Add docs aboout ivy #11

themodernlife · 2016-12-22T16:51:17Z

Hi Bryan,

Added some docs about the feature per comments on apache#15119.

Cheers,

…ings

…-settings

themodernlife · 2016-12-22T16:52:14Z

Wrong branch :/

…pressions ## What changes were proposed in this pull request? This PR changes the direction of expression transformation in the DecimalPrecision rule. Previously, the expressions were transformed down, which led to incorrect result types when decimal expressions had other decimal expressions as their operands. The root cause of this issue was in visiting outer nodes before their children. Consider the example below: ``` val inputSchema = StructType(StructField("col", DecimalType(26, 6)) :: Nil) val sc = spark.sparkContext val rdd = sc.parallelize(1 to 2).map(_ => Row(BigDecimal(12))) val df = spark.createDataFrame(rdd, inputSchema) // Works correctly since no nested decimal expression is involved // Expected result type: (26, 6) * (26, 6) = (38, 12) df.select($"col" * $"col").explain(true) df.select($"col" * $"col").printSchema() // Gives a wrong result since there is a nested decimal expression that should be visited first // Expected result type: ((26, 6) * (26, 6)) * (26, 6) = (38, 12) * (26, 6) = (38, 18) df.select($"col" * $"col" * $"col").explain(true) df.select($"col" * $"col" * $"col").printSchema() ``` The example above gives the following output: ``` // Correct result without sub-expressions == Parsed Logical Plan == 'Project [('col * 'col) AS (col * col)#4] +- LogicalRDD [col#1] == Analyzed Logical Plan == (col * col): decimal(38,12) Project [CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS (col * col)#4] +- LogicalRDD [col#1] == Optimized Logical Plan == Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4] +- LogicalRDD [col#1] == Physical Plan == *Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4] +- Scan ExistingRDD[col#1] // Schema root |-- (col * col): decimal(38,12) (nullable = true) // Incorrect result with sub-expressions == Parsed Logical Plan == 'Project [(('col * 'col) * 'col) AS ((col * col) * col)#11] +- LogicalRDD [col#1] == Analyzed Logical Plan == ((col * col) * col): decimal(38,12) Project [CheckOverflow((promote_precision(cast(CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS ((col * col) * col)#11] +- LogicalRDD [col#1] == Optimized Logical Plan == Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11] +- LogicalRDD [col#1] == Physical Plan == *Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11] +- Scan ExistingRDD[col#1] // Schema root |-- ((col * col) * col): decimal(38,12) (nullable = true) ``` ## How was this patch tested? This PR was tested with available unit tests. Moreover, there are tests to cover previously failing scenarios. Author: aokolnychyi <[email protected]> Closes apache#18583 from aokolnychyi/spark-21332.

…pressions ## What changes were proposed in this pull request? This PR changes the direction of expression transformation in the DecimalPrecision rule. Previously, the expressions were transformed down, which led to incorrect result types when decimal expressions had other decimal expressions as their operands. The root cause of this issue was in visiting outer nodes before their children. Consider the example below: ``` val inputSchema = StructType(StructField("col", DecimalType(26, 6)) :: Nil) val sc = spark.sparkContext val rdd = sc.parallelize(1 to 2).map(_ => Row(BigDecimal(12))) val df = spark.createDataFrame(rdd, inputSchema) // Works correctly since no nested decimal expression is involved // Expected result type: (26, 6) * (26, 6) = (38, 12) df.select($"col" * $"col").explain(true) df.select($"col" * $"col").printSchema() // Gives a wrong result since there is a nested decimal expression that should be visited first // Expected result type: ((26, 6) * (26, 6)) * (26, 6) = (38, 12) * (26, 6) = (38, 18) df.select($"col" * $"col" * $"col").explain(true) df.select($"col" * $"col" * $"col").printSchema() ``` The example above gives the following output: ``` // Correct result without sub-expressions == Parsed Logical Plan == 'Project [('col * 'col) AS (col * col)#4] +- LogicalRDD [col#1] == Analyzed Logical Plan == (col * col): decimal(38,12) Project [CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS (col * col)#4] +- LogicalRDD [col#1] == Optimized Logical Plan == Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4] +- LogicalRDD [col#1] == Physical Plan == *Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4] +- Scan ExistingRDD[col#1] // Schema root |-- (col * col): decimal(38,12) (nullable = true) // Incorrect result with sub-expressions == Parsed Logical Plan == 'Project [(('col * 'col) * 'col) AS ((col * col) * col)#11] +- LogicalRDD [col#1] == Analyzed Logical Plan == ((col * col) * col): decimal(38,12) Project [CheckOverflow((promote_precision(cast(CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS ((col * col) * col)#11] +- LogicalRDD [col#1] == Optimized Logical Plan == Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11] +- LogicalRDD [col#1] == Physical Plan == *Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11] +- Scan ExistingRDD[col#1] // Schema root |-- ((col * col) * col): decimal(38,12) (nullable = true) ``` ## How was this patch tested? This PR was tested with available unit tests. Moreover, there are tests to cover previously failing scenarios. Author: aokolnychyi <[email protected]> Closes apache#18583 from aokolnychyi/spark-21332. (cherry picked from commit 0be5fb4) Signed-off-by: gatorsmile <[email protected]>

…/`to_avro` ## What changes were proposed in this pull request? Previously in from_avro/to_avro, we override the method `simpleString` and `sql` for the string output. However, the override only affects the alias naming: ``` Project [from_avro('col, ... , (mode,PERMISSIVE)) AS from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))#11] ``` It only makes the alias name quite long: `from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))`). We should follow `from_csv`/`from_json` here, to override the method prettyName only, and we will get a clean alias name ``` ... AS from_avro(col)#11 ``` ## How was this patch tested? Manual check Closes apache#22890 from gengliangwang/revise_from_to_avro. Authored-by: Gengliang Wang <[email protected]> Signed-off-by: gatorsmile <[email protected]>

### Why are the changes needed? `EnsureRequirements` adds `ShuffleExchangeExec` (RangePartitioning) after Sort if `RoundRobinPartitioning` behinds it. This will cause 2 shuffles, and the number of partitions in the final stage is not the number specified by `RoundRobinPartitioning. **Example SQL** ``` SELECT /*+ REPARTITION(5) */ * FROM test ORDER BY a ``` **BEFORE** ``` == Physical Plan == *(1) Sort [a#0 ASC NULLS FIRST], true, 0 +- Exchange rangepartitioning(a#0 ASC NULLS FIRST, 200), true, [id=#11] +- Exchange RoundRobinPartitioning(5), false, [id=#9] +- Scan hive default.test [a#0, b#1], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [a#0, b#1] ``` **AFTER** ``` == Physical Plan == *(1) Sort [a#0 ASC NULLS FIRST], true, 0 +- Exchange rangepartitioning(a#0 ASC NULLS FIRST, 5), true, [id=#11] +- Scan hive default.test [a#0, b#1], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [a#0, b#1] ``` ### Does this PR introduce any user-facing change? No ### How was this patch tested? Run suite Tests and add new test for this. Closes apache#26946 from stczwd/RoundRobinPartitioning. Lead-authored-by: lijunqing <[email protected]> Co-authored-by: stczwd <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>

…from_avro`/`to_avro` Back port apache#22890 to branch-2.4. It is a bug fix for this issue: https://issues.apache.org/jira/browse/SPARK-26063 ## What changes were proposed in this pull request? Previously in from_avro/to_avro, we override the method `simpleString` and `sql` for the string output. However, the override only affects the alias naming: ``` Project [from_avro('col, ... , (mode,PERMISSIVE)) AS from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))#11] ``` It only makes the alias name quite long: `from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))`). We should follow `from_csv`/`from_json` here, to override the method prettyName only, and we will get a clean alias name ``` ... AS from_avro(col)#11 ``` ## How was this patch tested? Manual check Closes apache#23047 from gengliangwang/backport_avro_pretty_name. Authored-by: Gengliang Wang <[email protected]> Signed-off-by: hyukjinkwon <[email protected]>

BryanCutler and others added 4 commits May 11, 2016 16:51

added option to load IvySettings from file instead of hard-coded sett…

cc1ad11

…ings

added parenthesis to be consistent

97ea3a4

Merge remote-tracking branch 'upstream/master' into wip-spark-package…

1f472ff

…-settings

Adjusts documentation for Ivy-related settings.

7ac0b68

themodernlife closed this Dec 22, 2016

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themodernlife commented Dec 22, 2016

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