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SHS-NG M4.5: Simplify API resource structure. #11
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With the new UI store, the API resource classes have a lot less code, since there's no need for complicated translations between the UI types and the API types. So the code ended up with a bunch of files with a single method declared in them. This change re-structures the API code so that it uses less classes; mainly, most sub-resources were removed, and the code to deal with single-attempt and multi-attempt apps was simplified. The only change was the addition of a method to return a single attempt's information; that was missing in the old API, so trying to retrieve "/v1/applications/appId/attemptId" would result in a 404 even if the attempt existed (and URIs under that one would return valid data). The streaming API resources also overtook the same treatment; the streaming backend is still not hooked up to the store, but once it is, the code in the remaining classes will be simplified even further.
vanzin
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Jul 20, 2017
…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 <anton.okolnychyi@sap.com> Closes apache#18583 from aokolnychyi/spark-21332.
vanzin
pushed a commit
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Oct 3, 2017
…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 <anton.okolnychyi@sap.com> Closes apache#18583 from aokolnychyi/spark-21332. (cherry picked from commit 0be5fb4) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
vanzin
pushed a commit
that referenced
this pull request
May 8, 2018
…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 <anton.okolnychyi@sap.com> Closes apache#18583 from aokolnychyi/spark-21332. (cherry picked from commit 0be5fb4) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
vanzin
pushed a commit
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Jun 21, 2018
…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 <anton.okolnychyi@sap.com> Closes apache#18583 from aokolnychyi/spark-21332. (cherry picked from commit 0be5fb4) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
vanzin
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Oct 31, 2018
…/`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 <gengliang.wang@databricks.com> Signed-off-by: gatorsmile <gatorsmile@gmail.com>
vanzin
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Jan 11, 2019
…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 <gengliang.wang@databricks.com> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
vanzin
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Jan 2, 2020
### 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 <lijunqing@baidu.com> Co-authored-by: stczwd <qcsd2011@163.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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With the new UI store, the API resource classes have a lot less code,
since there's no need for complicated translations between the UI
types and the API types. So the code ended up with a bunch of files
with a single method declared in them.
This change re-structures the API code so that it uses less classes;
mainly, most sub-resources were removed, and the code to deal with
single-attempt and multi-attempt apps was simplified.
The only change was the addition of a method to return a single
attempt's information; that was missing in the old API, so trying
to retrieve "/v1/applications/appId/attemptId" would result in a
404 even if the attempt existed (and URIs under that one would
return valid data).
The streaming API resources also overtook the same treatment; the
streaming backend is still not hooked up to the store, but once it
is, the code in the remaining classes will be simplified even
further.