## What changes were proposed in this pull request?
This adds `spark.executor.pyspark.memory` to configure Python's address space limit, [`resource.RLIMIT_AS`](https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS). Limiting Python's address space allows Python to participate in memory management. In practice, we see fewer cases of Python taking too much memory because it doesn't know to run garbage collection. This results in YARN killing fewer containers. This also improves error messages so users know that Python is consuming too much memory:
```
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item, []), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
```
The new pyspark memory setting is used to increase requested YARN container memory, instead of sharing overhead memory between python and off-heap JVM activity.
## How was this patch tested?
Tested memory limits in our YARN cluster and verified that MemoryError is thrown.
Author: Ryan Blue <blue@apache.org>
Closes#21977 from rdblue/SPARK-25004-add-python-memory-limit.
## What changes were proposed in this pull request?
In the PR, I propose to not perform recursive parallel listening of files in the `scanPartitions` method because it can cause a deadlock. Instead of that I propose to do `scanPartitions` in parallel for top level partitions only.
## How was this patch tested?
I extended an existing test to trigger the deadlock.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#22233 from MaxGekk/fix-recover-partitions.
## What changes were proposed in this pull request?
YARN `AmIpFilter` adds a new parameter "RM_HA_URLS" to support RM HA, but Spark on YARN doesn't provide a such parameter, so it will be failed to redirect when running on RM HA. The detailed exception can be checked from JIRA. So here fixing this issue by adding "RM_HA_URLS" parameter.
## How was this patch tested?
Local verification.
Closes#22164 from jerryshao/SPARK-23679.
Authored-by: jerryshao <sshao@hortonworks.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR implements the possibility of the user to override the maximum number of buckets when saving to a table.
Currently the limit is a hard-coded 100k, which might be insufficient for large workloads.
A new configuration entry is proposed: `spark.sql.bucketing.maxBuckets`, which defaults to the previous 100k.
## How was this patch tested?
Added unit tests in the following spark.sql test suites:
- CreateTableAsSelectSuite
- BucketedWriteSuite
Author: Fernando Pereira <fernando.pereira@epfl.ch>
Closes#21087 from ferdonline/enh/configurable_bucket_limit.
## What changes were proposed in this pull request?
As the user uses Kafka transactions to write data, the offsets in Kafka will be non-consecutive. It will contains some transaction (commit or abort) markers. In addition, if the consumer's `isolation.level` is `read_committed`, `poll` will not return aborted messages either. Hence, we will see non-consecutive offsets in the date returned by `poll`. However, as `seekToEnd` may move the offset point to these missing offsets, there are 4 possible corner cases we need to support:
- The whole batch contains no data messages
- The first offset in a batch is not a committed data message
- The last offset in a batch is not a committed data message
- There is a gap in the middle of a batch
They are all covered by the new unit tests.
## How was this patch tested?
The new unit tests.
Closes#22042 from zsxwing/kafka-transaction-read.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
Make sure TransportServer and SocketAuthHelper close the resources for all types of errors.
## How was this patch tested?
Jenkins
Closes#22210 from zsxwing/SPARK-25218.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
The PR excludes Python UDFs filters in FileSourceStrategy so that they don't ExtractPythonUDF rule to throw exception. It doesn't make sense to pass Python UDF filters in FileSourceStrategy anyway because they cannot be used as push down filters.
## How was this patch tested?
Add a new regression test
Closes#22104 from icexelloss/SPARK-24721-udf-filter.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Updated documentation for Spark on Kubernetes for the upcoming 2.4.0.
Please review http://spark.apache.org/contributing.html before opening a pull request.
mccheah erikerlandson
Closes#22224 from liyinan926/master.
Authored-by: Yinan Li <ynli@google.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Our HDFS cluster configured 5 nameservices: `nameservices1`, `nameservices2`, `nameservices3`, `nameservices-dev1` and `nameservices4`, but `nameservices-dev1` unstable. So sometimes an error occurred and causing the entire job failed since [SPARK-24149](https://issues.apache.org/jira/browse/SPARK-24149):
I think it's best to add a switch here.
## How was this patch tested?
manual tests
Closes#21734 from wangyum/SPARK-24149.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Fix flaky synchronization in Kafka tests - we need to use the scan config that was persisted rather than reconstructing it to identify the stream's current configuration.
We caught most instances of this in the original PR, but this one slipped through.
## How was this patch tested?
n/a
Closes#22245 from jose-torres/fixflake.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This PR adds a unit test for OpenHashMap , this can help developers to distinguish between the 0/0.0/0L and null
## How was this patch tested?
Closes#22241 from 10110346/openhashmap.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
this pr add a configuration parameter to configure the capacity of fast aggregation.
Performance comparison:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Windows 7 6.1
Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
Aggregate w multiple keys: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
fasthash = default 5612 / 5882 3.7 267.6 1.0X
fasthash = config 3586 / 3595 5.8 171.0 1.6X
```
## How was this patch tested?
the existed test cases.
Closes#21931 from heary-cao/FastHashCapacity.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is based on the discussion https://github.com/apache/spark/pull/16677/files#r212805327.
As SQL standard doesn't mandate that a nested order by followed by a limit has to respect that ordering clause, this patch removes the `child.outputOrdering` check.
## How was this patch tested?
Unit tests.
Closes#22239 from viirya/improve-global-limit-parallelism-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`__version__` in `setup.py` is currently being dynamically read by `exec`; so the linter complains. Better just switch it off for this line for now.
**Before:**
```bash
$ python -m flake8 . --count --select=E9,F82 --show-source --statistics
./setup.py:37:11: F821 undefined name '__version__'
VERSION = __version__
^
1 F821 undefined name '__version__'
1
```
**After:**
```bash
$ python -m flake8 . --count --select=E9,F82 --show-source --statistics
0
```
## How was this patch tested?
Manually tested.
Closes#22235 from HyukjinKwon/SPARK-23698.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Improved the documentation for the datetime functions in `org.apache.spark.sql.functions` by adding details about the supported column input types, the column return type, behaviour on invalid input, supporting examples and clarifications.
## How was this patch tested?
Manually testing each of the datetime functions with different input to ensure that the corresponding Javadoc/Scaladoc matches the behaviour of the function. Successfully ran the `unidoc` SBT process.
Closes#20901 from abradbury/SPARK-23792.
Authored-by: Adam Bradbury <abradbury@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is a follow up PR for #22207 to fix a potential flaky test. `processAllAvailable` doesn't work for continuous processing so we should not use it for a continuous query.
## How was this patch tested?
Jenkins.
Closes#22230 from zsxwing/SPARK-25214-2.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
An RDD is created using LabeledPoint, but the comment is like #LabeledPoint(feature, label).
Although in the method ChiSquareTest.test, the second parameter is feature and the third parameter is label, it it better to write label in front of feature here because if an RDD is created using LabeldPoint, what we get are actually (label, feature) pairs.
Now it is changed as LabeledPoint(label, feature).
The comments in Scala and Java example have the same typos.
## How was this patch tested?
tested
https://issues.apache.org/jira/browse/SPARK-24688
Author: Weizhe Huang 492816239qq.com
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21665 from uzmijnlm/my_change.
Authored-by: Huangweizhe <huangweizhe@bbdservice.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
[SPARK-25095](ad45299d04) introduced `ambiguous reference to overloaded definition`
```
[error] /Users/d_tsai/dev/apache-spark/core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala:242: ambiguous reference to overloaded definition,
[error] both method addTaskCompletionListener in class TaskContext of type [U](f: org.apache.spark.TaskContext => U)org.apache.spark.TaskContext
[error] and method addTaskCompletionListener in class TaskContext of type (listener: org.apache.spark.util.TaskCompletionListener)org.apache.spark.TaskContext
[error] match argument types (org.apache.spark.TaskContext => Unit)
[error] context.addTaskCompletionListener(_ => server.close())
[error] ^
[error] one error found
[error] Compile failed at Aug 24, 2018 1:56:06 PM [31.582s]
```
which fails the Scala 2.12 branch build.
## How was this patch tested?
Existing tests
Closes#22229 from dbtsai/fix-2.12-build.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22189 from movrsprbp/patch-1.
Authored-by: jaroslav chládek <mastermism@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`parallelize` uses integer multiplication to determine the split indices. It might cause integer overflow.
## How was this patch tested?
unit test
Closes#22225 from mengxr/SPARK-25234.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
When using older versions of spark releases, a use case generated a huge code-gen file which hit the limitation `Constant pool has grown past JVM limit of 0xFFFF`. In this situation, it should fail immediately. But the diagnosis message sent to RM is too large, the ApplicationMaster suspended and RM's ZKStateStore was crashed. For 2.3 or later spark releases the limitation of code-gen has been removed, but maybe there are still some uncaught exceptions that contain oversized error message will cause such a problem.
This PR is aim to cut down the diagnosis message size.
## How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22180 from yaooqinn/SPARK-25174.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When there are missing offsets, Kafka v2 source may return duplicated records when `failOnDataLoss=false` because it doesn't skip missing offsets.
This PR fixes the issue and also adds regression tests for all Kafka readers.
## How was this patch tested?
New tests.
Closes#22207 from zsxwing/SPARK-25214.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
**## What changes were proposed in this pull request?**
When the yarn.nodemanager.resource.memory-mb or yarn.scheduler.maximum-allocation-mb
memory assignment is insufficient, Spark always reports an error request to adjust
yarn.scheduler.maximum-allocation-mb even though in message it shows the memory value
of yarn.nodemanager.resource.memory-mb parameter,As the error Message is bit misleading to the user we can modify the same, We can keep the error message same as executor memory validation message.
Defintion of **yarn.nodemanager.resource.memory-mb:**
Amount of physical memory, in MB, that can be allocated for containers. It means the amount of memory YARN can utilize on this node and therefore this property should be lower then the total memory of that machine.
**yarn.scheduler.maximum-allocation-mb:**
It defines the maximum memory allocation available for a container in MB
it means RM can only allocate memory to containers in increments of "yarn.scheduler.minimum-allocation-mb" and not exceed "yarn.scheduler.maximum-allocation-mb" and It should not be more than total allocated memory of the Node.
**## How was this patch tested?**
Manually tested in hdfs-Yarn clustaer
Closes#22199 from sujith71955/maste_am_log.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR generates the code that to refer a `StructType` generated in the scala code instead of generating `StructType` in Java code.
The original code has two issues.
1. Avoid to used the field name such as `key.name`
1. Support complicated schema (e.g. nested DataType)
At first, [the JIRA entry](https://issues.apache.org/jira/browse/SPARK-25178) proposed to change the generated field name of the keySchema / valueSchema to a dummy name in `RowBasedHashMapGenerator` and `VectorizedHashMapGenerator.scala`. This proposal can addresse issue 1.
Ueshin suggested an approach to refer to a `StructType` generated in the scala code using `ctx.addReferenceObj()`. This approach can address issues 1 and 2. Finally, this PR uses this approach.
## How was this patch tested?
Existing UTs
Closes#22187 from kiszk/SPARK-25178.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Update to janino 3.0.9 to address Java 8 + Scala 2.12 incompatibility. The error manifests as test failures like this in `ExpressionEncoderSuite`:
```
- encode/decode for seq of string: List(abc, xyz) *** FAILED ***
java.lang.RuntimeException: Error while encoding: org.codehaus.janino.InternalCompilerException: failed to compile: org.codehaus.janino.InternalCompilerException: Compiling "GeneratedClass": Two non-abstract methods "public int scala.collection.TraversableOnce.size()" have the same parameter types, declaring type and return type
```
It comes up pretty immediately in any generated code that references Scala collections, and virtually always concerning the `size()` method.
## How was this patch tested?
Existing tests
Closes#22203 from srowen/SPARK-25029.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
(Link to Jira: https://issues.apache.org/jira/browse/SPARK-4502)
_N.B. This is a restart of PR #16578 which includes a subset of that code. Relevant review comments from that PR should be considered incorporated by reference. Please avoid duplication in review by reviewing that PR first. The summary below is an edited copy of the summary of the previous PR._
## What changes were proposed in this pull request?
One of the hallmarks of a column-oriented data storage format is the ability to read data from a subset of columns, efficiently skipping reads from other columns. Spark has long had support for pruning unneeded top-level schema fields from the scan of a parquet file. For example, consider a table, `contacts`, backed by parquet with the following Spark SQL schema:
```
root
|-- name: struct
| |-- first: string
| |-- last: string
|-- address: string
```
Parquet stores this table's data in three physical columns: `name.first`, `name.last` and `address`. To answer the query
```SQL
select address from contacts
```
Spark will read only from the `address` column of parquet data. However, to answer the query
```SQL
select name.first from contacts
```
Spark will read `name.first` and `name.last` from parquet.
This PR modifies Spark SQL to support a finer-grain of schema pruning. With this patch, Spark reads only the `name.first` column to answer the previous query.
### Implementation
There are two main components of this patch. First, there is a `ParquetSchemaPruning` optimizer rule for gathering the required schema fields of a `PhysicalOperation` over a parquet file, constructing a new schema based on those required fields and rewriting the plan in terms of that pruned schema. The pruned schema fields are pushed down to the parquet requested read schema. `ParquetSchemaPruning` uses a new `ProjectionOverSchema` extractor for rewriting a catalyst expression in terms of a pruned schema.
Second, the `ParquetRowConverter` has been patched to ensure the ordinals of the parquet columns read are correct for the pruned schema. `ParquetReadSupport` has been patched to address a compatibility mismatch between Spark's built in vectorized reader and the parquet-mr library's reader.
### Limitation
Among the complex Spark SQL data types, this patch supports parquet column pruning of nested sequences of struct fields only.
## How was this patch tested?
Care has been taken to ensure correctness and prevent regressions. A more advanced version of this patch incorporating optimizations for rewriting queries involving aggregations and joins has been running on a production Spark cluster at VideoAmp for several years. In that time, one bug was found and fixed early on, and we added a regression test for that bug.
We forward-ported this patch to Spark master in June 2016 and have been running this patch against Spark 2.x branches on ad-hoc clusters since then.
Closes#21320 from mallman/spark-4502-parquet_column_pruning-foundation.
Lead-authored-by: Michael Allman <msa@allman.ms>
Co-authored-by: Adam Jacques <adam@technowizardry.net>
Co-authored-by: Michael Allman <michael@videoamp.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Dataset.apply calls dataset.deserializer (to provide an early error) which ends up calling the full Analyzer on the deserializer. This can take tens of milliseconds, depending on how big the plan is.
Since Dataset.apply is called for many Dataset operations such as Dataset.where it can be a significant overhead for short queries.
According to a comment in the PR that introduced this check, we can at least remove this check for DataFrames: https://github.com/apache/spark/pull/20402#discussion_r164338267
## How was this patch tested?
Existing tests + manual benchmark
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#22201 from bogdanrdc/deserializer-fix.
## What changes were proposed in this pull request?
**Problem statement**
load data command with hdfs file paths consists of wild card strings like * are not working
eg:
"load data inpath 'hdfs://hacluster/user/ext* into table t1"
throws Analysis exception while executing this query
**Analysis -**
Currently fs.exists() API which is used for path validation in load command API cannot resolve the path with wild card pattern, To mitigate this problem i am using globStatus() API another api which can resolve the paths with hdfs supported wildcards like *,? etc(inline with hive wildcard support).
**Improvement identified as part of this issue -**
Currently system wont support wildcard character to be used for folder level path in a local file system. This PR has handled this scenario, the same globStatus API will unify the validation logic of local and non local file systems, this will ensure the behavior consistency between the hdfs and local file path in load command.
with this improvement user will be able to use a wildcard character in folder level path of a local file system in load command inline with hive behaviour, in older versions user can use wildcards only in file path of the local file system if they use in folder path system use to give an error by mentioning that not supported.
eg: load data local inpath '/localfilesystem/folder* into table t1
## How was this patch tested?
a) Manually tested by executing test-cases in HDFS yarn cluster. Reports is been attached in below section.
b) Existing test-case can verify the impact and functionality for local file path scenarios
c) A test-case is been added for verifying the functionality when wild card is been used in folder level path of a local file system
## Test Results
Note: all ip's were updated to localhost for security reasons.
HDFS path details
```
vm1:/opt/ficlient # hadoop fs -ls /user/data/sujith1
Found 2 items
-rw-r--r-- 3 shahid hadoop 4802 2018-03-26 15:45 /user/data/sujith1/typeddata60.txt
-rw-r--r-- 3 shahid hadoop 4883 2018-03-26 15:45 /user/data/sujith1/typeddata61.txt
vm1:/opt/ficlient # hadoop fs -ls /user/data/sujith2
Found 2 items
-rw-r--r-- 3 shahid hadoop 4802 2018-03-26 15:45 /user/data/sujith2/typeddata60.txt
-rw-r--r-- 3 shahid hadoop 4883 2018-03-26 15:45 /user/data/sujith2/typeddata61.txt
```
positive scenario by specifying complete file path to know about record size
```
0: jdbc:hive2://localhost:22550/default> create table wild_spark (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.217 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/typeddata60.txt' into table wild_spark;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (4.236 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/typeddata61.txt' into table wild_spark;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.602 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from wild_spark;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
1 row selected (18.529 seconds)
0: jdbc:hive2://localhost:22550/default>
```
With wild card character in file path
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.409 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/type*' into table spark_withWildChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.502 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
```
with ? wild card scenario
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar_DiffChar (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.489 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/?ypeddata60.txt' into table spark_withWildChar_DiffChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.152 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/?ypeddata61.txt' into table spark_withWildChar_DiffChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.644 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar_DiffChar;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
1 row selected (16.078 seconds)
```
with folder level wild card scenario
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar_folderlevel (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.489 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/suji*/*' into table spark_withWildChar_folderlevel;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.152 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar_folderlevel;
+-----------+--+
| count(1) |
+-----------+--+
| 242 |
+-----------+--+
1 row selected (16.078 seconds)
```
Negative scenario invalid path
```
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujiinvalid*/*' into table spark_withWildChar_folder;
Error: org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: /user/data/sujiinvalid*/*; (state=,code=0)
0: jdbc:hive2://localhost:22550/default>
```
Hive Test results- file level
```
0: jdbc:hive2://localhost:21066/> create table hive_withWildChar_files (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) stored as TEXTFILE;
No rows affected (0.723 seconds)
0: jdbc:hive2://localhost:21066/> load data inpath '/user/data/sujith1/type*' into table hive_withWildChar_files;
INFO : Loading data to table default.hive_withwildchar_files from hdfs://hacluster/user/sujith1/type*
No rows affected (0.682 seconds)
0: jdbc:hive2://localhost:21066/> select count(*) from hive_withWildChar_files;
+------+--+
| _c0 |
+------+--+
| 121 |
+------+--+
1 row selected (50.832 seconds)
```
Hive Test results- folder level
```
0: jdbc:hive2://localhost:21066/> create table hive_withWildChar_folder (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) stored as TEXTFILE;
No rows affected (0.459 seconds)
0: jdbc:hive2://localhost:21066/> load data inpath '/user/data/suji*/*' into table hive_withWildChar_folder;
INFO : Loading data to table default.hive_withwildchar_folder from hdfs://hacluster/user/data/suji*/*
No rows affected (0.76 seconds)
0: jdbc:hive2://localhost:21066/> select count(*) from hive_withWildChar_folder;
+------+--+
| _c0 |
+------+--+
| 242 |
+------+--+
1 row selected (46.483 seconds)
```
Closes#20611 from sujith71955/master_wldcardsupport.
Lead-authored-by: s71955 <sujithchacko.2010@gmail.com>
Co-authored-by: sujith71955 <sujithchacko.2010@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In feature.py, VectorSizeHint setSize and getSize don't return value. Add return.
## How was this patch tested?
I tested the changes on my local.
Closes#22136 from huaxingao/spark-25124.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Joseph K. Bradley <joseph@databricks.com>
## What changes were proposed in this pull request?
Fix a race in the rate source tests. We need a better way of testing restart behavior.
## How was this patch tested?
unit test
Closes#22191 from jose-torres/racetest.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
Casting to `DecimalType` is not always needed to force nullable.
If the decimal type to cast is wider than original type, or only truncating or precision loss, the casted value won't be `null`.
## How was this patch tested?
Added and modified tests.
Closes#22200 from ueshin/issues/SPARK-25208/cast_nullable_decimal.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
[SPARK-25126] (https://issues.apache.org/jira/browse/SPARK-25126)
reports loading a large number of orc files consumes a lot of memory
in both 2.0 and 2.3. The issue is caused by creating a Reader for every
orc file in order to infer the schema.
In OrFileOperator.ReadSchema, a Reader is created for every file
although only the first valid one is used. This uses significant
amount of memory when there `paths` have a lot of files. In 2.3
a different code path (OrcUtils.readSchema) is used for inferring
schema for orc files. This commit changes both functions to create
Reader lazily.
## How was this patch tested?
Pass the Jenkins with a newly added test case by dongjoon-hyun
Closes#22157 from raofu/SPARK-25126.
Lead-authored-by: Rao Fu <rao@coupang.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Rao Fu <raofu04@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/21838, the class `AvroDataToCatalyst` and `CatalystDataToAvro` were put in package `org.apache.spark.sql`.
They should be moved to package `org.apache.spark.sql.avro`.
Also optimize imports in Avro module.
## How was this patch tested?
Unit test
Closes#22196 from gengliangwang/avro_revise_package_name.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
VectorizedParquetRecordReader::initializeInternal rebuilds the column list and path list once for each column. Therefore, it indirectly iterates 2\*colCount\*colCount times for each parquet file.
This inefficiency impacts jobs that read parquet-backed tables with many columns and many files. Jobs that read tables with few columns or few files are not impacted.
This PR changes initializeInternal so that it builds each list only once.
I ran benchmarks on my laptop with 1 worker thread, running this query:
<pre>
sql("select * from parquet_backed_table where id1 = 1").collect
</pre>
There are roughly one matching row for every 425 rows, and the matching rows are sprinkled pretty evenly throughout the table (that is, every page for column <code>id1</code> has at least one matching row).
6000 columns, 1 million rows, 67 32M files:
master | branch | improvement
-------|---------|-----------
10.87 min | 6.09 min | 44%
6000 columns, 1 million rows, 23 98m files:
master | branch | improvement
-------|---------|-----------
7.39 min | 5.80 min | 21%
600 columns 10 million rows, 67 32M files:
master | branch | improvement
-------|---------|-----------
1.95 min | 1.96 min | -0.5%
60 columns, 100 million rows, 67 32M files:
master | branch | improvement
-------|---------|-----------
0.55 min | 0.55 min | 0%
## How was this patch tested?
- sql unit tests
- pyspark-sql tests
Closes#22188 from bersprockets/SPARK-25164.
Authored-by: Bruce Robbins <bersprockets@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr proposed to show RDD/relation names in RDD/Hive table scan nodes.
This change made these names show up in the webUI and explain results.
For example;
```
scala> sql("CREATE TABLE t(c1 int) USING hive")
scala> sql("INSERT INTO t VALUES(1)")
scala> spark.table("t").explain()
== Physical Plan ==
Scan hive default.t [c1#8], HiveTableRelation `default`.`t`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [c1#8]
^^^^^^^^^^^
```
<img width="212" alt="spark-pr-hive" src="https://user-images.githubusercontent.com/692303/44501013-51264c80-a6c6-11e8-94f8-0704aee83bb6.png">
Closes#20226
## How was this patch tested?
Added tests in `DataFrameSuite`, `DatasetSuite`, and `HiveExplainSuite`
Closes#22153 from maropu/pr20226.
Lead-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: Tejas Patil <tejasp@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #22031 which added `zip_with` function to fix an example.
## How was this patch tested?
Existing tests.
Closes#22194 from ueshin/issues/SPARK-23932/fix_examples.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
A few SQL tests for R were failing in my development environment. In this PR, i am attempting to
address some of them. Below are the reasons for the failure.
- The catalog api tests assumes catalog artifacts named "foo" to be non existent. I think name such as foo and bar are common and i use it frequently. I have changed it to a string that i hope is less likely to collide.
- One test assumes that we only have one database in the system. I had more than one and it caused the test to fail. I have changed that check.
- One more test which compares two timestamp values fail - i am debugging this now. I will send it as a followup - may be.
## How was this patch tested?
Its a test fix.
Closes#22161 from dilipbiswal/r-sql-test-fix1.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
They depend on internal Expression APIs. Let's see how far we can get without it.
## How was this patch tested?
Just some code removal. There's no existing tests as far as I can tell so it's easy to remove.
Closes#22185 from rxin/SPARK-25127.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/21495 the welcome message is printed first, and then Scala prompt will be shown before the Spark UI info is printed.
Although it's a minor issue, but visually, it doesn't look as nice as the existing behavior. This PR intends to fix it by duplicating the Scala `process` code to arrange the printing order. However, one variable is private, so reflection has to be used which is not desirable.
We can use this PR to brainstorm how to handle it properly and how Scala can change their APIs to fit our need.
## How was this patch tested?
Existing test
Closes#21749 from dbtsai/repl-followup.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
`ExternalAppendOnlyMapSuiteCheck` test is flaky.
We use a `SparkListener` to collect spill metrics of completed stages. `withListener` runs the code that does spill. Spill status was checked after the code finishes but it was still in `withListener`. At that time it was possibly not all events to the listener bus are processed.
We should check spill status after all events are processed.
## How was this patch tested?
Locally ran unit tests.
Closes#22181 from viirya/SPARK-25163.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
The race condition that caused test failure is between 2 threads.
- The MicrobatchExecution thread that processes inputs to produce answers and then generates progress events.
- The test thread that generates some input data, checked the answer and then verified the query generated progress event.
The synchronization structure between these threads is as follows
1. MicrobatchExecution thread, in every batch, does the following in order.
a. Processes batch input to generate answer.
b. Signals `awaitProgressLockCondition` to wake up threads waiting for progress using `awaitOffset`
c. Generates progress event
2. Test execution thread
a. Calls `awaitOffset` to wait for progress, which waits on `awaitProgressLockCondition`.
b. As soon as `awaitProgressLockCondition` is signaled, it would move on the in the test to check answer.
c. Finally, it would verify the last generated progress event.
What can happen is the following sequence of events: 2a -> 1a -> 1b -> 2b -> 2c -> 1c.
In other words, the progress event may be generated after the test tries to verify it.
The solution has two steps.
1. Signal the waiting thread after the progress event has been generated, that is, after `finishTrigger()`.
2. Increase the timeout of `awaitProgressLockCondition.await(100 ms)` to a large value.
This latter is to ensure that test thread for keeps waiting on `awaitProgressLockCondition`until the MicroBatchExecution thread explicitly signals it. With the existing small timeout of 100ms the following sequence can occur.
- MicroBatchExecution thread updates committed offsets
- Test thread waiting on `awaitProgressLockCondition` accidentally times out after 100 ms, finds that the committed offsets have been updated, therefore returns from `awaitOffset` and moves on to the progress event tests.
- MicroBatchExecution thread then generates progress event and signals. But the test thread has already attempted to verify the event and failed.
By increasing the timeout to large (e.g., `streamingTimeoutMs = 60 seconds`, similar to `awaitInitialization`), this above type of race condition is also avoided.
## How was this patch tested?
Ran locally many times.
Closes#22182 from tdas/SPARK-25184.
Authored-by: Tathagata Das <tathagata.das1565@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
Limit Thread Pool size in BlockManager Master and Slave endpoints.
Currently, BlockManagerMasterEndpoint and BlockManagerSlaveEndpoint both have thread pools with nearly unbounded (Integer.MAX_VALUE) numbers of threads. In certain cases, this can lead to driver OOM errors. This change limits the thread pools to 100 threads; this should not break any existing behavior because any tasks beyond that number will get queued.
## How was this patch tested?
Manual testing
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22176 from mukulmurthy/25181-threads.
Authored-by: Mukul Murthy <mukul.murthy@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
Include PandasUDFType in the import all of pyspark.sql.functions
## How was this patch tested?
Run the test case from the pyspark shell from the jira [spark-25105](https://jira.apache.org/jira/browse/SPARK-25105?jql=project%20%3D%20SPARK%20AND%20component%20in%20(ML%2C%20PySpark%2C%20SQL%2C%20%22Structured%20Streaming%22))
I manually test on pyspark-shell:
before:
`
>>> from pyspark.sql.functions import *
>>> foo = pandas_udf(lambda x: x, 'v int', PandasUDFType.GROUPED_MAP)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'PandasUDFType' is not defined
>>>
`
after:
`
>>> from pyspark.sql.functions import *
>>> foo = pandas_udf(lambda x: x, 'v int', PandasUDFType.GROUPED_MAP)
>>>
`
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22100 from kevinyu98/spark-25105.
Authored-by: Kevin Yu <qyu@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Fix issues arising from the fact that builtins __file__, __long__, __raw_input()__, __unicode__, __xrange()__, etc. were all removed from Python 3. __Undefined names__ have the potential to raise [NameError](https://docs.python.org/3/library/exceptions.html#NameError) at runtime.
## How was this patch tested?
* $ __python2 -m flake8 . --count --select=E9,F82 --show-source --statistics__
* $ __python3 -m flake8 . --count --select=E9,F82 --show-source --statistics__
holdenk
flake8 testing of https://github.com/apache/spark on Python 3.6.3
$ __python3 -m flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics__
```
./dev/merge_spark_pr.py:98:14: F821 undefined name 'raw_input'
result = raw_input("\n%s (y/n): " % prompt)
^
./dev/merge_spark_pr.py:136:22: F821 undefined name 'raw_input'
primary_author = raw_input(
^
./dev/merge_spark_pr.py:186:16: F821 undefined name 'raw_input'
pick_ref = raw_input("Enter a branch name [%s]: " % default_branch)
^
./dev/merge_spark_pr.py:233:15: F821 undefined name 'raw_input'
jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id)
^
./dev/merge_spark_pr.py:278:20: F821 undefined name 'raw_input'
fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions)
^
./dev/merge_spark_pr.py:317:28: F821 undefined name 'raw_input'
raw_assignee = raw_input(
^
./dev/merge_spark_pr.py:430:14: F821 undefined name 'raw_input'
pr_num = raw_input("Which pull request would you like to merge? (e.g. 34): ")
^
./dev/merge_spark_pr.py:442:18: F821 undefined name 'raw_input'
result = raw_input("Would you like to use the modified title? (y/n): ")
^
./dev/merge_spark_pr.py:493:11: F821 undefined name 'raw_input'
while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y":
^
./dev/create-release/releaseutils.py:58:16: F821 undefined name 'raw_input'
response = raw_input("%s [y/n]: " % msg)
^
./dev/create-release/releaseutils.py:152:38: F821 undefined name 'unicode'
author = unidecode.unidecode(unicode(author, "UTF-8")).strip()
^
./python/setup.py:37:11: F821 undefined name '__version__'
VERSION = __version__
^
./python/pyspark/cloudpickle.py:275:18: F821 undefined name 'buffer'
dispatch[buffer] = save_buffer
^
./python/pyspark/cloudpickle.py:807:18: F821 undefined name 'file'
dispatch[file] = save_file
^
./python/pyspark/sql/conf.py:61:61: F821 undefined name 'unicode'
if not isinstance(obj, str) and not isinstance(obj, unicode):
^
./python/pyspark/sql/streaming.py:25:21: F821 undefined name 'long'
intlike = (int, long)
^
./python/pyspark/streaming/dstream.py:405:35: F821 undefined name 'long'
return self._sc._jvm.Time(long(timestamp * 1000))
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:21:10: F821 undefined name 'xrange'
for i in xrange(50):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:22:14: F821 undefined name 'xrange'
for j in xrange(5):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:23:18: F821 undefined name 'xrange'
for k in xrange(20022):
^
20 F821 undefined name 'raw_input'
20
```
Closes#20838 from cclauss/fix-undefined-names.
Authored-by: cclauss <cclauss@bluewin.ch>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Improve the data source v2 API according to the [design doc](https://docs.google.com/document/d/1DDXCTCrup4bKWByTalkXWgavcPdvur8a4eEu8x1BzPM/edit?usp=sharing)
summary of the changes
1. rename `ReadSupport` -> `DataSourceReader` -> `InputPartition` -> `InputPartitionReader` to `BatchReadSupportProvider` -> `BatchReadSupport` -> `InputPartition`/`PartitionReaderFactory` -> `PartitionReader`. Similar renaming also happens at streaming and write APIs.
2. create `ScanConfig` to store query specific information like operator pushdown result, streaming offsets, etc. This makes batch and streaming `ReadSupport`(previouslly named `DataSourceReader`) immutable. All other methods take `ScanConfig` as input, which implies applying operator pushdown and getting streaming offsets happen before all other things(get input partitions, report statistics, etc.).
3. separate `InputPartition` to `InputPartition` and `PartitionReaderFactory`. This is a natural separation, data splitting and reading are orthogonal and we should not mix them in one interfaces. This also makes the naming consistent between read and write API: `PartitionReaderFactory` vs `DataWriterFactory`.
4. separate the batch and streaming interfaces. Sometimes it's painful to force the streaming interface to extend batch interface, as we may need to override some batch methods to return false, or even leak the streaming concept to batch API(e.g. `DataWriterFactory#createWriter(partitionId, taskId, epochId)`)
Some follow-ups we should do after this PR (tracked by https://issues.apache.org/jira/browse/SPARK-25186 ):
1. Revisit the life cycle of `ReadSupport` instances. Currently I keep it same as the previous `DataSourceReader`, i.e. the life cycle is bound to the batch/stream query. This fits streaming very well but may not be perfect for batch source. We can also consider to let `ReadSupport.newScanConfigBuilder` take `DataSourceOptions` as parameter, if we decide to change the life cycle.
2. Add `WriteConfig`. This is similar to `ScanConfig` and makes the write API more flexible. But it's only needed when we add the `replaceWhere` support, and it needs to change the streaming execution engine for this new concept, which I think is better to be done in another PR.
3. Refine the document. This PR adds/changes a lot of document and it's very likely that some people may have better ideas.
4. Figure out the life cycle of `CustomMetrics`. It looks to me that it should be bound to a `ScanConfig`, but we need to change `ProgressReporter` to get the `ScanConfig`. Better to be done in another PR.
5. Better operator pushdown API. This PR keeps the pushdown API as it was, i.e. using the `SupportsPushdownXYZ` traits. We can design a better API using build pattern, but this is a complicated design and deserves an individual JIRA ticket and design doc.
6. Improve the continuous streaming engine to only create a new `ScanConfig` when re-configuring.
7. Remove `SupportsPushdownCatalystFilter`. This is actually not a must-have for file source, we can change the hive partition pruning to use the public `Filter`.
## How was this patch tested?
existing tests.
Closes#22009 from cloud-fan/redesign.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR moves the compilation of the regexp for code formatting outside the method which is called for each code block when splitting expressions, in order to avoid recompiling the regexp every time.
Credit should be given to Izek Greenfield.
## How was this patch tested?
existing UTs
Closes#22135 from mgaido91/SPARK-25093.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This fixes a perf regression caused by https://github.com/apache/spark/pull/21376 .
We should not use `RDD#toLocalIterator`, which triggers one Spark job per RDD partition. This is very bad for RDDs with a lot of small partitions.
To fix it, this PR introduces a way to access SQLConf in the scheduler event loop thread, so that we don't need to use `RDD#toLocalIterator` anymore in `JsonInferSchema`.
## How was this patch tested?
a new test
Closes#22152 from cloud-fan/conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This pr is to fix bugs when expr codegen fails; we need to catch `java.util.concurrent.ExecutionException` instead of `InternalCompilerException` and `CompileException` . This handling is the same with the `WholeStageCodegenExec ` one: 60af2501e1/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala (L585)
## How was this patch tested?
Added tests in `CodeGeneratorWithInterpretedFallbackSuite`
Closes#22154 from maropu/SPARK-25140.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
