[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
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[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user CodingCat commented on the issue: https://github.com/apache/spark/pull/19810 When I contributed it back, the community is as looking at something else, so I didnât spend too much time to convince the people to reviewbut if the interests are raised again now, I am happy to pick it up again, --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user maropu commented on the issue: https://github.com/apache/spark/pull/19810 If the author is inactive, its ok to take this over by someone. But, we should first discuss more to get consensus for this feature. I'm not sure we could get enough performance gains at the expense of the current simple cache logic. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user eyalfa commented on the issue: https://github.com/apache/spark/pull/19810 @maropu , this looks rather cold :sunglasses: , but extremely interesting and relevant. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user CodingCat commented on the issue: https://github.com/apache/spark/pull/19810 @cloud-fan for this case, if the data has been dumped to disk or some non-local tasks are started, I/O is involved in addition to the overhead to start extra tasks. If all data is in-memory, only the overhead related to tasks' launching is there > It sounds like something can be done better in 3rd party data sources, or we need to change the Spark core just for a better table cache, which seems risky. Yes, some work can be done in 3rd party data sources, e.g. to avoid parsing overhead in parquet, Regarding the risk, in the current implementation, I directly modify the core part to add a new type of block and make it recognizable by BlockManager. The new RDD and dependency implementation is in SQL module. An alternative way to do that is implementing this new type block in SQL as well (but it needs some small refactoring to make BlockManager open to anything outside of Spark Core) I personally think it's a good feature to add without that much threat to existing code --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/19810 > store_sales is a 1TB-sized table in cloud storage, which is partitioned by 'location'. The first query, Q1, wants to output several metrics A, B, C for all stores in all locations. After that, a small team of 3 data scientists wants to do some causal analysis for the sales in different locations. To avoid unnecessary I/O and parquet/orc parsing overhead, they want to cache the whole table in memory in Q1. Reading your use case, it sounds like you are trying to optimize the case that data is too large to fit in memory. For that case, if a partition is on the disk, Spark needs to load the entire partition to memory before filtering blocks. It sounds like something can be done better in 3rd party data sources, or we need to change the Spark core just for a better table cache, which seems risky. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user CodingCat commented on the issue: https://github.com/apache/spark/pull/19810 @sadikovi thanks for the review, I replied in comments --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user CodingCat commented on the issue: https://github.com/apache/spark/pull/19810 reading less data is a observation from the input metrics in Spark UI which includes both of local/remote read in BlockManagers, and also the overhead in BlockManager layer itself (especially when the user chooses to cache with serialized format) but I didn't count how much it contributes to the speedup (and a small portion of data is in disk in my perf test) --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/19810 When all data is in memory, what do you mean by reading less data? Starting less tasks makes sense. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user CodingCat commented on the issue: https://github.com/apache/spark/pull/19810 Hi, @cloud-fan, this PR is not only for the case where the data size is larger than the memory size, even when all data is in-memory, I observed up to 10-40% speedup because the implementation here (1) read less data (2) started less tasks you can understand this PR as it implement the functionality of Parquet's footer for the in-memory table --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/19810 are you trying to optimize the case that data is too large to fit in memory? Spark RDD cache doesn't work well for this case. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #19810: [SPARK-22599][SQL] In-Memory Table Pruning without Extra...
Github user SparkQA commented on the issue: https://github.com/apache/spark/pull/19810 **[Test build #84152 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/84152/testReport)** for PR 19810 at commit [`accd549`](https://github.com/apache/spark/commit/accd5493464d5bd5f7401285defbd8ca2628ae68). --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
