What bothers me is that you are making sweeping statements about Spark inability to handle quote " ... the key weakness of Spark is 1) its poor performance when executing concurrent queries and 2) its poor resource utilization when executing multiple Spark applications concurrently" and conversely overstating Hive ability on handling MR. In fairness anything published in a public forum is fair game for analysis or criticism. Thenyou are expected to back it up. I cannot see how anyone could object to the statement: if you make a claim, be prepared to prove it.
I am open minded on this so please clarify the above statement HTH view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Sun, 8 Jan 2023 at 05:21, Sungwoo Park <glap...@gmail.com> wrote: > >> [image: image.png] >> >> from your posting, the result is amazing. glad to know hive on mr3 has >> that nice performance. >> > > Hive on MR3 is similar to Hive-LLAP in performance, so we can interpret > the above result as Hive being much faster than SparkSQL. For executing > concurrent queries, the performance gap is even greater. In my (rather > biased) opinion, the key weakness of Spark is 1) its poor performance when > executing concurrent queries and 2) its poor resource utilization when > executing multiple Spark applications concurrently. > > We released Hive on MR3 1.6 a couple of weeks ago. Now we have backported > about 700 patches to Hive 3.1. If interested, please check it out: > https://www.datamonad.com/ > > Sungwoo >
