[GitHub] [arrow-ballista] dariusgm commented on issue #30: [Discuss] Ballista Future Direction

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dariusgm commented on issue #30:
URL: https://github.com/apache/arrow-ballista/issues/30#issuecomment-1155694167

   What are the downsides of apache spark, why somebody should use ballista?
   
   imo as far as I read posts and watched a talk on yt, the memory consumption of spark is huge. Even a hello world in spark will consume a lot of memory. The reduced memory consumption can be a big advantage for ballista. I always have to increase the memory of our production spark jobs - sometimes to even 32GB per executor.
   
   What can do apache spark good?
   
   Its integration very good with the hadoop distributed file system. This is from my perspective the big advantage: the computation is taken place as close to the data as possible, moving only data when really required. Again, as far as I understand, this is currently not possible with ballista?
   
   And, as I am a data engineer and doing a lot of analytics, I really like the spark-shell to play around with the data. 
   
   I just started using rust, so maybe I am not the biggest help for implementing features, but I know spark from a developer perspective quite well as I am using it daily.
   
   What could be a direction: Have the data mostly/only in ram in the cluster, reducing the (slow) HDD/SSD reads and running the computation than on the arrow In-memory data frames. That maybe a niche to fit into. 
   
   


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