TopSubgraphItems.scala
package org.wikidata.query.rdf.spark.metrics.queries.subgraphs
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.expressions.WindowSpec
import org.apache.spark.sql.functions.{col, countDistinct, expr, row_number}
import org.wikidata.query.rdf.spark.utils.SubgraphUtils.{getPercentileExpr, sparkDfColumnsToMap}
object TopSubgraphItems {
/** Gets the list and distribution of the topN matched items in queries per subgraph.
*
* @param subgraphQItemsMatchInQuery List of queries that matched with subgraphs due to an item.
* Expected columns: id, subgraph, item
* @param topN Number of top matched items to extract.
* @param subgraphWindow Window.partitionBy("subgraph").orderBy(desc("count"))
* @return spark dataframes:
* - topMatchedItems: expected columns: subgraph, top_items
* - matchedItemsDistribution: expected columns: subgraph, matched_items_percentiles, matched_items_mean
*/
def getSubgraphItemsInfo(subgraphQItemsMatchInQuery: DataFrame, topN: Long, subgraphWindow: WindowSpec): (DataFrame, DataFrame) = {
// Top items that caused queries to map to subgraphs and their distribution
val matchedItemsPerSubgraph = subgraphQItemsMatchInQuery
.groupBy("subgraph", "item")
.agg(countDistinct("id").alias("count"))
// Top items
val topMatchedItems = sparkDfColumnsToMap(
matchedItemsPerSubgraph
.withColumn("rank", row_number().over(subgraphWindow))
.filter(col("rank") <= topN),
"item",
"count",
"top_items",
List("subgraph")
)
// distribution of item usage in each subgraph
val matchedItemsDistribution = matchedItemsPerSubgraph
.groupBy("subgraph")
.agg(
expr(getPercentileExpr("count", "matched_items_percentiles")),
expr("mean(count) as matched_items_mean")
)
(topMatchedItems, matchedItemsDistribution)
}
}