Spark >= 3.0 Since 3.0 Spark provides built-in optimizations for handling skewed joins – which can be enabled using spark.sql.adaptive.optimizeSkewedJoin.enabled property. See SPARK-29544 for details. Spark < 3.0 You clearly have a problem with a huge right data skew. Lets take a look a the statistics you’ve provided: df1 = [mean=4.989209978967438, stddev=2255.654165352454, count=2400088] df2 = … Read more
Use: SELECT p.upc, p.name, ph.price, ph.date FROM PRODUCT p LEFT JOIN PRICE_H ph ON ph.product_id = p.id JOIN (SELECT a.product_id, MAX(a.date) AS max_date FROM PRICE_H a GROUP BY a.product_id) x ON x.product_id = ph.product_id AND x.max_date = ph.date
As the docs you quote indicate, you can’t use $lookup on a sharded collection. So the best practice workaround is to perform the lookup yourself in a separate query. Perform your aggregate query. Pull the “localField” values from your query results into an array, possibly using Array#map. Perform a find query against the “from” collection, … Read more
No. You cannot specify which exact field you need to distinct with. It only works with the whole row. As of your problem – just make your query a subquery and in outer one GROUP BY user_id SELECT * FROM (SELECT a.user_id,a.updatecontents as city,b.country FROM userprofiletemp AS a LEFT JOIN userattributes AS b ON a.user_id=b.user_id … Read more
Try something like this: SELECT s.*, ss.* FROM student AS s LEFT JOIN student_story AS ss ON (ss.studentid = s.studentid) WHERE ss.dateline = ( SELECT MAX(dateline) FROM student_story AS ss2 WHERE ss2.studentid = s.studentid )
Simply put the “qa bug” criteria in the join: select t1.*, t2.name from #bug t1 left join #blocking t2 on t1.id = t2.id AND t2.name=”qa bug”
SELECT post.id, post.title, comment.id, comment.message FROM post OUTER APPLY ( SELECT TOP 1 * FROM comment с WHERE c.post_id = post.id ORDER BY date DESC ) comment or SELECT * FROM ( SELECT post.id, post.title, comment.id, comment.message, ROW_NUMBER() OVER (PARTITION BY post.id ORDER BY comment.date DESC) AS rn FROM post LEFT JOIN comment ON comment.post_id … Read more
Yes, you can use groupby to remove your duplicate lines. Do everything you’ve done to define left and right. Now, I define a new dataframe on your last line: left2=left.merge( right, how=’left’, on=’age’ ) df= left2.groupby([‘age’])[‘salary’].first().reset_index() df At first I used a .min(), which will give you the minimum salary at each age, as such: … Read more
select a.ip, a.os, a.hostname, a.port, a.protocol, b.state from a left join b on a.ip = b.ip and a.port = b.port