[SPARK-22515] [SQL] Estimation relation size based on numRows * rowSize #19743
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Test build #83841 has finished for PR 19743 at commit
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Test build #84256 has finished for PR 19743 at commit
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ping @cloud-fan Could you review this? |
cloud-fan
reviewed
Nov 28, 2017
| val attrStats = AttributeMap(planOutput.flatMap(a => colStats.get(a.name).map(a -> _))) | ||
| // Estimate size as number of rows * row size. | ||
| val size = EstimationUtils.getOutputSize(planOutput, rowCount.get, attrStats) | ||
| Statistics(sizeInBytes = size, rowCount = rowCount, attributeStats = attrStats) | ||
| } else { | ||
| // When CBO is disabled, we apply the size-only estimation strategy, so there's no need to |
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now we need to update the comment: when CBP is disabled or the table doesn't have statistics
cloud-fan
reviewed
Nov 28, 2017
| @@ -41,7 +41,35 @@ import org.apache.spark.sql.types._ | |||
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| class StatisticsSuite extends StatisticsCollectionTestBase with TestHiveSingleton { | |||
| test("Hive serde tables should fallback to HDFS for size estimation") { | |||
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| test("size estimation for relations based on row size * number of rows") { | |||
cloud-fan
reviewed
Nov 28, 2017
| val hiveTbl = "rel_est_hive_table" | ||
| withTable(dsTbl, hiveTbl) { | ||
| spark.range(1000L).write.format("parquet").saveAsTable(dsTbl) | ||
| sql(s"CREATE TABLE $hiveTbl STORED AS parquet AS SELECT * FROM $dsTbl") |
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nit: spark.range(1000L).write.format("hive").saveAsTable(hiveTbl)
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LGTM |
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Test build #84263 has finished for PR 19743 at commit
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Thanks! Merged to master. |
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What changes were proposed in this pull request?
Currently, relation size is computed as the sum of file size, which is error-prone because storage format like parquet may have a much smaller file size compared to in-memory size. When we choose broadcast join based on file size, there's a risk of OOM. But if the number of rows is available in statistics, we can get a better estimation by
numRows * rowSize, which helps to alleviate this problem.How was this patch tested?
Added a new test case for data source table and hive table.