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Posted to commits@spark.apache.org by fe...@apache.org on 2018/01/09 05:58:31 UTC
spark git commit: [SPARK-21292][DOCS] refreshtable example
Repository: spark
Updated Branches:
refs/heads/master f20131dd3 -> 8486ad419
[SPARK-21292][DOCS] refreshtable example
## What changes were proposed in this pull request?
doc update
Author: Felix Cheung <fe...@hotmail.com>
Closes #20198 from felixcheung/rrefreshdoc.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/8486ad41
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/8486ad41
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/8486ad41
Branch: refs/heads/master
Commit: 8486ad419d8f1779e277ec71c39e1516673a83ab
Parents: f20131d
Author: Felix Cheung <fe...@hotmail.com>
Authored: Mon Jan 8 21:58:26 2018 -0800
Committer: Felix Cheung <fe...@apache.org>
Committed: Mon Jan 8 21:58:26 2018 -0800
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docs/sql-programming-guide.md | 16 ++++++++++++----
1 file changed, 12 insertions(+), 4 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/8486ad41/docs/sql-programming-guide.md
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diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 3ccaaf4..72f79d6 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -915,6 +915,14 @@ spark.catalog.refreshTable("my_table")
</div>
+<div data-lang="r" markdown="1">
+
+{% highlight r %}
+refreshTable("my_table")
+{% endhighlight %}
+
+</div>
+
<div data-lang="sql" markdown="1">
{% highlight sql %}
@@ -1498,10 +1506,10 @@ that these options will be deprecated in future release as more optimizations ar
## Broadcast Hint for SQL Queries
The `BROADCAST` hint guides Spark to broadcast each specified table when joining them with another table or view.
-When Spark deciding the join methods, the broadcast hash join (i.e., BHJ) is preferred,
+When Spark deciding the join methods, the broadcast hash join (i.e., BHJ) is preferred,
even if the statistics is above the configuration `spark.sql.autoBroadcastJoinThreshold`.
When both sides of a join are specified, Spark broadcasts the one having the lower statistics.
-Note Spark does not guarantee BHJ is always chosen, since not all cases (e.g. full outer join)
+Note Spark does not guarantee BHJ is always chosen, since not all cases (e.g. full outer join)
support BHJ. When the broadcast nested loop join is selected, we still respect the hint.
<div class="codetabs">
@@ -1780,7 +1788,7 @@ options.
Note that, for <b>DecimalType(38,0)*</b>, the table above intentionally does not cover all other combinations of scales and precisions because currently we only infer decimal type like `BigInteger`/`BigInt`. For example, 1.1 is inferred as double type.
- In PySpark, now we need Pandas 0.19.2 or upper if you want to use Pandas related functionalities, such as `toPandas`, `createDataFrame` from Pandas DataFrame, etc.
- In PySpark, the behavior of timestamp values for Pandas related functionalities was changed to respect session timezone. If you want to use the old behavior, you need to set a configuration `spark.sql.execution.pandas.respectSessionTimeZone` to `False`. See [SPARK-22395](https://issues.apache.org/jira/browse/SPARK-22395) for details.
-
+
- Since Spark 2.3, when either broadcast hash join or broadcast nested loop join is applicable, we prefer to broadcasting the table that is explicitly specified in a broadcast hint. For details, see the section [Broadcast Hint](#broadcast-hint-for-sql-queries) and [SPARK-22489](https://issues.apache.org/jira/browse/SPARK-22489).
- Since Spark 2.3, when all inputs are binary, `functions.concat()` returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set `spark.sql.function.concatBinaryAsString` to `true`.
@@ -2167,7 +2175,7 @@ Not all the APIs of the Hive UDF/UDTF/UDAF are supported by Spark SQL. Below are
Spark SQL currently does not support the reuse of aggregation.
* `getWindowingEvaluator` (`GenericUDAFEvaluator`) is a function to optimize aggregation by evaluating
an aggregate over a fixed window.
-
+
### Incompatible Hive UDF
Below are the scenarios in which Hive and Spark generate different results:
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