TopSubgraphUris.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 TopSubgraphUris {
/** Gets the list and distribution of the topN matched URIs in queries per subgraph.
*
* @param subgraphUrisMatchInQuery List of queries that matched with subgraphs due to a URI.
* Expected columns: id, subgraph, uri
* @param topN Number of top matched items to extract.
* @param subgraphWindow Window.partitionBy("subgraph").orderBy(desc("count"))
* @return spark dataframes:
* - topMatchedUris: expected columns: subgraph, top_uris
* - matchedUrisDistribution: expected columns: subgraph, matched_uris_percentiles, matched_uris_mean
*/
def getSubgraphUrisInfo(subgraphUrisMatchInQuery: DataFrame, topN: Long, subgraphWindow: WindowSpec): (DataFrame, DataFrame) = {
// Top URIs and distribution matched
// Add percent of query if necessary (q*100/total_q)
val matchedUrisPerSubgraph = subgraphUrisMatchInQuery
.groupBy("subgraph", "uri")
.agg(countDistinct("id").alias("count"))
// Top URIs
val topMatchedUris = sparkDfColumnsToMap(
matchedUrisPerSubgraph
.withColumn("rank", row_number().over(subgraphWindow))
.filter(col("rank") <= topN),
"uri",
"count",
"top_uris",
List("subgraph")
)
// distribution of URI usage in each subgraph
val matchedUrisDistribution = matchedUrisPerSubgraph
.groupBy("subgraph")
.agg(
expr(getPercentileExpr("count", "matched_uris_percentiles")),
expr("mean(count) as matched_uris_mean")
)
(topMatchedUris, matchedUrisDistribution)
}
}