[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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Github user MaxGekk commented on the issue: https://github.com/apache/spark/pull/21909 > Please document it in the migration guide. @gatorsmile Updated by the notice: - Since Spark 2.4, text-based datasources like CSV and JSON don't parse input lines if the required schema pushed down to the datasources in read is empty. The schema can be empty in the case of count(), for example. To set `true` to `spark.sql.legacy.bypassParserForEmptySchema` restores the previous behavior when the underlying parser is always invoked even for the empty schema. This option will be removed in Spark 3.0. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/21909 Please document it in the migration guide. --- - 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 #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
Github user felixcheung commented on the issue: https://github.com/apache/spark/pull/21909 got it --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
Github user MaxGekk commented on the issue: https://github.com/apache/spark/pull/21909 > does this benchmark result vary if we select col2 or col10? @felixcheung Not so much. Here is the benchmark for CSV. ``` JJava HotSpot(TM) 64-Bit Server VM 1.8.0_172-b11 on Mac OS X 10.13.6 Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz Count a dataset with 10 columns: Best/Avg Time(ms)Rate(M/s) Per Row(ns) Relative col0 + count()9097 / 9167 1.1 909.7 1.0X col2 + count()9294 / 9302 1.1 929.4 1.0X col5 + count()9346 / 9394 1.1 934.6 1.0X col7 + count()9227 / 9231 1.1 922.7 1.0X col9 + count()9141 / 9233 1.1 914.1 1.0X ``` --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
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[GitHub] spark issue #21909: [SPARK-24959][SQL] Speed up count() for JSON and CSV
Github user holdensmagicalunicorn commented on the issue: https://github.com/apache/spark/pull/21909 @MaxGekk, thanks! I am a bot who has found some folks who might be able to help with the review:@HyukjinKwon, @gatorsmile and @cloud-fan --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
