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SPARQL query and distinct count

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If you’re using Java and Jena’s ARQ, you can use ARQ’s extensions for aggregates. Your query would look something like: SELECT ?tag (count(distinct ?tag) as ?count) WHERE { ?r ns9:taggedWithTag ?tagresource. ?tagresource ns9:name ?tag } LIMIT 5000 The original SPARQL specification from 2008 didn’t include aggregates, but the current version, 1.1, from 2013 does.

pyspark count rows on condition

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count doesn’t sum Trues, it only counts the number of non null values. To count the True values, you need to convert the conditions to 1 / 0 and then sum: import pyspark.sql.functions as F cnt_cond = lambda cond: F.sum(F.when(cond, 1).otherwise(0)) test.groupBy(‘x’).agg( cnt_cond(F.col(‘y’) > 12453).alias(‘y_cnt’), cnt_cond(F.col(‘z’) > 230).alias(‘z_cnt’) ).show() +—+—–+—–+ | x|y_cnt|z_cnt| +—+—–+—–+ | bn| … Read more

Spark: How to translate count(distinct(value)) in Dataframe API’s

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What you need is the DataFrame aggregation function countDistinct: import sqlContext.implicits._ import org.apache.spark.sql.functions._ case class Log(page: String, visitor: String) val logs = data.map(p => Log(p._1,p._2)) .toDF() val result = logs.select(“page”,”visitor”) .groupBy(‘page) .agg(‘page, countDistinct(‘visitor)) result.foreach(println)

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