Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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---
@@ -444,7 +444,7 @@ class SessionCatalogSuite
Github user rxin commented on the issue:
https://github.com/apache/spark/pull/15135
I understand the reasons why you want to add this -- but I feel this is too
esoteric and if we add this one, there are also a lot of other cases that can
be added and I don't know where we would stop.
Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala
---
@@ -282,6 +271,24 @@ class SessionCatalog(
}
Github user zjffdu commented on the issue:
https://github.com/apache/spark/pull/14959
```
The internal SparkConf of the context will not be the same instance as conf.
```
This is the existing implementation that python is different from scala.
But I think it is correct. I
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Github user citoubest commented on the issue:
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@rxin @davies @srowen
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Github user sethah commented on the issue:
https://github.com/apache/spark/pull/14808
https://github.com/apache/spark/pull/14834 is merged now. We did not
implement a new API, but we can still update the logistic regression examples
to show the new multiclass functionality.
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Github user HyukjinKwon commented on the issue:
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I mean the problem in the JIRA is not reproduced in the master branch and
therefore I believe we need another JIRA to describe the support for other time
formats as the same one as casting
Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/15054#discussion_r79539702
--- Diff:
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---
@@ -357,6 +346,21 @@ class SessionCatalog(
Github user zjffdu commented on a diff in the pull request:
https://github.com/apache/spark/pull/14959#discussion_r79539542
--- Diff: python/pyspark/java_gateway.py ---
@@ -50,13 +50,18 @@ def launch_gateway():
# proper classpath and settings from spark-env.sh
Github user viirya commented on the issue:
https://github.com/apache/spark/pull/14452
@davies Thanks for comment.
In our initial benchmark of the TPC-DS queries (totally 13) using CTE, this
PR helps about half (6) of them, 5 queries are not affected, 2 queries are
regressed.
Github user sethah commented on the issue:
https://github.com/apache/spark/pull/14852
Now that https://github.com/apache/spark/pull/14834 has been merged, we can
make the updates to Python API. There is no new interface to implement, but it
would be great if this PR could take care
Github user sethah commented on the issue:
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A few high-level comments/questions:
* Should this go into the `feature` package as a feature
estimator/transformer? That is where other dimensionality reduction techniques
have gone and
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Merging in master/2.0.
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Github user rxin commented on the issue:
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@frreiss can you close this now?
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Github user rxin commented on the issue:
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Since @frreiss hasn't updated the pr yet, I'm going to merge this one and
assign the jira ticket to Fred.
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Github user HyukjinKwon commented on the issue:
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@nbeyer Thanks for your investigation. I think that sounds reasonable
though I think it might be arguable because adding more cases virtually means
more time and computation to parse/infer
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GitHub user gatorsmile opened a pull request:
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[SPARK-17603] [SQL] Utilize Hive-generated Statistics For Partitioned Tables
### What changes were proposed in this pull request?
For non-partitioned tables, Hive-generated statistics are
Github user wzhfy commented on the issue:
https://github.com/apache/spark/pull/15090
This pr has been updated based on all the above comments, changes are as
follows:
1. Modify analyze syntax a little bit: `identifierSeq` is now non-optional,
i.e. users must specify column names
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Github user dbtsai commented on the issue:
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Merged into master. Thanks.
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Github user nbeyer commented on the issue:
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@HyukjinKwon Based on my further reading of the code, I'd like to suggest
that add a deprecation to the stringToTime method and then update the
stringToTimestamp method, specifically here
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Github user cloud-fan commented on the issue:
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I re-targeted it to 2.1 only.
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Github user viirya commented on a diff in the pull request:
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/lsh/LSH.scala ---
@@ -0,0 +1,270 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under
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--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/test/DataFrameReaderWriterSuite.scala
---
@@ -293,6 +293,39 @@ class DataFrameReaderWriterSuite
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/15046#discussion_r79533310
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -327,8 +327,13 @@ case class DataSource(
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/15046#discussion_r79533043
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala ---
@@ -345,34 +345,72 @@ class TableScanSuite extends DataSourceTest
Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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@@ -345,34 +345,72 @@ class TableScanSuite extends DataSourceTest
Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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@@ -65,6 +65,26 @@ class InsertSuite extends DataSourceTest with
Github user viirya commented on the issue:
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@Yunni Thanks for working on this.
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/lsh/LSH.scala ---
@@ -0,0 +1,270 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/15155
Yea. I meant if we want to read old/new Parquet files without user-given
schema with enabling merging schemas, then, we'd face SPARK-15516 first. This
is why I thought that JIRA blocks this
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Github user viirya commented on the issue:
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I guess if using the same analyzed plan increases the chance to reuse
exchange, then it may improve the performance. Anyway, it is not the purpose of
this change. Because the analyzed subquery plan
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Github user wgtmac commented on the issue:
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@HyukjinKwon Yup this PR is very similar to yours.
For merging parquet schema, it won't work. Think about this: the table
contains two parquet files, one has int, one has long. The DataFrame
Github user viirya commented on the issue:
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@hvanhovell This is for analyzer change and adds CTE in CTE feature. I
don't expect there is performance improvement.
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Github user viirya commented on the issue:
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@marmbrus
>
* I think that for all but text you have to include the partition columns
in the schema if inference is turned off (which it is by default).
For text format, when
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Github user JoshRosen commented on the issue:
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I'm going to merge this to master and branch-2.0. Thanks!
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Github user jerryshao commented on a diff in the pull request:
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--- Diff:
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+/*
+ * Licensed to
Github user zjffdu commented on the issue:
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Close it as it is resolved somewhere else.
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cc @vanzin
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Revert "[SPARK-17549][SQL] Only collect table size stat in driver for
cached relation."
This reverts commit 39e2bad6a866d27c3ca594d15e574a1da3ee84cc because of the
problem mentioned at
Github user viirya commented on the issue:
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>
* If the partition directories are not present when the stream starts
then I believe this breaks.
Yes. Schema inference only happens when starting the stream.
>
* I
Github user yhuai commented on the issue:
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This change looks good. Let's add a regression test.
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Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/15090#discussion_r79521372
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsColumnSuite.scala
---
@@ -0,0 +1,228 @@
+/*
+ * Licensed to the Apache
Github user JoshRosen commented on the issue:
https://github.com/apache/spark/pull/15034
@jkbradley, it looks like this is legitimately failing MiMa (not sure why
it passed on the first run...):
```
[error] * the type hierarchy of object
Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/15090#discussion_r79520564
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsColumnSuite.scala
---
@@ -0,0 +1,228 @@
+/*
+ * Licensed to the Apache Software
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