PySpark ```DecisionTreeClassifier``` & ```DecisionTreeRegressor``` should support ```setSeed``` like what we do at Scala side.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9807 from yanboliang/spark-11815.
Add ```computeCost``` to ```KMeansModel``` as evaluator for PySpark spark.ml.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9931 from yanboliang/SPARK-11945.
From JIRA:
Currently, PySpark wrappers for spark.ml Scala classes are brittle when accepting Param types. E.g., Normalizer's "p" param cannot be set to "2" (an integer); it must be set to "2.0" (a float). Fixing this is not trivial since there does not appear to be a natural place to insert the conversion before Python wrappers call Java's Params setter method.
A possible fix will be to include a method "_checkType" to PySpark's Param class which checks the type, prints an error if needed, and converts types when relevant (e.g., int to float, or scipy matrix to array). The Java wrapper method which copies params to Scala can call this method when available.
This fix instead checks the types at set time since I think failing sooner is better, but I can switch it around to check at copy time if that would be better. So far this only converts int to float and other conversions (like scipymatrix to array) are left for the future.
Author: Holden Karau <holden@us.ibm.com>
Closes#9581 from holdenk/SPARK-7675-PySpark-sparkml-Params-type-conversion.
For queries like :
select <> from table group by a distribute by a
we can eliminate distribute by ; since group by will anyways do a hash partitioning
Also applicable when user uses Dataframe API
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#9858 from saucam/eliminatedistribute.
Whole code of Vector.scala, VectorSuite.scala and GraphKryoRegistrator.scala are no longer used so it's time to remove them in Spark 2.0.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10613 from sarutak/SPARK-12665.
JIRA: https://issues.apache.org/jira/browse/SPARK-12578
Slightly update to Hive parser. We should keep the distinct keyword when used in an aggregate function with OVER clause. So the CheckAnalysis will detect it and throw exception later.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10557 from viirya/keep-distinct-hivesql.
Add ```read.text``` and ```write.text``` for SparkR.
cc sun-rui felixcheung shivaram
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10348 from yanboliang/spark-12393.
Cartesian product use UnsafeExternalSorter without comparator to do spilling, it will NPE if spilling happens.
This bug also hitted by #10605
cc JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#10606 from davies/fix_spilling.
This fix masks JDBC credentials in the explain output. URL patterns to specify credential seems to be vary between different databases. Added a new method to dialect to mask the credentials according to the database specific URL pattern.
While adding tests I noticed explain output includes array variable for partitions ([Lorg.apache.spark.Partition;3ff74546,). Modified the code to include the first, and last partition information.
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#10452 from sureshthalamati/mask_jdbc_credentials_spark-12504.
Add `columnSimilarities` to IndexedRowMatrix for PySpark spark.mllib.linalg.
Author: Kai Jiang <jiangkai@gmail.com>
Closes#10158 from vectorijk/spark-12041.
Successfully ran kinesis demo on a live, aws hosted kinesis stream against master and 1.6 branches. For reasons I don't entirely understand it required a manual merge to 1.5 which I did as shown here: 075c22e89b
The demo ran successfully on the 1.5 branch as well.
According to `mvn dependency:tree` it is still pulling a fairly old version of the aws-java-sdk (1.9.37), but this appears to have fixed the kinesis regression in 1.5.2.
Author: BrianLondon <brian@seatgeek.com>
Closes#10492 from BrianLondon/remove-only.
Update user guide doc for ```DecisionTreeRegressor``` providing variance of prediction.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10594 from yanboliang/spark-12570.
There is an issue that Py4J's PythonProxyHandler.finalize blocks forever. (https://github.com/bartdag/py4j/pull/184)
Py4j will create a PythonProxyHandler in Java for "transformer_serializer" when calling "registerSerializer". If we call "registerSerializer" twice, the second PythonProxyHandler will override the first one, then the first one will be GCed and trigger "PythonProxyHandler.finalize". To avoid that, we should not call"registerSerializer" more than once, so that "PythonProxyHandler" in Java side won't be GCed.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10514 from zsxwing/SPARK-12511.
As noted in the code, this change is to make this component easier to test in isolation.
Author: Nong <nongli@gmail.com>
Closes#10581 from nongli/spark-12636.
This patch added Py4jCallbackConnectionCleaner to clean the leak sockets of Py4J every 30 seconds. This is a workaround before Py4J fixes the leak issue https://github.com/bartdag/py4j/issues/187
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10579 from zsxwing/SPARK-12617.
JIRA: https://issues.apache.org/jira/browse/SPARK-12439
In toCatalystArray, we should look at the data type returned by dataTypeFor instead of silentSchemaFor, to determine if the element is native type. An obvious problem is when the element is Option[Int] class, catalsilentSchemaFor will return Int, then we will wrongly recognize the element is native type.
There is another problem when using Option as array element. When we encode data like Seq(Some(1), Some(2), None) with encoder, we will use MapObjects to construct an array for it later. But in MapObjects, we don't check if the return value of lambdaFunction is null or not. That causes a bug that the decoded data for Seq(Some(1), Some(2), None) would be Seq(1, 2, -1), instead of Seq(1, 2, null).
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10391 from viirya/fix-catalystarray.
I looked at each case individually and it looks like they can all be removed. The only one that I had to think twice was toArray (I even thought about un-deprecating it, until I realized it was a problem in Java to have toArray returning java.util.List).
Author: Reynold Xin <rxin@databricks.com>
Closes#10569 from rxin/SPARK-12615.
address comments in #10435
This makes the API easier to use if user programmatically generate the call to hash, and they will get analysis exception if the arguments of hash is empty.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10588 from cloud-fan/hash.
JIRA: https://issues.apache.org/jira/browse/SPARK-12643
Without setting lib directory for antlr, the updates of imported grammar files can not be detected. So SparkSqlParser.g will not be rebuilt automatically.
Since it is a minor update, no JIRA ticket is opened. Let me know if it is needed. Thanks.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10571 from viirya/antlr-build.
Modified the definition of R^2 for regression through origin. Added modified test for regression metrics.
Author: Imran Younus <iyounus@us.ibm.com>
Author: Imran Younus <imranyounus@gmail.com>
Closes#10384 from iyounus/SPARK_12331_R2_for_regression_through_origin.
Currently we don't support Hadoop 0.23 but there is a few code related to it so let's clean it up.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10590 from sarutak/SPARK-12641.
rxin davies shivaram
Took save mode from my PR #10480, and move everything to writer methods. This is related to PR #10559
- [x] it seems jsonRDD() is broken, need to investigate - this is not a public API though; will look into some more tonight. (fixed)
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#10584 from felixcheung/rremovedeprecated.
just write the arguments into unsafe row and use murmur3 to calculate hash code
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10435 from cloud-fan/hash-expr.
Currently, when we call corr or cov on dataframe with invalid input we see these error messages for both corr and cov:
- "Currently cov supports calculating the covariance between two columns"
- "Covariance calculation for columns with dataType "[DataType Name]" not supported."
I've fixed this issue by passing the function name as an argument. We could also do the input checks separately for each function. I avoided doing that because of code duplication.
Thanks!
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Closes#10458 from NarineK/sparksqlstatsmessages.
The reader was previously not setting the row length meaning it was wrong if there were variable
length columns. This problem does not manifest usually, since the value in the column is correct and
projecting the row fixes the issue.
Author: Nong Li <nong@databricks.com>
Closes#10576 from nongli/spark-12589.
This PR enable cube/rollup as function, so they can be used as this:
```
select a, b, sum(c) from t group by rollup(a, b)
```
Author: Davies Liu <davies@databricks.com>
Closes#10522 from davies/rollup.
DecisionTreeRegressor will provide variance of prediction as a Double column.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8866 from yanboliang/spark-9622.
It is currently possible to change the values of the supposedly immutable ```GenericRow``` and ```GenericInternalRow``` classes. This is caused by the fact that scala's ArrayOps ```toArray``` (returned by calling ```toSeq```) will return the backing array instead of a copy. This PR fixes this problem.
This PR was inspired by https://github.com/apache/spark/pull/10374 by apo1.
cc apo1 sarutak marmbrus cloud-fan nongli (everyone in the previous conversation).
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10553 from hvanhovell/SPARK-12421.
Before #9264, submitJob would create a separate thread to wait for the job result. `submitJobThreadPool` was a workaround in `ReceiverTracker` to run these waiting-job-result threads. Now #9264 has been merged to master and resolved this blocking issue, `submitJobThreadPool` can be removed now.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10560 from zsxwing/remove-submitJobThreadPool.
Spark SQL's JDBC data source allows users to specify an explicit JDBC driver to load (using the `driver` argument), but in the current code it's possible that the user-specified driver will not be used when it comes time to actually create a JDBC connection.
In a nutshell, the problem is that you might have multiple JDBC drivers on the classpath that claim to be able to handle the same subprotocol, so simply registering the user-provided driver class with the our `DriverRegistry` and JDBC's `DriverManager` is not sufficient to ensure that it's actually used when creating the JDBC connection.
This patch addresses this issue by first registering the user-specified driver with the DriverManager, then iterating over the driver manager's loaded drivers in order to obtain the correct driver and use it to create a connection (previously, we just called `DriverManager.getConnection()` directly).
If a user did not specify a JDBC driver to use, then we call `DriverManager.getDriver` to figure out the class of the driver to use, then pass that class's name to executors; this guards against corner-case bugs in situations where the driver and executor JVMs might have different sets of JDBC drivers on their classpaths (previously, there was the (rare) potential for `DriverManager.getConnection()` to use different drivers on the driver and executors if the user had not explicitly specified a JDBC driver class and the classpaths were different).
This patch is inspired by a similar patch that I made to the `spark-redshift` library (https://github.com/databricks/spark-redshift/pull/143), which contains its own modified fork of some of Spark's JDBC data source code (for cross-Spark-version compatibility reasons).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10519 from JoshRosen/jdbc-driver-precedence.
This patch updates the ExecutorRunner's terminate path to use the new java 8 API
to terminate processes more forcefully if possible. If the executor is unhealthy,
it would previously ignore the destroy() call. Presumably, the new java API was
added to handle cases like this.
We could update the termination path in the future to use OS specific commands
for older java versions.
Author: Nong Li <nong@databricks.com>
Closes#10438 from nongli/spark-12486-executors.
Explicitly close client side socket connection before restart socket receiver.
Author: guoxu1231 <guoxu1231@gmail.com>
Author: Shawn Guo <guoxu1231@gmail.com>
Closes#10464 from guoxu1231/SPARK-12513.
This patch aims to fix another potential source of flakiness in the `dev/test-dependencies.sh` script.
pwendell's original patch and my version used `$(date +%s | tail -c6)` to generate a suffix to use when installing temporary Spark versions into the local Maven cache, but this value only changes once per second and thus is highly collision-prone when concurrent builds launch on AMPLab Jenkins. In order to reduce the potential for conflicts, this patch updates the script to call Python's random number generator instead.
I also fixed a bug in how we captured the original project version; the bug was causing the exit handler code to fail.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10558 from JoshRosen/build-dep-tests-round-3.
