ISTM `lib` is better because `datanucleus` jars are located in `lib` for release builds.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#10901 from maropu/DocFix.
JIRA: https://issues.apache.org/jira/browse/SPARK-12705
**Scope:**
This PR is a general fix for sorting reference resolution when the child's `outputSet` does not have the order-by attributes (called, *missing attributes*):
- UnaryNode support is limited to `Project`, `Window`, `Aggregate`, `Distinct`, `Filter`, `RepartitionByExpression`.
- We will not try to resolve the missing references inside a subquery, unless the outputSet of this subquery contains it.
**General Reference Resolution Rules:**
- Jump over the nodes with the following types: `Distinct`, `Filter`, `RepartitionByExpression`. Do not need to add missing attributes. The reason is their `outputSet` is decided by their `inputSet`, which is the `outputSet` of their children.
- Group-by expressions in `Aggregate`: missing order-by attributes are not allowed to be added into group-by expressions since it will change the query result. Thus, in RDBMS, it is not allowed.
- Aggregate expressions in `Aggregate`: if the group-by expressions in `Aggregate` contains the missing attributes but aggregate expressions do not have it, just add them into the aggregate expressions. This can resolve the analysisExceptions thrown by the three TCPDS queries.
- `Project` and `Window` are special. We just need to add the missing attributes to their `projectList`.
**Implementation:**
1. Traverse the whole tree in a pre-order manner to find all the resolvable missing order-by attributes.
2. Traverse the whole tree in a post-order manner to add the found missing order-by attributes to the node if their `inputSet` contains the attributes.
3. If the origins of the missing order-by attributes are different nodes, each pass only resolves the missing attributes that are from the same node.
**Risk:**
Low. This rule will be trigger iff ```!s.resolved && child.resolved``` is true. Thus, very few cases are affected.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10678 from gatorsmile/sortWindows.
JIRA: https://issues.apache.org/jira/browse/SPARK-12989
In the rule `ExtractWindowExpressions`, we simply replace alias by the corresponding attribute. However, this will cause an issue exposed by the following case:
```scala
val data = Seq(("a", "b", "c", 3), ("c", "b", "a", 3)).toDF("A", "B", "C", "num")
.withColumn("Data", struct("A", "B", "C"))
.drop("A")
.drop("B")
.drop("C")
val winSpec = Window.partitionBy("Data.A", "Data.B").orderBy($"num".desc)
data.select($"*", max("num").over(winSpec) as "max").explain(true)
```
In this case, both `Data.A` and `Data.B` are `alias` in `WindowSpecDefinition`. If we replace these alias expression by their alias names, we are unable to know what they are since they will not be put in `missingExpr` too.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10963 from gatorsmile/seletStarAfterColDrop.
Add a local property to indicate if checkpointing all RDDs that are marked with the checkpoint flag, and enable it in Streaming
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10934 from zsxwing/recursive-checkpoint.
The current implementation is sub-optimal:
* If an expression is always nullable, e.g. `Unhex`, we can still remove null check for children if they are not nullable.
* If an expression has some non-nullable children, we can still remove null check for these children and keep null check for others.
This PR improves this by making the null check elimination more fine-grained.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10987 from cloud-fan/null-check.
This PR adds the ability to specify the ```ignoreNulls``` option to the functions dsl, e.g:
```df.select($"id", last($"value", ignoreNulls = true).over(Window.partitionBy($"id").orderBy($"other"))```
This PR is some where between a bug fix (see the JIRA) and a new feature. I am not sure if we should backport to 1.6.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10957 from hvanhovell/SPARK-13049.
JIRA: https://issues.apache.org/jira/browse/SPARK-12689
DDLParser processes three commands: createTable, describeTable and refreshTable.
This patch migrates the three commands to newly absorbed parser.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10723 from viirya/migrate-ddl-describe.
Make sure we throw better error messages when Parquet schema merging fails.
Author: Cheng Lian <lian@databricks.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10979 from viirya/schema-merging-failure-message.
In jdk1.7 TimeZone.getTimeZone() is synchronized, so use an instance variable to hold an GMT TimeZone object instead of instantiate it every time.
Author: wangyang <wangyang@haizhi.com>
Closes#10994 from wangyang1992/datetimeUtil.
This patch changes Spark's build to make Scala 2.11 the default Scala version. To be clear, this does not mean that Spark will stop supporting Scala 2.10: users will still be able to compile Spark for Scala 2.10 by following the instructions on the "Building Spark" page; however, it does mean that Scala 2.11 will be the default Scala version used by our CI builds (including pull request builds).
The Scala 2.11 compiler is faster than 2.10, so I think we'll be able to look forward to a slight speedup in our CI builds (it looks like it's about 2X faster for the Maven compile-only builds, for instance).
After this patch is merged, I'll update Jenkins to add new compile-only jobs to ensure that Scala 2.10 compilation doesn't break.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10608 from JoshRosen/SPARK-6363.
This class is only used for serialization of Python DataFrame. However, we don't require internal row there, so `GenericRowWithSchema` can also do the job.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10992 from cloud-fan/python.
This PR add support for grouping keys for generated TungstenAggregate.
Spilling and performance improvements for BytesToBytesMap will be done by followup PR.
Author: Davies Liu <davies@databricks.com>
Closes#10855 from davies/gen_keys.
This issue is causing tests to fail consistently in master with Hadoop 2.6 / 2.7. This is because for Hadoop 2.5+ we overwrite existing values of `InputMetrics#bytesRead` in each call to `HadoopRDD#compute`. In the case of coalesce, e.g.
```
sc.textFile(..., 4).coalesce(2).count()
```
we will call `compute` multiple times in the same task, overwriting `bytesRead` values from previous calls to `compute`.
For a regression test, see `InputOutputMetricsSuite.input metrics for old hadoop with coalesce`. I did not add a new regression test because it's impossible without significant refactoring; there's a lot of existing duplicate code in this corner of Spark.
This was caused by #10835.
Author: Andrew Or <andrew@databricks.com>
Closes#10973 from andrewor14/fix-input-metrics-coalesce.
Apparently chrome removed `SVGElement.prototype.getTransformToElement`, which is used by our JS library dagre-d3 when creating edges. The real diff can be found here: 7d6c0002e4, which is taken from the fix in the main repo: 1ef067f1c6
Upstream issue: https://github.com/cpettitt/dagre-d3/issues/202
Author: Andrew Or <andrew@databricks.com>
Closes#10986 from andrewor14/fix-dag-viz.
And ClientWrapper -> HiveClientImpl.
I have some followup pull requests to introduce a new internal catalog, and I think this new naming reflects better the functionality of the two classes.
Author: Reynold Xin <rxin@databricks.com>
Closes#10981 from rxin/SPARK-13076.
This is an existing issue uncovered recently by #10835. The reason for the exception was because the `SQLHistoryListener` gets all sorts of accumulators, not just the ones that represent SQL metrics. For example, the listener gets the `internal.metrics.shuffleRead.remoteBlocksFetched`, which is an Int, then it proceeds to cast the Int to a Long, which fails.
The fix is to mark accumulators representing SQL metrics using some internal metadata. Then we can identify which ones are SQL metrics and only process those in the `SQLHistoryListener`.
Author: Andrew Or <andrew@databricks.com>
Closes#10971 from andrewor14/fix-sql-history.
Our current Intersect physical operator simply delegates to RDD.intersect. We should remove the Intersect physical operator and simply transform a logical intersect into a semi-join with distinct. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins).
After a search, I found one of the mainstream RDBMS did the same. In their query explain, Intersect is replaced by Left-semi Join. Left-semi Join could help outer-join elimination in Optimizer, as shown in the PR: https://github.com/apache/spark/pull/10566
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10630 from gatorsmile/IntersectBySemiJoin.
[SPARK-10873] Support column sort and search for History Server using jQuery DataTable and REST API. Before this commit, the history server was generated hard-coded html and can not support search, also, the sorting was disabled if there is any application that has more than one attempt. Supporting search and sort (over all applications rather than the 20 entries in the current page) in any case will greatly improve user experience.
1. Create the historypage-template.html for displaying application information in datables.
2. historypage.js uses jQuery to access the data from /api/v1/applications REST API, and use DataTable to display each application's information. For application that has more than one attempt, the RowsGroup is used to merge such entries while at the same time supporting sort and search.
3. "duration" and "lastUpdated" rest API are added to application's "attempts".
4. External javascirpt and css files for datatables, RowsGroup and jquery plugins are added with licenses clarified.
Snapshots for how it looks like now:
History page view:
Search:
Sort by started time:
Author: zhuol <zhuol@yahoo-inc.com>
Closes#10648 from zhuoliu/10873.
* Implement ```MLWriter/MLWritable/MLReader/MLReadable``` for PySpark.
* Making ```LinearRegression``` to support ```save/load``` as example. After this merged, the work for other transformers/estimators will be easy, then we can list and distribute the tasks to the community.
cc mengxr jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#10469 from yanboliang/spark-11939.
1. enable whole stage codegen during tests even there is only one operator supports that.
2. split doProduce() into two APIs: upstream() and doProduce()
3. generate prefix for fresh names of each operator
4. pass UnsafeRow to parent directly (avoid getters and create UnsafeRow again)
5. fix bugs and tests.
This PR re-open #10944 and fix the bug.
Author: Davies Liu <davies@databricks.com>
Closes#10977 from davies/gen_refactor.
A dependency on the spark test tags was left out of the sketch module pom file causing builds to fail when test tags were used. This dependency is found in the pom file for every other module in spark.
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#10954 from ajbozarth/spark13050.
A simple workaround to avoid getting parameter types when convert a
logical plan to json.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10970 from cloud-fan/reflection.
JIRA: https://issues.apache.org/jira/browse/SPARK-12968
Implement command to set current database.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10916 from viirya/ddl-use-database.
JIRA: https://issues.apache.org/jira/browse/SPARK-11955
Currently we simply skip pushdowning filters in parquet if we enable schema merging.
However, we can actually mark particular fields in merging schema for safely pushdowning filters in parquet.
Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#9940 from viirya/safe-pushdown-parquet-filters.
I tried to add this via `USE_BIG_DECIMAL_FOR_FLOATS` option from Jackson with no success.
Added test for non-complex types. Should I add a test for complex types?
Author: Brandon Bradley <bradleytastic@gmail.com>
Closes#10936 from blbradley/spark-12749.
We can handle posgresql-specific enum types as strings in jdbc.
So, we should just add tests and close the corresponding JIRA ticket.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#10596 from maropu/AddTestsInIntegration.
Implement ```IterativelyReweightedLeastSquares``` solver for GLM. I consider it as a solver rather than estimator, it only used internal so I keep it ```private[ml]```.
There are two limitations in the current implementation compared with R:
* It can not support ```Tuple``` as response for ```Binomial``` family, such as the following code:
```
glm( cbind(using, notUsing) ~ age + education + wantsMore , family = binomial)
```
* It does not support ```offset```.
Because I considered that ```RFormula``` did not support ```Tuple``` as label and ```offset``` keyword, so I simplified the implementation. But to add support for these two functions is not very hard, I can do it in follow-up PR if it is necessary. Meanwhile, we can also add R-like statistic summary for IRLS.
The implementation refers R, [statsmodels](https://github.com/statsmodels/statsmodels) and [sparkGLM](https://github.com/AlteryxLabs/sparkGLM).
Please focus on the main structure and overpass minor issues/docs that I will update later. Any comments and opinions will be appreciated.
cc mengxr jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10639 from yanboliang/spark-9835.
1. enable whole stage codegen during tests even there is only one operator supports that.
2. split doProduce() into two APIs: upstream() and doProduce()
3. generate prefix for fresh names of each operator
4. pass UnsafeRow to parent directly (avoid getters and create UnsafeRow again)
5. fix bugs and tests.
Author: Davies Liu <davies@databricks.com>
Closes#10944 from davies/gen_refactor.
Users unknowingly try to set core Spark configs in SQLContext but later realise that it didn't work. eg. sqlContext.sql("SET spark.shuffle.memoryFraction=0.4"). This PR adds a warning message when such operations are done.
Author: Tejas Patil <tejasp@fb.com>
Closes#10849 from tejasapatil/SPARK-12926.
This PR is a follow-up of #10911. It adds specialized update methods for `CountMinSketch` so that we can avoid doing internal/external row format conversion in `DataFrame.countMinSketch()`.
Author: Cheng Lian <lian@databricks.com>
Closes#10968 from liancheng/cms-specialized.
this is stated for --packages and --repositories. Without stating it for --jars, people expect a standard java classpath to work, with expansion and using a different delimiter than a comma. Currently this is only state in the --help for spark-submit "Comma-separated list of local jars to include on the driver and executor classpaths."
Author: James Lohse <jimlohse@users.noreply.github.com>
Closes#10890 from jimlohse/patch-1.
by explicitly marking annotated parameters as vals (SI-8813).
Caused by #10835.
Author: Andrew Or <andrew@databricks.com>
Closes#10955 from andrewor14/fix-scala211.
This PR moves all the functionality provided by the SparkSQLParser/ExtendedHiveQlParser to the new Parser hierarchy (SparkQl/HiveQl). This also improves the current SET command parsing: the current implementation swallows ```set role ...``` and ```set autocommit ...``` commands, this PR respects these commands (and passes them on to Hive).
This PR and https://github.com/apache/spark/pull/10723 end the use of Parser-Combinator parsers for SQL parsing. As a result we can also remove the ```AbstractSQLParser``` in Catalyst.
The PR is marked WIP as long as it doesn't pass all tests.
cc rxin viirya winningsix (this touches https://github.com/apache/spark/pull/10144)
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10905 from hvanhovell/SPARK-12866.
This PR integrates Bloom filter from spark-sketch into DataFrame. This version resorts to RDD.aggregate for building the filter. A more performant UDAF version can be built in future follow-up PRs.
This PR also add 2 specify `put` version(`putBinary` and `putLong`) into `BloomFilter`, which makes it easier to build a Bloom filter over a `DataFrame`.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10937 from cloud-fan/bloom-filter.
Spark's `Partition` and `RDD.partitions` APIs have a contract which requires custom implementations of `RDD.partitions` to ensure that for all `x`, `rdd.partitions(x).index == x`; in other words, the `index` reported by a repartition needs to match its position in the partitions array.
If a custom RDD implementation violates this contract, then Spark has the potential to become stuck in an infinite recomputation loop when recomputing a subset of an RDD's partitions, since the tasks that are actually run will not correspond to the missing output partitions that triggered the recomputation. Here's a link to a notebook which demonstrates this problem: 5e8a5aa8d2/Violating%2520RDD.partitions%2520contract.html
In order to guard against this infinite loop behavior, this patch modifies Spark so that it fails fast and refuses to compute RDDs' whose `partitions` violate the API contract.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10932 from JoshRosen/SPARK-13021.
The high level idea is that instead of having the executors send both accumulator updates and TaskMetrics, we should have them send only accumulator updates. This eliminates the need to maintain both code paths since one can be implemented in terms of the other. This effort is split into two parts:
**SPARK-12895: Implement TaskMetrics using accumulators.** TaskMetrics is basically just a bunch of accumulable fields. This patch makes TaskMetrics a syntactic wrapper around a collection of accumulators so we don't need to send TaskMetrics from the executors to the driver.
**SPARK-12896: Send only accumulator updates to the driver.** Now that TaskMetrics are expressed in terms of accumulators, we can capture all TaskMetrics values if we just send accumulator updates from the executors to the driver. This completes the parent issue SPARK-10620.
While an effort has been made to preserve as much of the public API as possible, there were a few known breaking DeveloperApi changes that would be very awkward to maintain. I will gather the full list shortly and post it here.
Note: This was once part of #10717. This patch is split out into its own patch from there to make it easier for others to review. Other smaller pieces of already been merged into master.
Author: Andrew Or <andrew@databricks.com>
Closes#10835 from andrewor14/task-metrics-use-accums.
The error message is now changed from "Do not support type class scala.Tuple2." to "Do not support type class org.json4s.JsonAST$JNull$" to be more informative about what is not supported. Also, StructType metadata now handles JNull correctly, i.e., {'a': None}. test_metadata_null is added to tests.py to show the fix works.
Author: Jason Lee <cjlee@us.ibm.com>
Closes#8969 from jasoncl/SPARK-10847.
There's a minor bug in how we handle the `root` module in the `modules_to_test()` function in `dev/run-tests.py`: since `root` now depends on `build` (since every test needs to run on any build test), we now need to check for the presence of root in `modules_to_test` instead of `changed_modules`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10933 from JoshRosen/build-module-fix.
JIRA 1680 added a property called spark.yarn.appMasterEnv. This PR draws users' attention to this special case by adding an explanation in configuration.html#environment-variables
Author: Andrew <weiner.andrew.j@gmail.com>
Closes#10869 from weineran/branch-yarn-docs.
There are some typos or plain unintelligible sentences in the metrics template.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes#10902 from BenFradet/SPARK-12983.
If there's an RPC issue while sparkContext is alive but stopped (which would happen only when executing SparkContext.stop), log a warning instead. This is a common occurrence.
vanzin
Author: Nishkam Ravi <nishkamravi@gmail.com>
Author: nishkamravi2 <nishkamravi@gmail.com>
Closes#10881 from nishkamravi2/master_netty.
This PR is a follow-up of PR #10541. It integrates the newly introduced SQL generation feature with native view to make native view canonical.
In this PR, a new SQL option `spark.sql.nativeView.canonical` is added. When this option and `spark.sql.nativeView` are both `true`, Spark SQL tries to handle `CREATE VIEW` DDL statements using SQL query strings generated from view definition logical plans. If we failed to map the plan to SQL, we fallback to the original native view approach.
One important issue this PR fixes is that, now we can use CTE when defining a view. Originally, when native view is turned on, we wrap the view definition text with an extra `SELECT`. However, HiveQL parser doesn't allow CTE appearing as a subquery. Namely, something like this is disallowed:
```sql
SELECT n
FROM (
WITH w AS (SELECT 1 AS n)
SELECT * FROM w
) v
```
This PR fixes this issue because the extra `SELECT` is no longer needed (also, CTE expressions are inlined as subqueries during analysis phase, thus there won't be CTE expressions in the generated SQL query string).
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#10733 from liancheng/spark-12728.integrate-sql-gen-with-native-view.
This PR integrates Count-Min Sketch from spark-sketch into DataFrame. This version resorts to `RDD.aggregate` for building the sketch. A more performant UDAF version can be built in future follow-up PRs.
Author: Cheng Lian <lian@databricks.com>
Closes#10911 from liancheng/cms-df-api.
Add ```covar_samp``` and ```covar_pop``` for SparkR.
Should we also provide ```cov``` alias for ```covar_samp```? There is ```cov``` implementation at stats.R which masks ```stats::cov``` already, but may bring to breaking API change.
cc sun-rui felixcheung shivaram
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10829 from yanboliang/spark-12903.
