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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/10/08 08:31:52 UTC
[GitHub] [spark] dcoliversun opened a new pull request, #38160: [SPARK-40710][DOCS] Supplement undocumented parquet configurations in documentation
dcoliversun opened a new pull request, #38160:
URL: https://github.com/apache/spark/pull/38160
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### What changes were proposed in this pull request?
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This PR aims to supplement undocumented parquet configurations in documentation.
### Why are the changes needed?
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Help users to confirm configurations through documentation instead of code.
### Does this PR introduce _any_ user-facing change?
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Yes, more configurations in documentation.
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Pass the GA.
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[GitHub] [spark] dcoliversun commented on a diff in pull request #38160: [SPARK-40710][DOCS] Supplement undocumented parquet configurations in documentation
Posted by GitBox <gi...@apache.org>.
dcoliversun commented on code in PR #38160:
URL: https://github.com/apache/spark/pull/38160#discussion_r990613064
##########
docs/sql-data-sources-parquet.md:
##########
@@ -454,6 +454,28 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.3.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.int96TimestampConversion</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L899-L905
##########
docs/sql-data-sources-parquet.md:
##########
@@ -493,6 +526,17 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.5.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.respectSummaryFiles</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L872-L879
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1033-L1038
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized parquet decoding.
+ </td>
+ <td>2.0.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableNestedColumnVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized Parquet decoding for nested columns (e.g., struct, list, map).
+ Requires <code>spark.sql.parquet.enableVectorizedReader</code> to be enabled.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.recordLevelFilter.enabled</code></td>
+ <td>false</td>
+ <td>
+ If true, enables Parquet's native record-level filtering using the pushed down filters.
+ This configuration only has an effect when <code>spark.sql.parquet.filterPushdown</code>
+ is enabled and the vectorized reader is not used. You can ensure the vectorized reader
+ is not used by setting <code>spark.sql.parquet.enableVectorizedReader</code> to false.
+ </td>
+ <td>2.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.columnarReaderBatchSize</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1058-L1063
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized parquet decoding.
+ </td>
+ <td>2.0.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableNestedColumnVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized Parquet decoding for nested columns (e.g., struct, list, map).
+ Requires <code>spark.sql.parquet.enableVectorizedReader</code> to be enabled.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.recordLevelFilter.enabled</code></td>
+ <td>false</td>
+ <td>
+ If true, enables Parquet's native record-level filtering using the pushed down filters.
+ This configuration only has an effect when <code>spark.sql.parquet.filterPushdown</code>
+ is enabled and the vectorized reader is not used. You can ensure the vectorized reader
+ is not used by setting <code>spark.sql.parquet.enableVectorizedReader</code> to false.
+ </td>
+ <td>2.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.columnarReaderBatchSize</code></td>
+ <td>4096</td>
+ <td>
+ The number of rows to include in a parquet vectorized reader batch. The number should
+ be carefully chosen to minimize overhead and avoid OOMs in reading data.
+ </td>
+ <td>2.4.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.write.enabled</code></td>
+ <td>true</td>
+ <td>
+ Field ID is a native field of the Parquet schema spec. When enabled,
+ Parquet writers will populate the field Id metadata (if present) in the Spark schema to the Parquet schema.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.read.enabled</code></td>
+ <td>false</td>
+ <td>
+ Field ID is a native field of the Parquet schema spec. When enabled, Parquet readers
+ will use field IDs (if present) in the requested Spark schema to look up Parquet
+ fields instead of using column names.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.read.ignoreMissing</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1083-L1090
##########
docs/sql-data-sources-parquet.md:
##########
@@ -473,6 +495,17 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
<td>Enables Parquet filter push-down optimization when set to true.</td>
<td>1.2.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.aggregatePushdown</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1003-L1010
##########
docs/sql-data-sources-parquet.md:
##########
@@ -454,6 +454,28 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.3.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.int96TimestampConversion</code></td>
+ <td>false</td>
+ <td>
+ This controls whether timestamp adjustments should be applied to INT96 data when
+ converting to timestamps, for data written by Impala. This is necessary because Impala
+ stores INT96 data with a different timezone offset than Hive & Spark.
+ </td>
+ <td>2.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.outputTimestampType</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L911-L921
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized parquet decoding.
+ </td>
+ <td>2.0.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableNestedColumnVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized Parquet decoding for nested columns (e.g., struct, list, map).
+ Requires <code>spark.sql.parquet.enableVectorizedReader</code> to be enabled.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.recordLevelFilter.enabled</code></td>
+ <td>false</td>
+ <td>
+ If true, enables Parquet's native record-level filtering using the pushed down filters.
+ This configuration only has an effect when <code>spark.sql.parquet.filterPushdown</code>
+ is enabled and the vectorized reader is not used. You can ensure the vectorized reader
+ is not used by setting <code>spark.sql.parquet.enableVectorizedReader</code> to false.
+ </td>
+ <td>2.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.columnarReaderBatchSize</code></td>
+ <td>4096</td>
+ <td>
+ The number of rows to include in a parquet vectorized reader batch. The number should
+ be carefully chosen to minimize overhead and avoid OOMs in reading data.
+ </td>
+ <td>2.4.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.write.enabled</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1065-L1072
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized parquet decoding.
+ </td>
+ <td>2.0.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableNestedColumnVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized Parquet decoding for nested columns (e.g., struct, list, map).
+ Requires <code>spark.sql.parquet.enableVectorizedReader</code> to be enabled.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.recordLevelFilter.enabled</code></td>
+ <td>false</td>
+ <td>
+ If true, enables Parquet's native record-level filtering using the pushed down filters.
+ This configuration only has an effect when <code>spark.sql.parquet.filterPushdown</code>
+ is enabled and the vectorized reader is not used. You can ensure the vectorized reader
+ is not used by setting <code>spark.sql.parquet.enableVectorizedReader</code> to false.
+ </td>
+ <td>2.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.columnarReaderBatchSize</code></td>
+ <td>4096</td>
+ <td>
+ The number of rows to include in a parquet vectorized reader batch. The number should
+ be carefully chosen to minimize overhead and avoid OOMs in reading data.
+ </td>
+ <td>2.4.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.write.enabled</code></td>
+ <td>true</td>
+ <td>
+ Field ID is a native field of the Parquet schema spec. When enabled,
+ Parquet writers will populate the field Id metadata (if present) in the Spark schema to the Parquet schema.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.read.enabled</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1074-L1081
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized parquet decoding.
+ </td>
+ <td>2.0.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableNestedColumnVectorizedReader</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1040-L1046
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized parquet decoding.
+ </td>
+ <td>2.0.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableNestedColumnVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized Parquet decoding for nested columns (e.g., struct, list, map).
+ Requires <code>spark.sql.parquet.enableVectorizedReader</code> to be enabled.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.recordLevelFilter.enabled</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1048-L1056
##########
docs/sql-data-sources-parquet.md:
##########
@@ -505,6 +549,84 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
</td>
<td>1.6.0</td>
</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized parquet decoding.
+ </td>
+ <td>2.0.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.enableNestedColumnVectorizedReader</code></td>
+ <td>true</td>
+ <td>
+ Enables vectorized Parquet decoding for nested columns (e.g., struct, list, map).
+ Requires <code>spark.sql.parquet.enableVectorizedReader</code> to be enabled.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.recordLevelFilter.enabled</code></td>
+ <td>false</td>
+ <td>
+ If true, enables Parquet's native record-level filtering using the pushed down filters.
+ This configuration only has an effect when <code>spark.sql.parquet.filterPushdown</code>
+ is enabled and the vectorized reader is not used. You can ensure the vectorized reader
+ is not used by setting <code>spark.sql.parquet.enableVectorizedReader</code> to false.
+ </td>
+ <td>2.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.columnarReaderBatchSize</code></td>
+ <td>4096</td>
+ <td>
+ The number of rows to include in a parquet vectorized reader batch. The number should
+ be carefully chosen to minimize overhead and avoid OOMs in reading data.
+ </td>
+ <td>2.4.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.write.enabled</code></td>
+ <td>true</td>
+ <td>
+ Field ID is a native field of the Parquet schema spec. When enabled,
+ Parquet writers will populate the field Id metadata (if present) in the Spark schema to the Parquet schema.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.read.enabled</code></td>
+ <td>false</td>
+ <td>
+ Field ID is a native field of the Parquet schema spec. When enabled, Parquet readers
+ will use field IDs (if present) in the requested Spark schema to look up Parquet
+ fields instead of using column names.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.fieldId.read.ignoreMissing</code></td>
+ <td>false</td>
+ <td>
+ When the Parquet file doesn't have any field IDs but the
+ Spark read schema is using field IDs to read, we will silently return nulls
+ when this flag is enabled, or error otherwise.
+ </td>
+ <td>3.3.0</td>
+</tr>
+<tr>
+ <td><code>spark.sql.parquet.timestampNTZ.enabled</code></td>
Review Comment:
https://github.com/apache/spark/blob/309638eeefbfb13dae8dbded0279bf44390389ee/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L1092-L1101
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[GitHub] [spark] AmplabJenkins commented on pull request #38160: [SPARK-40710][DOCS] Supplement undocumented parquet configurations in documentation
Posted by GitBox <gi...@apache.org>.
AmplabJenkins commented on PR #38160:
URL: https://github.com/apache/spark/pull/38160#issuecomment-1273027726
Can one of the admins verify this patch?
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[GitHub] [spark] dcoliversun commented on pull request #38160: [SPARK-40710][DOCS] Supplement undocumented parquet configurations in documentation
Posted by GitBox <gi...@apache.org>.
dcoliversun commented on PR #38160:
URL: https://github.com/apache/spark/pull/38160#issuecomment-1272428305
@srowen Yes, related configurations are grouped together and are in logical order with each other.
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[GitHub] [spark] srowen closed pull request #38160: [SPARK-40710][DOCS] Supplement undocumented parquet configurations in documentation
Posted by GitBox <gi...@apache.org>.
srowen closed pull request #38160: [SPARK-40710][DOCS] Supplement undocumented parquet configurations in documentation
URL: https://github.com/apache/spark/pull/38160
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