Spark 615 map partitions with index callable from java #16
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Hey Holden, wait on this a bit until #17 is merged. Then we'll also want to make sure it works with Java 8 (you'll need to make the class an interface and such). |
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Sure, I'll give this a shot today :) On Sat, Mar 8, 2014 at 11:24 AM, Patrick Wendell
Cell : 425-233-8271 |
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Sorry to necro the oldest open PR, but do you mind closing this now that |
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Fix java.util.MissingFormatArgumentException in statsd module
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## What changes were proposed in this pull request?
This PR brings the support for chained Python UDFs, for example
```sql
select udf1(udf2(a))
select udf1(udf2(a) + 3)
select udf1(udf2(a) + udf3(b))
```
Also directly chained unary Python UDFs are put in single batch of Python UDFs, others may require multiple batches.
For example,
```python
>>> sqlContext.sql("select double(double(1))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#10 AS double(double(1))#9]
: +- INPUT
+- !BatchPythonEvaluation double(double(1)), [pythonUDF#10]
+- Scan OneRowRelation[]
>>> sqlContext.sql("select double(double(1) + double(2))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#19 AS double((double(1) + double(2)))#16]
: +- INPUT
+- !BatchPythonEvaluation double((pythonUDF#17 + pythonUDF#18)), [pythonUDF#17,pythonUDF#18,pythonUDF#19]
+- !BatchPythonEvaluation double(2), [pythonUDF#17,pythonUDF#18]
+- !BatchPythonEvaluation double(1), [pythonUDF#17]
+- Scan OneRowRelation[]
```
TODO: will support multiple unrelated Python UDFs in one batch (another PR).
## How was this patch tested?
Added new unit tests for chained UDFs.
Author: Davies Liu <[email protected]>
Closes #12014 from davies/py_udfs.
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…-1656 to netflix/1.6.1 * commit '5b54d2fbb11b45298440d77deb06514f12c47b40': [DSEPLAT-1656] Upgrade the version of metacat client, benjamin and bdurl.
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Fix dev tools and add some new, Criteo specific ones.
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* Documentation for the current state of the world. * Adding navigation links from other pages * Address comments, add TODO for things that should be fixed * Address comments, mostly making images section clearer * Virtual runtime -> container runtime
lins05
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* Documentation for the current state of the world. * Adding navigation links from other pages * Address comments, add TODO for things that should be fixed * Address comments, mostly making images section clearer * Virtual runtime -> container runtime
icexelloss
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Apr 28, 2017
Move column writers to Arrow.scala Add support for more types; Switch to arrow NullableVector closes apache#16
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* Documentation for the current state of the world. * Adding navigation links from other pages * Address comments, add TODO for things that should be fixed * Address comments, mostly making images section clearer * Virtual runtime -> container runtime
sven0726
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修改 hiveContext permission
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upgrade spark version to 2.4.1-kylin-r5
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upgrade spark version to 2.4.1-kylin-r5
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ringtail
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Feature/proxy user
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[YSPARK-1523] Cleanup hbaseread.py
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…onnect ### What changes were proposed in this pull request? Implement Arrow-optimized Python UDFs in Spark Connect. Please see #39384 for motivation and performance improvements of Arrow-optimized Python UDFs. ### Why are the changes needed? Parity with vanilla PySpark. ### Does this PR introduce _any_ user-facing change? Yes. In Spark Connect Python Client, users can: 1. Set `useArrow` parameter True to enable Arrow optimization for a specific Python UDF. ```sh >>> df = spark.range(2) >>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).show() +------------+ |<lambda>(id)| +------------+ | 1| | 2| +------------+ # ArrowEvalPython indicates Arrow optimization >>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).explain() == Physical Plan == *(2) Project [pythonUDF0#18 AS <lambda>(id)#16] +- ArrowEvalPython [<lambda>(id#14L)#15], [pythonUDF0#18], 200 +- *(1) Range (0, 2, step=1, splits=1) ``` 2. Enable `spark.sql.execution.pythonUDF.arrow.enabled` Spark Conf to make all Python UDFs Arrow-optimized. ```sh >>> spark.conf.set("spark.sql.execution.pythonUDF.arrow.enabled", True) >>> df.select(udf(lambda x : x + 1)('id')).show() +------------+ |<lambda>(id)| +------------+ | 1| | 2| +------------+ # ArrowEvalPython indicates Arrow optimization >>> df.select(udf(lambda x : x + 1)('id')).explain() == Physical Plan == *(2) Project [pythonUDF0#30 AS <lambda>(id)#28] +- ArrowEvalPython [<lambda>(id#26L)#27], [pythonUDF0#30], 200 +- *(1) Range (0, 2, step=1, splits=1) ``` ### How was this patch tested? Parity unit tests. Closes #40725 from xinrong-meng/connect_arrow_py_udf. Authored-by: Xinrong Meng <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]>
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