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Cherry picked this into 2.0.
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Github user vectorijk commented on a diff in the pull request:
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+/*
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Github user srowen commented on the issue:
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OK, later we'd have to make the base version 2.0.0 right? that's fine
though either way we have to bump something later, so was going to wait, but,
no big deal either way, especially if it reduces co
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Github user jiangxb1987 commented on the issue:
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@hvanhovell Thank you for your kindly review, the suggestions are quite
useful for me. I'll try to get some time later today to update some fixes.
Thanks!
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Github user rxin commented on the issue:
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I'm going to merge this in master.
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Github user rxin commented on the issue:
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Yup once we actually release we can bump the base version. The good thing
with the new approach is that we can now define mima excludes based on 2.0 or
2.1 versions already.
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Github user dongjoon-hyun commented on the issue:
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If pissible, I hope. :)
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Github user srowen commented on the issue:
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@rxin I added configurability; does that look reasonable? there are several
ways to play this one.
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Github user rxin commented on the issue:
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That's why I did https://github.com/apache/spark/pull/14128
Base mima version is still 1.6, and 2.0 and 2.1 currently has the same set
of excludes.
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Should this be in 2.0?
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Github user petermaxlee commented on the issue:
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I've added the checkEvaluation fix here too.
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Github user rxin commented on the issue:
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@hvanhovell please leave a message on the pr next time when you merge one
-- and also indicate the branch this was merged in (master in this case).
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Github user phalodi commented on the issue:
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@srowen yeah you are right i will close this request can you please look
into documentation pull request https://github.com/apache/spark/pull/14104
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Github user dongjoon-hyun commented on the issue:
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Hi, @rxin.
Could you review this again?
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Thank you for review and merging, @rxin and @hvanhovell .
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+/*
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Github user rdblue commented on the issue:
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+1
Nice work, I think we may be able to do the same thing in parquet-avro as
well.
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Github user srowen commented on the issue:
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I don't think that's true. Each iteration makes a new RDD that depends on
the previous iteration's RDD. The execution is actually just a long chain of
RDDs if you 'unrolled' it. That's why I think ca
Github user praveendareddy21 commented on the issue:
https://github.com/apache/spark/pull/13248
@MechCoder @drcrallen @jjthomas @vectorijk
Kindly, review my new changes.
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Github user wesolowskim commented on the issue:
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Whole computation is iterative and depends on previous state of
sccWorkGraph. It iterates on sccWorkGraph.numVertices which is action under the
hood and without caching whole algorithm would be
Github user lianhuiwang commented on a diff in the pull request:
https://github.com/apache/spark/pull/13494#discussion_r70267348
--- Diff:
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---
@@ -0,0 +1,153 @@
+/*
+ * Licensed to
Github user liancheng commented on a diff in the pull request:
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--- Diff: docs/sql-programming-guide.md ---
@@ -1380,17 +949,17 @@ metadata.
{% highlight scala %}
-// spark is an existing HiveContext
Github user liancheng commented on the issue:
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Can we add actual stdout output after each `.show()` call?
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Github user srowen commented on the issue:
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You are right, the code already had a `.cache()` call without
`.unpersist()`. It eventually gets cleaned up, but not good form and probably
didn't actually do much either. Except perhaps break the li
Github user liancheng commented on the issue:
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This looks pretty good! Only found a few minor issues. Thanks for working
on it!
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--- Diff:
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@@ -41,43 +35,47 @@ object HiveFromSpark {
// in
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--- Diff:
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@@ -0,0 +1,133 @@
+/*
+ * Licensed to the Apache So
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@@ -0,0 +1,201 @@
+/*
+ * Licensed to the Apache Software
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+package org.apache.spark.examples.
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@@ -0,0 +1,106 @@
+/*
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--- Diff: docs/sql-programming-guide.md ---
@@ -679,43 +435,7 @@ a `DataFrame` can be created programmatically with
three steps.
by `SparkSession`.
Github user liancheng commented on a diff in the pull request:
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--- Diff: docs/sql-programming-guide.md ---
@@ -732,62 +452,7 @@ a `Dataset` can be created programmatically with
three steps.
by `SparkSession`.
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Github user tgravescs commented on the issue:
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ah, thanks @vanzin for the reminder. @sharkdtu could you add a small unit
test for this.
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Github user srowen commented on the issue:
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This doesn't actually show use of the API other than calling it. I honestly
don't think this is something worth much more mention.
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Github user liancheng commented on the issue:
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Since you've added `JavaSparkSqlExample.scala`, we can remove
`JavaSparkSQL.scala` now. (I guess that file was from my original WIP branch?)
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add to whitelist
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test this please
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--- Diff:
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@@ -536,6 +536,25 @@ object functions {
def min(columnName: String): Colu
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+/*
+ * Licensed to
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+/*
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Github user lianhuiwang commented on the issue:
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@hvanhovell I have addressed your comments. Thanks. If I missed something,
please tell me.
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Github user wesolowskim commented on the issue:
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Let me introduce some data first:
1. SCC run computed on randomly generated graph just like one provided by
me on databrics notebook takes about 120s
2. When doing sccGraph.vertices.count
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