[SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide #22453

seancxmao · 2018-09-18T14:38:06Z

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

…erty in Spark SQL, DataFrames and Datasets Guide

seancxmao · 2018-09-18T14:45:45Z

@HyukjinKwon Could you please help review this?

HyukjinKwon · 2018-09-18T16:09:18Z

ok to test

SparkQA · 2018-09-18T16:31:09Z

Test build #96187 has finished for PR 22453 at commit 3af33a3.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

srowen · 2018-09-24T00:39:57Z

docs/sql-programming-guide.md

@@ -1002,6 +1002,15 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
    </p>
  </td>
 </tr>
+<tr>
+  <td><code>spark.sql.parquet.writeLegacyFormat</code></td>


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.

@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, 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.

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.

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.

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.

++1 for more information actually.

OK, I will update the doc and describe scenarios and reasons why we need this flag.

HyukjinKwon · 2018-09-24T02:51:45Z

cc @jaceklaskowski FYI

seancxmao · 2018-09-24T04:48:39Z

FYI. I had a brief survey on Parquet decimal support of computing engines at the time of writing.

Hive

HIVE-19069 Hive can't read int32 and int64 Parquet decimal. Not resolved yet.

Impala:

IMPALA-5628 Parquet support for additional valid decimal representations. This is an umbrella JIRA.
IMPALA-2494 Impala Unable to scan a Decimal column stored as Bytes. Fix Version/s: Impala 2.11.0.
IMPALA-5542 Impala cannot scan Parquet decimal stored as int64_t/int32_t. Fix Version/s: Impala 3.1.0, not released yet.

Presto

issues/7232. Can't read decimal type in parquet files written by spark and referenced as external in the hive metastore
issues/7533. Improve decimal type support in the new Parquet reader. Fixed Version: 0.182

srowen · 2018-09-24T13:04:51Z

docs/sql-programming-guide.md

+    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


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?

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.

I think it somehow leads to confusion since we call the option something "legacy" which isn't actually legacy in Parquet's decimal side.

Hive and Impala do NOT support new Parquet format yet.

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: 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: Improve decimal type support in the new Parquet reader. This patch is included in 0.182. Blow is the excerpt:

Fix reading decimal values in the optimized Parquet reader when they are backed by the int32 or int64 types.

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.

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

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?

spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala

Lines 450 to 458 in 473d0d8

// ===================================

// ArrayType and MapType (legacy mode)

// ===================================

// Spark 1.4.x and prior versions convert `ArrayType` with nullable elements into a 3-level

// `LIST` structure. This behavior is somewhat a hybrid of parquet-hive and parquet-avro

// (1.6.0rc3): the 3-level structure is similar to parquet-hive while the 3rd level element

// field name "array" is borrowed from parquet-avro.

case ArrayType(elementType, nullable @ true) if writeLegacyParquetFormat =>

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 (based upon Sean's comment):

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

SparkQA · 2018-09-24T15:00:42Z

Test build #96507 has finished for PR 22453 at commit e6f67c1.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

HyukjinKwon · 2018-09-24T16:00:26Z

docs/sql-programming-guide.md

+    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


BTW, let's match the doc in SQLConf as well.

Thanks for your suggestion. I have updated the doc in SQLConf.

SparkQA · 2018-09-25T06:36:50Z

Test build #96532 has finished for PR 22453 at commit 6e2680f.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2018-09-26T06:28:21Z

Test build #96590 has finished for PR 22453 at commit 3e51bd9.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

seancxmao · 2018-09-26T07:02:28Z

Retest this please.

SparkQA · 2018-09-26T11:06:39Z

Test build #96608 has finished for PR 22453 at commit 3e51bd9.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

srowen

I think the text is close enough.

HyukjinKwon · 2018-09-26T14:13:38Z

Merged to master and branch-2.4.

…erty 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 Closes #22453 from seancxmao/SPARK-20937. Authored-by: seancxmao <seancxmao@gmail.com> Signed-off-by: hyukjinkwon <gurwls223@apache.org> (cherry picked from commit cf5c9c4) Signed-off-by: hyukjinkwon <gurwls223@apache.org>

…erty 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 Closes apache#22453 from seancxmao/SPARK-20937. Authored-by: seancxmao <seancxmao@gmail.com> Signed-off-by: hyukjinkwon <gurwls223@apache.org>

[SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat prop…

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…erty in Spark SQL, DataFrames and Datasets Guide

HyukjinKwon approved these changes Sep 19, 2018

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srowen reviewed Sep 24, 2018

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add more information: reasons, scenarios, etc

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srowen reviewed Sep 24, 2018

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HyukjinKwon reviewed Sep 24, 2018

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match the doc in SQLConf with programming guide

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make doc concise and to the point

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HyukjinKwon approved these changes Sep 26, 2018

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[SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide #22453

[SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide #22453

seancxmao commented Sep 18, 2018

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HyukjinKwon commented Sep 26, 2018

	// ===================================
	// ArrayType and MapType (legacy mode)
	// ===================================

	// Spark 1.4.x and prior versions convert `ArrayType` with nullable elements into a 3-level
	// `LIST` structure. This behavior is somewhat a hybrid of parquet-hive and parquet-avro
	// (1.6.0rc3): the 3-level structure is similar to parquet-hive while the 3rd level element
	// field name "array" is borrowed from parquet-avro.
	case ArrayType(elementType, nullable @ true) if writeLegacyParquetFormat =>

[SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide #22453

[SPARK-20937][DOCS] Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide #22453

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seancxmao commented Sep 18, 2018

What changes were proposed in this pull request?

How was this patch tested?

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