[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user asfgit closed the pull request at: https://github.com/apache/spark/pull/22453 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r220409331 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- Let's make it short and get rid of all other things orthogonal with the issue itself (I think the issue is specific to decimals). For instance, we could say: If `true`, it writes Parquet file in a way of Spark 1.4 and earlier, for instance, decimal values will be written in Apache Parquet's fixed-length byte array format, which other systems such as Apache Hive and Apache Impala use. If `false`, the newer format in Parquet will be used, for instance, decimals will be written based on int. If Parquet output is intended for use with systems that do not support this newer format, set to `true`. Please feel free to change words as what you think is righter --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user seancxmao commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r220407692 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- If we must call it "legacy", I'd think of it legacy implementation in Spark side, rather than legacy format in Parquet side. As comment in [SPARK-20297](https://issues.apache.org/jira/browse/SPARK-20297?focusedCommentId=15975559=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15975559) > The standard doesn't say that smaller decimals have to be stored in int32/int64, it just is an option for subset of decimal types. int32 and int64 are valid representations for a subset of decimal types. fixed_len_byte_array and binary are a valid representation of any decimal type. > >The int32/int64 options were present in the original version of the decimal spec, they just weren't widely implemented. So its not a new/old version thing, it was just an alternative representation that many systems didn't implement. Anyway, it really leads to confusion. Really appreciate your suggestion @srowen to make the doc shorter, the doc you suggested is more concise and to the point. One more thing I want to discuss. After investigating the usage of this option, I found this option is not only related to decimals, but also complex types (Array, Map), see below source code. Should we mention this in the doc? https://github.com/apache/spark/blob/473d0d862de54ec1c7a8f0354fa5e06f3d66e455/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala#L450-L458 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user srowen commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r220200276 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- It sounds like it isn't quite a legacy format, but one still used by Hive and even considered valid if not current by Parquet? This part I am not sure of, but basing it on Hyukjin's comment above. I suggest a somewhat shorter text like this, what do you think? its length would be more suitable as a config doc below. If `true`, then decimal values will be written in Apache Parquet's fixed-length byte array format. This is used by Spark 1.4 and earlier, and systems like Apache Hive and Apache Impala. If `false`, decimals will be written using the newer int format in Parquet. If Parquet output is intended for use with systems that do not support this newer format, set to `true`. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user seancxmao commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r220042478 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- Thanks for your suggestion. I have updated the doc in SQLConf. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user seancxmao commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r220038438 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- Hive and Impala do NOT support new Parquet format yet. * [HIVE-19069](https://jira.apache.org/jira/browse/HIVE-19069): Hive can't read int32 and int64 Parquet decimal. This issue is not resolved yet. This is consistent with source code check by @HyukjinKwon * [IMPALA-5542](https://issues.apache.org/jira/browse/IMPALA-5542): Impala cannot scan Parquet decimal stored as int64_t/int32_t. This is resolved, however targeted to Impala 3.1.0, which is a version not released yet. The latest release is 3.0.0 (https://impala.apache.org/downloads.html). Presto began to support new Parquet format since 0.182. * [issues/7533](https://github.com/prestodb/presto/issues/7533): Improve decimal type support in the new Parquet reader. This patch is included in [0.182](https://prestodb.io/docs/current/release/release-0.182.html). Blow is the excerpt: > Fix reading decimal values in the optimized Parquet reader when they are backed by the int32 or int64 types. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219895824 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- This is another issue since we call the option something "legacy" which isn't actually legacy in Parquet's decimal side. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219895047 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- I haven't checked closely but I think Hive still uses binary for decimals (https://github.com/apache/hive/blob/ae008b79b5d52ed6a38875b73025a505725828eb/ql/src/java/org/apache/hadoop/hive/ql/io/parquet/write/DataWritableWriter.java#L503-L541). Given my past investigation, thing is, Parquet supports both ways to write out (https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#decimal) IIRC. They deprecated timestamp based on int 96 (https://github.com/apache/parquet-format/blob/master/src/main/thrift/parquet.thrift#L782) but not decimals. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219892751 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- BTW, let's match the doc in `SQLConf` as well. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user srowen commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219827092 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,21 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat + false + +This configuration indicates whether we should use legacy Parquet format adopted by Spark 1.4 +and prior versions or the standard format defined in parquet-format specification to write +Parquet files. This is not only related to compatibility with old Spark ones, but also other +systems like Hive, Impala, Presto, etc. This is especially important for decimals. If this +configuration is not enabled, decimals will be written in int-based format in Spark 1.5 and +above, other systems that only support legacy decimal format (fixed length byte array) will not +be able to read what Spark has written. Note other systems may have added support for the +standard format in more recent versions, which will make this configuration unnecessary. Please --- End diff -- Yeah, I think Hive and Impala also use newer Parquet versions/format. Isn't it sufficient to say older versions of Spark (<= 1.4) and older versions of Hive, Impala (do we know which?) use older Parquet formats and this enables writing it that way? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user seancxmao commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219729166 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat --- End diff -- OK, I will update the doc and describe scenarios and reasons why we need this flag. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219722950 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat --- End diff -- ++1 for more information actually. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user srowen commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219722694 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat --- End diff -- OK that sounds important to document. But the reasoning in this thread is also more useful information I think. Instead of describing it as a legacy format (implying it's not valid Parquet or something) and that it's required for Hive and Impala, can we mention or point to the specific reason that would cause you to need this? The value of the documentation here is in whether it helps the user know when to set it one way or the other. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user seancxmao commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219721110 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat --- End diff -- I'd like to add my 2 cents. We use both Spark and Hive in our Hadoop/Spark clusters. And we have 2 types of tables, working tables and target tables. Working tables are only used by Spark jobs, while target tables are populated by Spark and exposed to downstream jobs including Hive jobs. Our data engineers frequently meet with this issue when they use Hive to read target tables. Finally we decided to set spark.sql.parquet.writeLegacyFormat=true as the default value for target tables and explicitly describe this in our internal developer guide. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219719299 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat --- End diff -- This is, of course, something we should remove in long term but my impression is that it's better to expose and explicitly mention we deprecate this later, and the remove it out. I already argued a bit (for instance in SPARK-20297) to explain how to workaround and why it is. Was thinking it's better document this and reduce such overhead at least. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219719166 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat --- End diff -- @srowen, actually, this configuration specifically related with compatibility with other systems like Impala (not only old Spark ones) where decimals are written based on fixed binary format (nowdays it's written in int-based in Spark). If this configurations is not enabled, they are unable to read what Spark wrote. Given https://stackoverflow.com/questions/44279870/why-cant-impala-read-parquet-files-after-spark-sqls-write and JIRA like [SPARK-20297](https://issues.apache.org/jira/browse/SPARK-20297), I think this configuration is kind of important. I even expected more documentation about this configuration specifically at the first place. Personally I have been thinking it would better to leave this configuration after 3.0 as well for better compatibility. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
Github user srowen commented on a diff in the pull request: https://github.com/apache/spark/pull/22453#discussion_r219717918 --- Diff: docs/sql-programming-guide.md --- @@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession + + spark.sql.parquet.writeLegacyFormat --- End diff -- This should go with the other parquet properties if anything, but, this one is so old I don't think it's worth documenting. It shouldn't be used today. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22453: [SPARK-20937][DOCS] Describe spark.sql.parquet.wr...
GitHub user seancxmao opened a pull request: https://github.com/apache/spark/pull/22453 [SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide ## What changes were proposed in this pull request? Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide. ## How was this patch tested? N/A You can merge this pull request into a Git repository by running: $ git pull https://github.com/seancxmao/spark SPARK-20937 Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/22453.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #22453 commit 3af33a31f528059b5f4a66e8ba10bf945eb6fa53 Author: seancxmao Date: 2018-09-18T14:32:18Z [SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
