Ohad Raviv created SPARK-18747: ---------------------------------- Summary: UDF multiple evaluations causes very poor performance Key: SPARK-18747 URL: https://issues.apache.org/jira/browse/SPARK-18747 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 1.6.1 Reporter: Ohad Raviv
We have a use case where we have a relatively expensive UDF that needs to be calculated. The problem is that instead of being calculated once, it gets calculated over and over again. for example: {quote} def veryExpensiveCalc(str:String) = \{println("blahblah1"); "nothing"\} hiveContext.udf.register("veryExpensiveCalc", veryExpensiveCalc _) hiveContext.sql("select * from (select veryExpensiveCalc('a') c)z where c is not null and c<>''").show {quote} with the output: {quote} blahblah1 blahblah1 blahblah1 +-------+ | c| +-------+ |nothing| +-------+ {quote} You can see that for each reference of column "c" you will get the println. that causes very poor performance for our real use case. This also came out on StackOverflow: http://stackoverflow.com/questions/40320563/spark-udf-called-more-than-once-per-record-when-df-has-too-many-columns http://stackoverflow.com/questions/34587596/trying-to-turn-a-blob-into-multiple-columns-in-spark/ with two problematic work-arounds: 1. cache() after the first time. e.g. {quote} hiveContext.sql("select veryExpensiveCalc('a') as c").cache().where("c is not null and c<>''").show {quote} while it works, in our case we can't do that because the table is too big to cache. 2. move back and forth to rdd: {quote} val df = hiveContext.sql("select veryExpensiveCalc('a') as c") hiveContext.createDataFrame(df.rdd, df.schema).where("c is not null and c<>''").show {quote} which works but then we loose some of the optimizations like push down predicate features, etc. and its very ugly. Any ideas on how we can make the UDF get calculated just once in a reasonable way? -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org