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Provide Highcharts Support for Spark either in Zepplin or spark-shell
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.knockdata.spark.highcharts
import org.apache.spark.sql.functions._
import org.apache.spark.sql.{Column, DataFrame, Row}
import scala.collection.mutable
import model._
import SeriesHolder.Defs
object convert {
def getFieldColumnMap(defs: Defs, withDrilldownField: Boolean): List[(String, String)] = {
if (withDrilldownField)
("drilldown" -> "drilldown") :: defs.allColsMap
else
defs.allColsMap
}
/**
* get data from dataFrame with drill definition
* using groupBy and agg
*
* @param dataFrame
* @param defs (jsonFieldName -> columnName) or
* (jsonField -> Column) the Column is with agg function
* ("orderBy" -> Column)
* @return (columns, rows)
*/
private[spark] def getRows(dataFrame: DataFrame,
currentKeys: List[String],
defs: Defs,
withDrilldownField: Boolean): Array[Row] = {
val dfAgg =
if (defs.aggDefs.isEmpty || defs.nameDefs.isEmpty) {
dataFrame
}
else {
val groupByCols = defs.nameDefs.map {
case (jsonFieldName, colName: String) =>
col(colName)
}
val aggCols = defs.aggDefs.map {
case (jsonFieldName, aggCol: Column) =>
aggCol
}
dataFrame
.groupBy(groupByCols: _*)
.agg(aggCols.head, aggCols.tail: _*)
}
val dfCustomized: DataFrame = (dfAgg /: defs.df2dfDefs)((temp: DataFrame, df2df) => df2df(temp))
val wantedCols = defs.wantedCol
val dfSelected = dfCustomized.select(wantedCols.head, wantedCols.tail:_*)
val dfDrill =
if (withDrilldownField)
withDrilldown(currentKeys, defs.nameDefs, dfSelected)
else
dfSelected
val rows: Array[Row] = defs.getRows(dfDrill)
val rowsCustomized = (rows /: defs.ar2arDefs)((ar: Array[Row], ar2ar) => ar2ar(ar))
rowsCustomized
}
private[spark] def withDrilldown(currentKeys: List[String],
colNameDefs: List[(String, Any)],
aggDF: DataFrame): DataFrame = {
val previousDrillField =
if (currentKeys.isEmpty)
""
else
currentKeys.mkString(",") + ","
colNameDefs match {
// groupBy one column
case (firstJsonFieldName, firstColName: String) :: Nil =>
val udfDrilldown1 = udf[String, String] {
(v1: String) => s"$previousDrillField$firstColName=$v1"
}
aggDF.withColumn("drilldown",
udfDrilldown1(col(firstColName)))
// groupBy two columns
case (firstJsonFieldName, firstColName: String)
:: (secondJsonFieldName, secondColName: String)
:: Nil =>
val udfDrilldown2 = udf[String, String, String] {
(v1: String, v2: String) => s"$previousDrillField$firstColName=$v1,$secondColName=$v2"
}
aggDF.withColumn("drilldown",
udfDrilldown2(col(firstColName), col(secondColName)))
case _ =>
throw new Exception("Only groupBy 1 or 2 columns are supported")
}
}
/**
* From the current dataFrame and what data is now
* get the next level DataFrames wanna drilldown
*
* @param currentDataFrame the current dataFrame
* @param row the current row for the data point.
* highchart will drilldown to next level when click it.
* @param groupByColumns (jsonFieldName -> columnName)
* @return (keys, nextDataFrame)
* example: (List("marital=single", "job=entrepreneur"), nextDataFrame)
*/
private[spark] def nextDFs(currentDataFrame: DataFrame,
row: Row,
groupByColumns: List[(String, String)]): (List[String], DataFrame) = {
val keys = groupByColumns.map {
case (jsonFieldName, columnName) =>
// the original column has been alias to jsonFieldName
// for easy convert to map data point
val colVal = row.getAs[Any](columnName)
s"$columnName=$colVal"
}
// groupBy columns does not need for next level drilldown
// for each level of drill down, columns getting less and less
// e.g the original columns are
// marital, job, education, balance
// if we drilldown marital, then the next level only need
// job, education, balance
// if we drilldown job, then the next level only need
// education, balance
val wantedCols = currentDataFrame.columns.toList diff groupByColumns
// in case of there are multiple groupByColumns
// filter one groupBy column a time
// start from currentDataFrame
val nextDF = (currentDataFrame /: groupByColumns) (
(df: DataFrame, pair: (String, String)) => {
val (_, colName) = pair
df.filter(col(colName) === row.getAs[Any](colName))
}
).selectExpr(wantedCols: _*)
(keys, nextDF)
}
private case class Data(keys: List[String],
withDrilldownField: Boolean,
defs: Defs,
rows: Array[Row]) {
var _name: String = if (keys.isEmpty) "" else keys.last
def setName(name: Any): Data = {
_name = name.toString
this
}
def name: String = _name
}
/**
* drilldown one level at time from currentDataFrame with a recursive call.
* All drilldown data store in the buffer
*
* @param currentDataFrame
* @param allDefs List(List(jsonFieldName, groupByColumnName/aggColumn))
* the number of left drills will decrease for each level drilldown
* when it's empty, it does not need be drilldown any further
* @param currentKeys Nil for root.
* The number of keys will increase for each level drilldown
* @param buffer to store the drilldown data
*/
private def drilldown(currentDataFrame: DataFrame,
allDefs: List[Defs],
currentKeys: List[String],
buffer: mutable.ListBuffer[Data]): Unit = {
val currentDrilldown :: restDrills = allDefs
val withDrilldownField = restDrills.nonEmpty
val rows = getRows(currentDataFrame, currentKeys, currentDrilldown, withDrilldownField)
buffer += Data(currentKeys, withDrilldownField, currentDrilldown, rows)
// drilldown if there are more levels
if (restDrills.nonEmpty) {
// val drillColumns = currentDrilldown.filter(_._2.isInstanceOf[String])
// .map(pair => (pair._1, pair._2.asInstanceOf[String]))
// only collect if it's groupByColumnName
val drillColumns = currentDrilldown.nameDefs
// for each row, we need drilldown once (create one more data series)
rows.foreach {
row =>
val (nextKeys, nextDF) = nextDFs(currentDataFrame, row, drillColumns)
val fullNextKeys: List[String] = currentKeys ::: nextKeys
drilldown(nextDF,
restDrills,
fullNextKeys,
buffer)
}
}
}
/**
* create a drilldown highchart with the rootDataFrame
*
* @param rootDataFrame
* @param allDefs List((jsonFieldName, groupByColumnName/aggColumn))*
* @return highchart
*/
def apply(rootDataFrame: DataFrame,
allDefs: List[Defs]): (List[Series], List[Series]) = {
val buffer = mutable.ListBuffer[Data]()
drilldown(rootDataFrame, allDefs, Nil, buffer)
val normalData :: drilldownData = buffer.result()
val normalSeries = toSeries(normalData)
val drilldownSeriesList =
drilldownData match {
case Nil =>
Nil
case xs =>
toSeriesList(drilldownData)
}
(List(normalSeries), drilldownSeriesList)
}
def getCategories(rootDataFrame: DataFrame,
defs: Defs): List[(String, Map[String, Any])] = {
// if there is only one column, then no category needed
if ((defs.nameDefs.size + defs.aggDefs.size) == 1) {
Nil
}
else {
val dfOrdered =
if (defs.orderByCols.isEmpty) {
rootDataFrame
} else {
rootDataFrame.orderBy(defs.orderByCols: _*)
}
val (categoryFieldName, categoryCol) = defs.nameDefs.head
val rows = dfOrdered.selectExpr(categoryCol).distinct.collect.toList
val nulls: Map[String, Any] = (defs.nameDefs.tail ++ defs.aggDefs).map {
case (jsonFieldName, column) =>
jsonFieldName -> null
}.toMap
rows.map {
row =>
val category = row.get(0)
val defaultValues = nulls.updated(categoryFieldName, category)
category.toString -> defaultValues
}
}
}
def apply(rootDataFrame: DataFrame,
seriesCol: String,
allDefs: List[Defs]): (List[Series], List[Series]) = {
val allSeriesValues = rootDataFrame.select(seriesCol).distinct.orderBy(col(seriesCol)).collect.map(_.get(0))
val categories = getCategories(rootDataFrame, allDefs.head)
val column = col(seriesCol)
val wantedCols = rootDataFrame.columns.filter(_ != seriesCol)
val bufferNormalSeries = mutable.ListBuffer[Data]()
val bufferDrilldownSeries = mutable.ListBuffer[Data]()
for (aSeriesValue <- allSeriesValues) {
val seriesDataFrame =
rootDataFrame.filter(column === aSeriesValue).selectExpr(wantedCols: _*)
val keys = s"$seriesCol=${aSeriesValue.toString}" :: Nil
val buffer = mutable.ListBuffer[Data]()
drilldown(seriesDataFrame, allDefs, keys, buffer)
val normalSeries :: drilldownSeries = buffer.result()
bufferNormalSeries += normalSeries.setName(aSeriesValue)
bufferDrilldownSeries ++= drilldownSeries
}
val normalSeriesList = toSeriesList(bufferNormalSeries.result(), categories)
val drilldownSeriesList = toSeriesList(bufferDrilldownSeries.result())
(normalSeriesList, drilldownSeriesList)
}
private def toSeries(data: Data): Series = {
val colsMap =
if (data.withDrilldownField)
"drilldown" -> "drilldown" :: data.defs.allColsMap
else
data.defs.allColsMap
val mapData: Array[Map[String, Any]] = data.rows.map {
row =>
colsMap.map {
case (jsonFieldName, columnName) =>
jsonFieldName -> row.getAs[Any](columnName)
}.toMap
}
val series =
new Series(mapData.toList)
.id(data.keys.mkString(","))
.name(data.name)
(series /: data.defs.s2sDefs)((s: Series, s2s) => s2s(s))
}
def getNameRows(data: Data): Map[String, Row] = {
data.rows.map {
row =>
val category = row.get(0).toString
(category, row)
}.toMap
}
private def toSeries(data: Data, categories: List[(String, Map[String, Any])]): Series = {
val nameRows = getNameRows(data)
val colsMap =
if (data.withDrilldownField)
"drilldown" -> "drilldown" :: data.defs.allColsMap
else
data.defs.allColsMap
val mapData: List[Map[String, Any]] =
categories.map {
case (category, defaultValues) =>
if (nameRows.contains(category)) {
val row = nameRows(category)
colsMap.map {
case (jsonFieldName, columnName) =>
jsonFieldName -> row.getAs[Any](columnName)
}.toMap
}
else {
defaultValues
}
}
val series = new Series(mapData)
.id(data.keys.mkString(","))
.name(data.name)
(series /: data.defs.s2sDefs)((s: Series, s2s) => s2s(s))
}
/**
* convert data to series
*
* @param allData
* @return
*/
private def toSeriesList(allData: List[Data]): List[Series] = {
val allSeries = allData.map(toSeries)
allSeries
}
private def toSeriesList(allData: List[Data], categories: List[(String, Map[String, Any])]): List[Series] = {
val allSeries = allData.map {
categories match {
case Nil =>
toSeries
case xs =>
toSeries(_, categories)
}
}
allSeries
}
}