## What changes were proposed in this pull request?
Unfortunately, it seems that we missed this in 2.4.0. In Spark 2.4, if the default file system is not the local file system, `LOAD DATA LOCAL INPATH` only works in case of absolute paths. This PR aims to fix it to support relative paths. This is a regression in 2.4.0.
```scala
$ ls kv1.txt
kv1.txt
scala> spark.sql("LOAD DATA LOCAL INPATH 'kv1.txt' INTO TABLE t")
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: kv1.txt;
```
## How was this patch tested?
Pass the Jenkins
Closes#22927 from dongjoon-hyun/SPARK-LOAD.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
* Update `AppStatusListener` `cleanupStages` method to remove tasks for those stages in a single pass instead of 1 for each stage.
* This fixes an issue where the cleanupStages method would get backed up, causing a backup in the executor in ElementTrackingStore, resulting in stages and jobs not getting cleaned up properly.
Tasks seem most susceptible to this as there are a lot of them, however a similar issue could arise in other locations the `KVStore` `view` method is used. A broader fix might involve updates to `KVStoreView` and `InMemoryView` as it appears this interface and implementation can lead to multiple and inefficient traversals of the stored data.
## How was this patch tested?
Using existing tests in AppStatusListenerSuite
This is my original work and I license the work to the project under the project’s open source license.
Closes#22883 from patrickbrownsync/cleanup-stages-fix.
Authored-by: Patrick Brown <patrick.brown@blyncsy.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Currently K8S integration tests are hardcoded to use a `minikube` based backend. `minikube` is VM based so can be resource hungry and also doesn't cope well with certain networking setups (for example using Cisco AnyConnect software VPN `minikube` is unusable as it detects its own IP incorrectly).
This PR Adds a new K8S integration testing backend that allows for using the Kubernetes support in [Docker for Desktop](https://blog.docker.com/2018/07/kubernetes-is-now-available-in-docker-desktop-stable-channel/). It also generalises the framework to be able to run the integration tests against an arbitrary Kubernetes cluster.
To Do:
- [x] General Kubernetes cluster backend
- [x] Documentation on Kubernetes integration testing
- [x] Testing of general K8S backend
- [x] Check whether change from timestamps being `Time` to `String` in Fabric 8 upgrade needs additional fix up
## How was this patch tested?
Ran integration tests with Docker for Desktop and all passed:
Suggested Reviewers: ifilonenko srowen
Author: Rob Vesse <rvesse@dotnetrdf.org>
Closes#22805 from rvesse/SPARK-25809.
## What changes were proposed in this pull request?
a followup of https://github.com/apache/spark/pull/22749.
When we construct the new serializer in `ExpressionEncoder.tuple`, we don't need to add `if(isnull ...)` check for each field. They are either simple expressions that can propagate null correctly(e.g. `GetStructField(GetColumnByOrdinal(0, schema), index)`), or complex expression that already have the isnull check.
## How was this patch tested?
existing tests
Closes#22898 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When `SHOW CREATE TABLE` for Datasource tables, we are missing `TBLPROPERTIES` and `COMMENT`, and we should use `LOCATION` instead of path in `OPTION`.
## How was this patch tested?
Splitted `ShowCreateTableSuite` to confirm to work with both `InMemoryCatalog` and `HiveExternalCatalog`, and added some tests.
Closes#22892 from ueshin/issues/SPARK-25884/show_create_table.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to add new function - *schema_of_csv()* which infers schema of CSV string literal. The result of the function is a string containing a schema in DDL format. For example:
```sql
select schema_of_csv('1|abc', map('delimiter', '|'))
```
```
struct<_c0:int,_c1:string>
```
## How was this patch tested?
Added new tests to `CsvFunctionsSuite`, `CsvExpressionsSuite` and SQL tests to `csv-functions.sql`
Closes#22666 from MaxGekk/schema_of_csv-function.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
I saw CoarseGrainedSchedulerBackendSuite failed in my PR and finally reproduced the following error on a very busy machine:
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedDueToTimeoutException: The code passed to eventually never returned normally. Attempted 400 times over 10.009828643999999 seconds. Last failure message: ArrayBuffer("2", "0", "3") had length 3 instead of expected length 4.
```
The logs in this test shows executor 1 was not up when the test failed.
```
18/10/30 11:34:03.563 dispatcher-event-loop-12 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (172.17.0.2:43656) with ID 2
18/10/30 11:34:03.593 dispatcher-event-loop-3 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (172.17.0.2:43658) with ID 3
18/10/30 11:34:03.629 dispatcher-event-loop-6 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (172.17.0.2:43654) with ID 0
18/10/30 11:34:03.885 pool-1-thread-1-ScalaTest-running-CoarseGrainedSchedulerBackendSuite INFO CoarseGrainedSchedulerBackendSuite:
===== FINISHED o.a.s.scheduler.CoarseGrainedSchedulerBackendSuite: 'compute max number of concurrent tasks can be launched' =====
```
And the following logs in executor 1 shows it was still doing the initialization when the timeout happened (at 18/10/30 11:34:03.885).
```
18/10/30 11:34:03.463 netty-rpc-connection-0 INFO TransportClientFactory: Successfully created connection to 54b6b6217301/172.17.0.2:33741 after 37 ms (0 ms spent in bootstraps)
18/10/30 11:34:03.959 main INFO DiskBlockManager: Created local directory at /home/jenkins/workspace/core/target/tmp/spark-383518bc-53bd-4d9c-885b-d881f03875bf/executor-61c406e4-178f-40a6-ac2c-7314ee6fb142/blockmgr-03fb84a1-eedc-4055-8743-682eb3ac5c67
18/10/30 11:34:03.993 main INFO MemoryStore: MemoryStore started with capacity 546.3 MB
```
Hence, I think our current 10 seconds is not enough on a slow Jenkins machine. This PR just increases the timeout from 10 seconds to 60 seconds to make the test more stable.
## How was this patch tested?
Jenkins
Closes#22910 from zsxwing/fix-flaky-test.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR proposes a new optimization rule that replaces `Literal(null, _)` with `FalseLiteral` in conditions in `Join` and `Filter`, predicates in `If`, conditions in `CaseWhen`.
The idea is that some expressions evaluate to `false` if the underlying expression is `null` (as an example see `GeneratePredicate$create` or `doGenCode` and `eval` methods in `If` and `CaseWhen`). Therefore, we can replace `Literal(null, _)` with `FalseLiteral`, which can lead to more optimizations later on.
Let’s consider a few examples.
```
val df = spark.range(1, 100).select($"id".as("l"), ($"id" > 50).as("b"))
df.createOrReplaceTempView("t")
df.createOrReplaceTempView("p")
```
**Case 1**
```
spark.sql("SELECT * FROM t WHERE if(l > 10, false, NULL)").explain(true)
// without the new rule
…
== Optimized Logical Plan ==
Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- Filter if ((id#0L > 10)) false else null
+- Range (1, 100, step=1, splits=Some(12))
== Physical Plan ==
*(1) Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- *(1) Filter if ((id#0L > 10)) false else null
+- *(1) Range (1, 100, step=1, splits=12)
// with the new rule
…
== Optimized Logical Plan ==
LocalRelation <empty>, [l#2L, s#3]
== Physical Plan ==
LocalTableScan <empty>, [l#2L, s#3]
```
**Case 2**
```
spark.sql("SELECT * FROM t WHERE CASE WHEN l < 10 THEN null WHEN l > 40 THEN false ELSE null END”).explain(true)
// without the new rule
...
== Optimized Logical Plan ==
Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- Filter CASE WHEN (id#0L < 10) THEN null WHEN (id#0L > 40) THEN false ELSE null END
+- Range (1, 100, step=1, splits=Some(12))
== Physical Plan ==
*(1) Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- *(1) Filter CASE WHEN (id#0L < 10) THEN null WHEN (id#0L > 40) THEN false ELSE null END
+- *(1) Range (1, 100, step=1, splits=12)
// with the new rule
...
== Optimized Logical Plan ==
LocalRelation <empty>, [l#2L, s#3]
== Physical Plan ==
LocalTableScan <empty>, [l#2L, s#3]
```
**Case 3**
```
spark.sql("SELECT * FROM t JOIN p ON IF(t.l > p.l, null, false)").explain(true)
// without the new rule
...
== Optimized Logical Plan ==
Join Inner, if ((l#2L > l#37L)) null else false
:- Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
: +- Range (1, 100, step=1, splits=Some(12))
+- Project [id#0L AS l#37L, cast(id#0L as string) AS s#38]
+- Range (1, 100, step=1, splits=Some(12))
== Physical Plan ==
BroadcastNestedLoopJoin BuildRight, Inner, if ((l#2L > l#37L)) null else false
:- *(1) Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
: +- *(1) Range (1, 100, step=1, splits=12)
+- BroadcastExchange IdentityBroadcastMode
+- *(2) Project [id#0L AS l#37L, cast(id#0L as string) AS s#38]
+- *(2) Range (1, 100, step=1, splits=12)
// with the new rule
...
== Optimized Logical Plan ==
LocalRelation <empty>, [l#2L, s#3, l#37L, s#38]
```
## How was this patch tested?
This PR comes with a set of dedicated tests.
Closes#22857 from aokolnychyi/spark-25860.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
This avoids having two classes to deal with tokens; now the above
class is a one-stop shop for dealing with delegation tokens. The
YARN backend extends that class instead of doing composition like
before, resulting in a bit less code there too.
The renewer functionality is basically the same code that used to
be in YARN's AMCredentialRenewer. That is also the reason why the
public API of HadoopDelegationTokenManager is a little bit odd;
the YARN AM has some odd requirements for how this all should be
initialized, and the weirdness is needed currently to support that.
Tested:
- YARN with stress app for DT renewal
- Mesos and K8S with basic kerberos tests (both tgt and keytab)
Closes#22624 from vanzin/SPARK-23781.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
This turns off hdfs erasure coding by default for event logs, regardless of filesystem defaults. Because this requires apis only available in hadoop 3, this uses reflection. EC isn't a very good choice for event logs, as hflush() is a no-op, and so updates to the file are not visible for a long time. This can still be configured by setting "spark.eventLog.allowErasureCoding=true", which will use filesystem defaults.
## How was this patch tested?
deployed a cluster with the changes with HDFS EC on. By default, event logs didn't use EC, but configuration still would allow EC. Also tried writing to the local fs (which doesn't support EC at all) and things worked fine.
Closes#22881 from squito/SPARK-25855.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Py4J 0.10.8.1 is released on October 21st and is the first release of Py4J to support Python 3.7 officially. We had better have this to get the official support. Also, there are some patches related to garbage collections.
https://www.py4j.org/changelog.html#py4j-0-10-8-and-py4j-0-10-8-1
## How was this patch tested?
Pass the Jenkins.
Closes#22901 from dongjoon-hyun/SPARK-25891.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
When `spark.sql.hive.metastore.version` is misconfigured, we had better give a directional error message.
**BEFORE**
```scala
scala> sql("show databases").show
scala.MatchError: 2.4 (of class java.lang.String)
```
**AFTER**
```scala
scala> sql("show databases").show
java.lang.UnsupportedOperationException: Unsupported Hive Metastore version (2.4).
Please set spark.sql.hive.metastore.version with a valid version.
```
## How was this patch tested?
Manual.
Closes#22902 from dongjoon-hyun/SPARK-25893.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently in `FailureSafeParser` and `from_avro`, the exception is created with such code
```
throw new SparkException("Malformed records are detected in record parsing. " +
s"Parse Mode: ${FailFastMode.name}.", e.cause)
```
1. The cause part should be `e` instead of `e.cause`
2. If `e` contains non-null message, it should be shown in `from_json`/`from_csv`/`from_avro`, e.g.
```
com.fasterxml.jackson.core.JsonParseException: Unexpected character ('1' (code 49)): was expecting a colon to separate field name and value
at [Source: (InputStreamReader); line: 1, column: 7]
```
3.Kindly show hint for trying PERMISSIVE in error message.
## How was this patch tested?
Unit test.
Closes#22895 from gengliangwang/improve_error_msg.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Refactor BuiltInDataSourceWriteBenchmark, DataSourceWriteBenchmark and AvroWriteBenchmark to use main method.
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.BuiltInDataSourceWriteBenchmark"
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "avro/test:runMain org.apache.spark.sql.execution.benchmark.AvroWriteBenchmark"
```
## How was this patch tested?
manual tests
Closes#22861 from yucai/BuiltInDataSourceWriteBenchmark.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In this test, i have reduced the test time to 20 secs from 1 minute while reducing the sleep time from 1 sec to 100 milliseconds.
With this change, i was able to run the test in 20+ seconds consistently on my laptop. I would like see if it passes in jenkins consistently.
## How was this patch tested?
Its a test fix.
Closes#22900 from dilipbiswal/SPARK-25618.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Previously in from_avro/to_avro, we override the method `simpleString` and `sql` for the string output. However, the override only affects the alias naming:
```
Project [from_avro('col,
...
, (mode,PERMISSIVE)) AS from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))#11]
```
It only makes the alias name quite long: `from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))`).
We should follow `from_csv`/`from_json` here, to override the method prettyName only, and we will get a clean alias name
```
... AS from_avro(col)#11
```
## How was this patch tested?
Manual check
Closes#22890 from gengliangwang/revise_from_to_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Both Spark and Hive support views. However in some cases views created by Hive are not readable by Spark. For example, if column aliases are not specified in view definition queries, both Spark and Hive will generate alias names, but in different ways. In order for Spark to be able to read views created by Hive, users should explicitly specify column aliases in view definition queries.
Given that it's not uncommon that Hive and Spark are used together in enterprise data warehouse, this PR aims to explicitly describe this compatibility issue to help users troubleshoot this issue easily.
## How was this patch tested?
Docs are manually generated and checked locally.
```
SKIP_API=1 jekyll serve
```
Closes#22868 from seancxmao/SPARK-25833.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch removes the rangeBetween functions introduced in SPARK-21608. As explained in SPARK-25841, these functions are confusing and don't quite work. We will redesign them and introduce better ones in SPARK-25843.
## How was this patch tested?
Removed relevant test cases as well. These test cases will need to be added back in SPARK-25843.
Closes#22870 from rxin/SPARK-25862.
Lead-authored-by: Reynold Xin <rxin@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Refactor JSONBenchmark to use main method
use spark-submit:
`bin/spark-submit --class org.apache.spark.sql.execution.datasources.json.JSONBenchmark --jars ./core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar,./sql/catalyst/target/spark-catalyst_2.11-3.0.0-SNAPSHOT-tests.jar ./sql/core/target/spark-sql_2.11-3.0.0-SNAPSHOT-tests.jar`
Generate benchmark result:
`SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.datasources.json.JSONBenchmark"`
## How was this patch tested?
manual tests
Closes#22844 from heary-cao/JSONBenchmarks.
Lead-authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Co-authored-by: heary <cao.xuewen@zte.com.cn>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When we compare attributes, in general, we should always refer to semantic equality, as the default `equal` method can return false when there are "cosmetic" differences between them, but still they are the same thing; at least we have to consider them so when analyzing/optimizing queries.
The PR focuses on the usage and comparison of the `output` of a `LogicalPlan`, which is a `Seq[Attribute]` in `AliasViewChild`. In this case, using equality implicitly fails to check the semantic equality. This results in the operator failing to stabilize.
## How was this patch tested?
running the tests with the patch provided by maryannxue in https://github.com/apache/spark/pull/22060Closes#22713 from mgaido91/SPARK-25691.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
New feature to pass podspec files for driver and executor pods.
## How was this patch tested?
new unit and integration tests
- [x] more overwrites in integration tests
- [ ] invalid template integration test, documentation
Author: Onur Satici <osatici@palantir.com>
Author: Yifei Huang <yifeih@palantir.com>
Author: onursatici <onursatici@gmail.com>
Closes#22146 from onursatici/pod-template.
## What changes were proposed in this pull request?
When a job finishes, there may be some zombie tasks still running due to stage retry. Since a result stage will never be used by other jobs, running these tasks are just wasting the cluster resource. This PR just asks TaskScheduler to cancel the running tasks of a result stage when it's already finished. Credits go to srinathshankar who suggested this idea to me.
This PR also fixes two minor issues while I'm touching DAGScheduler:
- Invalid spark.job.interruptOnCancel should not crash DAGScheduler.
- Non fatal errors should not crash DAGScheduler.
## How was this patch tested?
The new unit tests.
Closes#22771 from zsxwing/SPARK-25773.
Lead-authored-by: Shixiong Zhu <zsxwing@gmail.com>
Co-authored-by: Shixiong Zhu <shixiong@databricks.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
use spark-submit:
`bin/spark-submit --class org.apache.spark.sql.execution.datasources.csv.CSVBenchmark --jars ./core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar,./sql/catalyst/target/spark-catalyst_2.11-3.0.0-SNAPSHOT-tests.jar ./sql/core/target/spark-sql_2.11-3.0.0-SNAPSHOT-tests.jar`
Generate benchmark result:
`SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.datasources.csv.CSVBenchmark"`
## How was this patch tested?
manual tests
Closes#22845 from heary-cao/CSVBenchmarks.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
MetricGetter should rename to ExecutorMetricType in comments.
## How was this patch tested?
Just comments, no need to test.
Closes#22884 from LantaoJin/SPARK-23429_FOLLOWUP.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Spark PCA supports maximum only ~65,535 columns matrix. This is due to the fact that, it computes the Covariance matrix first, then compute principle components. The main bottle neck was computing **covariance matrix.** The limit 65,500 came due to the integer size limit. Because we are passing an array of size n*(n+1)/2 to the breeze library and the size cannot be more than INT_MAX. so, the maximum column size we can give is 65,500.
Currently we don't have such limitation for computing SVD in spark. So, we can make use of Spark SVD to compute the PCA, if the number of columns exceeds the limit.
Computation of PCA can be done directly using SVD of matrix, instead of finding the covariance matrix.
Following are the papers/links for the reference.
https://arxiv.org/pdf/1404.1100.pdfhttps://en.wikipedia.org/wiki/Principal_component_analysis#Singular_value_decompositionhttp://www.ifis.uni-luebeck.de/~moeller/Lectures/WS-16-17/Web-Mining-Agents/PCA-SVD.pdf
## How was this patch tested?
added UT, also manually verified with the existing test for pca, by removing the limit condition in the fit method.
Closes#22784 from shahidki31/PCA.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
When running the release script, you will be interrupted unexpectedly
```
ATTENTION! Your password for authentication realm:
<https://dist.apache.org:443> ASF Committers
can only be stored to disk unencrypted! You are advised to configure
your system so that Subversion can store passwords encrypted, if
possible. See the documentation for details.
You can avoid future appearances of this warning by setting the value
of the 'store-plaintext-passwords' option to either 'yes' or 'no' in
'/home/spark-rm/.subversion/servers'.
-----------------------------------------------------------------------
Store password unencrypted (yes/no)?
```
We can avoid it by adding `--no-auth-cache` when running svn command.
## How was this patch tested?
manually verified with 2.4.0 RC5
Closes#22885 from cloud-fan/svn.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, the BroadcastHashJoinExec physical plan supports CodeGen and non-codegen, but only CodeGen code is tested in the unit tests of InnerJoinSuite、OuterJoinSuite、ExistenceJoinSuite, and non-codegen code is not tested. This PR supplements this part of the test.
## How was this patch tested?
add new unit tested.
Closes#22755 from heary-cao/AddTestToBroadcastHashJoinExec.
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 PR proposes to avoid hardcorded configuration keys in SQLConf's `doc.
## How was this patch tested?
Manually verified.
Closes#22877 from HyukjinKwon/minor-conf-name.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
These two classes were added for regr_ expression support (SPARK-23907). These have been removed and hence we can remove these base classes and inline the logic in the concrete classes.
## How was this patch tested?
Existing tests.
Closes#22856 from dilipbiswal/average_cleanup.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Code generation is incorrect if `outputVars` parameter of `consume` method in `CodegenSupport` contains a lazily evaluated stream of expressions.
This PR fixes the issue by forcing the evaluation of `inputVars` before generating the code for UnsafeRow.
## How was this patch tested?
Tested with the sample program provided in https://issues.apache.org/jira/browse/SPARK-25767Closes#22789 from peter-toth/SPARK-25767.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Set main args correctly in BenchmarkBase, to make it accessible for its subclass.
It will benefit:
- BuiltInDataSourceWriteBenchmark
- AvroWriteBenchmark
## How was this patch tested?
manual tests
Closes#22872 from yucai/main_args.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR targets to document binary type in "Apache Arrow in Spark".
## How was this patch tested?
Manually built the documentation and checked.
Closes#22871 from HyukjinKwon/SPARK-25179.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Extractors are made of 2 expressions, one of them defines the the value to be extract from (called `child`) and the other defines the way of extraction (called `extraction`). In this term extractors have 2 children so they shouldn't be `UnaryExpression`s.
`ResolveReferences` was changed in this commit: 36b826f5d1 which resulted a regression with nested extractors. An extractor need to define its children as the set of both `child` and `extraction`; and should try to resolve both in `ResolveReferences`.
This PR changes `UnresolvedExtractValue` to a `BinaryExpression`.
## How was this patch tested?
added UT
Closes#22817 from peter-toth/SPARK-25816.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The instance of `FileSplit` is redundant for `ParquetFileFormat` and `hive\orc\OrcFileFormat` class.
## How was this patch tested?
Existing unit tests in `ParquetQuerySuite.scala` and `HiveOrcQuerySuite.scala`
Closes#22802 from 10110346/FileSplitnotneed.
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?
WindowSpecDefinition checks start < last, but CalendarIntervalType is not comparable, so it would throw the following exception at runtime:
```
scala.MatchError: CalendarIntervalType (of class org.apache.spark.sql.types.CalendarIntervalType$) at
org.apache.spark.sql.catalyst.util.TypeUtils$.getInterpretedOrdering(TypeUtils.scala:58) at
org.apache.spark.sql.catalyst.expressions.BinaryComparison.ordering$lzycompute(predicates.scala:592) at
org.apache.spark.sql.catalyst.expressions.BinaryComparison.ordering(predicates.scala:592) at
org.apache.spark.sql.catalyst.expressions.GreaterThan.nullSafeEval(predicates.scala:797) at org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:496) at org.apache.spark.sql.catalyst.expressions.SpecifiedWindowFrame.isGreaterThan(windowExpressions.scala:245) at
org.apache.spark.sql.catalyst.expressions.SpecifiedWindowFrame.checkInputDataTypes(windowExpressions.scala:216) at
org.apache.spark.sql.catalyst.expressions.Expression.resolved$lzycompute(Expression.scala:171) at
org.apache.spark.sql.catalyst.expressions.Expression.resolved(Expression.scala:171) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
scala.collection.IndexedSeqOptimized$class.prefixLengthImpl(IndexedSeqOptimized.scala:38) at scala.collection.IndexedSeqOptimized$class.forall(IndexedSeqOptimized.scala:43) at scala.collection.mutable.ArrayBuffer.forall(ArrayBuffer.scala:48) at
org.apache.spark.sql.catalyst.expressions.Expression.childrenResolved(Expression.scala:183) at
org.apache.spark.sql.catalyst.expressions.WindowSpecDefinition.resolved$lzycompute(windowExpressions.scala:48) at
org.apache.spark.sql.catalyst.expressions.WindowSpecDefinition.resolved(windowExpressions.scala:48) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
scala.collection.LinearSeqOptimized$class.forall(LinearSeqOptimized.scala:83)
```
We fix the issue by only perform the check on boundary expressions that are AtomicType.
## How was this patch tested?
Add new test case in `DataFrameWindowFramesSuite`
Closes#22853 from jiangxb1987/windowBoundary.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
## What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/22745 , Dataset encoder supports the combination of java bean and map type. This PR is to fix the Scala side.
The reason why it didn't work before is, `CatalystToExternalMap` tries to get the data type of the input map expression, while it can be unresolved and its data type is known. To fix it, we can follow `UnresolvedMapObjects`, to create a `UnresolvedCatalystToExternalMap`, and only create `CatalystToExternalMap` when the input map expression is resolved and the data type is known.
## How was this patch tested?
enable a old test case
Closes#22812 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implements Every, Some, Any aggregates in SQL. These new aggregate expressions are analyzed in normal way and rewritten to equivalent existing aggregate expressions in the optimizer.
Every(x) => Min(x) where x is boolean.
Some(x) => Max(x) where x is boolean.
Any is a synonym for Some.
SQL
```
explain extended select every(v) from test_agg group by k;
```
Plan :
```
== Parsed Logical Plan ==
'Aggregate ['k], [unresolvedalias('every('v), None)]
+- 'UnresolvedRelation `test_agg`
== Analyzed Logical Plan ==
every(v): boolean
Aggregate [k#0], [every(v#1) AS every(v)#5]
+- SubqueryAlias `test_agg`
+- Project [k#0, v#1]
+- SubqueryAlias `test_agg`
+- LocalRelation [k#0, v#1]
== Optimized Logical Plan ==
Aggregate [k#0], [min(v#1) AS every(v)#5]
+- LocalRelation [k#0, v#1]
== Physical Plan ==
*(2) HashAggregate(keys=[k#0], functions=[min(v#1)], output=[every(v)#5])
+- Exchange hashpartitioning(k#0, 200)
+- *(1) HashAggregate(keys=[k#0], functions=[partial_min(v#1)], output=[k#0, min#7])
+- LocalTableScan [k#0, v#1]
Time taken: 0.512 seconds, Fetched 1 row(s)
```
## How was this patch tested?
Added tests in SQLQueryTestSuite, DataframeAggregateSuite
Closes#22809 from dilipbiswal/SPARK-19851-specific-rewrite.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove deprecated functions which includes:
SQLContext/HiveContext stuff
sparkR.init
jsonFile
parquetFile
registerTempTable
saveAsParquetFile
unionAll
createExternalTable
dropTempTable
## How was this patch tested?
jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#22843 from felixcheung/rrddapi.
## What changes were proposed in this pull request?
Includes python document style checking.
- Use sphinx like check, run only if pydocstyle installed on machine/jenkins
- use pydocstyle rather than single file version pep257.py, which is much older and had some known issues
- verify pydocstyle latest 3.0.0 is in use, to ensure latest doc checks are getting executed
- ignore (inclusion/exclusion error codes) features and support via tox.ini
- Be non-breaking change and allow updating docstyle to standards at easy pace
## How was this patch tested?
./dev/run-tests
Closes#22425 from rekhajoshm/SPARK-23367-2.
Authored-by: Rekha Joshi <rekhajoshm@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Change the version number in comment of `HiveUtils.newClientForExecution` from `13` to `1.2.1` .
## How was this patch tested?
N/A
Closes#22850 from laskfla/HiveUtils-Comment.
Authored-by: laskfla <wwlsax11@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Changed the `kubernetes-client` version and refactored code that broke as a result
## How was this patch tested?
Unit and Integration tests
Closes#22820 from ifilonenko/SPARK-25828.
Authored-by: Ilan Filonenko <ifilondz@gmail.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
## What changes were proposed in this pull request?
Remove SQLContext methods deprecated in 1.4
## How was this patch tested?
Existing tests.
Closes#22815 from srowen/SPARK-25821.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/21596, Jackson is upgraded to 2.9.6.
There are some deprecated API warnings in SQLListener.
Create a trivial PR to fix them.
```
[warn] SQLListener.scala:92: method uncheckedSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] val objectType = typeFactory.uncheckedSimpleType(classOf[Object])
[warn]
[warn] SQLListener.scala:93: method constructSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] typeFactory.constructSimpleType(classOf[(_, _)], classOf[(_, _)], Array(objectType, objectType))
[warn]
[warn] SQLListener.scala:97: method uncheckedSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] val longType = typeFactory.uncheckedSimpleType(classOf[Long])
[warn]
[warn] SQLListener.scala:98: method constructSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] typeFactory.constructSimpleType(classOf[(_, _)], classOf[(_, _)], Array(longType, longType))
```
## How was this patch tested?
Existing unit tests.
Closes#22848 from gengliangwang/fixSQLListenerWarning.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
the final line in the mvn helper script in build/ attempts to shut down the zinc server. due to the zinc server being set up w/a 30min timeout, by the time the mvn test instantiation finishes, the server times out.
this means that when the mvn script tries to shut down zinc, it returns w/an exit code of 1. this will then automatically fail the entire build (even if the build passes).
## How was this patch tested?
i set up a test build:
https://amplab.cs.berkeley.edu/jenkins/job/sknapp-testing-spark-branch-2.4-test-maven-hadoop-2.7/Closes#22854 from shaneknapp/fix-mvn-helper-script.
Authored-by: shane knapp <incomplete@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
add getActiveSession in session.py
## How was this patch tested?
add doctest
Closes#22295 from huaxingao/spark25255.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
Add note about AddJar return value change in migration guide
## How was this patch tested?
n/a
Closes#22826 from srowen/SPARK-25760.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
