### What changes were proposed in this pull request?
This PR intends to fix typos in the sub-modules:
* `R`
* `common`
* `dev`
* `mlib`
* `external`
* `project`
* `streaming`
* `resource-managers`
* `python`
Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618
NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
There are various fixes to documentation, etc...
### How was this patch tested?
No testing was performed
Closes#30402 from jsoref/spelling-R_common_dev_mlib_external_project_streaming_resource-managers_python.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Spark creates local server to serialize several type of data for python. The python code tries to connect to the server, immediately after it's created but there are several system calls in between (this may change in each Spark version):
* getaddrinfo
* socket
* settimeout
* connect
Under some circumstances in heavy user environments these calls can be super slow (more than 15 seconds). These issues must be analyzed one-by-one but since these are system calls the underlying OS and/or DNS servers must be debugged and fixed. This is not trivial task and at the same time data processing must work somehow. In this PR I'm only intended to add a configuration possibility to increase the mentioned timeouts in order to be able to provide temporary workaround. The rootcause analysis is ongoing but I think this can vary in each case.
Because the server part doesn't contain huge amount of log entries to with one can measure time, I've added some.
### Why are the changes needed?
Provide workaround when localhost python server connection timeout appears.
### Does this PR introduce _any_ user-facing change?
Yes, new configuration added.
### How was this patch tested?
Existing unit tests + manual test.
```
#Compile Spark
echo "spark.io.encryption.enabled true" >> conf/spark-defaults.conf
echo "spark.python.authenticate.socketTimeout 10" >> conf/spark-defaults.conf
$ ./bin/pyspark
Python 3.8.5 (default, Jul 21 2020, 10:48:26)
[Clang 11.0.3 (clang-1103.0.32.62)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
20/11/20 10:17:03 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/11/20 10:17:03 WARN SparkEnv: I/O encryption enabled without RPC encryption: keys will be visible on the wire.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Python version 3.8.5 (default, Jul 21 2020 10:48:26)
Spark context Web UI available at http://192.168.0.189:4040
Spark context available as 'sc' (master = local[*], app id = local-1605863824276).
SparkSession available as 'spark'.
>>> sc.setLogLevel("TRACE")
>>> sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect()
20/11/20 10:17:09 TRACE PythonParallelizeServer: Creating listening socket
20/11/20 10:17:09 TRACE PythonParallelizeServer: Setting timeout to 10 sec
20/11/20 10:17:09 TRACE PythonParallelizeServer: Waiting for connection on port 59726
20/11/20 10:17:09 TRACE PythonParallelizeServer: Connection accepted from address /127.0.0.1:59727
20/11/20 10:17:09 TRACE PythonParallelizeServer: Client authenticated
20/11/20 10:17:09 TRACE PythonParallelizeServer: Closing server
...
20/11/20 10:17:10 TRACE SocketFuncServer: Creating listening socket
20/11/20 10:17:10 TRACE SocketFuncServer: Setting timeout to 10 sec
20/11/20 10:17:10 TRACE SocketFuncServer: Waiting for connection on port 59735
20/11/20 10:17:10 TRACE SocketFuncServer: Connection accepted from address /127.0.0.1:59736
20/11/20 10:17:10 TRACE SocketFuncServer: Client authenticated
20/11/20 10:17:10 TRACE SocketFuncServer: Closing server
[[0], [2], [3], [4], [6]]
>>>
```
Closes#30389 from gaborgsomogyi/SPARK-33143.
Lead-authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes migration of Core to NumPy documentation style.
### Why are the changes needed?
To improve documentation style.
### Does this PR introduce _any_ user-facing change?
Yes, this changes both rendered HTML docs and console representation (SPARK-33243).
### How was this patch tested?
dev/lint-python and manual inspection.
Closes#30320 from zero323/SPARK-33254.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to drop Python 2.7, 3.4 and 3.5.
Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark.
### Why are the changes needed?
1. Unsupport EOL Python versions
2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2.
3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation.
4. Users can use Python type hints with Pandas UDFs without thinking about Python version
5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle.
### Does this PR introduce _any_ user-facing change?
Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version.
### How was this patch tested?
Manually tested and also tested in Jenkins.
Closes#28957 from HyukjinKwon/SPARK-32138.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As part of the Stage level scheduling features, add the Python api's to set resource profiles.
This also adds the functionality to properly apply the pyspark memory configuration when specified in the ResourceProfile. The pyspark memory configuration is being passed in the task local properties. This was an easy way to get it to the PythonRunner that needs it. I modeled this off how the barrier task scheduling is passing the addresses. As part of this I added in the JavaRDD api's because those are needed by python.
### Why are the changes needed?
python api for this feature
### Does this PR introduce any user-facing change?
Yes adds the java and python apis for user to specify a ResourceProfile to use stage level scheduling.
### How was this patch tested?
unit tests and manually tested on yarn. Tests also run to verify it errors properly on standalone and local mode where its not yet supported.
Closes#28085 from tgravescs/SPARK-29641-pr-base.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to add **Single threading model design (pinned thread model)** mode which is an experimental mode to sync threads on PVM and JVM. See https://www.py4j.org/advanced_topics.html#using-single-threading-model-pinned-thread
### Multi threading model
Currently, PySpark uses this model. Threads on PVM and JVM are independent. For instance, in a different Python thread, callbacks are received and relevant Python codes are executed. JVM threads are reused when possible.
Py4J will create a new thread every time a command is received and there is no thread available. See the current model we're using - https://www.py4j.org/advanced_topics.html#the-multi-threading-model
One problem in this model is that we can't sync threads on PVM and JVM out of the box. This leads to some problems in particular at some codes related to threading in JVM side. See:
7056e004ee/core/src/main/scala/org/apache/spark/SparkContext.scala (L334)
Due to reusing JVM threads, seems the job groups in Python threads cannot be set in each thread as described in the JIRA.
### Single threading model design (pinned thread model)
This mode pins and syncs the threads on PVM and JVM to work around the problem above. For instance, in the same Python thread, callbacks are received and relevant Python codes are executed. See https://www.py4j.org/advanced_topics.html#the-single-threading-model
Even though this mode can sync threads on PVM and JVM for other thread related code paths,
this might cause another problem: seems unable to inherit properties as below (assuming multi-thread mode still creates new threads when existing threads are busy, I suspect this issue already exists when multiple jobs are submitted in multi-thread mode; however, it can be always seen in single threading mode):
```bash
$ PYSPARK_PIN_THREAD=true ./bin/pyspark
```
```python
import threading
spark.sparkContext.setLocalProperty("a", "hi")
def print_prop():
print(spark.sparkContext.getLocalProperty("a"))
threading.Thread(target=print_prop).start()
```
```
None
```
Unlike Scala side:
```scala
spark.sparkContext.setLocalProperty("a", "hi")
new Thread(new Runnable {
def run() = println(spark.sparkContext.getLocalProperty("a"))
}).start()
```
```
hi
```
This behaviour potentially could cause weird issues but this PR currently does not target this fix this for now since this mode is experimental.
### How does this PR fix?
Basically there are two types of Py4J servers `GatewayServer` and `ClientServer`. The former is for multi threading and the latter is for single threading. This PR adds a switch to use the latter.
In Scala side:
The logic to select a server is encapsulated in `Py4JServer` and use `Py4JServer` at `PythonRunner` for Spark summit and `PythonGatewayServer` for Spark shell. Each uses `ClientServer` when `PYSPARK_PIN_THREAD` is `true` and `GatewayServer` otherwise.
In Python side:
Simply do an if-else to switch the server to talk. It uses `ClientServer` when `PYSPARK_PIN_THREAD` is `true` and `GatewayServer` otherwise.
This is disabled by default for now.
## How was this patch tested?
Manually tested. This can be tested via:
```python
PYSPARK_PIN_THREAD=true ./bin/pyspark
```
and/or
```bash
cd python
./run-tests --python-executables=python --testnames "pyspark.tests.test_pin_thread"
```
Also, ran the Jenkins tests with `PYSPARK_PIN_THREAD` enabled.
Closes#24898 from HyukjinKwon/pinned-thread.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Allow the caller to customize the py4j JVM subprocess pipes and buffers for programmatic capturing of its output.
https://issues.apache.org/jira/browse/SPARK-21094 has more detail about the use case.
## How was this patch tested?
Tested by running the pyspark unit tests locally.
Closes#18339 from parente/feature/SPARK-21094-popen-args.
Lead-authored-by: Peter Parente <parente@cs.unc.edu>
Co-authored-by: Peter Parente <peter.parente@maxpoint.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
Misc code cleanup from lgtm.com analysis. See comments below for details.
## How was this patch tested?
Existing tests.
Closes#23571 from srowen/SPARK-26640.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
HyukjinKwon
## What changes were proposed in this pull request?
add __from pyspark.util import \_exception_message__ to python/pyspark/java_gateway.py
## How was this patch tested?
[flake8](http://flake8.pycqa.org) testing of https://github.com/apache/spark on Python 3.7.0
$ __flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics__
```
./python/pyspark/java_gateway.py:172:20: F821 undefined name '_exception_message'
emsg = _exception_message(e)
^
1 F821 undefined name '_exception_message'
1
```
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22265 from cclauss/patch-2.
Authored-by: cclauss <cclauss@bluewin.ch>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
This eliminates some duplication in the code to connect to a server on localhost to talk directly to the jvm. Also it gives consistent ipv6 and error handling. Two other incidental changes, that shouldn't matter:
1) python barrier tasks perform authentication immediately (rather than waiting for the BARRIER_FUNCTION indicator)
2) for `rdd._load_from_socket`, the timeout is only increased after authentication.
Closes#22247 from squito/py_connection_refactor.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have better sense of buiding a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the microbatch output as a dataframe is useful.
- Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning).
- Reuse batch data sources for output whose streaming version does not exists (e.g. redshift data source).
- Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice.
The proposal is to add a method `foreachBatch(f: Dataset[T] => Unit)` to Scala/Java/Python `DataStreamWriter`.
## How was this patch tested?
New unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21571 from tdas/foreachBatch.
## What changes were proposed in this pull request?
This change uses Arrow to optimize the creation of a Spark DataFrame from a Pandas DataFrame. The input df is sliced according to the default parallelism. The optimization is enabled with the existing conf "spark.sql.execution.arrow.enabled" and is disabled by default.
## How was this patch tested?
Added new unit test to create DataFrame with and without the optimization enabled, then compare results.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19459 from BryanCutler/arrow-createDataFrame-from_pandas-SPARK-20791.
## What changes were proposed in this pull request?
This PR aims to provide a pip installable PySpark package. This does a bunch of work to copy the jars over and package them with the Python code (to prevent challenges from trying to use different versions of the Python code with different versions of the JAR). It does not currently publish to PyPI but that is the natural follow up (SPARK-18129).
Done:
- pip installable on conda [manual tested]
- setup.py installed on a non-pip managed system (RHEL) with YARN [manual tested]
- Automated testing of this (virtualenv)
- packaging and signing with release-build*
Possible follow up work:
- release-build update to publish to PyPI (SPARK-18128)
- figure out who owns the pyspark package name on prod PyPI (is it someone with in the project or should we ask PyPI or should we choose a different name to publish with like ApachePySpark?)
- Windows support and or testing ( SPARK-18136 )
- investigate details of wheel caching and see if we can avoid cleaning the wheel cache during our test
- consider how we want to number our dev/snapshot versions
Explicitly out of scope:
- Using pip installed PySpark to start a standalone cluster
- Using pip installed PySpark for non-Python Spark programs
*I've done some work to test release-build locally but as a non-committer I've just done local testing.
## How was this patch tested?
Automated testing with virtualenv, manual testing with conda, a system wide install, and YARN integration.
release-build changes tested locally as a non-committer (no testing of upload artifacts to Apache staging websites)
Author: Holden Karau <holden@us.ibm.com>
Author: Juliet Hougland <juliet@cloudera.com>
Author: Juliet Hougland <not@myemail.com>
Closes#15659 from holdenk/SPARK-1267-pip-install-pyspark.
## What changes were proposed in this pull request?
The root cause that we would ignore SparkConf when launching JVM is that SparkConf require JVM to be created first. https://github.com/apache/spark/blob/master/python/pyspark/conf.py#L106
In this PR, I would defer the launching of JVM until SparkContext is created so that we can pass SparkConf to JVM correctly.
## How was this patch tested?
Use the example code in the description of SPARK-17387,
```
$ SPARK_HOME=$PWD PYTHONPATH=python:python/lib/py4j-0.10.3-src.zip python
Python 2.7.12 (default, Jul 1 2016, 15:12:24)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from pyspark import SparkContext
>>> from pyspark import SparkConf
>>> conf = SparkConf().set("spark.driver.memory", "4g")
>>> sc = SparkContext(conf=conf)
```
And verify the spark.driver.memory is correctly picked up.
```
...op/ -Xmx4g org.apache.spark.deploy.SparkSubmit --conf spark.driver.memory=4g pyspark-shell
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14959 from zjffdu/SPARK-17387.
## What changes were proposed in this pull request?
Now we have PySpark picklers for new and old vector/matrix, individually. However, they are all implemented under `PythonMLlibAPI`. To separate spark.mllib from spark.ml, we should implement the picklers of new vector/matrix under `spark.ml.python` instead.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13219 from viirya/pyspark-pickler-ml.
Previously, we use 64MB as the default page size, which was way too big for a lot of Spark applications (especially for single node).
This patch changes it so that the default page size, if unset by the user, is determined by the number of cores available and the total execution memory available.
Author: Reynold Xin <rxin@databricks.com>
Closes#8012 from rxin/pagesize and squashes the following commits:
16f4756 [Reynold Xin] Fixed failing test.
5afd570 [Reynold Xin] private...
0d5fb98 [Reynold Xin] Update default value.
674a6cd [Reynold Xin] Address review feedback.
dc00e05 [Reynold Xin] Merge with master.
73ebdb6 [Reynold Xin] [SPARK-9700] Pick default page size more intelligently.
This pull request enables Unsafe mode by default in Spark SQL. In order to do this, we had to fix a number of small issues:
**List of fixed blockers**:
- [x] Make some default buffer sizes configurable so that HiveCompatibilitySuite can run properly (#7741).
- [x] Memory leak on grouped aggregation of empty input (fixed by #7560 to fix this)
- [x] Update planner to also check whether codegen is enabled before planning unsafe operators.
- [x] Investigate failing HiveThriftBinaryServerSuite test. This turns out to be caused by a ClassCastException that occurs when Exchange tries to apply an interpreted RowOrdering to an UnsafeRow when range partitioning an RDD. This could be fixed by #7408, but a shorter-term fix is to just skip the Unsafe exchange path when RangePartitioner is used.
- [x] Memory leak exceptions masking exceptions that actually caused tasks to fail (will be fixed by #7603).
- [x] ~~https://issues.apache.org/jira/browse/SPARK-9162, to implement code generation for ScalaUDF. This is necessary for `UDFSuite` to pass. For now, I've just ignored this test in order to try to find other problems while we wait for a fix.~~ This is no longer necessary as of #7682.
- [x] Memory leaks from Limit after UnsafeExternalSort cause the memory leak detector to fail tests. This is a huge problem in the HiveCompatibilitySuite (fixed by f4ac642a4e5b2a7931c5e04e086bb10e263b1db6).
- [x] Tests in `AggregationQuerySuite` are failing due to NaN-handling issues in UnsafeRow, which were fixed in #7736.
- [x] `org.apache.spark.sql.ColumnExpressionSuite.rand` needs to be updated so that the planner check also matches `TungstenProject`.
- [x] After having lowered the buffer sizes to 4MB so that most of HiveCompatibilitySuite runs:
- [x] Wrong answer in `join_1to1` (fixed by #7680)
- [x] Wrong answer in `join_nulls` (fixed by #7680)
- [x] Managed memory OOM / leak in `lateral_view`
- [x] Seems to hang indefinitely in `partcols1`. This might be a deadlock in script transformation or a bug in error-handling code? The hang was fixed by #7710.
- [x] Error while freeing memory in `partcols1`: will be fixed by #7734.
- [x] After fixing the `partcols1` hang, it appears that a number of later tests have issues as well.
- [x] Fix thread-safety bug in codegen fallback expression evaluation (#7759).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7564 from JoshRosen/unsafe-by-default and squashes the following commits:
83c0c56 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-by-default
f4cc859 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-by-default
963f567 [Josh Rosen] Reduce buffer size for R tests
d6986de [Josh Rosen] Lower page size in PySpark tests
013b9da [Josh Rosen] Also match TungstenProject in checkNumProjects
5d0b2d3 [Josh Rosen] Add task completion callback to avoid leak in limit after sort
ea250da [Josh Rosen] Disable unsafe Exchange path when RangePartitioning is used
715517b [Josh Rosen] Enable Unsafe by default
This commit parallelizes the Python unit test execution, significantly reducing Jenkins build times. Parallelism is now configurable by passing the `-p` or `--parallelism` flags to either `dev/run-tests` or `python/run-tests` (the default parallelism is 4, but I've successfully tested with higher parallelism).
To avoid flakiness, I've disabled the Spark Web UI for the Python tests, similar to what we've done for the JVM tests.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7031 from JoshRosen/parallelize-python-tests and squashes the following commits:
feb3763 [Josh Rosen] Re-enable other tests
f87ea81 [Josh Rosen] Only log output from failed tests
d4ded73 [Josh Rosen] Logging improvements
a2717e1 [Josh Rosen] Make parallelism configurable via dev/run-tests
1bacf1b [Josh Rosen] Merge remote-tracking branch 'origin/master' into parallelize-python-tests
110cd9d [Josh Rosen] Fix universal_newlines for Python 3
cd13db8 [Josh Rosen] Also log python_implementation
9e31127 [Josh Rosen] Log Python --version output for each executable.
a2b9094 [Josh Rosen] Bump up parallelism.
5552380 [Josh Rosen] Python 3 fix
866b5b9 [Josh Rosen] Fix lazy logging warnings in Prospector checks
87cb988 [Josh Rosen] Skip MLLib tests for PyPy
8309bfe [Josh Rosen] Temporarily disable parallelism to debug a failure
9129027 [Josh Rosen] Disable Spark UI in Python tests
037b686 [Josh Rosen] Temporarily disable JVM tests so we can test Python speedup in Jenkins.
af4cef4 [Josh Rosen] Initial attempt at parallelizing Python test execution
This PR enable auto_convert in JavaGateway, then we could register a converter for a given types, for example, date and datetime.
There are two bugs related to auto_convert, see [1] and [2], we workaround it in this PR.
[1] https://github.com/bartdag/py4j/issues/160
[2] https://github.com/bartdag/py4j/issues/161
cc rxin JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#5570 from davies/py4j_date and squashes the following commits:
eb4fa53 [Davies Liu] fix tests in python 3
d17d634 [Davies Liu] rollback changes in mllib
2e7566d [Davies Liu] convert tuple into ArrayList
ceb3779 [Davies Liu] Update rdd.py
3c373f3 [Davies Liu] support date and datetime by auto_convert
cb094ff [Davies Liu] enable auto convert
This PR update PySpark to support Python 3 (tested with 3.4).
Known issue: unpickle array from Pyrolite is broken in Python 3, those tests are skipped.
TODO: ec2/spark-ec2.py is not fully tested with python3.
Author: Davies Liu <davies@databricks.com>
Author: twneale <twneale@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#5173 from davies/python3 and squashes the following commits:
d7d6323 [Davies Liu] fix tests
6c52a98 [Davies Liu] fix mllib test
99e334f [Davies Liu] update timeout
b716610 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
cafd5ec [Davies Liu] adddress comments from @mengxr
bf225d7 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
179fc8d [Davies Liu] tuning flaky tests
8c8b957 [Davies Liu] fix ResourceWarning in Python 3
5c57c95 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
4006829 [Davies Liu] fix test
2fc0066 [Davies Liu] add python3 path
71535e9 [Davies Liu] fix xrange and divide
5a55ab4 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
125f12c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ed498c8 [Davies Liu] fix compatibility with python 3
820e649 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
e8ce8c9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ad7c374 [Davies Liu] fix mllib test and warning
ef1fc2f [Davies Liu] fix tests
4eee14a [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
20112ff [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
59bb492 [Davies Liu] fix tests
1da268c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ca0fdd3 [Davies Liu] fix code style
9563a15 [Davies Liu] add imap back for python 2
0b1ec04 [Davies Liu] make python examples work with Python 3
d2fd566 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
a716d34 [Davies Liu] test with python 3.4
f1700e8 [Davies Liu] fix test in python3
671b1db [Davies Liu] fix test in python3
692ff47 [Davies Liu] fix flaky test
7b9699f [Davies Liu] invalidate import cache for Python 3.3+
9c58497 [Davies Liu] fix kill worker
309bfbf [Davies Liu] keep compatibility
5707476 [Davies Liu] cleanup, fix hash of string in 3.3+
8662d5b [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
f53e1f0 [Davies Liu] fix tests
70b6b73 [Davies Liu] compile ec2/spark_ec2.py in python 3
a39167e [Davies Liu] support customize class in __main__
814c77b [Davies Liu] run unittests with python 3
7f4476e [Davies Liu] mllib tests passed
d737924 [Davies Liu] pass ml tests
375ea17 [Davies Liu] SQL tests pass
6cc42a9 [Davies Liu] rename
431a8de [Davies Liu] streaming tests pass
78901a7 [Davies Liu] fix hash of serializer in Python 3
24b2f2e [Davies Liu] pass all RDD tests
35f48fe [Davies Liu] run future again
1eebac2 [Davies Liu] fix conflict in ec2/spark_ec2.py
6e3c21d [Davies Liu] make cloudpickle work with Python3
2fb2db3 [Josh Rosen] Guard more changes behind sys.version; still doesn't run
1aa5e8f [twneale] Turned out `pickle.DictionaryType is dict` == True, so swapped it out
7354371 [twneale] buffer --> memoryview I'm not super sure if this a valid change, but the 2.7 docs recommend using memoryview over buffer where possible, so hoping it'll work.
b69ccdf [twneale] Uses the pure python pickle._Pickler instead of c-extension _pickle.Pickler. It appears pyspark 2.7 uses the pure python pickler as well, so this shouldn't degrade pickling performance (?).
f40d925 [twneale] xrange --> range
e104215 [twneale] Replaces 2.7 types.InstsanceType with 3.4 `object`....could be horribly wrong depending on how types.InstanceType is used elsewhere in the package--see http://bugs.python.org/issue8206
79de9d0 [twneale] Replaces python2.7 `file` with 3.4 _io.TextIOWrapper
2adb42d [Josh Rosen] Fix up some import differences between Python 2 and 3
854be27 [Josh Rosen] Run `futurize` on Python code:
7c5b4ce [Josh Rosen] Remove Python 3 check in shell.py.
....
In particular, this makes pyspark in yarn-cluster mode fail unless
SPARK_HOME is set, when it's not really needed.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#5405 from vanzin/SPARK-6506 and squashes the following commits:
e184507 [Marcelo Vanzin] [SPARK-6506] [pyspark] Do not try to retrieve SPARK_HOME when not needed.
SparkSubmit should be launched without setting PYSPARK_SUBMIT_ARGS
cc JoshRosen , this mode is actually used by python unit test, so I will not add more test for it.
Author: Davies Liu <davies@databricks.com>
Closes#5019 from davies/fix_submit and squashes the following commits:
2c20b0c [Davies Liu] fix launch spark-submit from python
This change encapsulates all the logic involved in launching a Spark job
into a small Java library that can be easily embedded into other applications.
The overall goal of this change is twofold, as described in the bug:
- Provide a public API for launching Spark processes. This is a common request
from users and currently there's no good answer for it.
- Remove a lot of the duplicated code and other coupling that exists in the
different parts of Spark that deal with launching processes.
A lot of the duplication was due to different code needed to build an
application's classpath (and the bootstrapper needed to run the driver in
certain situations), and also different code needed to parse spark-submit
command line options in different contexts. The change centralizes those
as much as possible so that all code paths can rely on the library for
handling those appropriately.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#3916 from vanzin/SPARK-4924 and squashes the following commits:
18c7e4d [Marcelo Vanzin] Fix make-distribution.sh.
2ce741f [Marcelo Vanzin] Add lots of quotes.
3b28a75 [Marcelo Vanzin] Update new pom.
a1b8af1 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
897141f [Marcelo Vanzin] Review feedback.
e2367d2 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
28cd35e [Marcelo Vanzin] Remove stale comment.
b1d86b0 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
00505f9 [Marcelo Vanzin] Add blurb about new API in the programming guide.
5f4ddcc [Marcelo Vanzin] Better usage messages.
92a9cfb [Marcelo Vanzin] Fix Win32 launcher, usage.
6184c07 [Marcelo Vanzin] Rename field.
4c19196 [Marcelo Vanzin] Update comment.
7e66c18 [Marcelo Vanzin] Fix pyspark tests.
0031a8e [Marcelo Vanzin] Review feedback.
c12d84b [Marcelo Vanzin] Review feedback. And fix spark-submit on Windows.
e2d4d71 [Marcelo Vanzin] Simplify some code used to launch pyspark.
43008a7 [Marcelo Vanzin] Don't make builder extend SparkLauncher.
b4d6912 [Marcelo Vanzin] Use spark-submit script in SparkLauncher.
28b1434 [Marcelo Vanzin] Add a comment.
304333a [Marcelo Vanzin] Fix propagation of properties file arg.
bb67b93 [Marcelo Vanzin] Remove unrelated Yarn change (that is also wrong).
8ec0243 [Marcelo Vanzin] Add missing newline.
95ddfa8 [Marcelo Vanzin] Fix handling of --help for spark-class command builder.
72da7ec [Marcelo Vanzin] Rename SparkClassLauncher.
62978e4 [Marcelo Vanzin] Minor cleanup of Windows code path.
9cd5b44 [Marcelo Vanzin] Make all non-public APIs package-private.
e4c80b6 [Marcelo Vanzin] Reorganize the code so that only SparkLauncher is public.
e50dc5e [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
de81da2 [Marcelo Vanzin] Fix CommandUtils.
86a87bf [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
2061967 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
46d46da [Marcelo Vanzin] Clean up a test and make it more future-proof.
b93692a [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
ad03c48 [Marcelo Vanzin] Revert "Fix a thread-safety issue in "local" mode."
0b509d0 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
23aa2a9 [Marcelo Vanzin] Read java-opts from conf dir, not spark home.
7cff919 [Marcelo Vanzin] Javadoc updates.
eae4d8e [Marcelo Vanzin] Fix new unit tests on Windows.
e570fb5 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
44cd5f7 [Marcelo Vanzin] Add package-info.java, clean up javadocs.
f7cacff [Marcelo Vanzin] Remove "launch Spark in new thread" feature.
7ed8859 [Marcelo Vanzin] Some more feedback.
54cd4fd [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
61919df [Marcelo Vanzin] Clean leftover debug statement.
aae5897 [Marcelo Vanzin] Use launcher classes instead of jars in non-release mode.
e584fc3 [Marcelo Vanzin] Rework command building a little bit.
525ef5b [Marcelo Vanzin] Rework Unix spark-class to handle argument with newlines.
8ac4e92 [Marcelo Vanzin] Minor test cleanup.
e946a99 [Marcelo Vanzin] Merge PySparkLauncher into SparkSubmitCliLauncher.
c617539 [Marcelo Vanzin] Review feedback round 1.
fc6a3e2 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
f26556b [Marcelo Vanzin] Fix a thread-safety issue in "local" mode.
2f4e8b4 [Marcelo Vanzin] Changes needed to make this work with SPARK-4048.
799fc20 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
bb5d324 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
53faef1 [Marcelo Vanzin] Merge branch 'master' into SPARK-4924
a7936ef [Marcelo Vanzin] Fix pyspark tests.
656374e [Marcelo Vanzin] Mima fixes.
4d511e7 [Marcelo Vanzin] Fix tools search code.
7a01e4a [Marcelo Vanzin] Fix pyspark on Yarn.
1b3f6e9 [Marcelo Vanzin] Call SparkSubmit from spark-class launcher for unknown classes.
25c5ae6 [Marcelo Vanzin] Centralize SparkSubmit command line parsing.
27be98a [Marcelo Vanzin] Modify Spark to use launcher lib.
6f70eea [Marcelo Vanzin] [SPARK-4924] Add a library for launching Spark jobs programatically.
This patch changes PySpark so that the GatewayServer's port is communicated back to the Python process that launches it over a local socket instead of a pipe. The old pipe-based approach was brittle and could fail if `spark-submit` printed unexpected to stdout.
To accomplish this, I wrote a custom `PythonGatewayServer.main()` function to use in place of Py4J's `GatewayServer.main()`.
Closes#3424.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#4603 from JoshRosen/SPARK-2313 and squashes the following commits:
6a7740b [Josh Rosen] Remove EchoOutputThread since it's no longer needed
0db501f [Josh Rosen] Use select() so that we don't block if GatewayServer dies.
9bdb4b6 [Josh Rosen] Handle case where getListeningPort returns -1
3fb7ed1 [Josh Rosen] Remove stdout=PIPE
2458934 [Josh Rosen] Use underscore to mark env var. as private
d12c95d [Josh Rosen] Use Logging and Utils.tryOrExit()
e5f9730 [Josh Rosen] Wrap everything in a giant try-block
2f70689 [Josh Rosen] Use stdin PIPE to share fate with driver
8bf956e [Josh Rosen] Initial cut at passing Py4J gateway port back to driver via socket
When JVM is started in a Python process, it should exit once the stdin is closed.
test: add spark.driver.memory in conf/spark-defaults.conf
```
daviesdm:~/work/spark$ cat conf/spark-defaults.conf
spark.driver.memory 8g
daviesdm:~/work/spark$ bin/pyspark
>>> quit
daviesdm:~/work/spark$ jps
4931 Jps
286
daviesdm:~/work/spark$ python wc.py
943738
0.719928026199
daviesdm:~/work/spark$ jps
286
4990 Jps
```
Author: Davies Liu <davies@databricks.com>
Closes#3274 from davies/exit and squashes the following commits:
df0e524 [Davies Liu] address comments
ce8599c [Davies Liu] address comments
050651f [Davies Liu] JVM should exit after Python exit
This is an effort to bring the Windows scripts up to speed after recent splashing changes in #1845.
Author: Andrew Or <andrewor14@gmail.com>
Closes#2129 from andrewor14/windows-config and squashes the following commits:
881a8f0 [Andrew Or] Add reference to Windows taskkill
92e6047 [Andrew Or] Update a few comments (minor)
22b1acd [Andrew Or] Fix style again (minor)
afcffea [Andrew Or] Fix style (minor)
72004c2 [Andrew Or] Actually respect --driver-java-options
803218b [Andrew Or] Actually respect SPARK_*_CLASSPATH
eeb34a0 [Andrew Or] Update outdated comment (minor)
35caecc [Andrew Or] In Windows, actually kill Java processes on exit
f97daa2 [Andrew Or] Fix Windows spark shell stdin issue
83ebe60 [Andrew Or] Parse special driver configs in Windows (broken)
We read the py4j port from the stdout of the `bin/spark-submit` subprocess. If there is interference in stdout (e.g. a random echo in `spark-submit`), we throw an exception with a warning message. We do not, however, distinguish between this case from the case where no stdout is produced at all.
I wasted a non-trivial amount of time being baffled by this exception in search of places where I print random whitespace (in vain, of course). A clearer exception message that distinguishes between these cases will prevent similar headaches that I have gone through.
Author: Andrew Or <andrewor14@gmail.com>
Closes#2067 from andrewor14/python-exception and squashes the following commits:
742f823 [Andrew Or] Further clarify warning messages
e96a7a0 [Andrew Or] Distinguish between unexpected output and no output at all
As sryza reported, spark-shell doesn't accept any flags.
The root cause is wrong usage of spark-submit in spark-shell and it come to the surface by #1801
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1715, Closes#1864, and Closes#1861Closes#1825 from sarutak/SPARK-2894 and squashes the following commits:
47f3510 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2894
2c899ed [Kousuke Saruta] Removed useless code from java_gateway.py
98287ed [Kousuke Saruta] Removed useless code from java_gateway.py
513ad2e [Kousuke Saruta] Modified util.sh to enable to use option including white spaces
28a374e [Kousuke Saruta] Modified java_gateway.py to recognize arguments
5afc584 [Cheng Lian] Filter out spark-submit options when starting Python gateway
e630d19 [Cheng Lian] Fixing pyspark and spark-shell CLI options
As described in [SPARK-2627](https://issues.apache.org/jira/browse/SPARK-2627), we'd like Python code to automatically be checked for PEP 8 compliance by Jenkins. This pull request aims to do that.
Notes:
* We may need to install [`pep8`](https://pypi.python.org/pypi/pep8) on the build server.
* I'm expecting tests to fail now that PEP 8 compliance is being checked as part of the build. I'm fine with cleaning up any remaining PEP 8 violations as part of this pull request.
* I did not understand why the RAT and scalastyle reports are saved to text files. I did the same for the PEP 8 check, but only so that the console output style can match those for the RAT and scalastyle checks. The PEP 8 report is removed right after the check is complete.
* Updates to the ["Contributing to Spark"](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark) guide will be submitted elsewhere, as I don't believe that text is part of the Spark repo.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Author: nchammas <nicholas.chammas@gmail.com>
Closes#1744 from nchammas/master and squashes the following commits:
274b238 [Nicholas Chammas] [SPARK-2627] [PySpark] minor indentation changes
983d963 [nchammas] Merge pull request #5 from apache/master
1db5314 [nchammas] Merge pull request #4 from apache/master
0e0245f [Nicholas Chammas] [SPARK-2627] undo erroneous whitespace fixes
bf30942 [Nicholas Chammas] [SPARK-2627] PEP8: comment spacing
6db9a44 [nchammas] Merge pull request #3 from apache/master
7b4750e [Nicholas Chammas] merge upstream changes
91b7584 [Nicholas Chammas] [SPARK-2627] undo unnecessary line breaks
44e3e56 [Nicholas Chammas] [SPARK-2627] use tox.ini to exclude files
b09fae2 [Nicholas Chammas] don't wrap comments unnecessarily
bfb9f9f [Nicholas Chammas] [SPARK-2627] keep up with the PEP 8 fixes
9da347f [nchammas] Merge pull request #2 from apache/master
aa5b4b5 [Nicholas Chammas] [SPARK-2627] follow Spark bash style for if blocks
d0a83b9 [Nicholas Chammas] [SPARK-2627] check that pep8 downloaded fine
dffb5dd [Nicholas Chammas] [SPARK-2627] download pep8 at runtime
a1ce7ae [Nicholas Chammas] [SPARK-2627] space out test report sections
21da538 [Nicholas Chammas] [SPARK-2627] it's PEP 8, not PEP8
6f4900b [Nicholas Chammas] [SPARK-2627] more misc PEP 8 fixes
fe57ed0 [Nicholas Chammas] removing merge conflict backups
9c01d4c [nchammas] Merge pull request #1 from apache/master
9a66cb0 [Nicholas Chammas] resolving merge conflicts
a31ccc4 [Nicholas Chammas] [SPARK-2627] miscellaneous PEP 8 fixes
beaa9ac [Nicholas Chammas] [SPARK-2627] fail check on non-zero status
723ed39 [Nicholas Chammas] always delete the report file
0541ebb [Nicholas Chammas] [SPARK-2627] call Python linter from run-tests
12440fa [Nicholas Chammas] [SPARK-2627] add Scala linter
61c07b9 [Nicholas Chammas] [SPARK-2627] add Python linter
75ad552 [Nicholas Chammas] make check output style consistent
Author: Andrew Or <andrewor14@gmail.com>
Closes#1365 from andrewor14/master-fs and squashes the following commits:
497f100 [Andrew Or] Sneak in a space and hope no one will notice
05ba6da [Andrew Or] Remove unused val
This reverts a change introduced in 3870248740, which redirected all stderr to the OS pipe instead of directly to the `bin/pyspark` shell output. This causes a simple job to hang in two ways:
1. If the cluster is not configured correctly or does not have enough resources, the job hangs without producing any output, because the relevant warning messages are masked.
2. If the stderr volume is large, this could lead to a deadlock if we redirect everything to the OS pipe. From the [python docs](https://docs.python.org/2/library/subprocess.html):
```
Note Do not use stdout=PIPE or stderr=PIPE with this function as that can deadlock
based on the child process output volume. Use Popen with the communicate() method
when you need pipes.
```
Note that we cannot remove `stdout=PIPE` in a similar way, because we currently use it to communicate the py4j port. However, it should be fine (as it has been for a long time) because we do not produce a ton of traffic through `stdout`.
That commit was not merged in branch-1.0, so this fix is for master only.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1178 from andrewor14/fix-python and squashes the following commits:
e68e870 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-python
20849a8 [Andrew Or] Tone down stdout interference message
a09805b [Andrew Or] Return more than 1 line of error message to user
6dfbd1e [Andrew Or] Don't swallow original exception
0d1861f [Andrew Or] Provide more helpful output if stdout is garbled
21c9d7c [Andrew Or] Do not mask stderr from output
If the gateway process fails to start correctly (e.g., because JAVA_HOME isn't set correctly, there's no Spark jar, etc.), right now pyspark fails because of a very difficult-to-understand error, where we try to parse stdout to get the port where Spark started and there's nothing there. This commit properly catches the error and throws an exception that includes the stderr output for much easier debugging.
Thanks to @shivaram and @stogers for helping to fix this issue!
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#383 from kayousterhout/pyspark and squashes the following commits:
36dd54b [Kay Ousterhout] [SPARK-1466] Raise exception if Gateway process doesn't start.
**Problem.** For `bin/pyspark`, there is currently no other way to specify Spark configuration properties other than through `SPARK_JAVA_OPTS` in `conf/spark-env.sh`. However, this mechanism is supposedly deprecated. Instead, it needs to pick up configurations explicitly specified in `conf/spark-defaults.conf`.
**Solution.** Have `bin/pyspark` invoke `bin/spark-submit`, like all of its counterparts in Scala land (i.e. `bin/spark-shell`, `bin/run-example`). This has the additional benefit of making the invocation of all the user facing Spark scripts consistent.
**Details.** `bin/pyspark` inherently handles two cases: (1) running python applications and (2) running the python shell. For (1), Spark submit already handles running python applications. For cases in which `bin/pyspark` is given a python file, we can simply call pass the file directly to Spark submit and let it handle the rest.
For case (2), `bin/pyspark` starts a python process as before, which launches the JVM as a sub-process. The existing code already provides a code path to do this. All we needed to change is to use `bin/spark-submit` instead of `spark-class` to launch the JVM. This requires modifications to Spark submit to handle the pyspark shell as a special case.
This has been tested locally (OSX and Windows 7), on a standalone cluster, and on a YARN cluster. Running IPython also works as before, except now it takes in Spark submit arguments too.
Author: Andrew Or <andrewor14@gmail.com>
Closes#799 from andrewor14/pyspark-submit and squashes the following commits:
bf37e36 [Andrew Or] Minor changes
01066fa [Andrew Or] bin/pyspark for Windows
c8cb3bf [Andrew Or] Handle perverse app names (with escaped quotes)
1866f85 [Andrew Or] Windows is not cooperating
456d844 [Andrew Or] Guard against shlex hanging if PYSPARK_SUBMIT_ARGS is not set
7eebda8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit
b7ba0d8 [Andrew Or] Address a few comments (minor)
06eb138 [Andrew Or] Use shlex instead of writing our own parser
05879fa [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit
a823661 [Andrew Or] Fix --die-on-broken-pipe not propagated properly
6fba412 [Andrew Or] Deal with quotes + address various comments
fe4c8a7 [Andrew Or] Update --help for bin/pyspark
afe47bf [Andrew Or] Fix spark shell
f04aaa4 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit
a371d26 [Andrew Or] Route bin/pyspark through Spark submit
This PR updates spark-submit to allow submitting Python scripts (currently only with deploy-mode=client, but that's all that was supported before) and updates the PySpark code to properly find various paths, etc. One significant change is that we assume we can always find the Python files either from the Spark assembly JAR (which will happen with the Maven assembly build in make-distribution.sh) or from SPARK_HOME (which will exist in local mode even if you use sbt assembly, and should be enough for testing). This means we no longer need a weird hack to modify the environment for YARN.
This patch also updates the Python worker manager to run python with -u, which means unbuffered output (send it to our logs right away instead of waiting a while after stuff was written); this should simplify debugging.
In addition, it fixes https://issues.apache.org/jira/browse/SPARK-1709, setting the main class from a JAR's Main-Class attribute if not specified by the user, and fixes a few help strings and style issues in spark-submit.
In the future we may want to make the `pyspark` shell use spark-submit as well, but it seems unnecessary for 1.0.
Author: Matei Zaharia <matei@databricks.com>
Closes#664 from mateiz/py-submit and squashes the following commits:
15e9669 [Matei Zaharia] Fix some uses of path.separator property
051278c [Matei Zaharia] Small style fixes
0afe886 [Matei Zaharia] Add license headers
4650412 [Matei Zaharia] Add pyFiles to PYTHONPATH in executors, remove old YARN stuff, add tests
15f8e1e [Matei Zaharia] Set PYTHONPATH in PythonWorkerFactory in case it wasn't set from outside
47c0655 [Matei Zaharia] More work to make spark-submit work with Python:
d4375bd [Matei Zaharia] Clean up description of spark-submit args a bit and add Python ones
This reopens https://github.com/apache/incubator-spark/pull/640 against the new repo
Author: Sandy Ryza <sandy@cloudera.com>
Closes#30 from sryza/sandy-spark-1004 and squashes the following commits:
89889d4 [Sandy Ryza] Move unzipping py4j to the generate-resources phase so that it gets included in the jar the first time
5165a02 [Sandy Ryza] Fix docs
fd0df79 [Sandy Ryza] PySpark on YARN
An initial API that exposes SparkSQL functionality in PySpark. A PythonRDD composed of dictionaries, with string keys and primitive values (boolean, float, int, long, string) can be converted into a SchemaRDD that supports sql queries.
```
from pyspark.context import SQLContext
sqlCtx = SQLContext(sc)
rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
srdd = sqlCtx.applySchema(rdd)
sqlCtx.registerRDDAsTable(srdd, "table1")
srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
srdd2.collect()
```
The last line yields ```[{"f1" : 1, "f2" : "row1"}, {"f1" : 2, "f2": "row2"}, {"f1" : 3, "f2": "row3"}]```
Author: Ahir Reddy <ahirreddy@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#363 from ahirreddy/pysql and squashes the following commits:
0294497 [Ahir Reddy] Updated log4j properties to supress Hive Warns
307d6e0 [Ahir Reddy] Style fix
6f7b8f6 [Ahir Reddy] Temporary fix MIMA checker. Since we now assemble Spark jar with Hive, we don't want to check the interfaces of all of our hive dependencies
3ef074a [Ahir Reddy] Updated documentation because classes moved to sql.py
29245bf [Ahir Reddy] Cache underlying SchemaRDD instead of generating and caching PythonRDD
f2312c7 [Ahir Reddy] Moved everything into sql.py
a19afe4 [Ahir Reddy] Doc fixes
6d658ba [Ahir Reddy] Remove the metastore directory created by the HiveContext tests in SparkSQL
521ff6d [Ahir Reddy] Trying to get spark to build with hive
ab95eba [Ahir Reddy] Set SPARK_HIVE=true on jenkins
ded03e7 [Ahir Reddy] Added doc test for HiveContext
22de1d4 [Ahir Reddy] Fixed maven pyrolite dependency
e4da06c [Ahir Reddy] Display message if hive is not built into spark
227a0be [Michael Armbrust] Update API links. Fix Hive example.
58e2aa9 [Michael Armbrust] Build Docs for pyspark SQL Api. Minor fixes.
4285340 [Michael Armbrust] Fix building of Hive API Docs.
38a92b0 [Michael Armbrust] Add note to future non-python developers about python docs.
337b201 [Ahir Reddy] Changed com.clearspring.analytics stream version from 2.4.0 to 2.5.1 to match SBT build, and added pyrolite to maven build
40491c9 [Ahir Reddy] PR Changes + Method Visibility
1836944 [Michael Armbrust] Fix comments.
e00980f [Michael Armbrust] First draft of python sql programming guide.
b0192d3 [Ahir Reddy] Added Long, Double and Boolean as usable types + unit test
f98a422 [Ahir Reddy] HiveContexts
79621cf [Ahir Reddy] cleaning up cruft
b406ba0 [Ahir Reddy] doctest formatting
20936a5 [Ahir Reddy] Added tests and documentation
e4d21b4 [Ahir Reddy] Added pyrolite dependency
79f739d [Ahir Reddy] added more tests
7515ba0 [Ahir Reddy] added more tests :)
d26ec5e [Ahir Reddy] added test
e9f5b8d [Ahir Reddy] adding tests
906d180 [Ahir Reddy] added todo explaining cost of creating Row object in python
251f99d [Ahir Reddy] for now only allow dictionaries as input
09b9980 [Ahir Reddy] made jrdd explicitly lazy
c608947 [Ahir Reddy] SchemaRDD now has all RDD operations
725c91e [Ahir Reddy] awesome row objects
55d1c76 [Ahir Reddy] return row objects
4fe1319 [Ahir Reddy] output dictionaries correctly
be079de [Ahir Reddy] returning dictionaries works
cd5f79f [Ahir Reddy] Switched to using Scala SQLContext
e948bd9 [Ahir Reddy] yippie
4886052 [Ahir Reddy] even better
c0fb1c6 [Ahir Reddy] more working
043ca85 [Ahir Reddy] working
5496f9f [Ahir Reddy] doesn't crash
b8b904b [Ahir Reddy] Added schema rdd class
67ba875 [Ahir Reddy] java to python, and python to java
bcc0f23 [Ahir Reddy] Java to python
ab6025d [Ahir Reddy] compiling
(Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615)
This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables:
SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY
The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public.
SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property.
SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory.
Other memory considerations:
- The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY.
- run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class).
This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however.
Author: Aaron Davidson <aaron@databricks.com>
Closes#99 from aarondav/sparkmem and squashes the following commits:
9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM
