[SPARK-26540][SQL] Support PostgreSQL numeric arrays without precision/scale by dongjoon-hyun · Pull Request #23458 · apache/spark

dongjoon-hyun · 2019-01-05T02:02:12Z

What changes were proposed in this pull request?

Currently, Spark cannot handle numeric[] type with decimal data because both precision and scale are considered as 0.

postgres=# CREATE TABLE t (v numeric[], d numeric);
CREATE TABLE
postgres=# INSERT INTO t VALUES('{1111.222,2222.332}', 222.4555);
INSERT 01

BEFORE

scala>valpgTable= spark.read.jdbc("jdbc:postgresql:postgres", "t", options)
pgTable: org.apache.spark.sql.DataFrame= [v: array<decimal(0,0)>, d: decimal(38,18)]
scala> pgTable.show
19/01/0418:07:38ERRORExecutor:Exception in task 0.0 in stage 0.0 (TID0)
java.lang.IllegalArgumentException: requirement failed: Decimal precision 4 exceeds max precision 0

AFTER

scala>valpgTable= spark.read.jdbc("jdbc:postgresql:postgres", "t", options)
pgTable: org.apache.spark.sql.DataFrame= [v: array<decimal(38,18)>, d: decimal(38,18)]
scala> pgTable.show()
+--------------------+--------------------+| v| d|+--------------------+--------------------+|[1111.22200000000...|222.4555000000000...|+--------------------+--------------------+

How was this patch tested?

Manual. Run the PostgresIntegrationSuite.

Closes#23456

…n/scale

dongjoon-hyun · 2019-01-05T02:03:55Z

cc @maropu

maropu · 2019-01-05T02:41:19Z

Thanks, @dongjoon-hyun! I’ll check tonight (away from a keyboard now)

maropu

This behavior is the same with v1.6?

dongjoon-hyun · 2019-01-05T02:44:55Z

Yes. Exactly. It uses decimal(38,18).

maropu · 2019-01-05T02:50:41Z

Also, did you check if PostgresIntegrationSuite passed in your env? It seems Jenkins doesnot run the integration tests before.

dongjoon-hyun · 2019-01-05T02:53:00Z

Yes. I ran it inside IntelliJ manually.

dongjoon-hyun · 2019-01-05T03:15:29Z

Also, cc @gatorsmile and @mgaido91 .

SparkQA · 2019-01-05T05:58:21Z

Test build #100764 has finished for PR 23458 at commit 9e1b8ce.

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

SparkQA · 2019-01-05T06:41:40Z

Test build #100765 has finished for PR 23458 at commit 74215de.

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

dongjoon-hyun · 2019-01-05T06:55:05Z

Retest this please.

SparkQA · 2019-01-05T08:05:01Z

Test build #100772 has finished for PR 23458 at commit 74215de.

This patch fails due to an unknown error code, -9.
This patch merges cleanly.
This patch adds no public classes.

dongjoon-hyun · 2019-01-05T09:01:16Z

Retest this please.

maropu · 2019-01-05T11:16:01Z

 case "timestamp" | "timestamptz" | "time" | "timetz" => Some(TimestampType)
 case "date" => Some(DateType)
- case "numeric" | "decimal" => Some(DecimalType.bounded(precision, scale))
+ case "numeric" | "decimal" if precision != 0 || scale != 0 =>


We need to check scale != 0, too?

Yes. Please see non-array type logic here https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L224-L226.

Aha, I see. Thanks.

Not a very important point, but I think we best use precision > 0 || scale > 0. I mean, there may happen that scale is negative. In that case, precision must be > 0. In the above code a numeric(0, -10) which is an invalid combination is parsed as DECIMAL(0, -10). Probably, we can rely that this case never happens, but I think having code robust also for error conditions is better.

Good point. If we consider negative scale cases, we should change all of the existing code together to be consistent.

+1 on this. We can probably also do that in a followup PR.

maropu · 2019-01-05T11:16:17Z

LGTM except for one question.

maropu · 2019-01-05T12:31:23Z

Can you also add 'Closes #23456' in the description? I found the duplicate jira ticket.

SparkQA · 2019-01-05T12:33:36Z

Test build #100777 has finished for PR 23458 at commit 74215de.

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

dongjoon-hyun · 2019-01-05T12:34:36Z

Oh, I didn't notice that. I see. Thanks.

dongjoon-hyun · 2019-01-05T12:35:05Z

Retest this please

maropu · 2019-01-05T13:29:01Z

my bad, I didn't notice the existing jira ticket, sorry.

dongjoon-hyun · 2019-01-05T14:11:58Z

BTW, we need to backport this to older branches, right?

maropu · 2019-01-05T14:28:00Z

Yea, I think so. Since v1.6 accepts this query, this is a kind of regression?

dongjoon-hyun · 2019-01-05T14:30:19Z

Yep. I agree.

mgaido91

I am just not sure why we have this PR duplicating #23456. Is that PR stale? It has been opened few hours ago, so I wouldn't consider it like that. Probably I am missing something. Thanks.

mgaido91 · 2019-01-05T15:19:27Z

 case "timestamp" | "timestamptz" | "time" | "timetz" => Some(TimestampType)
 case "date" => Some(DateType)
- case "numeric" | "decimal" => Some(DecimalType.bounded(precision, scale))
+ case "numeric" | "decimal" if precision != 0 || scale != 0 =>


Not a very important point, but I think we best use precision > 0 || scale > 0. I mean, there may happen that scale is negative. In that case, precision must be > 0. In the above code a numeric(0, -10) which is an invalid combination is parsed as DECIMAL(0, -10). Probably, we can rely that this case never happens, but I think having code robust also for error conditions is better.

dongjoon-hyun · 2019-01-05T15:38:41Z

Actually, two JIRA issues are created in a short time and I received a call on SPARK-26540. So, I didn't check other JIRA and other PR.

dongjoon-hyun · 2019-01-05T15:41:51Z

If @mgaido91 has objection for this PR, I'll close this one. I'm okay. It's fair.
Just tell him to receive this code together.

dongjoon-hyun · 2019-01-05T15:47:58Z

@maropu . I close this PR and JIRA issue together~

mgaido91

mine was just a question @dongjoon-hyun. I was wondering if the other PR had something wrong which I couldn't figure out. I'd really appreciate if you could provide your feedback and help there then. Thanks.

mgaido91 · 2019-01-05T15:59:52Z

 case "timestamp" | "timestamptz" | "time" | "timetz" => Some(TimestampType)
 case "date" => Some(DateType)
- case "numeric" | "decimal" => Some(DecimalType.bounded(precision, scale))
+ case "numeric" | "decimal" if precision != 0 || scale != 0 =>


+1 on this. We can probably also do that in a followup PR.

SparkQA · 2019-01-05T16:31:27Z

Test build #100783 has finished for PR 23458 at commit 74215de.

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

[SPARK-26540][SQL] Support PostgreSQL numeric arrays without precisio…
9e1b8ce
…n/scale

Add a test at PostgresIntegrationSuite
74215de

maropu reviewed Jan 5, 2019
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mgaido91 reviewed Jan 5, 2019
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dongjoon-hyun closed this Jan 5, 2019

dongjoon-hyun deleted the SPARK-26540-DECIMAL-ARRAY branch January 5, 2019 15:45

mgaido91 reviewed Jan 5, 2019
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maropu mentioned this pull request Jan 9, 2019
[SPARK-26538][SQL] Set default precision and scale for elements of postgres numeric array #23456
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