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Github user cloud-fan commented on the pull request:
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Like the `buildScan` in data source, we shoud not expose internal types
outside spark sql, but we also need to provide the ability to build efficient
data source by
Github user liancheng commented on the pull request:
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I feel like Impala and Hive did Julian day conversion in the wrong way at
first, but left it as is and made the wrong conversion logic the de facto
standard way? I
Github user sun-rui commented on a diff in the pull request:
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--- Diff: R/pkg/inst/tests/test_Serde.R ---
@@ -0,0 +1,67 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+#
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GitHub user koeninger opened a pull request:
https://github.com/apache/spark/pull/8413
[SPARK-9786][Streaming][Kafka] fix backpressure so it works with defaâ¦
â¦ult maxRatePerPartition setting of 0
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Github user rxin commented on the pull request:
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Jenkins, test this please.
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retest this please.
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--- Diff:
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type
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Github user yhuai commented on the pull request:
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what is the datatype of `a`?
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Github user asfgit closed the pull request at:
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--- Diff: R/pkg/R/DataFrame.R ---
@@ -945,6 +947,19 @@ setMethod([, signature(x = DataFrame, i =
missing),
select(x, j)
})
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Github user yhuai commented on the pull request:
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ok the type of `a` is `Decimal(38, 18)`. The plan of `df.selectExpr(b * a
/ b)` is
```
scala df.selectExpr(b * a / b).explain(true)
== Parsed Logical Plan ==
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`promote_precision(cast(cast(b#3 as decimal(38,18)) as decimal(38,18)))`
looks weird.
The precision and scale will be calculated based on `e1 / e2 p1 - s1 +
Github user davies commented on the pull request:
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@yhuai For Add/Sub/Multiply, we should not lose precision (we can't reduce
the scale of result, prefer overflow), but for division, we can't keep all the
precision under
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LGTM. some minor comments.
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Can you explain what they are overlapped means?
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