[GitHub] [spark] AmplabJenkins commented on issue #27538: [SPARK-30785][SQL] Create table like should keep tracksPartitionsInCatalog same with source table
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[GitHub] [spark] SparkQA commented on issue #27538: [SPARK-30785][SQL] Create table like should keep tracksPartitionsInCatalog same with source table
SparkQA commented on issue #27538: [SPARK-30785][SQL] Create table like should keep tracksPartitionsInCatalog same with source table URL: https://github.com/apache/spark/pull/27538#issuecomment-584514666 **[Test build #118210 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/118210/testReport)** for PR 27538 at commit [`eae0731`](https://github.com/apache/spark/commit/eae0731ab86a983bd11a0fcdf37c2e97e5858b7b). * This patch **fails Spark unit tests**. * This patch merges cleanly. * This patch adds no public classes. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] SparkQA removed a comment on issue #27538: [SPARK-30785][SQL] Create table like should keep tracksPartitionsInCatalog same with source table
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[GitHub] [spark] AmplabJenkins commented on issue #27539: [SPARK-30786] [BLOCK MANAGER] Fix Block replication failure propogation issue in BlockManager
AmplabJenkins commented on issue #27539: [SPARK-30786] [BLOCK MANAGER] Fix Block replication failure propogation issue in BlockManager URL: https://github.com/apache/spark/pull/27539#issuecomment-584514388 Can one of the admins verify this patch? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] prakharjain09 opened a new pull request #27539: [SPARK-30786] [BLOCK MANAGER] Fix Block replication failure propogation issue in BlockManager
prakharjain09 opened a new pull request #27539: [SPARK-30786] [BLOCK MANAGER] Fix Block replication failure propogation issue in BlockManager URL: https://github.com/apache/spark/pull/27539 What changes were proposed in this pull request? Currently the uploadBlockSync api in BlockTransferService always succeeds irrespective of whether the BlockManager was able to successfully replicate a block on peer block manager or not. This PR makes sure that the NettyBlockRpcServer invokes onFailure callback when it is not able to replicate the block to itself because of any reason. The onFailure callback makes sure that the BlockTransferService on client side gets the failure and retry replication the Block on some other BlockManager. Why are the changes needed? Currently the Spark Block replication retry logic is not working correctly. It doesn't retry on other Block managers even when replication fails on 1 of the peers. A user can cache an DataFrame with different replication factor. Ex - df.persist(StorageLevel.MEMORY_ONLY_2) - This will cache each partition at two different BlockManagers. When a DataFrame partition is computed first time, it is firstly stored locally on the local BlockManager and then it is replicated to other block managers based on replication factor config. The replication of block to other block managers might fail because of memory/network etc issues and so there is already provision to retry the replication on some other peer based on "spark.storage.maxReplicationFailures" config, Currently when this replication fails, the client does not know about the failure and so it doesn't retry on other peers. This PR fixes this issue. Does this PR introduce any user-facing change? No. How was this patch tested? Added Unit Test. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] SparkQA commented on issue #27488: [SPARK-26580][SQL][ML][FOLLOW-UP] Throw exception when use untyped UDF by default
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[GitHub] [spark] SparkQA commented on issue #27207: [WIP][SPARK-18886][CORE] Make Locality wait time measure resource under utilization due to delay scheduling.
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[GitHub] [spark] cloud-fan commented on issue #24902: [SPARK-28093][SQL] Fix TRIM/LTRIM/RTRIM function parameter order issue
cloud-fan commented on issue #24902: [SPARK-28093][SQL] Fix TRIM/LTRIM/RTRIM function parameter order issue URL: https://github.com/apache/spark/pull/24902#issuecomment-584511167 I don't see the specification of the TRIM function parameters order in the SQL standard, and I assume it's vendor-specific. I agree that most SQL systems use the new order, but it doesn't mean the old order is wrong. More importantly, the SQL standard does define the TRIM syntax as `TRIM ( [ characters FROM ] string )`, and I think this is the thing we should follow. I'd suggest we revert this commit (how can users figure out the parameter order of a function is changed? TRIM is a common function and this can break many queries silently.). We can implement `TRIM ( [ characters FROM ] string )` in 3.1. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support `SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support
`SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
URL: https://github.com/apache/spark/pull/27524#discussion_r377477732
##
File path: sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
##
@@ -541,7 +541,7 @@ object Decimal {
/** Maximum number of decimal digits a Long can represent */
val MAX_LONG_DIGITS = 18
- private val POW_10 = Array.tabulate[Long](MAX_LONG_DIGITS + 1)(i =>
math.pow(10, i).toLong)
+ val POW_10 = Array.tabulate[Long](MAX_LONG_DIGITS + 1)(i => math.pow(10,
i).toLong)
Review comment:
`POW_10` is need in the wrapper of `FastDateFormat` to support
parsing/formatting in microsecond precision. Similar changes were made in Spark
2.4.
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[GitHub] [spark] SparkQA commented on issue #27524: [WIP][SQL] Support `SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
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[GitHub] [spark] SparkQA commented on issue #27488: [SPARK-26580][SQL][ML][FOLLOW-UP] Throw exception when use untyped UDF by default
SparkQA commented on issue #27488: [SPARK-26580][SQL][ML][FOLLOW-UP] Throw exception when use untyped UDF by default URL: https://github.com/apache/spark/pull/27488#issuecomment-584510148 **[Test build #118216 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/118216/testReport)** for PR 27488 at commit [`549cdf4`](https://github.com/apache/spark/commit/549cdf4d1d22f34091439cc47f486dd76e2a0de8). This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] Ngone51 commented on issue #27488: [SPARK-26580][SQL][ML][FOLLOW-UP] Throw exception when use untyped UDF by default
Ngone51 commented on issue #27488: [SPARK-26580][SQL][ML][FOLLOW-UP] Throw exception when use untyped UDF by default URL: https://github.com/apache/spark/pull/27488#issuecomment-584509861 Jenkins, retest this please. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support `SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support
`SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
URL: https://github.com/apache/spark/pull/27524#discussion_r377476654
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala
##
@@ -831,12 +831,11 @@ abstract class ToTimestamp
}
case StringType =>
val zid = ctx.addReferenceObj("zoneId", zoneId,
classOf[ZoneId].getName)
-val locale = ctx.addReferenceObj("locale", Locale.US)
val tf = TimestampFormatter.getClass.getName.stripSuffix("$")
nullSafeCodeGen(ctx, ev, (string, format) => {
s"""
try {
- ${ev.value} = $tf$$.MODULE$$.apply($format.toString(), $zid,
$locale)
+ ${ev.value} =
$tf$$.MODULE$$.withStrongLegacy($format.toString(), $zid)
Review comment:
I removed `withStrongLegacy` by enum
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[GitHub] [spark] MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support `SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support
`SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
URL: https://github.com/apache/spark/pull/27524#discussion_r377476826
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TimestampFormatter.scala
##
@@ -90,47 +91,168 @@ class FractionTimestampFormatter(zoneId: ZoneId)
override protected lazy val formatter =
DateTimeFormatterHelper.fractionFormatter
}
-class LegacyTimestampFormatter(
+/**
+ * The custom sub-class of `GregorianCalendar` is needed to get access to
+ * protected `fields` immediately after parsing. We cannot use
+ * the `get()` method because it performs normalization of the fraction
+ * part. Accordingly, the `MILLISECOND` field doesn't contain original value.
+ *
+ * Also this class allows to set raw value to the `MILLISECOND` field
+ * directly before formatting.
+ */
+class MicrosCalendar(tz: TimeZone, digitsInFraction: Int)
+ extends GregorianCalendar(tz, Locale.US) {
+ // Converts parsed `MILLISECOND` field to seconds fraction in microsecond
precision.
+ // For example if the fraction pattern is `` then `digitsInFraction` =
4, and
+ // if the `MILLISECOND` field was parsed to `1234`.
+ def getMicros(): SQLTimestamp = {
+// Append 6 zeros to the field: 1234 -> 123400
+val d = fields(Calendar.MILLISECOND) * MICROS_PER_SECOND
+// Take the first 6 digits from `d`: 123400 -> 123400
+// The rest contains exactly `digitsInFraction`: `` = 10 ^
digitsInFraction
+// So, the result is `(1234 * 100) / (10 ^ digitsInFraction)
+d / Decimal.POW_10(digitsInFraction)
+ }
+
+ // Converts the seconds fraction in microsecond precision to a value
+ // that can be correctly formatted according to the specified fraction
pattern.
+ // The method performs operations opposite to `getMicros()`.
+ def setMicros(micros: Long): Unit = {
+val d = micros * Decimal.POW_10(digitsInFraction)
+fields(Calendar.MILLISECOND) = (d / MICROS_PER_SECOND).toInt
+ }
+}
+
+/**
+ * An instance of the class is aimed to re-use many times. It contains helper
objects
+ * `cal` which is reused between `parse()` and `format` invokes.
+ */
+class LegacyFastDateFormat(fastDateFormat: FastDateFormat) {
Review comment:
I removed the class
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[GitHub] [spark] Ngone51 commented on issue #22163: [SPARK-25166][CORE]Reduce the number of write operations for shuffle write.
Ngone51 commented on issue #22163: [SPARK-25166][CORE]Reduce the number of write operations for shuffle write. URL: https://github.com/apache/spark/pull/22163#issuecomment-584509149 After taking another detail look on this, I feel that this change may not bring expected performance improvement and is unnecessary. Before this PR, we'll only copy one record from `recordPage` to `writeBuffer` (even if there's remaining free space for following records) at each time and call `DiskBlockObjectWriter.write()` after copy. And this PR changes it to copy multiple records at each time until there's no free space in `writeBuffer`. Then, call `DiskBlockObjectWriter.write()` and write multiple records in batch. So it looks like that this PR tries to reduce the invocation on `DiskBlockObjectWriter.write()` and expectedly to reduce I/O operations(I guess). But please note that, `DiskBlockObjectWriter` itself has already backed by a buffer(which is more bigger than `writeBuffer`) in its `BufferedOutputStream`. So, it's unnecessary for us to bring the duplicate work upon `DiskBlockObjectWriter`. Any thoughts? @10110346 @kiszk @cloud-fan @maropu This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] SparkQA commented on issue #27533: [SPARK-30783] Exclude hive-service-rpc
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[GitHub] [spark] viirya commented on a change in pull request #27499: [SPARK-30590][SQL] Untyped select API cannot take typed column expression
viirya commented on a change in pull request #27499: [SPARK-30590][SQL] Untyped
select API cannot take typed column expression
URL: https://github.com/apache/spark/pull/27499#discussion_r377464564
##
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##
@@ -1430,6 +1430,11 @@ class Dataset[T] private[sql](
*/
@scala.annotation.varargs
def select(cols: Column*): DataFrame = withPlan {
Review comment:
Yea, I think so if I do not miss anything.
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[GitHub] [spark] AmplabJenkins removed a comment on issue #27537: [WIP][SPARK-30668][SQL][FOLLOWUP] Raise exception instead of silent change for new TimestampFormatter
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[GitHub] [spark] MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support `SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
MaxGekk commented on a change in pull request #27524: [WIP][SQL] Support
`SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp formatters
URL: https://github.com/apache/spark/pull/27524#discussion_r377463939
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala
##
@@ -831,12 +831,11 @@ abstract class ToTimestamp
}
case StringType =>
val zid = ctx.addReferenceObj("zoneId", zoneId,
classOf[ZoneId].getName)
-val locale = ctx.addReferenceObj("locale", Locale.US)
val tf = TimestampFormatter.getClass.getName.stripSuffix("$")
nullSafeCodeGen(ctx, ev, (string, format) => {
s"""
try {
- ${ev.value} = $tf$$.MODULE$$.apply($format.toString(), $zid,
$locale)
+ ${ev.value} =
$tf$$.MODULE$$.withStrongLegacy($format.toString(), $zid)
Review comment:
> is it because SIMPLE_DATE_FORMAT is an enum, and tricky for the codegen
part to reference?
Yes, let me figure out how to use Scala enum from Java
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[GitHub] [spark] SparkQA commented on issue #27537: [WIP][SPARK-30668][SQL][FOLLOWUP] Raise exception instead of silent change for new TimestampFormatter
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