` rows.
`Dataset<T>`	`hint(String name, Object... parameters)` Specifies some hint on the current Dataset.
`Dataset<T>`	`hint(String name, scala.collection.Seq<Object> parameters)` Specifies some hint on the current Dataset.
`String[]`	`inputFiles()` Returns a best-effort snapshot of the files that compose this Dataset.
`Dataset<T>`	`intersect(Dataset<T> other)` Returns a new Dataset containing rows only in both this Dataset and another Dataset.
`Dataset<T>`	`intersectAll(Dataset<T> other)` Returns a new Dataset containing rows only in both this Dataset and another Dataset while preserving the duplicates.
`boolean`	`isEmpty()` Returns true if the `Dataset` is empty.
`boolean`	`isLocal()` Returns true if the `collect` and `take` methods can be run locally (without any Spark executors).
`boolean`	`isStreaming()` Returns true if this Dataset contains one or more sources that continuously return data as it arrives.
`JavaRDD<T>`	`javaRDD()` Returns the content of the Dataset as a `JavaRDD` of `T`s.
`Dataset<Row>`	`join(Dataset<?> right)` Join with another `DataFrame`.
`Dataset<Row>`	`join(Dataset<?> right, Column joinExprs)` Inner join with another `DataFrame`, using the given join expression.
`Dataset<Row>`	`join(Dataset<?> right, Column joinExprs, String joinType)` Join with another `DataFrame`, using the given join expression.
`Dataset<Row>`	`join(Dataset<?> right, scala.collection.Seq<String> usingColumns)` (Scala-specific) Inner equi-join with another `DataFrame` using the given columns.
`Dataset<Row>`	`join(Dataset<?> right, scala.collection.Seq<String> usingColumns, String joinType)` (Scala-specific) Equi-join with another `DataFrame` using the given columns.
`Dataset<Row>`	`join(Dataset<?> right, String usingColumn)` Inner equi-join with another `DataFrame` using the given column.
`Dataset<Row>`	`join(Dataset<?> right, String[] usingColumns)` (Java-specific) Inner equi-join with another `DataFrame` using the given columns.
`Dataset<Row>`	`join(Dataset<?> right, String[] usingColumns, String joinType)` (Java-specific) Equi-join with another `DataFrame` using the given columns.
`Dataset<Row>`	`join(Dataset<?> right, String usingColumn, String joinType)` Equi-join with another `DataFrame` using the given column.
`<U> Dataset<scala.Tuple2<T,U>>`	`joinWith(Dataset<U> other, Column condition)` Using inner equi-join to join this Dataset returning a `Tuple2` for each pair where `condition` evaluates to true.
`<U> Dataset<scala.Tuple2<T,U>>`	`joinWith(Dataset<U> other, Column condition, String joinType)` Joins this Dataset returning a `Tuple2` for each pair where `condition` evaluates to true.
`Dataset<T>`	`limit(int n)` Returns a new Dataset by taking the first `n` rows.
`Dataset<T>`	`localCheckpoint()` Eagerly locally checkpoints a Dataset and return the new Dataset.
`Dataset<T>`	`localCheckpoint(boolean eager)` Locally checkpoints a Dataset and return the new Dataset.
`<U> Dataset<U>`	`map(scala.Function1<T,U> func, Encoder<U> evidence$6)` (Scala-specific) Returns a new Dataset that contains the result of applying `func` to each element.
`<U> Dataset<U>`	`map(MapFunction<T,U> func, Encoder<U> encoder)` (Java-specific) Returns a new Dataset that contains the result of applying `func` to each element.
`<U> Dataset<U>`	`mapPartitions(scala.Function1<scala.collection.Iterator<T>,scala.collection.Iterator<U>> func, Encoder<U> evidence$7)` (Scala-specific) Returns a new Dataset that contains the result of applying `func` to each partition.
`<U> Dataset<U>`	`mapPartitions(MapPartitionsFunction<T,U> f, Encoder<U> encoder)` (Java-specific) Returns a new Dataset that contains the result of applying `f` to each partition.
`Dataset<Row>`	`melt(Column[] ids, Column[] values, String variableColumnName, String valueColumnName)` Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set.
`Dataset<Row>`	`melt(Column[] ids, String variableColumnName, String valueColumnName)` Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set.
`Column`	`metadataColumn(String colName)` Selects a metadata column based on its logical column name, and returns it as a `Column`.
`DataFrameNaFunctions`	`na()` Returns a `DataFrameNaFunctions` for working with missing data.
`Dataset<T>`	`observe(Observation observation, Column expr, Column... exprs)` Observe (named) metrics through an `org.apache.spark.sql.Observation` instance.
`Dataset<T>`	`observe(Observation observation, Column expr, scala.collection.Seq<Column> exprs)` Observe (named) metrics through an `org.apache.spark.sql.Observation` instance.
`Dataset<T>`	`observe(String name, Column expr, Column... exprs)` Define (named) metrics to observe on the Dataset.
`Dataset<T>`	`observe(String name, Column expr, scala.collection.Seq<Column> exprs)` Define (named) metrics to observe on the Dataset.
`Dataset<T>`	`offset(int n)` Returns a new Dataset by skipping the first `n` rows.
`static Dataset<Row>`	`ofRows(SparkSession sparkSession, org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan)`
`static Dataset<Row>`	`ofRows(SparkSession sparkSession, org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan, org.apache.spark.sql.catalyst.QueryPlanningTracker tracker)` A variant of ofRows that allows passing in a tracker so we can track query parsing time.
`Dataset<T>`	`orderBy(Column... sortExprs)` Returns a new Dataset sorted by the given expressions.
`Dataset<T>`	`orderBy(scala.collection.Seq<Column> sortExprs)` Returns a new Dataset sorted by the given expressions.
`Dataset<T>`	`orderBy(String sortCol, scala.collection.Seq<String> sortCols)` Returns a new Dataset sorted by the given expressions.
`Dataset<T>`	`orderBy(String sortCol, String... sortCols)` Returns a new Dataset sorted by the given expressions.
`Dataset<T>`	`persist()` Persist this Dataset with the default storage level (`MEMORY_AND_DISK`).
`Dataset<T>`	`persist(StorageLevel newLevel)` Persist this Dataset with the given storage level.
`void`	`printSchema()` Prints the schema to the console in a nice tree format.
`void`	`printSchema(int level)` Prints the schema up to the given level to the console in a nice tree format.
`org.apache.spark.sql.execution.QueryExecution`	`queryExecution()`
`Dataset<T>[]`	`randomSplit(double[] weights)` Randomly splits this Dataset with the provided weights.
`Dataset<T>[]`	`randomSplit(double[] weights, long seed)` Randomly splits this Dataset with the provided weights.
`java.util.List<Dataset<T>>`	`randomSplitAsList(double[] weights, long seed)` Returns a Java list that contains randomly split Dataset with the provided weights.
`RDD<T>`	`rdd()`
`T`	`reduce(scala.Function2<T,T,T> func)` (Scala-specific) Reduces the elements of this Dataset using the specified binary function.
`T`	`reduce(ReduceFunction<T> func)` (Java-specific) Reduces the elements of this Dataset using the specified binary function.
`void`	`registerTempTable(String tableName)` Deprecated. Use createOrReplaceTempView(viewName) instead. Since 2.0.0.
`Dataset<T>`	`repartition(Column... partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions, using `spark.sql.shuffle.partitions` as number of partitions.
`Dataset<T>`	`repartition(int numPartitions)` Returns a new Dataset that has exactly `numPartitions` partitions.
`Dataset<T>`	`repartition(int numPartitions, Column... partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions into `numPartitions`.
`Dataset<T>`	`repartition(int numPartitions, scala.collection.Seq<Column> partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions into `numPartitions`.
`Dataset<T>`	`repartition(scala.collection.Seq<Column> partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions, using `spark.sql.shuffle.partitions` as number of partitions.
`Dataset<T>`	`repartitionByRange(Column... partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions, using `spark.sql.shuffle.partitions` as number of partitions.
`Dataset<T>`	`repartitionByRange(int numPartitions, Column... partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions into `numPartitions`.
`Dataset<T>`	`repartitionByRange(int numPartitions, scala.collection.Seq<Column> partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions into `numPartitions`.
`Dataset<T>`	`repartitionByRange(scala.collection.Seq<Column> partitionExprs)` Returns a new Dataset partitioned by the given partitioning expressions, using `spark.sql.shuffle.partitions` as number of partitions.
`RelationalGroupedDataset`	`rollup(Column... cols)` Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`rollup(scala.collection.Seq<Column> cols)` Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`rollup(String col1, scala.collection.Seq<String> cols)` Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.
`RelationalGroupedDataset`	`rollup(String col1, String... cols)` Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them.
`boolean`	`sameSemantics(Dataset<T> other)` Returns `true` when the logical query plans inside both `Dataset`s are equal and therefore return same results.
`Dataset<T>`	`sample(boolean withReplacement, double fraction)` Returns a new `Dataset` by sampling a fraction of rows, using a random seed.
`Dataset<T>`	`sample(boolean withReplacement, double fraction, long seed)` Returns a new `Dataset` by sampling a fraction of rows, using a user-supplied seed.
`Dataset<T>`	`sample(double fraction)` Returns a new `Dataset` by sampling a fraction of rows (without replacement), using a random seed.
`Dataset<T>`	`sample(double fraction, long seed)` Returns a new `Dataset` by sampling a fraction of rows (without replacement), using a user-supplied seed.
`StructType`	`schema()` Returns the schema of this Dataset.
`Dataset<Row>`	`select(Column... cols)` Selects a set of column based expressions.
`Dataset<Row>`	`select(scala.collection.Seq<Column> cols)` Selects a set of column based expressions.
`Dataset<Row>`	`select(String col, scala.collection.Seq<String> cols)` Selects a set of columns.
`Dataset<Row>`	`select(String col, String... cols)` Selects a set of columns.
`<U1> Dataset<U1>`	`select(TypedColumn<T,U1> c1)` Returns a new Dataset by computing the given `Column` expression for each element.
`<U1,U2> Dataset<scala.Tuple2<U1,U2>>`	`select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2)` Returns a new Dataset by computing the given `Column` expressions for each element.
`<U1,U2,U3> Dataset<scala.Tuple3<U1,U2,U3>>`	`select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2, TypedColumn<T,U3> c3)` Returns a new Dataset by computing the given `Column` expressions for each element.
`<U1,U2,U3,U4> Dataset<scala.Tuple4<U1,U2,U3,U4>>`	`select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2, TypedColumn<T,U3> c3, TypedColumn<T,U4> c4)` Returns a new Dataset by computing the given `Column` expressions for each element.
`<U1,U2,U3,U4,U5> Dataset<scala.Tuple5<U1,U2,U3,U4,U5>>`	`select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2, TypedColumn<T,U3> c3, TypedColumn<T,U4> c4, TypedColumn<T,U5> c5)` Returns a new Dataset by computing the given `Column` expressions for each element.
`Dataset<Row>`	`selectExpr(scala.collection.Seq<String> exprs)` Selects a set of SQL expressions.
`Dataset<Row>`	`selectExpr(String... exprs)` Selects a set of SQL expressions.
`int`	`semanticHash()` Returns a `hashCode` of the logical query plan against this `Dataset`.
`void`	`show()` Displays the top 20 rows of Dataset in a tabular form.
`void`	`show(boolean truncate)` Displays the top 20 rows of Dataset in a tabular form.
`void`	`show(int numRows)` Displays the Dataset in a tabular form.
`void`	`show(int numRows, boolean truncate)` Displays the Dataset in a tabular form.
`void`	`show(int numRows, int truncate)` Displays the Dataset in a tabular form.
`void`	`show(int numRows, int truncate, boolean vertical)` Displays the Dataset in a tabular form.
`Dataset<T>`	`sort(Column... sortExprs)` Returns a new Dataset sorted by the given expressions.
`Dataset<T>`	`sort(scala.collection.Seq<Column> sortExprs)` Returns a new Dataset sorted by the given expressions.
`Dataset<T>`	`sort(String sortCol, scala.collection.Seq<String> sortCols)` Returns a new Dataset sorted by the specified column, all in ascending order.
`Dataset<T>`	`sort(String sortCol, String... sortCols)` Returns a new Dataset sorted by the specified column, all in ascending order.
`Dataset<T>`	`sortWithinPartitions(Column... sortExprs)` Returns a new Dataset with each partition sorted by the given expressions.
`Dataset<T>`	`sortWithinPartitions(scala.collection.Seq<Column> sortExprs)` Returns a new Dataset with each partition sorted by the given expressions.
`Dataset<T>`	`sortWithinPartitions(String sortCol, scala.collection.Seq<String> sortCols)` Returns a new Dataset with each partition sorted by the given expressions.
`Dataset<T>`	`sortWithinPartitions(String sortCol, String... sortCols)` Returns a new Dataset with each partition sorted by the given expressions.
`SparkSession`	`sparkSession()`
`SQLContext`	`sqlContext()`
`DataFrameStatFunctions`	`stat()` Returns a `DataFrameStatFunctions` for working statistic functions support.
`StorageLevel`	`storageLevel()` Get the Dataset's current storage level, or StorageLevel.NONE if not persisted.
`Dataset<Row>`	`summary(scala.collection.Seq<String> statistics)` Computes specified statistics for numeric and string columns.
`Dataset<Row>`	`summary(String... statistics)` Computes specified statistics for numeric and string columns.
`Object`	`tail(int n)` Returns the last `n` rows in the Dataset.
`Object`	`take(int n)` Returns the first `n` rows in the Dataset.
`java.util.List<T>`	`takeAsList(int n)` Returns the first `n` rows in the Dataset as a list.
`Dataset<Row>`	`to(StructType schema)` Returns a new DataFrame where each row is reconciled to match the specified schema.
`Dataset<Row>`	`toDF()` Converts this strongly typed collection of data to generic Dataframe.
`Dataset<Row>`	`toDF(scala.collection.Seq<String> colNames)` Converts this strongly typed collection of data to generic `DataFrame` with columns renamed.
`Dataset<Row>`	`toDF(String... colNames)` Converts this strongly typed collection of data to generic `DataFrame` with columns renamed.
`JavaRDD<T>`	`toJavaRDD()` Returns the content of the Dataset as a `JavaRDD` of `T`s.
`Dataset<String>`	`toJSON()` Returns the content of the Dataset as a Dataset of JSON strings.
`java.util.Iterator<T>`	`toLocalIterator()` Returns an iterator that contains all rows in this Dataset.
`String`	`toString()`
`<U> Dataset<U>`	`transform(scala.Function1<Dataset<T>,Dataset<U>> t)` Concise syntax for chaining custom transformations.
`Dataset<T>`	`union(Dataset<T> other)` Returns a new Dataset containing union of rows in this Dataset and another Dataset.
`Dataset<T>`	`unionAll(Dataset<T> other)` Returns a new Dataset containing union of rows in this Dataset and another Dataset.
`Dataset<T>`	`unionByName(Dataset<T> other)` Returns a new Dataset containing union of rows in this Dataset and another Dataset.
`Dataset<T>`	`unionByName(Dataset<T> other, boolean allowMissingColumns)` Returns a new Dataset containing union of rows in this Dataset and another Dataset.
`Dataset<T>`	`unpersist()` Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.
`Dataset<T>`	`unpersist(boolean blocking)` Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.
`Dataset<Row>`	`unpivot(Column[] ids, Column[] values, String variableColumnName, String valueColumnName)` Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set.
`Dataset<Row>`	`unpivot(Column[] ids, String variableColumnName, String valueColumnName)` Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set.
`Dataset<T>`	`where(Column condition)` Filters rows using the given condition.
`Dataset<T>`	`where(String conditionExpr)` Filters rows using the given SQL expression.
`Dataset<Row>`	`withColumn(String colName, Column col)` Returns a new Dataset by adding a column or replacing the existing column that has the same name.
`Dataset<Row>`	`withColumnRenamed(String existingName, String newName)` Returns a new Dataset with a column renamed.
`Dataset<Row>`	`withColumns(scala.collection.immutable.Map<String,Column> colsMap)` (Scala-specific) Returns a new Dataset by adding columns or replacing the existing columns that has the same names.
`Dataset<Row>`	`withColumns(java.util.Map<String,Column> colsMap)` (Java-specific) Returns a new Dataset by adding columns or replacing the existing columns that has the same names.
`Dataset<Row>`	`withColumnsRenamed(scala.collection.immutable.Map<String,String> colsMap)` (Scala-specific) Returns a new Dataset with a columns renamed.
`Dataset<Row>`	`withColumnsRenamed(java.util.Map<String,String> colsMap)` (Java-specific) Returns a new Dataset with a columns renamed.
`Dataset<Row>`	`withMetadata(String columnName, Metadata metadata)` Returns a new Dataset by updating an existing column with metadata.
`Dataset<T>`	`withWatermark(String eventTime, String delayThreshold)` Defines an event time watermark for this `Dataset`.
`DataFrameWriter<T>`	`write()` Interface for saving the content of the non-streaming Dataset out into external storage.
`DataStreamWriter<T>`	`writeStream()` Interface for saving the content of the streaming Dataset out into external storage.
`DataFrameWriterV2<T>`	`writeTo(String table)` Create a write configuration builder for v2 sources.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
 - Dataset
```
public Dataset(SparkSession sparkSession,
 org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan,
 Encoder<T> encoder)
```
 - Dataset
```
public Dataset(SQLContext sqlContext,
 org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan,
 Encoder<T> encoder)
```
- Method Detail
 - curId
```
public static java.util.concurrent.atomic.AtomicLong curId()
```
 - DATASET_ID_KEY
```
public static String DATASET_ID_KEY()
```
 - COL_POS_KEY
```
public static String COL_POS_KEY()
```
 - DATASET_ID_TAG
```
public static org.apache.spark.sql.catalyst.trees.TreeNodeTag<scala.collection.mutable.HashSet<Object>> DATASET_ID_TAG()
```
 - ofRows
```
public static Dataset<Row> ofRows(SparkSession sparkSession,
 org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan)
```
 - ofRows
```
public static Dataset<Row> ofRows(SparkSession sparkSession,
 org.apache.spark.sql.catalyst.plans.logical.LogicalPlan logicalPlan,
 org.apache.spark.sql.catalyst.QueryPlanningTracker tracker)
```
 A variant of ofRows that allows passing in a tracker so we can track query parsing time.
 - toDF
```
public Dataset<Row> toDF(String... colNames)
```
 Converts this strongly typed collection of data to generic DataFrame with columns renamed. This can be quite convenient in conversion from an RDD of tuples into a DataFrame with meaningful names. For example:
```
 val rdd: RDD[(Int, String)] = ...
 rdd.toDF() // this implicit conversion creates a DataFrame with column name `_1` and `_2`
 rdd.toDF("id", "name") // this creates a DataFrame with column name "id" and "name"
 
```
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sortWithinPartitions
```
public Dataset<T> sortWithinPartitions(String sortCol,
 String... sortCols)
```
 Returns a new Dataset with each partition sorted by the given expressions.
 This is the same operation as "SORT BY" in SQL (Hive QL).
 
 Parameters:
 
 sortCol - (undocumented)
 
 sortCols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sortWithinPartitions
```
public Dataset<T> sortWithinPartitions(Column... sortExprs)
```
 Returns a new Dataset with each partition sorted by the given expressions.
 This is the same operation as "SORT BY" in SQL (Hive QL).
 
 Parameters:
 
 sortExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sort
```
public Dataset<T> sort(String sortCol,
 String... sortCols)
```
 Returns a new Dataset sorted by the specified column, all in ascending order.
```
 // The following 3 are equivalent
 ds.sort("sortcol")
 ds.sort($"sortcol")
 ds.sort($"sortcol".asc)
 
```
 Parameters:
 
 sortCol - (undocumented)
 
 sortCols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sort
```
public Dataset<T> sort(Column... sortExprs)
```
 Returns a new Dataset sorted by the given expressions. For example:
```
 ds.sort($"col1", $"col2".desc)
 
```
 Parameters:
 
 sortExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - orderBy
```
public Dataset<T> orderBy(String sortCol,
 String... sortCols)
```
 Returns a new Dataset sorted by the given expressions. This is an alias of the sort function.
 
 Parameters:
 
 sortCol - (undocumented)
 
 sortCols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - orderBy
```
public Dataset<T> orderBy(Column... sortExprs)
```
 Returns a new Dataset sorted by the given expressions. This is an alias of the sort function.
 
 Parameters:
 
 sortExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - hint
```
public Dataset<T> hint(String name,
 Object... parameters)
```
 Specifies some hint on the current Dataset. As an example, the following code specifies that one of the plan can be broadcasted:
```
 df1.join(df2.hint("broadcast"))
 
```
 Parameters:
 
 name - (undocumented)
 
 parameters - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.2.0
 - select
```
public Dataset<Row> select(Column... cols)
```
 Selects a set of column based expressions.
```
 ds.select($"colA", $"colB" + 1)
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - select
```
public Dataset<Row> select(String col,
 String... cols)
```
 Selects a set of columns. This is a variant of select that can only select existing columns using column names (i.e. cannot construct expressions).
```
 // The following two are equivalent:
 ds.select("colA", "colB")
 ds.select($"colA", $"colB")
 
```
 Parameters:
 
 col - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - selectExpr
```
public Dataset<Row> selectExpr(String... exprs)
```
 Selects a set of SQL expressions. This is a variant of select that accepts SQL expressions.
```
 // The following are equivalent:
 ds.selectExpr("colA", "colB as newName", "abs(colC)")
 ds.select(expr("colA"), expr("colB as newName"), expr("abs(colC)"))
 
```
 Parameters:
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - groupBy
```
public RelationalGroupedDataset groupBy(Column... cols)
```
 Groups the Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
```
 // Compute the average for all numeric columns grouped by department.
 ds.groupBy($"department").avg()

 // Compute the max age and average salary, grouped by department and gender.
 ds.groupBy($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - rollup
```
public RelationalGroupedDataset rollup(Column... cols)
```
 Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
```
 // Compute the average for all numeric columns rolled up by department and group.
 ds.rollup($"department", $"group").avg()

 // Compute the max age and average salary, rolled up by department and gender.
 ds.rollup($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - cube
```
public RelationalGroupedDataset cube(Column... cols)
```
 Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
```
 // Compute the average for all numeric columns cubed by department and group.
 ds.cube($"department", $"group").avg()

 // Compute the max age and average salary, cubed by department and gender.
 ds.cube($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - groupBy
```
public RelationalGroupedDataset groupBy(String col1,
 String... cols)
```
 Groups the Dataset using the specified columns, so that we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
 This is a variant of groupBy that can only group by existing columns using column names (i.e. cannot construct expressions).
```
 // Compute the average for all numeric columns grouped by department.
 ds.groupBy("department").avg()

 // Compute the max age and average salary, grouped by department and gender.
 ds.groupBy($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - rollup
```
public RelationalGroupedDataset rollup(String col1,
 String... cols)
```
 Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
 This is a variant of rollup that can only group by existing columns using column names (i.e. cannot construct expressions).
```
 // Compute the average for all numeric columns rolled up by department and group.
 ds.rollup("department", "group").avg()

 // Compute the max age and average salary, rolled up by department and gender.
 ds.rollup($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - cube
```
public RelationalGroupedDataset cube(String col1,
 String... cols)
```
 Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
 This is a variant of cube that can only group by existing columns using column names (i.e. cannot construct expressions).
```
 // Compute the average for all numeric columns cubed by department and group.
 ds.cube("department", "group").avg()

 // Compute the max age and average salary, cubed by department and gender.
 ds.cube($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - agg
```
public Dataset<Row> agg(Column expr,
 Column... exprs)
```
 Aggregates on the entire Dataset without groups.
```
 // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
 ds.agg(max($"age"), avg($"salary"))
 ds.groupBy().agg(max($"age"), avg($"salary"))
 
```
 Parameters:
 
 expr - (undocumented)
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - observe
```
public Dataset<T> observe(String name,
 Column expr,
 Column... exprs)
```
 Define (named) metrics to observe on the Dataset. This method returns an 'observed' Dataset that returns the same result as the input, with the following guarantees:
 - It will compute the defined aggregates (metrics) on all the data that is flowing through the Dataset at that point.
 - It will report the value of the defined aggregate columns as soon as we reach a completion point. A completion point is either the end of a query (batch mode) or the end of a streaming epoch. The value of the aggregates only reflects the data processed since the previous completion point.
 Please note that continuous execution is currently not supported.
 The metrics columns must either contain a literal (e.g. lit(42)), or should contain one or more aggregate functions (e.g. sum(a) or sum(a + b) + avg(c) - lit(1)). Expressions that contain references to the input Dataset's columns must always be wrapped in an aggregate function.
 A user can observe these metrics by either adding StreamingQueryListener or a QueryExecutionListener to the spark session.
```
 // Monitor the metrics using a listener.
 spark.streams.addListener(new StreamingQueryListener() {
 override def onQueryStarted(event: QueryStartedEvent): Unit = {}
 override def onQueryProgress(event: QueryProgressEvent): Unit = {
 event.progress.observedMetrics.asScala.get("my_event").foreach { row =>
 // Trigger if the number of errors exceeds 5 percent
 val num_rows = row.getAs[Long]("rc")
 val num_error_rows = row.getAs[Long]("erc")
 val ratio = num_error_rows.toDouble / num_rows
 if (ratio > 0.05) {
 // Trigger alert
 }
 }
 }
 override def onQueryTerminated(event: QueryTerminatedEvent): Unit = {}
 })
 // Observe row count (rc) and error row count (erc) in the streaming Dataset
 val observed_ds = ds.observe("my_event", count(lit(1)).as("rc"), count($"error").as("erc"))
 observed_ds.writeStream.format("...").start()
 
```
 Parameters:
 
 name - (undocumented)
 
 expr - (undocumented)
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.0.0
 - observe
```
public Dataset<T> observe(Observation observation,
 Column expr,
 Column... exprs)
```
 Observe (named) metrics through an org.apache.spark.sql.Observation instance. This is equivalent to calling observe(String, Column, Column*) but does not require adding org.apache.spark.sql.util.QueryExecutionListener to the spark session. This method does not support streaming datasets.
 A user can retrieve the metrics by accessing org.apache.spark.sql.Observation.get.
```
 // Observe row count (rows) and highest id (maxid) in the Dataset while writing it
 val observation = Observation("my_metrics")
 val observed_ds = ds.observe(observation, count(lit(1)).as("rows"), max($"id").as("maxid"))
 observed_ds.write.parquet("ds.parquet")
 val metrics = observation.get
 
```
 Parameters:
 
 observation - (undocumented)
 
 expr - (undocumented)
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Throws:
 
 IllegalArgumentException - If this is a streaming Dataset (this.isStreaming == true)
 
 Since:
 
 3.3.0
 - drop
```
public Dataset<Row> drop(String... colNames)
```
 Returns a new Dataset with columns dropped. This is a no-op if schema doesn't contain column name(s).
 This method can only be used to drop top level columns. the colName string is treated literally without further interpretation.
 
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - drop
```
public Dataset<Row> drop(Column col,
 Column... cols)
```
 Returns a new Dataset with columns dropped.
 This method can only be used to drop top level columns. This is a no-op if the Dataset doesn't have a columns with an equivalent expression.
 
 Parameters:
 
 col - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - dropDuplicates
```
public Dataset<T> dropDuplicates(String col1,
 String... cols)
```
 Returns a new Dataset with duplicate rows removed, considering only the subset of columns.
 For a static batch Dataset, it just drops duplicate rows. For a streaming Dataset, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark to limit how late the duplicate data can be and system will accordingly limit the state. In addition, too late data older than watermark will be dropped to avoid any possibility of duplicates.
 
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - dropDuplicatesWithinWatermark
```
public Dataset<T> dropDuplicatesWithinWatermark(String col1,
 String... cols)
```
 Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
 This only works with streaming Dataset, and watermark for the input Dataset must be set via withWatermark.
 For a streaming Dataset, this will keep all data across triggers as intermediate state to drop duplicated rows. The state will be kept to guarantee the semantic, "Events are deduplicated as long as the time distance of earliest and latest events are smaller than the delay threshold of watermark." Users are encouraged to set the delay threshold of watermark longer than max timestamp differences among duplicated events.
 Note: too late data older than watermark will be dropped.
 
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.5.0
 - describe
```
public Dataset<Row> describe(String... cols)
```
 Computes basic statistics for numeric and string columns, including count, mean, stddev, min, and max. If no columns are given, this function computes statistics for all numerical or string columns.
 This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting Dataset. If you want to programmatically compute summary statistics, use the agg function instead.
```
 ds.describe("age", "height").show()

 // output:
 // summary age height
 // count 10.0 10.0
 // mean 53.3 178.05
 // stddev 11.6 15.7
 // min 18.0 163.0
 // max 92.0 192.0
 
```
 Use summary for expanded statistics and control over which statistics to compute.
 Parameters:
 
 cols - Columns to compute statistics on.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - summary
```
public Dataset<Row> summary(String... statistics)
```
 Computes specified statistics for numeric and string columns. Available statistics are:
 - count
 - mean
 - stddev
 - min
 - max
 - arbitrary approximate percentiles specified as a percentage (e.g. 75%)
 - count_distinct
 - approx_count_distinct
 If no statistics are given, this function computes count, mean, stddev, min, approximate quartiles (percentiles at 25%, 50%, and 75%), and max.
 This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting Dataset. If you want to programmatically compute summary statistics, use the agg function instead.
```
 ds.summary().show()

 // output:
 // summary age height
 // count 10.0 10.0
 // mean 53.3 178.05
 // stddev 11.6 15.7
 // min 18.0 163.0
 // 25% 24.0 176.0
 // 50% 24.0 176.0
 // 75% 32.0 180.0
 // max 92.0 192.0
 
```
```
 ds.summary("count", "min", "25%", "75%", "max").show()

 // output:
 // summary age height
 // count 10.0 10.0
 // min 18.0 163.0
 // 25% 24.0 176.0
 // 75% 32.0 180.0
 // max 92.0 192.0
 
```
 To do a summary for specific columns first select them:
```
 ds.select("age", "height").summary().show()
 
```
 Specify statistics to output custom summaries:
```
 ds.summary("count", "count_distinct").show()
 
```
 The distinct count isn't included by default.
 You can also run approximate distinct counts which are faster:
```
 ds.summary("count", "approx_count_distinct").show()
 
```
 See also describe for basic statistics.
 Parameters:
 
 statistics - Statistics from above list to be computed.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - repartition
```
public Dataset<T> repartition(int numPartitions,
 Column... partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions into numPartitions. The resulting Dataset is hash partitioned.
 This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
 
 Parameters:
 
 numPartitions - (undocumented)
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - repartition
```
public Dataset<T> repartition(Column... partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions, using spark.sql.shuffle.partitions as number of partitions. The resulting Dataset is hash partitioned.
 This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
 
 Parameters:
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - repartitionByRange
```
public Dataset<T> repartitionByRange(int numPartitions,
 Column... partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions into numPartitions. The resulting Dataset is range partitioned.
 At least one partition-by expression must be specified. When no explicit sort order is specified, "ascending nulls first" is assumed. Note, the rows are not sorted in each partition of the resulting Dataset.
 Note that due to performance reasons this method uses sampling to estimate the ranges. Hence, the output may not be consistent, since sampling can return different values. The sample size can be controlled by the config spark.sql.execution.rangeExchange.sampleSizePerPartition.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - repartitionByRange
```
public Dataset<T> repartitionByRange(Column... partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions, using spark.sql.shuffle.partitions as number of partitions. The resulting Dataset is range partitioned.
 At least one partition-by expression must be specified. When no explicit sort order is specified, "ascending nulls first" is assumed. Note, the rows are not sorted in each partition of the resulting Dataset.
 Note that due to performance reasons this method uses sampling to estimate the ranges. Hence, the output may not be consistent, since sampling can return different values. The sample size can be controlled by the config spark.sql.execution.rangeExchange.sampleSizePerPartition.
 
 Parameters:
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - queryExecution
```
public org.apache.spark.sql.execution.QueryExecution queryExecution()
```
 - encoder
```
public Encoder<T> encoder()
```
 - sparkSession
```
public SparkSession sparkSession()
```
 - classTag
```
public scala.reflect.ClassTag<T> classTag()
```
 - sqlContext
```
public SQLContext sqlContext()
```
 - toString
```
public String toString()
```
 Overrides:
 
 toString in class Object
 - toDF
```
public Dataset<Row> toDF()
```
 Converts this strongly typed collection of data to generic Dataframe. In contrast to the strongly typed objects that Dataset operations work on, a Dataframe returns generic Row objects that allow fields to be accessed by ordinal or name.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - as
```
public  Dataset as(Encoder evidence$2)
```
 Returns a new Dataset where each record has been mapped on to the specified type. The method used to map columns depend on the type of U:
 - When U is a class, fields for the class will be mapped to columns of the same name (case sensitivity is determined by spark.sql.caseSensitive).
 - When U is a tuple, the columns will be mapped by ordinal (i.e. the first column will be assigned to _1).
 - When U is a primitive type (i.e. String, Int, etc), then the first column of the DataFrame will be used.
 If the schema of the Dataset does not match the desired U type, you can use select along with alias or as to rearrange or rename as required.
 Note that as[] only changes the view of the data that is passed into typed operations, such as map(), and does not eagerly project away any columns that are not present in the specified class.
 Parameters:
 
 evidence$2 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - to
```
public Dataset<Row> to(StructType schema)
```
 Returns a new DataFrame where each row is reconciled to match the specified schema. Spark will:
 - Reorder columns and/or inner fields by name to match the specified schema.
 - Project away columns and/or inner fields that are not needed by the specified schema. Missing columns and/or inner fields (present in the specified schema but not input DataFrame) lead to failures.
 - Cast the columns and/or inner fields to match the data types in the specified schema, if the types are compatible, e.g., numeric to numeric (error if overflows), but not string to int.
 - Carry over the metadata from the specified schema, while the columns and/or inner fields still keep their own metadata if not overwritten by the specified schema.
 - Fail if the nullability is not compatible. For example, the column and/or inner field is nullable but the specified schema requires them to be not nullable.
 Parameters:
 
 schema - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - toDF
```
public Dataset<Row> toDF(scala.collection.Seq<String> colNames)
```
 Converts this strongly typed collection of data to generic DataFrame with columns renamed. This can be quite convenient in conversion from an RDD of tuples into a DataFrame with meaningful names. For example:
```
 val rdd: RDD[(Int, String)] = ...
 rdd.toDF() // this implicit conversion creates a DataFrame with column name `_1` and `_2`
 rdd.toDF("id", "name") // this creates a DataFrame with column name "id" and "name"
 
```
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - schema
```
public StructType schema()
```
 Returns the schema of this Dataset.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - printSchema
```
public void printSchema()
```
 Prints the schema to the console in a nice tree format.
 
 Since:
 
 1.6.0
 - printSchema
```
public void printSchema(int level)
```
 Prints the schema up to the given level to the console in a nice tree format.
 
 Parameters:
 
 level - (undocumented)
 
 Since:
 
 3.0.0
 - explain
```
public void explain(String mode)
```
 Prints the plans (logical and physical) with a format specified by a given explain mode.
 Parameters:
 mode - specifies the expected output format of plans.
 
 simple Print only a physical plan.
 
 extended: Print both logical and physical plans.
 
 codegen: Print a physical plan and generated codes if they are available.
 
 cost: Print a logical plan and statistics if they are available.
 
 formatted: Split explain output into two sections: a physical plan outline and node details.
 Since:
 
 3.0.0
 - explain
```
public void explain(boolean extended)
```
 Prints the plans (logical and physical) to the console for debugging purposes.
 
 Parameters:
 
 extended - default false. If false, prints only the physical plan.
 
 Since:
 
 1.6.0
 - explain
```
public void explain()
```
 Prints the physical plan to the console for debugging purposes.
 
 Since:
 
 1.6.0
 - dtypes
```
public scala.Tuple2<String,String>[] dtypes()
```
 Returns all column names and their data types as an array.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - columns
```
public String[] columns()
```
 Returns all column names as an array.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - isLocal
```
public boolean isLocal()
```
 Returns true if the collect and take methods can be run locally (without any Spark executors).
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - isEmpty
```
public boolean isEmpty()
```
 Returns true if the Dataset is empty.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.4.0
 - isStreaming
```
public boolean isStreaming()
```
 Returns true if this Dataset contains one or more sources that continuously return data as it arrives. A Dataset that reads data from a streaming source must be executed as a StreamingQuery using the start() method in DataStreamWriter. Methods that return a single answer, e.g. count() or collect(), will throw an AnalysisException when there is a streaming source present.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - checkpoint
```
public Dataset<T> checkpoint()
```
 Eagerly checkpoint a Dataset and return the new Dataset. Checkpointing can be used to truncate the logical plan of this Dataset, which is especially useful in iterative algorithms where the plan may grow exponentially. It will be saved to files inside the checkpoint directory set with SparkContext#setCheckpointDir.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.1.0
 - checkpoint
```
public Dataset<T> checkpoint(boolean eager)
```
 Returns a checkpointed version of this Dataset. Checkpointing can be used to truncate the logical plan of this Dataset, which is especially useful in iterative algorithms where the plan may grow exponentially. It will be saved to files inside the checkpoint directory set with SparkContext#setCheckpointDir.
 
 Parameters:
 
 eager - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.1.0
 - localCheckpoint
```
public Dataset<T> localCheckpoint()
```
 Eagerly locally checkpoints a Dataset and return the new Dataset. Checkpointing can be used to truncate the logical plan of this Dataset, which is especially useful in iterative algorithms where the plan may grow exponentially. Local checkpoints are written to executor storage and despite potentially faster they are unreliable and may compromise job completion.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - localCheckpoint
```
public Dataset<T> localCheckpoint(boolean eager)
```
 Locally checkpoints a Dataset and return the new Dataset. Checkpointing can be used to truncate the logical plan of this Dataset, which is especially useful in iterative algorithms where the plan may grow exponentially. Local checkpoints are written to executor storage and despite potentially faster they are unreliable and may compromise job completion.
 
 Parameters:
 
 eager - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - withWatermark
```
public Dataset<T> withWatermark(String eventTime,
 String delayThreshold)
```
 Defines an event time watermark for this Dataset. A watermark tracks a point in time before which we assume no more late data is going to arrive.
 Spark will use this watermark for several purposes:
 - To know when a given time window aggregation can be finalized and thus can be emitted when using output modes that do not allow updates.
 - To minimize the amount of state that we need to keep for on-going aggregations, mapGroupsWithState and dropDuplicates operators.
 The current watermark is computed by looking at the MAX(eventTime) seen across all of the partitions in the query minus a user specified delayThreshold. Due to the cost of coordinating this value across partitions, the actual watermark used is only guaranteed to be at least delayThreshold behind the actual event time. In some cases we may still process records that arrive more than delayThreshold late.
 Parameters:
 
 eventTime - the name of the column that contains the event time of the row.
 
 delayThreshold - the minimum delay to wait to data to arrive late, relative to the latest record that has been processed in the form of an interval (e.g. "1 minute" or "5 hours"). NOTE: This should not be negative.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.1.0
 - show
```
public void show(int numRows)
```
 Displays the Dataset in a tabular form. Strings more than 20 characters will be truncated, and all cells will be aligned right. For example:
```
 year month AVG('Adj Close) MAX('Adj Close)
 1980 12 0.503218 0.595103
 1981 01 0.523289 0.570307
 1982 02 0.436504 0.475256
 1983 03 0.410516 0.442194
 1984 04 0.450090 0.483521
 
```
 Parameters:
 
 numRows - Number of rows to show
 
 Since:
 
 1.6.0
 - show
```
public void show()
```
 Displays the top 20 rows of Dataset in a tabular form. Strings more than 20 characters will be truncated, and all cells will be aligned right.
 
 Since:
 
 1.6.0
 - show
```
public void show(boolean truncate)
```
 Displays the top 20 rows of Dataset in a tabular form.
 
 Parameters:
 
 truncate - Whether truncate long strings. If true, strings more than 20 characters will be truncated and all cells will be aligned right
 
 Since:
 
 1.6.0
 - show
```
public void show(int numRows,
 boolean truncate)
```
 Displays the Dataset in a tabular form. For example:
```
 year month AVG('Adj Close) MAX('Adj Close)
 1980 12 0.503218 0.595103
 1981 01 0.523289 0.570307
 1982 02 0.436504 0.475256
 1983 03 0.410516 0.442194
 1984 04 0.450090 0.483521
 
```
 Parameters:
 
 numRows - Number of rows to show
 
 truncate - Whether truncate long strings. If true, strings more than 20 characters will be truncated and all cells will be aligned right
 
 Since:
 
 1.6.0
 - show
```
public void show(int numRows,
 int truncate)
```
 Displays the Dataset in a tabular form. For example:
```
 year month AVG('Adj Close) MAX('Adj Close)
 1980 12 0.503218 0.595103
 1981 01 0.523289 0.570307
 1982 02 0.436504 0.475256
 1983 03 0.410516 0.442194
 1984 04 0.450090 0.483521
 
```
 Parameters:
 
 numRows - Number of rows to show
 
 truncate - If set to more than 0, truncates strings to truncate characters and all cells will be aligned right.
 
 Since:
 
 1.6.0
 - show
```
public void show(int numRows,
 int truncate,
 boolean vertical)
```
 Displays the Dataset in a tabular form. For example:
```
 year month AVG('Adj Close) MAX('Adj Close)
 1980 12 0.503218 0.595103
 1981 01 0.523289 0.570307
 1982 02 0.436504 0.475256
 1983 03 0.410516 0.442194
 1984 04 0.450090 0.483521
 
```
 If vertical enabled, this command prints output rows vertically (one line per column value)?
```
 -RECORD 0-------------------
 year | 1980
 month | 12
 AVG('Adj Close) | 0.503218
 AVG('Adj Close) | 0.595103
 -RECORD 1-------------------
 year | 1981
 month | 01
 AVG('Adj Close) | 0.523289
 AVG('Adj Close) | 0.570307
 -RECORD 2-------------------
 year | 1982
 month | 02
 AVG('Adj Close) | 0.436504
 AVG('Adj Close) | 0.475256
 -RECORD 3-------------------
 year | 1983
 month | 03
 AVG('Adj Close) | 0.410516
 AVG('Adj Close) | 0.442194
 -RECORD 4-------------------
 year | 1984
 month | 04
 AVG('Adj Close) | 0.450090
 AVG('Adj Close) | 0.483521
 
```
 Parameters:
 
 numRows - Number of rows to show
 
 truncate - If set to more than 0, truncates strings to truncate characters and all cells will be aligned right.
 
 vertical - If set to true, prints output rows vertically (one line per column value).
 
 Since:
 
 2.3.0
 - na
```
public DataFrameNaFunctions na()
```
 Returns a DataFrameNaFunctions for working with missing data.
```
 // Dropping rows containing any null values.
 ds.na.drop()
 
```
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - stat
```
public DataFrameStatFunctions stat()
```
 Returns a DataFrameStatFunctions for working statistic functions support.
```
 // Finding frequent items in column with name 'a'.
 ds.stat.freqItems(Seq("a"))
 
```
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - join
```
public Dataset<Row> join(Dataset<?> right)
```
 Join with another DataFrame.
 Behaves as an INNER JOIN and requires a subsequent join predicate.
 
 Parameters:
 
 right - Right side of the join operation.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - join
```
public Dataset<Row> join(Dataset<?> right,
 String usingColumn)
```
 Inner equi-join with another DataFrame using the given column.
 Different from other join functions, the join column will only appear once in the output, i.e. similar to SQL's JOIN USING syntax.
```
 // Joining df1 and df2 using the column "user_id"
 df1.join(df2, "user_id")
 
```
 Parameters:
 
 right - Right side of the join operation.
 
 usingColumn - Name of the column to join on. This column must exist on both sides.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 If you perform a self-join using this function without aliasing the input DataFrames, you will NOT be able to reference any columns after the join, since there is no way to disambiguate which side of the join you would like to reference.
 - join
```
public Dataset<Row> join(Dataset<?> right,
 String[] usingColumns)
```
 (Java-specific) Inner equi-join with another DataFrame using the given columns. See the Scala-specific overload for more details.
 
 Parameters:
 
 right - Right side of the join operation.
 
 usingColumns - Names of the columns to join on. This columns must exist on both sides.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - join
```
public Dataset<Row> join(Dataset<?> right,
 scala.collection.Seq<String> usingColumns)
```
 (Scala-specific) Inner equi-join with another DataFrame using the given columns.
 Different from other join functions, the join columns will only appear once in the output, i.e. similar to SQL's JOIN USING syntax.
```
 // Joining df1 and df2 using the columns "user_id" and "user_name"
 df1.join(df2, Seq("user_id", "user_name"))
 
```
 Parameters:
 
 right - Right side of the join operation.
 
 usingColumns - Names of the columns to join on. This columns must exist on both sides.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 If you perform a self-join using this function without aliasing the input DataFrames, you will NOT be able to reference any columns after the join, since there is no way to disambiguate which side of the join you would like to reference.
 - join
```
public Dataset<Row> join(Dataset<?> right,
 String usingColumn,
 String joinType)
```
 Equi-join with another DataFrame using the given column. A cross join with a predicate is specified as an inner join. If you would explicitly like to perform a cross join use the crossJoin method.
 Different from other join functions, the join column will only appear once in the output, i.e. similar to SQL's JOIN USING syntax.
 
 Parameters:
 
 right - Right side of the join operation.
 
 usingColumn - Name of the column to join on. This column must exist on both sides.
 
 joinType - Type of join to perform. Default inner. Must be one of: inner, cross, outer, full, fullouter, full_outer, left, leftouter, left_outer, right, rightouter, right_outer, semi, leftsemi, left_semi, anti, leftanti, left_anti.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 
 Note:
 
 If you perform a self-join using this function without aliasing the input DataFrames, you will NOT be able to reference any columns after the join, since there is no way to disambiguate which side of the join you would like to reference.
 - join
```
public Dataset<Row> join(Dataset<?> right,
 String[] usingColumns,
 String joinType)
```
 (Java-specific) Equi-join with another DataFrame using the given columns. See the Scala-specific overload for more details.
 
 Parameters:
 
 right - Right side of the join operation.
 
 usingColumns - Names of the columns to join on. This columns must exist on both sides.
 
 joinType - Type of join to perform. Default inner. Must be one of: inner, cross, outer, full, fullouter, full_outer, left, leftouter, left_outer, right, rightouter, right_outer, semi, leftsemi, left_semi, anti, leftanti, left_anti.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - join
```
public Dataset<Row> join(Dataset<?> right,
 scala.collection.Seq<String> usingColumns,
 String joinType)
```
 (Scala-specific) Equi-join with another DataFrame using the given columns. A cross join with a predicate is specified as an inner join. If you would explicitly like to perform a cross join use the crossJoin method.
 Different from other join functions, the join columns will only appear once in the output, i.e. similar to SQL's JOIN USING syntax.
 
 Parameters:
 
 right - Right side of the join operation.
 
 usingColumns - Names of the columns to join on. This columns must exist on both sides.
 
 joinType - Type of join to perform. Default inner. Must be one of: inner, cross, outer, full, fullouter, full_outer, left, leftouter, left_outer, right, rightouter, right_outer, semi, leftsemi, left_semi, anti, leftanti, left_anti.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 If you perform a self-join using this function without aliasing the input DataFrames, you will NOT be able to reference any columns after the join, since there is no way to disambiguate which side of the join you would like to reference.
 - join
```
public Dataset<Row> join(Dataset<?> right,
 Column joinExprs)
```
 Inner join with another DataFrame, using the given join expression.
```
 // The following two are equivalent:
 df1.join(df2, $"df1Key" === $"df2Key")
 df1.join(df2).where($"df1Key" === $"df2Key")
 
```
 Parameters:
 
 right - (undocumented)
 
 joinExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - join
```
public Dataset<Row> join(Dataset<?> right,
 Column joinExprs,
 String joinType)
```
 Join with another DataFrame, using the given join expression. The following performs a full outer join between df1 and df2.
```
 // Scala:
 import org.apache.spark.sql.functions._
 df1.join(df2, $"df1Key" === $"df2Key", "outer")

 // Java:
 import static org.apache.spark.sql.functions.*;
 df1.join(df2, col("df1Key").equalTo(col("df2Key")), "outer");
 
```
 Parameters:
 
 right - Right side of the join.
 
 joinExprs - Join expression.
 
 joinType - Type of join to perform. Default inner. Must be one of: inner, cross, outer, full, fullouter, full_outer, left, leftouter, left_outer, right, rightouter, right_outer, semi, leftsemi, left_semi, anti, leftanti, left_anti.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - crossJoin
```
public Dataset<Row> crossJoin(Dataset<?> right)
```
 Explicit cartesian join with another DataFrame.
 
 Parameters:
 
 right - Right side of the join operation.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.1.0
 
 Note:
 
 Cartesian joins are very expensive without an extra filter that can be pushed down.
 - joinWith
```
public  Dataset<scala.Tuple2<T,U>> joinWith(Dataset other,
 Column condition,
 String joinType)
```
 Joins this Dataset returning a Tuple2 for each pair where condition evaluates to true.
 This is similar to the relation join function with one important difference in the result schema. Since joinWith preserves objects present on either side of the join, the result schema is similarly nested into a tuple under the column names _1 and _2.
 This type of join can be useful both for preserving type-safety with the original object types as well as working with relational data where either side of the join has column names in common.
 
 Parameters:
 
 other - Right side of the join.
 
 condition - Join expression.
 
 joinType - Type of join to perform. Default inner. Must be one of: inner, cross, outer, full, fullouter,full_outer, left, leftouter, left_outer, right, rightouter, right_outer.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - joinWith
```
public  Dataset<scala.Tuple2<T,U>> joinWith(Dataset other,
 Column condition)
```
 Using inner equi-join to join this Dataset returning a Tuple2 for each pair where condition evaluates to true.
 
 Parameters:
 
 other - Right side of the join.
 
 condition - Join expression.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - sortWithinPartitions
```
public Dataset<T> sortWithinPartitions(String sortCol,
 scala.collection.Seq<String> sortCols)
```
 Returns a new Dataset with each partition sorted by the given expressions.
 This is the same operation as "SORT BY" in SQL (Hive QL).
 
 Parameters:
 
 sortCol - (undocumented)
 
 sortCols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sortWithinPartitions
```
public Dataset<T> sortWithinPartitions(scala.collection.Seq<Column> sortExprs)
```
 Returns a new Dataset with each partition sorted by the given expressions.
 This is the same operation as "SORT BY" in SQL (Hive QL).
 
 Parameters:
 
 sortExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sort
```
public Dataset<T> sort(String sortCol,
 scala.collection.Seq<String> sortCols)
```
 Returns a new Dataset sorted by the specified column, all in ascending order.
```
 // The following 3 are equivalent
 ds.sort("sortcol")
 ds.sort($"sortcol")
 ds.sort($"sortcol".asc)
 
```
 Parameters:
 
 sortCol - (undocumented)
 
 sortCols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sort
```
public Dataset<T> sort(scala.collection.Seq<Column> sortExprs)
```
 Returns a new Dataset sorted by the given expressions. For example:
```
 ds.sort($"col1", $"col2".desc)
 
```
 Parameters:
 
 sortExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - orderBy
```
public Dataset<T> orderBy(String sortCol,
 scala.collection.Seq<String> sortCols)
```
 Returns a new Dataset sorted by the given expressions. This is an alias of the sort function.
 
 Parameters:
 
 sortCol - (undocumented)
 
 sortCols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - orderBy
```
public Dataset<T> orderBy(scala.collection.Seq<Column> sortExprs)
```
 Returns a new Dataset sorted by the given expressions. This is an alias of the sort function.
 
 Parameters:
 
 sortExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - apply
```
public Column apply(String colName)
```
 Selects column based on the column name and returns it as a Column.
 
 Parameters:
 
 colName - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 The column name can also reference to a nested column like a.b.
 - hint
```
public Dataset<T> hint(String name,
 scala.collection.Seq<Object> parameters)
```
 Specifies some hint on the current Dataset. As an example, the following code specifies that one of the plan can be broadcasted:
```
 df1.join(df2.hint("broadcast"))
 
```
 Parameters:
 
 name - (undocumented)
 
 parameters - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.2.0
 - col
```
public Column col(String colName)
```
 Selects column based on the column name and returns it as a Column.
 
 Parameters:
 
 colName - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 The column name can also reference to a nested column like a.b.
 - metadataColumn
```
public Column metadataColumn(String colName)
```
 Selects a metadata column based on its logical column name, and returns it as a Column.
 A metadata column can be accessed this way even if the underlying data source defines a data column with a conflicting name.
 
 Parameters:
 
 colName - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.5.0
 - colRegex
```
public Column colRegex(String colName)
```
 Selects column based on the column name specified as a regex and returns it as Column.
 
 Parameters:
 
 colName - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - as
```
public Dataset<T> as(String alias)
```
 Returns a new Dataset with an alias set.
 
 Parameters:
 
 alias - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - as
```
public Dataset<T> as(scala.Symbol alias)
```
 (Scala-specific) Returns a new Dataset with an alias set.
 
 Parameters:
 
 alias - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - alias
```
public Dataset<T> alias(String alias)
```
 Returns a new Dataset with an alias set. Same as as.
 
 Parameters:
 
 alias - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - alias
```
public Dataset<T> alias(scala.Symbol alias)
```
 (Scala-specific) Returns a new Dataset with an alias set. Same as as.
 
 Parameters:
 
 alias - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - select
```
public Dataset<Row> select(scala.collection.Seq<Column> cols)
```
 Selects a set of column based expressions.
```
 ds.select($"colA", $"colB" + 1)
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - select
```
public Dataset<Row> select(String col,
 scala.collection.Seq<String> cols)
```
 Selects a set of columns. This is a variant of select that can only select existing columns using column names (i.e. cannot construct expressions).
```
 // The following two are equivalent:
 ds.select("colA", "colB")
 ds.select($"colA", $"colB")
 
```
 Parameters:
 
 col - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - selectExpr
```
public Dataset<Row> selectExpr(scala.collection.Seq<String> exprs)
```
 Selects a set of SQL expressions. This is a variant of select that accepts SQL expressions.
```
 // The following are equivalent:
 ds.selectExpr("colA", "colB as newName", "abs(colC)")
 ds.select(expr("colA"), expr("colB as newName"), expr("abs(colC)"))
 
```
 Parameters:
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - select
```
public <U1> Dataset<U1> select(TypedColumn<T,U1> c1)
```
 Returns a new Dataset by computing the given Column expression for each element.
```
 val ds = Seq(1, 2, 3).toDS()
 val newDS = ds.select(expr("value + 1").as[Int])
 
```
 Parameters:
 
 c1 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - select
```
public <U1,U2> Dataset<scala.Tuple2<U1,U2>> select(TypedColumn<T,U1> c1,
 TypedColumn<T,U2> c2)
```
 Returns a new Dataset by computing the given Column expressions for each element.
 
 Parameters:
 
 c1 - (undocumented)
 
 c2 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - select
```
public <U1,U2,U3> Dataset<scala.Tuple3<U1,U2,U3>> select(TypedColumn<T,U1> c1,
 TypedColumn<T,U2> c2,
 TypedColumn<T,U3> c3)
```
 Returns a new Dataset by computing the given Column expressions for each element.
 
 Parameters:
 
 c1 - (undocumented)
 
 c2 - (undocumented)
 
 c3 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - select
```
public <U1,U2,U3,U4> Dataset<scala.Tuple4<U1,U2,U3,U4>> select(TypedColumn<T,U1> c1,
 TypedColumn<T,U2> c2,
 TypedColumn<T,U3> c3,
 TypedColumn<T,U4> c4)
```
 Returns a new Dataset by computing the given Column expressions for each element.
 
 Parameters:
 
 c1 - (undocumented)
 
 c2 - (undocumented)
 
 c3 - (undocumented)
 
 c4 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - select
```
public <U1,U2,U3,U4,U5> Dataset<scala.Tuple5<U1,U2,U3,U4,U5>> select(TypedColumn<T,U1> c1,
 TypedColumn<T,U2> c2,
 TypedColumn<T,U3> c3,
 TypedColumn<T,U4> c4,
 TypedColumn<T,U5> c5)
```
 Returns a new Dataset by computing the given Column expressions for each element.
 
 Parameters:
 
 c1 - (undocumented)
 
 c2 - (undocumented)
 
 c3 - (undocumented)
 
 c4 - (undocumented)
 
 c5 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - filter
```
public Dataset<T> filter(Column condition)
```
 Filters rows using the given condition.
```
 // The following are equivalent:
 peopleDs.filter($"age" > 15)
 peopleDs.where($"age" > 15)
 
```
 Parameters:
 
 condition - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - filter
```
public Dataset<T> filter(String conditionExpr)
```
 Filters rows using the given SQL expression.
```
 peopleDs.filter("age > 15")
 
```
 Parameters:
 
 conditionExpr - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - where
```
public Dataset<T> where(Column condition)
```
 Filters rows using the given condition. This is an alias for filter.
```
 // The following are equivalent:
 peopleDs.filter($"age" > 15)
 peopleDs.where($"age" > 15)
 
```
 Parameters:
 
 condition - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - where
```
public Dataset<T> where(String conditionExpr)
```
 Filters rows using the given SQL expression.
```
 peopleDs.where("age > 15")
 
```
 Parameters:
 
 conditionExpr - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - groupBy
```
public RelationalGroupedDataset groupBy(scala.collection.Seq<Column> cols)
```
 Groups the Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
```
 // Compute the average for all numeric columns grouped by department.
 ds.groupBy($"department").avg()

 // Compute the max age and average salary, grouped by department and gender.
 ds.groupBy($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - rollup
```
public RelationalGroupedDataset rollup(scala.collection.Seq<Column> cols)
```
 Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
```
 // Compute the average for all numeric columns rolled up by department and group.
 ds.rollup($"department", $"group").avg()

 // Compute the max age and average salary, rolled up by department and gender.
 ds.rollup($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - cube
```
public RelationalGroupedDataset cube(scala.collection.Seq<Column> cols)
```
 Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
```
 // Compute the average for all numeric columns cubed by department and group.
 ds.cube($"department", $"group").avg()

 // Compute the max age and average salary, cubed by department and gender.
 ds.cube($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - groupBy
```
public RelationalGroupedDataset groupBy(String col1,
 scala.collection.Seq<String> cols)
```
 Groups the Dataset using the specified columns, so that we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
 This is a variant of groupBy that can only group by existing columns using column names (i.e. cannot construct expressions).
```
 // Compute the average for all numeric columns grouped by department.
 ds.groupBy("department").avg()

 // Compute the max age and average salary, grouped by department and gender.
 ds.groupBy($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - reduce
```
public T reduce(scala.Function2<T,T,T> func)
```
 (Scala-specific) Reduces the elements of this Dataset using the specified binary function. The given func must be commutative and associative or the result may be non-deterministic.
 
 Parameters:
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - reduce
```
public T reduce(ReduceFunction<T> func)
```
 (Java-specific) Reduces the elements of this Dataset using the specified binary function. The given func must be commutative and associative or the result may be non-deterministic.
 
 Parameters:
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - groupByKey
```
public <K> KeyValueGroupedDataset<K,T> groupByKey(scala.Function1<T,K> func,
 Encoder<K> evidence$3)
```
 (Scala-specific) Returns a KeyValueGroupedDataset where the data is grouped by the given key func.
 
 Parameters:
 
 func - (undocumented)
 
 evidence$3 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - groupByKey
```
public <K> KeyValueGroupedDataset<K,T> groupByKey(MapFunction<T,K> func,
 Encoder<K> encoder)
```
 (Java-specific) Returns a KeyValueGroupedDataset where the data is grouped by the given key func.
 
 Parameters:
 
 func - (undocumented)
 
 encoder - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - rollup
```
public RelationalGroupedDataset rollup(String col1,
 scala.collection.Seq<String> cols)
```
 Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
 This is a variant of rollup that can only group by existing columns using column names (i.e. cannot construct expressions).
```
 // Compute the average for all numeric columns rolled up by department and group.
 ds.rollup("department", "group").avg()

 // Compute the max age and average salary, rolled up by department and gender.
 ds.rollup($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - cube
```
public RelationalGroupedDataset cube(String col1,
 scala.collection.Seq<String> cols)
```
 Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.
 This is a variant of cube that can only group by existing columns using column names (i.e. cannot construct expressions).
```
 // Compute the average for all numeric columns cubed by department and group.
 ds.cube("department", "group").avg()

 // Compute the max age and average salary, cubed by department and gender.
 ds.cube($"department", $"gender").agg(Map(
 "salary" -> "avg",
 "age" -> "max"
 ))
 
```
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - agg
```
public Dataset<Row> agg(scala.Tuple2<String,String> aggExpr,
 scala.collection.Seq<scala.Tuple2<String,String>> aggExprs)
```
 (Scala-specific) Aggregates on the entire Dataset without groups.
```
 // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
 ds.agg("age" -> "max", "salary" -> "avg")
 ds.groupBy().agg("age" -> "max", "salary" -> "avg")
 
```
 Parameters:
 
 aggExpr - (undocumented)
 
 aggExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - agg
```
public Dataset<Row> agg(scala.collection.immutable.Map<String,String> exprs)
```
 (Scala-specific) Aggregates on the entire Dataset without groups.
```
 // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
 ds.agg(Map("age" -> "max", "salary" -> "avg"))
 ds.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
 
```
 Parameters:
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - agg
```
public Dataset<Row> agg(java.util.Map<String,String> exprs)
```
 (Java-specific) Aggregates on the entire Dataset without groups.
```
 // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
 ds.agg(Map("age" -> "max", "salary" -> "avg"))
 ds.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
 
```
 Parameters:
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - agg
```
public Dataset<Row> agg(Column expr,
 scala.collection.Seq<Column> exprs)
```
 Aggregates on the entire Dataset without groups.
```
 // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
 ds.agg(max($"age"), avg($"salary"))
 ds.groupBy().agg(max($"age"), avg($"salary"))
 
```
 Parameters:
 
 expr - (undocumented)
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - unpivot
```
public Dataset<Row> unpivot(Column[] ids,
 Column[] values,
 String variableColumnName,
 String valueColumnName)
```
 Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set. This is the reverse to groupBy(...).pivot(...).agg(...), except for the aggregation, which cannot be reversed.
 This function is useful to massage a DataFrame into a format where some columns are identifier columns ("ids"), while all other columns ("values") are "unpivoted" to the rows, leaving just two non-id columns, named as given by variableColumnName and valueColumnName.
```
 val df = Seq((1, 11, 12L), (2, 21, 22L)).toDF("id", "int", "long")
 df.show()
 // output:
 // +---+---+----+
 // | id|int|long|
 // +---+---+----+
 // | 1| 11| 12|
 // | 2| 21| 22|
 // +---+---+----+

 df.unpivot(Array($"id"), Array($"int", $"long"), "variable", "value").show()
 // output:
 // +---+--------+-----+
 // | id|variable|value|
 // +---+--------+-----+
 // | 1| int| 11|
 // | 1| long| 12|
 // | 2| int| 21|
 // | 2| long| 22|
 // +---+--------+-----+
 // schema:
 //root
 // |-- id: integer (nullable = false)
 // |-- variable: string (nullable = false)
 // |-- value: long (nullable = true)
 
```
 When no "id" columns are given, the unpivoted DataFrame consists of only the "variable" and "value" columns.
 All "value" columns must share a least common data type. Unless they are the same data type, all "value" columns are cast to the nearest common data type. For instance, types IntegerType and LongType are cast to LongType, while IntegerType and StringType do not have a common data type and unpivot fails with an AnalysisException.
 Parameters:
 
 ids - Id columns
 
 values - Value columns to unpivot
 
 variableColumnName - Name of the variable column
 
 valueColumnName - Name of the value column
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - unpivot
```
public Dataset<Row> unpivot(Column[] ids,
 String variableColumnName,
 String valueColumnName)
```
 Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set. This is the reverse to groupBy(...).pivot(...).agg(...), except for the aggregation, which cannot be reversed.
 
 Parameters:
 
 ids - Id columns
 
 variableColumnName - Name of the variable column
 
 valueColumnName - Name of the value column
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 
 See Also:
 
 org.apache.spark.sql.Dataset.unpivot(Array, Array, String, String)
 This is equivalent to calling Dataset#unpivot(Array, Array, String, String) where values is set to all non-id columns that exist in the DataFrame.
 - melt
```
public Dataset<Row> melt(Column[] ids,
 Column[] values,
 String variableColumnName,
 String valueColumnName)
```
 Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set. This is the reverse to groupBy(...).pivot(...).agg(...), except for the aggregation, which cannot be reversed. This is an alias for unpivot.
 
 Parameters:
 
 ids - Id columns
 
 values - Value columns to unpivot
 
 variableColumnName - Name of the variable column
 
 valueColumnName - Name of the value column
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 
 See Also:
 
 org.apache.spark.sql.Dataset.unpivot(Array, Array, String, String)
 - melt
```
public Dataset<Row> melt(Column[] ids,
 String variableColumnName,
 String valueColumnName)
```
 Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set. This is the reverse to groupBy(...).pivot(...).agg(...), except for the aggregation, which cannot be reversed. This is an alias for unpivot.
 
 Parameters:
 
 ids - Id columns
 
 variableColumnName - Name of the variable column
 
 valueColumnName - Name of the value column
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 
 See Also:
 
 org.apache.spark.sql.Dataset.unpivot(Array, Array, String, String)
 This is equivalent to calling Dataset#unpivot(Array, Array, String, String) where values is set to all non-id columns that exist in the DataFrame.
 - observe
```
public Dataset<T> observe(String name,
 Column expr,
 scala.collection.Seq<Column> exprs)
```
 Define (named) metrics to observe on the Dataset. This method returns an 'observed' Dataset that returns the same result as the input, with the following guarantees:
 - It will compute the defined aggregates (metrics) on all the data that is flowing through the Dataset at that point.
 - It will report the value of the defined aggregate columns as soon as we reach a completion point. A completion point is either the end of a query (batch mode) or the end of a streaming epoch. The value of the aggregates only reflects the data processed since the previous completion point.
 Please note that continuous execution is currently not supported.
 The metrics columns must either contain a literal (e.g. lit(42)), or should contain one or more aggregate functions (e.g. sum(a) or sum(a + b) + avg(c) - lit(1)). Expressions that contain references to the input Dataset's columns must always be wrapped in an aggregate function.
 A user can observe these metrics by either adding StreamingQueryListener or a QueryExecutionListener to the spark session.
```
 // Monitor the metrics using a listener.
 spark.streams.addListener(new StreamingQueryListener() {
 override def onQueryStarted(event: QueryStartedEvent): Unit = {}
 override def onQueryProgress(event: QueryProgressEvent): Unit = {
 event.progress.observedMetrics.asScala.get("my_event").foreach { row =>
 // Trigger if the number of errors exceeds 5 percent
 val num_rows = row.getAs[Long]("rc")
 val num_error_rows = row.getAs[Long]("erc")
 val ratio = num_error_rows.toDouble / num_rows
 if (ratio > 0.05) {
 // Trigger alert
 }
 }
 }
 override def onQueryTerminated(event: QueryTerminatedEvent): Unit = {}
 })
 // Observe row count (rc) and error row count (erc) in the streaming Dataset
 val observed_ds = ds.observe("my_event", count(lit(1)).as("rc"), count($"error").as("erc"))
 observed_ds.writeStream.format("...").start()
 
```
 Parameters:
 
 name - (undocumented)
 
 expr - (undocumented)
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.0.0
 - observe
```
public Dataset<T> observe(Observation observation,
 Column expr,
 scala.collection.Seq<Column> exprs)
```
 Observe (named) metrics through an org.apache.spark.sql.Observation instance. This is equivalent to calling observe(String, Column, Column*) but does not require adding org.apache.spark.sql.util.QueryExecutionListener to the spark session. This method does not support streaming datasets.
 A user can retrieve the metrics by accessing org.apache.spark.sql.Observation.get.
```
 // Observe row count (rows) and highest id (maxid) in the Dataset while writing it
 val observation = Observation("my_metrics")
 val observed_ds = ds.observe(observation, count(lit(1)).as("rows"), max($"id").as("maxid"))
 observed_ds.write.parquet("ds.parquet")
 val metrics = observation.get
 
```
 Parameters:
 
 observation - (undocumented)
 
 expr - (undocumented)
 
 exprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Throws:
 
 IllegalArgumentException - If this is a streaming Dataset (this.isStreaming == true)
 
 Since:
 
 3.3.0
 - limit
```
public Dataset<T> limit(int n)
```
 Returns a new Dataset by taking the first n rows. The difference between this function and head is that head is an action and returns an array (by triggering query execution) while limit returns a new Dataset.
 
 Parameters:
 
 n - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - offset
```
public Dataset<T> offset(int n)
```
 Returns a new Dataset by skipping the first n rows.
 
 Parameters:
 
 n - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - union
```
public Dataset<T> union(Dataset<T> other)
```
 Returns a new Dataset containing union of rows in this Dataset and another Dataset.
 This is equivalent to UNION ALL in SQL. To do a SQL-style set union (that does deduplication of elements), use this function followed by a distinct.
 Also as standard in SQL, this function resolves columns by position (not by name):
```
 val df1 = Seq((1, 2, 3)).toDF("col0", "col1", "col2")
 val df2 = Seq((4, 5, 6)).toDF("col1", "col2", "col0")
 df1.union(df2).show

 // output:
 // +----+----+----+
 // |col0|col1|col2|
 // +----+----+----+
 // | 1| 2| 3|
 // | 4| 5| 6|
 // +----+----+----+
 
```
 Notice that the column positions in the schema aren't necessarily matched with the fields in the strongly typed objects in a Dataset. This function resolves columns by their positions in the schema, not the fields in the strongly typed objects. Use unionByName to resolve columns by field name in the typed objects.
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - unionAll
```
public Dataset<T> unionAll(Dataset<T> other)
```
 Returns a new Dataset containing union of rows in this Dataset and another Dataset. This is an alias for union.
 This is equivalent to UNION ALL in SQL. To do a SQL-style set union (that does deduplication of elements), use this function followed by a distinct.
 Also as standard in SQL, this function resolves columns by position (not by name).
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - unionByName
```
public Dataset<T> unionByName(Dataset<T> other)
```
 Returns a new Dataset containing union of rows in this Dataset and another Dataset.
 This is different from both UNION ALL and UNION DISTINCT in SQL. To do a SQL-style set union (that does deduplication of elements), use this function followed by a distinct.
 The difference between this function and union is that this function resolves columns by name (not by position):
```
 val df1 = Seq((1, 2, 3)).toDF("col0", "col1", "col2")
 val df2 = Seq((4, 5, 6)).toDF("col1", "col2", "col0")
 df1.unionByName(df2).show

 // output:
 // +----+----+----+
 // |col0|col1|col2|
 // +----+----+----+
 // | 1| 2| 3|
 // | 6| 4| 5|
 // +----+----+----+
 
```
 Note that this supports nested columns in struct and array types. Nested columns in map types are not currently supported.
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - unionByName
```
public Dataset<T> unionByName(Dataset<T> other,
 boolean allowMissingColumns)
```
 Returns a new Dataset containing union of rows in this Dataset and another Dataset.
 The difference between this function and union is that this function resolves columns by name (not by position).
 When the parameter allowMissingColumns is true, the set of column names in this and other Dataset can differ; missing columns will be filled with null. Further, the missing columns of this Dataset will be added at the end in the schema of the union result:
```
 val df1 = Seq((1, 2, 3)).toDF("col0", "col1", "col2")
 val df2 = Seq((4, 5, 6)).toDF("col1", "col0", "col3")
 df1.unionByName(df2, true).show

 // output: "col3" is missing at left df1 and added at the end of schema.
 // +----+----+----+----+
 // |col0|col1|col2|col3|
 // +----+----+----+----+
 // | 1| 2| 3|NULL|
 // | 5| 4|NULL| 6|
 // +----+----+----+----+

 df2.unionByName(df1, true).show

 // output: "col2" is missing at left df2 and added at the end of schema.
 // +----+----+----+----+
 // |col1|col0|col3|col2|
 // +----+----+----+----+
 // | 4| 5| 6|NULL|
 // | 2| 1|NULL| 3|
 // +----+----+----+----+
 
```
 Note that this supports nested columns in struct and array types. With allowMissingColumns, missing nested columns of struct columns with the same name will also be filled with null values and added to the end of struct. Nested columns in map types are not currently supported.
 Parameters:
 
 other - (undocumented)
 
 allowMissingColumns - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.1.0
 - intersect
```
public Dataset<T> intersect(Dataset<T> other)
```
 Returns a new Dataset containing rows only in both this Dataset and another Dataset. This is equivalent to INTERSECT in SQL.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 
 Note:
 
 Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T.
 - intersectAll
```
public Dataset<T> intersectAll(Dataset<T> other)
```
 Returns a new Dataset containing rows only in both this Dataset and another Dataset while preserving the duplicates. This is equivalent to INTERSECT ALL in SQL.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.4.0
 
 Note:
 
 Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T. Also as standard in SQL, this function resolves columns by position (not by name).
 - except
```
public Dataset<T> except(Dataset<T> other)
```
 Returns a new Dataset containing rows in this Dataset but not in another Dataset. This is equivalent to EXCEPT DISTINCT in SQL.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T.
 - exceptAll
```
public Dataset<T> exceptAll(Dataset<T> other)
```
 Returns a new Dataset containing rows in this Dataset but not in another Dataset while preserving the duplicates. This is equivalent to EXCEPT ALL in SQL.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.4.0
 
 Note:
 
 Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T. Also as standard in SQL, this function resolves columns by position (not by name).
 - sample
```
public Dataset<T> sample(double fraction,
 long seed)
```
 Returns a new Dataset by sampling a fraction of rows (without replacement), using a user-supplied seed.
 
 Parameters:
 
 fraction - Fraction of rows to generate, range [0.0, 1.0].
 
 seed - Seed for sampling.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 
 Note:
 
 This is NOT guaranteed to provide exactly the fraction of the count of the given Dataset.
 - sample
```
public Dataset<T> sample(double fraction)
```
 Returns a new Dataset by sampling a fraction of rows (without replacement), using a random seed.
 
 Parameters:
 
 fraction - Fraction of rows to generate, range [0.0, 1.0].
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 
 Note:
 
 This is NOT guaranteed to provide exactly the fraction of the count of the given Dataset.
 - sample
```
public Dataset<T> sample(boolean withReplacement,
 double fraction,
 long seed)
```
 Returns a new Dataset by sampling a fraction of rows, using a user-supplied seed.
 
 Parameters:
 
 withReplacement - Sample with replacement or not.
 
 fraction - Fraction of rows to generate, range [0.0, 1.0].
 
 seed - Seed for sampling.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 
 Note:
 
 This is NOT guaranteed to provide exactly the fraction of the count of the given Dataset.
 - sample
```
public Dataset<T> sample(boolean withReplacement,
 double fraction)
```
 Returns a new Dataset by sampling a fraction of rows, using a random seed.
 
 Parameters:
 
 withReplacement - Sample with replacement or not.
 
 fraction - Fraction of rows to generate, range [0.0, 1.0].
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 
 Note:
 
 This is NOT guaranteed to provide exactly the fraction of the total count of the given Dataset.
 - randomSplit
```
public Dataset<T>[] randomSplit(double[] weights,
 long seed)
```
 Randomly splits this Dataset with the provided weights.
 
 Parameters:
 
 weights - weights for splits, will be normalized if they don't sum to 1.
 
 seed - Seed for sampling.
 For Java API, use randomSplitAsList.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - randomSplitAsList
```
public java.util.List<Dataset<T>> randomSplitAsList(double[] weights,
 long seed)
```
 Returns a Java list that contains randomly split Dataset with the provided weights.
 
 Parameters:
 
 weights - weights for splits, will be normalized if they don't sum to 1.
 
 seed - Seed for sampling.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - randomSplit
```
public Dataset<T>[] randomSplit(double[] weights)
```
 Randomly splits this Dataset with the provided weights.
 
 Parameters:
 
 weights - weights for splits, will be normalized if they don't sum to 1.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - explode
```
public <A extends scala.Product> Dataset<Row> explode(scala.collection.Seq<Column> input,
 scala.Function1<Row,scala.collection.TraversableOnce<A>> f,
 scala.reflect.api.TypeTags.TypeTag<A> evidence$4)
```
 Deprecated. use flatMap() or select() with functions.explode() instead. Since 2.0.0.
 (Scala-specific) Returns a new Dataset where each row has been expanded to zero or more rows by the provided function. This is similar to a LATERAL VIEW in HiveQL. The columns of the input row are implicitly joined with each row that is output by the function.
 Given that this is deprecated, as an alternative, you can explode columns either using functions.explode() or flatMap(). The following example uses these alternatives to count the number of books that contain a given word:
```
 case class Book(title: String, words: String)
 val ds: Dataset[Book]

 val allWords = ds.select($"title", explode(split($"words", " ")).as("word"))

 val bookCountPerWord = allWords.groupBy("word").agg(count_distinct("title"))
 
```
 Using flatMap() this can similarly be exploded as:
```
 ds.flatMap(_.words.split(" "))
 
```
 Parameters:
 
 input - (undocumented)
 
 f - (undocumented)
 
 evidence$4 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - explode
```
public <A,B> Dataset<Row> explode(String inputColumn,
 String outputColumn,
 scala.Function1<A,scala.collection.TraversableOnce> f,
 scala.reflect.api.TypeTags.TypeTag evidence$5)
```
 Deprecated. use flatMap() or select() with functions.explode() instead. Since 2.0.0.
 (Scala-specific) Returns a new Dataset where a single column has been expanded to zero or more rows by the provided function. This is similar to a LATERAL VIEW in HiveQL. All columns of the input row are implicitly joined with each value that is output by the function.
 Given that this is deprecated, as an alternative, you can explode columns either using functions.explode():
```
 ds.select(explode(split($"words", " ")).as("word"))
 
```
 or flatMap():
```
 ds.flatMap(_.words.split(" "))
 
```
 Parameters:
 
 inputColumn - (undocumented)
 
 outputColumn - (undocumented)
 
 f - (undocumented)
 
 evidence$5 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - withColumn
```
public Dataset<Row> withColumn(String colName,
 Column col)
```
 Returns a new Dataset by adding a column or replacing the existing column that has the same name.
 column's expression must only refer to attributes supplied by this Dataset. It is an error to add a column that refers to some other Dataset.
 
 Parameters:
 
 colName - (undocumented)
 
 col - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 this method introduces a projection internally. Therefore, calling it multiple times, for instance, via loops in order to add multiple columns can generate big plans which can cause performance issues and even StackOverflowException. To avoid this, use select with the multiple columns at once.
 - withColumns
```
public Dataset<Row> withColumns(scala.collection.immutable.Map<String,Column> colsMap)
```
 (Scala-specific) Returns a new Dataset by adding columns or replacing the existing columns that has the same names.
 colsMap is a map of column name and column, the column must only refer to attributes supplied by this Dataset. It is an error to add columns that refers to some other Dataset.
 
 Parameters:
 
 colsMap - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.3.0
 - withColumns
```
public Dataset<Row> withColumns(java.util.Map<String,Column> colsMap)
```
 (Java-specific) Returns a new Dataset by adding columns or replacing the existing columns that has the same names.
 colsMap is a map of column name and column, the column must only refer to attribute supplied by this Dataset. It is an error to add columns that refers to some other Dataset.
 
 Parameters:
 
 colsMap - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.3.0
 - withColumnRenamed
```
public Dataset<Row> withColumnRenamed(String existingName,
 String newName)
```
 Returns a new Dataset with a column renamed. This is a no-op if schema doesn't contain existingName.
 
 Parameters:
 
 existingName - (undocumented)
 
 newName - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - withColumnsRenamed
```
public Dataset<Row> withColumnsRenamed(scala.collection.immutable.Map<String,String> colsMap)
 throws AnalysisException
```
 (Scala-specific) Returns a new Dataset with a columns renamed. This is a no-op if schema doesn't contain existingName.
 colsMap is a map of existing column name and new column name.
 
 Parameters:
 
 colsMap - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Throws:
 
 AnalysisException - if there are duplicate names in resulting projection
 
 Since:
 
 3.4.0
 - withColumnsRenamed
```
public Dataset<Row> withColumnsRenamed(java.util.Map<String,String> colsMap)
```
 (Java-specific) Returns a new Dataset with a columns renamed. This is a no-op if schema doesn't contain existingName.
 colsMap is a map of existing column name and new column name.
 
 Parameters:
 
 colsMap - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - withMetadata
```
public Dataset<Row> withMetadata(String columnName,
 Metadata metadata)
```
 Returns a new Dataset by updating an existing column with metadata.
 
 Parameters:
 
 columnName - (undocumented)
 
 metadata - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.3.0
 - drop
```
public Dataset<Row> drop(String colName)
```
 Returns a new Dataset with a column dropped. This is a no-op if schema doesn't contain column name.
 This method can only be used to drop top level columns. the colName string is treated literally without further interpretation.
 Note: drop(colName) has different semantic with drop(col(colName)), for example: 1, multi column have the same colName:
```
 val df1 = spark.range(0, 2).withColumn("key1", lit(1))
 val df2 = spark.range(0, 2).withColumn("key2", lit(2))
 val df3 = df1.join(df2)

 df3.show
 // +---+----+---+----+
 // | id|key1| id|key2|
 // +---+----+---+----+
 // | 0| 1| 0| 2|
 // | 0| 1| 1| 2|
 // | 1| 1| 0| 2|
 // | 1| 1| 1| 2|
 // +---+----+---+----+

 df3.drop("id").show()
 // output: the two 'id' columns are both dropped.
 // |key1|key2|
 // +----+----+
 // | 1| 2|
 // | 1| 2|
 // | 1| 2|
 // | 1| 2|
 // +----+----+

 df3.drop(col("id")).show()
 // ...AnalysisException: [AMBIGUOUS_REFERENCE] Reference `id` is ambiguous...
 
```
 2, colName contains special characters, like dot.
```
 val df = spark.range(0, 2).withColumn("a.b.c", lit(1))

 df.show()
 // +---+-----+
 // | id|a.b.c|
 // +---+-----+
 // | 0| 1|
 // | 1| 1|
 // +---+-----+

 df.drop("a.b.c").show()
 // +---+
 // | id|
 // +---+
 // | 0|
 // | 1|
 // +---+

 df.drop(col("a.b.c")).show()
 // no column match the expression 'a.b.c'
 // +---+-----+
 // | id|a.b.c|
 // +---+-----+
 // | 0| 1|
 // | 1| 1|
 // +---+-----+
 
```
 Parameters:
 
 colName - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - drop
```
public Dataset<Row> drop(scala.collection.Seq<String> colNames)
```
 Returns a new Dataset with columns dropped. This is a no-op if schema doesn't contain column name(s).
 This method can only be used to drop top level columns. the colName string is treated literally without further interpretation.
 
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - drop
```
public Dataset<Row> drop(Column col)
```
 Returns a new Dataset with column dropped.
 This method can only be used to drop top level column. This version of drop accepts a Column rather than a name. This is a no-op if the Dataset doesn't have a column with an equivalent expression.
 Note: drop(col(colName)) has different semantic with drop(colName), please refer to Dataset#drop(colName: String).
 
 Parameters:
 
 col - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - drop
```
public Dataset<Row> drop(Column col,
 scala.collection.Seq<Column> cols)
```
 Returns a new Dataset with columns dropped.
 This method can only be used to drop top level columns. This is a no-op if the Dataset doesn't have a columns with an equivalent expression.
 
 Parameters:
 
 col - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.4.0
 - dropDuplicates
```
public Dataset<T> dropDuplicates()
```
 Returns a new Dataset that contains only the unique rows from this Dataset. This is an alias for distinct.
 For a static batch Dataset, it just drops duplicate rows. For a streaming Dataset, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark to limit how late the duplicate data can be and system will accordingly limit the state. In addition, too late data older than watermark will be dropped to avoid any possibility of duplicates.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - dropDuplicates
```
public Dataset<T> dropDuplicates(scala.collection.Seq<String> colNames)
```
 (Scala-specific) Returns a new Dataset with duplicate rows removed, considering only the subset of columns.
 For a static batch Dataset, it just drops duplicate rows. For a streaming Dataset, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark to limit how late the duplicate data can be and system will accordingly limit the state. In addition, too late data older than watermark will be dropped to avoid any possibility of duplicates.
 
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - dropDuplicates
```
public Dataset<T> dropDuplicates(String[] colNames)
```
 Returns a new Dataset with duplicate rows removed, considering only the subset of columns.
 For a static batch Dataset, it just drops duplicate rows. For a streaming Dataset, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark to limit how late the duplicate data can be and system will accordingly limit the state. In addition, too late data older than watermark will be dropped to avoid any possibility of duplicates.
 
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - dropDuplicates
```
public Dataset<T> dropDuplicates(String col1,
 scala.collection.Seq<String> cols)
```
 Returns a new Dataset with duplicate rows removed, considering only the subset of columns.
 For a static batch Dataset, it just drops duplicate rows. For a streaming Dataset, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark to limit how late the duplicate data can be and system will accordingly limit the state. In addition, too late data older than watermark will be dropped to avoid any possibility of duplicates.
 
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - dropDuplicatesWithinWatermark
```
public Dataset<T> dropDuplicatesWithinWatermark()
```
 Returns a new Dataset with duplicates rows removed, within watermark.
 This only works with streaming Dataset, and watermark for the input Dataset must be set via withWatermark.
 For a streaming Dataset, this will keep all data across triggers as intermediate state to drop duplicated rows. The state will be kept to guarantee the semantic, "Events are deduplicated as long as the time distance of earliest and latest events are smaller than the delay threshold of watermark." Users are encouraged to set the delay threshold of watermark longer than max timestamp differences among duplicated events.
 Note: too late data older than watermark will be dropped.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.5.0
 - dropDuplicatesWithinWatermark
```
public Dataset<T> dropDuplicatesWithinWatermark(scala.collection.Seq<String> colNames)
```
 Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
 This only works with streaming Dataset, and watermark for the input Dataset must be set via withWatermark.
 For a streaming Dataset, this will keep all data across triggers as intermediate state to drop duplicated rows. The state will be kept to guarantee the semantic, "Events are deduplicated as long as the time distance of earliest and latest events are smaller than the delay threshold of watermark." Users are encouraged to set the delay threshold of watermark longer than max timestamp differences among duplicated events.
 Note: too late data older than watermark will be dropped.
 
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.5.0
 - dropDuplicatesWithinWatermark
```
public Dataset<T> dropDuplicatesWithinWatermark(String[] colNames)
```
 Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
 This only works with streaming Dataset, and watermark for the input Dataset must be set via withWatermark.
 For a streaming Dataset, this will keep all data across triggers as intermediate state to drop duplicated rows. The state will be kept to guarantee the semantic, "Events are deduplicated as long as the time distance of earliest and latest events are smaller than the delay threshold of watermark." Users are encouraged to set the delay threshold of watermark longer than max timestamp differences among duplicated events.
 Note: too late data older than watermark will be dropped.
 
 Parameters:
 
 colNames - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.5.0
 - dropDuplicatesWithinWatermark
```
public Dataset<T> dropDuplicatesWithinWatermark(String col1,
 scala.collection.Seq<String> cols)
```
 Returns a new Dataset with duplicates rows removed, considering only the subset of columns, within watermark.
 This only works with streaming Dataset, and watermark for the input Dataset must be set via withWatermark.
 For a streaming Dataset, this will keep all data across triggers as intermediate state to drop duplicated rows. The state will be kept to guarantee the semantic, "Events are deduplicated as long as the time distance of earliest and latest events are smaller than the delay threshold of watermark." Users are encouraged to set the delay threshold of watermark longer than max timestamp differences among duplicated events.
 Note: too late data older than watermark will be dropped.
 
 Parameters:
 
 col1 - (undocumented)
 
 cols - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.5.0
 - describe
```
public Dataset<Row> describe(scala.collection.Seq<String> cols)
```
 Computes basic statistics for numeric and string columns, including count, mean, stddev, min, and max. If no columns are given, this function computes statistics for all numerical or string columns.
 This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting Dataset. If you want to programmatically compute summary statistics, use the agg function instead.
```
 ds.describe("age", "height").show()

 // output:
 // summary age height
 // count 10.0 10.0
 // mean 53.3 178.05
 // stddev 11.6 15.7
 // min 18.0 163.0
 // max 92.0 192.0
 
```
 Use summary for expanded statistics and control over which statistics to compute.
 Parameters:
 
 cols - Columns to compute statistics on.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - summary
```
public Dataset<Row> summary(scala.collection.Seq<String> statistics)
```
 Computes specified statistics for numeric and string columns. Available statistics are:
 - count
 - mean
 - stddev
 - min
 - max
 - arbitrary approximate percentiles specified as a percentage (e.g. 75%)
 - count_distinct
 - approx_count_distinct
 If no statistics are given, this function computes count, mean, stddev, min, approximate quartiles (percentiles at 25%, 50%, and 75%), and max.
 This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting Dataset. If you want to programmatically compute summary statistics, use the agg function instead.
```
 ds.summary().show()

 // output:
 // summary age height
 // count 10.0 10.0
 // mean 53.3 178.05
 // stddev 11.6 15.7
 // min 18.0 163.0
 // 25% 24.0 176.0
 // 50% 24.0 176.0
 // 75% 32.0 180.0
 // max 92.0 192.0
 
```
```
 ds.summary("count", "min", "25%", "75%", "max").show()

 // output:
 // summary age height
 // count 10.0 10.0
 // min 18.0 163.0
 // 25% 24.0 176.0
 // 75% 32.0 180.0
 // max 92.0 192.0
 
```
 To do a summary for specific columns first select them:
```
 ds.select("age", "height").summary().show()
 
```
 Specify statistics to output custom summaries:
```
 ds.summary("count", "count_distinct").show()
 
```
 The distinct count isn't included by default.
 You can also run approximate distinct counts which are faster:
```
 ds.summary("count", "approx_count_distinct").show()
 
```
 See also describe for basic statistics.
 Parameters:
 
 statistics - Statistics from above list to be computed.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - head
```
public Object head(int n)
```
 Returns the first n rows.
 
 Parameters:
 
 n - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 
 Note:
 
 this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
 - head
```
public T head()
```
 Returns the first row.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - first
```
public T first()
```
 Returns the first row. Alias for head().
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - transform
```
public  Dataset transform(scala.Function1<Dataset<T>,Dataset> t)
```
 Concise syntax for chaining custom transformations.
```
 def featurize(ds: Dataset[T]): Dataset[U] = ...

 ds
 .transform(featurize)
 .transform(...)
 
```
 Parameters:
 
 t - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - filter
```
public Dataset<T> filter(scala.Function1<T,Object> func)
```
 (Scala-specific) Returns a new Dataset that only contains elements where func returns true.
 
 Parameters:
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - filter
```
public Dataset<T> filter(FilterFunction<T> func)
```
 (Java-specific) Returns a new Dataset that only contains elements where func returns true.
 
 Parameters:
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - map
```
public  Dataset map(scala.Function1<T,U> func,
 Encoder evidence$6)
```
 (Scala-specific) Returns a new Dataset that contains the result of applying func to each element.
 
 Parameters:
 
 func - (undocumented)
 
 evidence$6 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - map
```
public  Dataset map(MapFunction<T,U> func,
 Encoder encoder)
```
 (Java-specific) Returns a new Dataset that contains the result of applying func to each element.
 
 Parameters:
 
 func - (undocumented)
 
 encoder - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - mapPartitions
```
public  Dataset mapPartitions(scala.Function1<scala.collection.Iterator<T>,scala.collection.Iterator> func,
 Encoder evidence$7)
```
 (Scala-specific) Returns a new Dataset that contains the result of applying func to each partition.
 
 Parameters:
 
 func - (undocumented)
 
 evidence$7 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - mapPartitions
```
public  Dataset mapPartitions(MapPartitionsFunction<T,U> f,
 Encoder encoder)
```
 (Java-specific) Returns a new Dataset that contains the result of applying f to each partition.
 
 Parameters:
 
 f - (undocumented)
 
 encoder - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - flatMap
```
public  Dataset flatMap(scala.Function1<T,scala.collection.TraversableOnce> func,
 Encoder evidence$8)
```
 (Scala-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.
 
 Parameters:
 
 func - (undocumented)
 
 evidence$8 - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - flatMap
```
public  Dataset flatMap(FlatMapFunction<T,U> f,
 Encoder encoder)
```
 (Java-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.
 
 Parameters:
 
 f - (undocumented)
 
 encoder - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - foreach
```
public void foreach(scala.Function1<T,scala.runtime.BoxedUnit> f)
```
 Applies a function f to all rows.
 
 Parameters:
 
 f - (undocumented)
 
 Since:
 
 1.6.0
 - foreach
```
public void foreach(ForeachFunction<T> func)
```
 (Java-specific) Runs func on each element of this Dataset.
 
 Parameters:
 
 func - (undocumented)
 
 Since:
 
 1.6.0
 - foreachPartition
```
public void foreachPartition(scala.Function1<scala.collection.Iterator<T>,scala.runtime.BoxedUnit> f)
```
 Applies a function f to each partition of this Dataset.
 
 Parameters:
 
 f - (undocumented)
 
 Since:
 
 1.6.0
 - foreachPartition
```
public void foreachPartition(ForeachPartitionFunction<T> func)
```
 (Java-specific) Runs func on each partition of this Dataset.
 
 Parameters:
 
 func - (undocumented)
 
 Since:
 
 1.6.0
 - take
```
public Object take(int n)
```
 Returns the first n rows in the Dataset.
 Running take requires moving data into the application's driver process, and doing so with a very large n can crash the driver process with OutOfMemoryError.
 
 Parameters:
 
 n - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - tail
```
public Object tail(int n)
```
 Returns the last n rows in the Dataset.
 Running tail requires moving data into the application's driver process, and doing so with a very large n can crash the driver process with OutOfMemoryError.
 
 Parameters:
 
 n - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.0.0
 - takeAsList
```
public java.util.List<T> takeAsList(int n)
```
 Returns the first n rows in the Dataset as a list.
 Running take requires moving data into the application's driver process, and doing so with a very large n can crash the driver process with OutOfMemoryError.
 
 Parameters:
 
 n - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - collect
```
public Object collect()
```
 Returns an array that contains all rows in this Dataset.
 Running collect requires moving all the data into the application's driver process, and doing so on a very large dataset can crash the driver process with OutOfMemoryError.
 For Java API, use collectAsList.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - collectAsList
```
public java.util.List<T> collectAsList()
```
 Returns a Java list that contains all rows in this Dataset.
 Running collect requires moving all the data into the application's driver process, and doing so on a very large dataset can crash the driver process with OutOfMemoryError.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - toLocalIterator
```
public java.util.Iterator<T> toLocalIterator()
```
 Returns an iterator that contains all rows in this Dataset.
 The iterator will consume as much memory as the largest partition in this Dataset.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 this results in multiple Spark jobs, and if the input Dataset is the result of a wide transformation (e.g. join with different partitioners), to avoid recomputing the input Dataset should be cached first.
 - count
```
public long count()
```
 Returns the number of rows in the Dataset.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - repartition
```
public Dataset<T> repartition(int numPartitions)
```
 Returns a new Dataset that has exactly numPartitions partitions.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - repartition
```
public Dataset<T> repartition(int numPartitions,
 scala.collection.Seq<Column> partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions into numPartitions. The resulting Dataset is hash partitioned.
 This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
 
 Parameters:
 
 numPartitions - (undocumented)
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - repartition
```
public Dataset<T> repartition(scala.collection.Seq<Column> partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions, using spark.sql.shuffle.partitions as number of partitions. The resulting Dataset is hash partitioned.
 This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
 
 Parameters:
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - repartitionByRange
```
public Dataset<T> repartitionByRange(int numPartitions,
 scala.collection.Seq<Column> partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions into numPartitions. The resulting Dataset is range partitioned.
 At least one partition-by expression must be specified. When no explicit sort order is specified, "ascending nulls first" is assumed. Note, the rows are not sorted in each partition of the resulting Dataset.
 Note that due to performance reasons this method uses sampling to estimate the ranges. Hence, the output may not be consistent, since sampling can return different values. The sample size can be controlled by the config spark.sql.execution.rangeExchange.sampleSizePerPartition.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - repartitionByRange
```
public Dataset<T> repartitionByRange(scala.collection.Seq<Column> partitionExprs)
```
 Returns a new Dataset partitioned by the given partitioning expressions, using spark.sql.shuffle.partitions as number of partitions. The resulting Dataset is range partitioned.
 At least one partition-by expression must be specified. When no explicit sort order is specified, "ascending nulls first" is assumed. Note, the rows are not sorted in each partition of the resulting Dataset.
 Note that due to performance reasons this method uses sampling to estimate the ranges. Hence, the output may not be consistent, since sampling can return different values. The sample size can be controlled by the config spark.sql.execution.rangeExchange.sampleSizePerPartition.
 
 Parameters:
 
 partitionExprs - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.3.0
 - coalesce
```
public Dataset<T> coalesce(int numPartitions)
```
 Returns a new Dataset that has exactly numPartitions partitions, when the fewer partitions are requested. If a larger number of partitions is requested, it will stay at the current number of partitions. Similar to coalesce defined on an RDD, this operation results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions.
 However, if you're doing a drastic coalesce, e.g. to numPartitions = 1, this may result in your computation taking place on fewer nodes than you like (e.g. one node in the case of numPartitions = 1). To avoid this, you can call repartition. This will add a shuffle step, but means the current upstream partitions will be executed in parallel (per whatever the current partitioning is).
 
 Parameters:
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - distinct
```
public Dataset<T> distinct()
```
 Returns a new Dataset that contains only the unique rows from this Dataset. This is an alias for dropDuplicates.
 Note that for a streaming Dataset, this method returns distinct rows only once regardless of the output mode, which the behavior may not be same with DISTINCT in SQL against streaming Dataset.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 
 Note:
 
 Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T.
 - persist
```
public Dataset<T> persist()
```
 Persist this Dataset with the default storage level (MEMORY_AND_DISK).
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - cache
```
public Dataset<T> cache()
```
 Persist this Dataset with the default storage level (MEMORY_AND_DISK).
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - persist
```
public Dataset<T> persist(StorageLevel newLevel)
```
 Persist this Dataset with the given storage level.
 
 Parameters:
 
 newLevel - One of: MEMORY_ONLY, MEMORY_AND_DISK, MEMORY_ONLY_SER, MEMORY_AND_DISK_SER, DISK_ONLY, MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - storageLevel
```
public StorageLevel storageLevel()
```
 Get the Dataset's current storage level, or StorageLevel.NONE if not persisted.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.1.0
 - unpersist
```
public Dataset<T> unpersist(boolean blocking)
```
 Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk. This will not un-persist any cached data that is built upon this Dataset.
 
 Parameters:
 
 blocking - Whether to block until all blocks are deleted.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - unpersist
```
public Dataset<T> unpersist()
```
 Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk. This will not un-persist any cached data that is built upon this Dataset.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - rdd
```
public RDD<T> rdd()
```
 - toJavaRDD
```
public JavaRDD<T> toJavaRDD()
```
 Returns the content of the Dataset as a JavaRDD of Ts.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - javaRDD
```
public JavaRDD<T> javaRDD()
```
 Returns the content of the Dataset as a JavaRDD of Ts.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - registerTempTable
```
public void registerTempTable(String tableName)
```
 Deprecated. Use createOrReplaceTempView(viewName) instead. Since 2.0.0.
 
 Registers this Dataset as a temporary table using the given name. The lifetime of this temporary table is tied to the SparkSession that was used to create this Dataset.
 
 Parameters:
 
 tableName - (undocumented)
 
 Since:
 
 1.6.0
 - createTempView
```
public void createTempView(String viewName)
 throws AnalysisException
```
 Creates a local temporary view using the given name. The lifetime of this temporary view is tied to the SparkSession that was used to create this Dataset.
 Local temporary view is session-scoped. Its lifetime is the lifetime of the session that created it, i.e. it will be automatically dropped when the session terminates. It's not tied to any databases, i.e. we can't use db1.view1 to reference a local temporary view.
 
 Parameters:
 
 viewName - (undocumented)
 
 Throws:
 
 AnalysisException - if the view name is invalid or already exists
 
 Since:
 
 2.0.0
 - createOrReplaceTempView
```
public void createOrReplaceTempView(String viewName)
```
 Creates a local temporary view using the given name. The lifetime of this temporary view is tied to the SparkSession that was used to create this Dataset.
 
 Parameters:
 
 viewName - (undocumented)
 
 Since:
 
 2.0.0
 - createGlobalTempView
```
public void createGlobalTempView(String viewName)
 throws AnalysisException
```
 Creates a global temporary view using the given name. The lifetime of this temporary view is tied to this Spark application.
 Global temporary view is cross-session. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database global_temp, and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.
 
 Parameters:
 
 viewName - (undocumented)
 
 Throws:
 
 AnalysisException - if the view name is invalid or already exists
 
 Since:
 
 2.1.0
 - createOrReplaceGlobalTempView
```
public void createOrReplaceGlobalTempView(String viewName)
```
 Creates or replaces a global temporary view using the given name. The lifetime of this temporary view is tied to this Spark application.
 Global temporary view is cross-session. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database global_temp, and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.
 
 Parameters:
 
 viewName - (undocumented)
 
 Since:
 
 2.2.0
 - write
```
public DataFrameWriter<T> write()
```
 Interface for saving the content of the non-streaming Dataset out into external storage.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 1.6.0
 - writeTo
```
public DataFrameWriterV2<T> writeTo(String table)
```
 Create a write configuration builder for v2 sources.
 This builder is used to configure and execute write operations. For example, to append to an existing table, run:
```
 df.writeTo("catalog.db.table").append()
 
```
 This can also be used to create or replace existing tables:
```
 df.writeTo("catalog.db.table").partitionedBy($"col").createOrReplace()
 
```
 Parameters:
 
 table - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.0.0
 - writeStream
```
public DataStreamWriter<T> writeStream()
```
 Interface for saving the content of the streaming Dataset out into external storage.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - toJSON
```
public Dataset<String> toJSON()
```
 Returns the content of the Dataset as a Dataset of JSON strings.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - inputFiles
```
public String[] inputFiles()
```
 Returns a best-effort snapshot of the files that compose this Dataset. This method simply asks each constituent BaseRelation for its respective files and takes the union of all results. Depending on the source relations, this may not find all input files. Duplicates are removed.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 2.0.0
 - sameSemantics
```
public boolean sameSemantics(Dataset<T> other)
```
 Returns true when the logical query plans inside both Datasets are equal and therefore return same results.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.1.0
 
 Note:
 
 The equality comparison here is simplified by tolerating the cosmetic differences such as attribute names., This API can compare both Datasets very fast but can still return false on the Dataset that return the same results, for instance, from different plans. Such false negative semantic can be useful when caching as an example.
 - semanticHash
```
public int semanticHash()
```
 Returns a hashCode of the logical query plan against this Dataset.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.1.0
 
 Note:
 
 Unlike the standard hashCode, the hash is calculated against the query plan simplified by tolerating the cosmetic differences such as attribute names.

Class Dataset<T>

Constructor Summary

Method Summary

Methods inherited from class Object

Constructor Detail

Dataset

Dataset

Method Detail

curId

DATASET_ID_KEY

COL_POS_KEY

DATASET_ID_TAG

ofRows

ofRows

toDF

sortWithinPartitions

sortWithinPartitions

sort

sort

orderBy

orderBy

hint

select

select

selectExpr

groupBy

rollup

cube

groupBy

rollup

cube

agg

observe

observe

drop

drop

dropDuplicates

dropDuplicatesWithinWatermark

describe

summary

repartition

repartition

repartitionByRange

repartitionByRange

queryExecution

encoder

sparkSession

classTag

sqlContext

toString

toDF

as

to

toDF

schema

printSchema

printSchema

explain

explain

explain

dtypes

columns

isLocal

isEmpty

isStreaming

checkpoint

checkpoint

localCheckpoint

localCheckpoint

withWatermark

show

show

show

show

show

show

na

stat

join

join