For example, we can write `SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY key + 1` in PostgreSQL, and we should support this in Spark SQL.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8548 from cloud-fan/support-order-by-non-attribute.
Before #8371, there was a bug for `Sort` on `Aggregate` that we can't use aggregate expressions named `_aggOrdering` and can't use more than one ordering expressions which contains aggregate functions. The reason of this bug is that: The aggregate expression in `SortOrder` never get resolved, we alias it with `_aggOrdering` and call `toAttribute` which gives us an `UnresolvedAttribute`. So actually we are referencing aggregate expression by name, not by exprId like we thought. And if there is already an aggregate expression named `_aggOrdering` or there are more than one ordering expressions having aggregate functions, we will have conflict names and can't search by name.
However, after #8371 got merged, the `SortOrder`s are guaranteed to be resolved and we are always referencing aggregate expression by exprId. The Bug doesn't exist anymore and this PR add regression tests for it.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8231 from cloud-fan/sort-agg.
This PR addresses issue [SPARK-10392](https://issues.apache.org/jira/browse/SPARK-10392)
The problem is that for "start of epoch" date (01 Jan 1970) PySpark class DateType returns 0 instead of the `datetime.date` due to implementation of its return statement
Issue reproduction on master:
```
>>> from pyspark.sql.types import *
>>> a = DateType()
>>> a.fromInternal(0)
0
>>> a.fromInternal(1)
datetime.date(1970, 1, 2)
```
Author: 0x0FFF <programmerag@gmail.com>
Closes#8556 from 0x0FFF/SPARK-10392.
This PR addresses [SPARK-10162](https://issues.apache.org/jira/browse/SPARK-10162)
The issue is with DataFrame filter() function, if datetime.datetime is passed to it:
* Timezone information of this datetime is ignored
* This datetime is assumed to be in local timezone, which depends on the OS timezone setting
Fix includes both code change and regression test. Problem reproduction code on master:
```python
import pytz
from datetime import datetime
from pyspark.sql import *
from pyspark.sql.types import *
sqc = SQLContext(sc)
df = sqc.createDataFrame([], StructType([StructField("dt", TimestampType())]))
m1 = pytz.timezone('UTC')
m2 = pytz.timezone('Etc/GMT+3')
df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
```
It gives the same timestamp ignoring time zone:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946713600000000)
Scan PhysicalRDD[dt#0]
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946713600000000)
Scan PhysicalRDD[dt#0]
```
After the fix:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946684800000000)
Scan PhysicalRDD[dt#0]
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946695600000000)
Scan PhysicalRDD[dt#0]
```
PR [8536](https://github.com/apache/spark/pull/8536) was occasionally closed by me dropping the repo
Author: 0x0FFF <programmerag@gmail.com>
Closes#8555 from 0x0FFF/SPARK-10162.
SPARK-4223.
Currently we support setting view and modify acls but you have to specify a list of users. It would be nice to support * meaning all users have access.
Manual tests to verify that: "*" works for any user in:
a. Spark ui: view and kill stage. Done.
b. Spark history server. Done.
c. Yarn application killing. Done.
Author: zhuol <zhuol@yahoo-inc.com>
Closes#8398 from zhuoliu/4223.
Migrate Apache download closer.cgi refs to new closer.lua
This is the bit of the change that affects the project docs; I'm implementing the changes to the Apache site separately.
Author: Sean Owen <sowen@cloudera.com>
Closes#8557 from srowen/SPARK-10398.
This PR can be quite challenging to review. I'm trying to give a detailed description of the problem as well as its solution here.
When reading Parquet files, we need to specify a potentially nested Parquet schema (of type `MessageType`) as requested schema for column pruning. This Parquet schema is translated from a Catalyst schema (of type `StructType`), which is generated by the query planner and represents all requested columns. However, this translation can be fairly complicated because of several reasons:
1. Requested schema must conform to the real schema of the physical file to be read.
This means we have to tailor the actual file schema of every individual physical Parquet file to be read according to the given Catalyst schema. Fortunately we are already doing this in Spark 1.5 by pushing request schema conversion to executor side in PR #7231.
1. Support for schema merging.
A single Parquet dataset may consist of multiple physical Parquet files come with different but compatible schemas. This means we may request for a column path that doesn't exist in a physical Parquet file. All requested column paths can be nested. For example, for a Parquet file schema
```
message root {
required group f0 {
required group f00 {
required int32 f000;
required binary f001 (UTF8);
}
}
}
```
we may request for column paths defined in the following schema:
```
message root {
required group f0 {
required group f00 {
required binary f001 (UTF8);
required float f002;
}
}
optional double f1;
}
```
Notice that we pruned column path `f0.f00.f000`, but added `f0.f00.f002` and `f1`.
The good news is that Parquet handles non-existing column paths properly and always returns null for them.
1. The map from `StructType` to `MessageType` is a one-to-many map.
This is the most unfortunate part.
Due to historical reasons (dark histories!), schemas of Parquet files generated by different libraries have different "flavors". For example, to handle a schema with a single non-nullable column, whose type is an array of non-nullable integers, parquet-protobuf generates the following Parquet schema:
```
message m0 {
repeated int32 f;
}
```
while parquet-avro generates another version:
```
message m1 {
required group f (LIST) {
repeated int32 array;
}
}
```
and parquet-thrift spills this:
```
message m1 {
required group f (LIST) {
repeated int32 f_tuple;
}
}
```
All of them can be mapped to the following _unique_ Catalyst schema:
```
StructType(
StructField(
"f",
ArrayType(IntegerType, containsNull = false),
nullable = false))
```
This greatly complicates Parquet requested schema construction, since the path of a given column varies in different cases. To read the array elements from files with the above schemas, we must use `f` for `m0`, `f.array` for `m1`, and `f.f_tuple` for `m2`.
In earlier Spark versions, we didn't try to fix this issue properly. Spark 1.4 and prior versions simply translate the Catalyst schema in a way more or less compatible with parquet-hive and parquet-avro, but is broken in many other cases. Earlier revisions of Spark 1.5 only try to tailor the Parquet file schema at the first level, and ignore nested ones. This caused [SPARK-10301] [spark-10301] as well as [SPARK-10005] [spark-10005]. In PR #8228, I tried to avoid the hard part of the problem and made a minimum change in `CatalystRowConverter` to fix SPARK-10005. However, when taking SPARK-10301 into consideration, keeping hacking `CatalystRowConverter` doesn't seem to be a good idea. So this PR is an attempt to fix the problem in a proper way.
For a given physical Parquet file with schema `ps` and a compatible Catalyst requested schema `cs`, we use the following algorithm to tailor `ps` to get the result Parquet requested schema `ps'`:
For a leaf column path `c` in `cs`:
- if `c` exists in `cs` and a corresponding Parquet column path `c'` can be found in `ps`, `c'` should be included in `ps'`;
- otherwise, we convert `c` to a Parquet column path `c"` using `CatalystSchemaConverter`, and include `c"` in `ps'`;
- no other column paths should exist in `ps'`.
Then comes the most tedious part:
> Given `cs`, `ps`, and `c`, how to locate `c'` in `ps`?
Unfortunately, there's no quick answer, and we have to enumerate all possible structures defined in parquet-format spec. They are:
1. the standard structure of nested types, and
1. cases defined in all backwards-compatibility rules for `LIST` and `MAP`.
The core part of this PR is `CatalystReadSupport.clipParquetType()`, which tailors a given Parquet file schema according to a requested schema in its Catalyst form. Backwards-compatibility rules of `LIST` and `MAP` are covered in `clipParquetListType()` and `clipParquetMapType()` respectively. The column path selection algorithm is implemented in `clipParquetGroupFields()`.
With this PR, we no longer need to do schema tailoring in `CatalystReadSupport` and `CatalystRowConverter`. Another benefit is that, now we can also read Parquet datasets consist of files with different physical Parquet schema but share the same logical schema, for example, files generated by different Parquet libraries. This situation is illustrated by [this test case] [test-case].
[spark-10301]: https://issues.apache.org/jira/browse/SPARK-10301
[spark-10005]: https://issues.apache.org/jira/browse/SPARK-10005
[test-case]: 38644d8a45 (diff-a9b98e28ce3ae30641829dffd1173be2R26)
Author: Cheng Lian <lian@databricks.com>
Closes#8509 from liancheng/spark-10301/fix-parquet-requested-schema.
They don't bring much value since we now have better unit test coverage for hash joins. This will also help reduce the test time.
Author: Reynold Xin <rxin@databricks.com>
Closes#8542 from rxin/SPARK-10378.
Currently OneVsRest use UDF to generate new binary label during training.
Considering that [SPARK-7321](https://issues.apache.org/jira/browse/SPARK-7321) has been merged, we can use ```when ... otherwise``` which will be more efficiency.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8519 from yanboliang/spark-10349.
In SMJ, the first ExternalSorter could consume all the memory before spilling, then the second can not even acquire the first page.
Before we have a better memory allocator, SMJ should call prepare() before call any compute() of it's children.
cc rxin JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#8511 from davies/smj_memory.
This could help reduce hash collisions, e.g., in `RDD[Vector].repartition`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#8182 from mengxr/SPARK-9954.
Data frame write to DB2 database is failing because by default JDBC data source implementation is generating a table schema with DB2 unsupported data types TEXT for String, and BIT1(1) for Boolean.
This patch registers DB2 JDBC Dialect that maps String, Boolean to valid DB2 data types.
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#8393 from sureshthalamati/db2_dialect_spark-10170.
`deregisterReceiver` should not remove `ReceiverTrackingInfo`. Otherwise, it will throw `java.util.NoSuchElementException: key not found` when restarting it.
Author: zsxwing <zsxwing@gmail.com>
Closes#8538 from zsxwing/SPARK-10369.
* do not cache first cost RDD
* change following cost RDD cache level to MEMORY_AND_DISK
* remove Vector wrapper to save a object per instance
Further improvements will be addressed in SPARK-10329
cc: yu-iskw HuJiayin
Author: Xiangrui Meng <meng@databricks.com>
Closes#8526 from mengxr/SPARK-10354.
This is just some small glue code to actually make use of the
AMPLAB_JENKINS_BUILD_TOOL switch. As far as I can tell, we actually
don't currently use the Maven support in the tool even though it exists.
This patch switches to Maven when the PR title contains "test-maven".
There are a few small other pieces of cleanup in the patch as well.
Author: Patrick Wendell <patrick@databricks.com>
Closes#7878 from pwendell/maven-tests.
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-10184
Change `cumWeight > target` to `cumWeight >= target` in `RangePartitioner.determineBounds` method to make the output partitions more balanced.
Author: ihainan <ihainan72@gmail.com>
Closes#8397 from ihainan/opt_for_rangepartitioner.
* The example code was added in 1.2, before `createDataFrame`. This PR switches to `createDataFrame`. Java code still uses JavaBean.
* assume `sqlContext` is available
* fix some minor issues from previous code review
jkbradley srowen feynmanliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#8518 from mengxr/SPARK-10331.
* replace `ML Dataset` by `DataFrame` to unify the abstraction
* ML algorithms -> pipeline components to describe the main concept
* remove Scala API doc links from the main guide
* `Section Title` -> `Section tile` to be consistent with other section titles in MLlib guide
* modified lines break at 100 chars or periods
jkbradley feynmanliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#8517 from mengxr/SPARK-10348.
This PR includes the following changes:
- Add `LocalNodeTest` for local operator tests and add unit tests for FilterNode and ProjectNode.
- Add `LimitNode` and `UnionNode` and their unit tests to show how to use `LocalNodeTest`. (SPARK-9991, SPARK-9993)
Author: zsxwing <zsxwing@gmail.com>
Closes#8464 from zsxwing/local-execution.
This fixes the problem that scanning partitioned table causes driver have a high memory pressure and takes down the cluster. Also, with this fix, we will be able to correctly show the query plan of a query consuming partitioned tables.
https://issues.apache.org/jira/browse/SPARK-10339https://issues.apache.org/jira/browse/SPARK-10334
Finally, this PR squeeze in a "quick fix" for SPARK-10301. It is not a real fix, but it just throw a better error message to let user know what to do.
Author: Yin Huai <yhuai@databricks.com>
Closes#8515 from yhuai/partitionedTableScan.
SparkHadoopUtil contains methods that use reflection to work around TaskAttemptContext binary incompatibilities between Hadoop 1.x and 2.x. We should use these methods in more places.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8499 from JoshRosen/use-hadoop-reflection-in-more-places.
When I tested the latest version of spark with exclamation mark, I got some errors. Then I reseted the spark version and found that commit id "a2409d1c8e8ddec04b529ac6f6a12b5993f0eeda" brought the bug. With jline version changing from 0.9.94 to 2.12 after this commit, exclamation mark would be treated as a special character in ConsoleReader.
Author: wangwei <wangwei82@huawei.com>
Closes#8420 from small-wang/jline-SPARK-10226.
Actually using this API requires access to a lot of classes that we might make private by accident. I've added some tests to prevent this.
Author: Michael Armbrust <michael@databricks.com>
Closes#8516 from marmbrus/extraStrategiesTests.
This PR introduces a direct write API for testing Parquet. It's a DSL flavored version of the [`writeDirect` method] [1] comes with parquet-avro testing code. With this API, it's much easier to construct arbitrary Parquet structures. It's especially useful when adding regression tests for various compatibility corner cases.
Sample usage of this API can be found in the new test case added in `ParquetThriftCompatibilitySuite`.
[1]: https://github.com/apache/parquet-mr/blob/apache-parquet-1.8.1/parquet-avro/src/test/java/org/apache/parquet/avro/TestArrayCompatibility.java#L945-L972
Author: Cheng Lian <lian@databricks.com>
Closes#8454 from liancheng/spark-10289/parquet-testing-direct-write-api.
Add subset and transform
Also reorganize `[` & `[[` to subset instead of select
Note: for transform, transform is very similar to mutate. Spark doesn't seem to replace existing column with the name in mutate (ie. `mutate(df, age = df$age + 2)` - returned DataFrame has 2 columns with the same name 'age'), so therefore not doing that for now in transform.
Though it is clearly stated it should replace column with matching name (should I open a JIRA for mutate/transform?)
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#8503 from felixcheung/rsubset_transform.
After this PR, In/InSet/ArrayContain will return null if value is null, instead of false. They also will return null even if there is a null in the set/array.
Author: Davies Liu <davies@databricks.com>
Closes#8492 from davies/fix_in.
This PR updates the MLlib user guide and adds migration guide for 1.4->1.5.
* merge migration guide for `spark.mllib` and `spark.ml` packages
* remove dependency section from `spark.ml` guide
* move the paragraph about `spark.mllib` and `spark.ml` to the top and recommend `spark.ml`
* move Sam's talk to footnote to make the section focus on dependencies
Minor changes to code examples and other wording will be in a separate PR.
jkbradley srowen feynmanliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#8498 from mengxr/SPARK-9671.
`fitIntercept` is a command line option but not set in the main program.
dbtsai
Author: Shuo Xiang <sxiang@pinterest.com>
Closes#8510 from coderxiang/intercept and squashes the following commits:
57c9b7d [Shuo Xiang] fix not being able to set intercept in LR example
This change aims at speeding up the dev cycle a little bit, by making
sure that all tests behave the same w.r.t. where the code to be tested
is loaded from. Namely, that means that tests don't rely on the assembly
anymore, rather loading all needed classes from the build directories.
The main change is to make sure all build directories (classes and test-classes)
are added to the classpath of child processes when running tests.
YarnClusterSuite required some custom code since the executors are run
differently (i.e. not through the launcher library, like standalone and
Mesos do).
I also found a couple of tests that could leak a SparkContext on failure,
and added code to handle those.
With this patch, it's possible to run the following command from a clean
source directory and have all tests pass:
mvn -Pyarn -Phadoop-2.4 -Phive-thriftserver install
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#7629 from vanzin/SPARK-9284.
This commit fixes an issue where the public SQL `Row` class did not override `hashCode`, causing it to violate the hashCode() + equals() contract. To fix this, I simply ported the `hashCode` implementation from the 1.4.x version of `Row`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8500 from JoshRosen/SPARK-10325 and squashes the following commits:
51ffea1 [Josh Rosen] Override hashCode() for public Row.
This is based on davies comment on SPARK-8952 which suggests to only call normalizePath() when path starts with '~'
Author: Luciano Resende <lresende@apache.org>
Closes#8343 from lresende/SPARK-8952.
Current port number is fixed as default (7337) in test, this will introduce port contention exception, better to change to a random number in unit test.
squito , seems you're author of this unit test, mind taking a look at this fix? Thanks a lot.
```
[info] - executor state kept across NM restart *** FAILED *** (597 milliseconds)
[info] org.apache.hadoop.service.ServiceStateException: java.net.BindException: Address already in use
[info] at org.apache.hadoop.service.ServiceStateException.convert(ServiceStateException.java:59)
[info] at org.apache.hadoop.service.AbstractService.init(AbstractService.java:172)
[info] at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply$mcV$sp(YarnShuffleServiceSuite.scala:72)
[info] at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply(YarnShuffleServiceSuite.scala:70)
[info] at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply(YarnShuffleServiceSuite.scala:70)
[info] at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
[info] at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
[info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info] at org.scalatest.Transformer.apply(Transformer.scala:22)
[info] at org.scalatest.Transformer.apply(Transformer.scala:20)
[info] at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
[info] at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:42)
...
```
Author: jerryshao <sshao@hortonworks.com>
Closes#8502 from jerryshao/avoid-hardcode-port.
Remove obsolete warning about dynamic allocation not working with cached RDDs
See discussion in https://issues.apache.org/jira/browse/SPARK-10295
Author: Sean Owen <sowen@cloudera.com>
Closes#8489 from srowen/SPARK-10295.
* Added isLargerBetter() method to Pyspark Evaluator to match the Scala version.
* JavaEvaluator delegates isLargerBetter() to underlying Scala object.
* Added check for isLargerBetter() in CrossValidator to determine whether to use argmin or argmax.
* Added test cases for where smaller is better (RMSE) and larger is better (R-Squared).
(This contribution is my original work and that I license the work to the project under Sparks' open source license)
Author: noelsmith <mail@noelsmith.com>
Closes#8399 from noel-smith/pyspark-rmse-xval-fix.
* Adds user guide for `LinearRegressionSummary`
* Fixes unresolved issues in #8197
CC jkbradley mengxr
Author: Feynman Liang <fliang@databricks.com>
Closes#8491 from feynmanliang/SPARK-9905.
