Missing Dataset methods

Here is an exhaustive status of the API implemented by frameless.TypeDataset compared to Spark's Dataset. We are getting pretty close to 100% API coverage 😄

Won't fix:

• Dataset alias(String alias) inherently unsafe
• Dataset withColumnRenamed(String existingName, String newName) inherently unsafe
• void createGlobalTempView(String viewName) inherently unsafe
• void createOrReplaceTempView(String viewName) inherently unsafe
• void createTempView(String viewName) inherently unsafe
• void registerTempTable(String tableName) inherently unsafe
• Dataset where(String conditionExpr) use select instead

TODO:

• KeyValueGroupedDataset<K,T> groupByKey(scala.Function1<T,K> func, Encoder evidence3)
• DataFrameNaFunctions na()
• DataFrameStatFunctions stat()
• Dataset dropDuplicates(String col1, String... cols)
• Dataset describe(String... cols)
• DataStreamWriter writeStream() (see #232)
• Dataset withWatermark(String eventTime, String delayThreshold) (see #232)
• RelationalGroupedDataset cube(Column... cols) (WIP #246)
• RelationalGroupedDataset rollup(String col1, String... cols) (WIP #246)

Done:

• Dataset sort(String sortCol, String... sortCols) (#248)
• Dataset sortWithinPartitions(String sortCol, String... sortCols) (#248)
• Dataset repartition(int numPartitions, Column... partitionExprs)
• Dataset drop(String... colNames) (#209)
• Dataset join(Dataset<?> right, Column joinExprs, String joinType)
• Dataset<scala.Tuple2<T,U>> joinWith(Dataset other, Column condition, String joinType)
• Dataset crossJoin(Dataset<?> right)
• Dataset agg(Column expr, Column... exprs)
• Column apply(String colName)
• Dataset as(Encoder evidence2)
• Dataset cache()
• Dataset coalesce(int numPartitions)
• Column col(String colName)
• Object collect()
• long count()
• Dataset distinct()
• Dataset except(Dataset other)
• void explain(boolean extended)
• <A,B> Dataset explode(String inputColumn, String outputColumn, scala.Function1<A,TraversableOnce<B f)
• Dataset filter(Column condition)
• Dataset filter(scala.Function1<T,Object> func)
• T first() (as firstOption)
• Dataset flatMap(scala.Function1<T,TraversableOnce> func, Encoder evidence8)
• void foreach(ForeachFunction func)
• void foreachPartition(scala.Function1<Iterator,scala.runtime.BoxedUnit> f)
• RelationalGroupedDataset groupBy(String col1, String... cols)
• Dataset intersect(Dataset other)
• Dataset limit(int n)
• Dataset map(scala.Function1<T,U> func, Encoder evidence6)
• Dataset mapPartitions(MapPartitionsFunction<T,U> f, Encoder encoder)
• Dataset persist(StorageLevel newLevel)
• void printSchema()
• RDD rdd()
• T reduce(scala.Function2<T,T,T> func) (as reduceOption)
• Dataset repartition(int numPartitions)
• Dataset sample(boolean withReplacement, double fraction, long seed)
• Dataset select(String col, String... cols)
• void show(int numRows, boolean truncate)
• Object take(int n)
• Dataset toDF()
• String toString()
• Dataset transform(scala.Function1<Dataset,Dataset> t)
• Dataset union(Dataset other)
• Dataset unpersist(boolean blocking)
• Dataset withColumn(String colName, Column col)
• Dataset orderBy(String sortCol, String... sortCols)
• String[] columns()
• org.apache.spark.sql.execution.QueryExecution queryExecution()
• StructType schema()
• SparkSession sparkSession()
• SQLContext sqlContext()
• Dataset checkpoint(boolean eager)
• String[] inputFiles()
• boolean isLocal()
• boolean isStreaming()
• Dataset[] randomSplit(double[] weights, long seed)
• StorageLevel storageLevel()
• Dataset toJSON()
• java.util.Iterator toLocalIterator()
• DataFrameWriter write()

Start by reviewing frameless.TypeDataset and comparing its API with the listed Spark Dataset TODO entries. Check the referenced issues for writeStream and withWatermark before choosing a method. Done means the selected missing API methods are implemented and the TODO list accurately reflects their status.