Except inner join, maybe the other join types are also useful when users are using the joinWith function. Thus, added the joinType into the existing joinWith call in Dataset APIs.
Also providing another joinWith interface for the cartesian-join-like functionality.
Please provide your opinions. marmbrus rxin cloud-fan Thank you!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9921 from gatorsmile/joinWith.
This patch makes it consistent to use varargs in all DataFrameReader methods, including Parquet, JSON, text, and the generic load function.
Also added a few more API tests for the Java API.
Author: Reynold Xin <rxin@databricks.com>
Closes#9945 from rxin/SPARK-11967.
This PR is to provide two common `coalesce` and `repartition` in Dataset APIs.
After reading the comments of SPARK-9999, I am unclear about the plan for supporting re-partitioning in Dataset APIs. Currently, both RDD APIs and Dataframe APIs provide users such a flexibility to control the number of partitions.
In most traditional RDBMS, they expose the number of partitions, the partitioning columns, the table partitioning methods to DBAs for performance tuning and storage planning. Normally, these parameters could largely affect the query performance. Since the actual performance depends on the workload types, I think it is almost impossible to automate the discovery of the best partitioning strategy for all the scenarios.
I am wondering if Dataset APIs are planning to hide these APIs from users? Feel free to reject my PR if it does not match the plan.
Thank you for your answers. marmbrus rxin cloud-fan
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9899 from gatorsmile/coalesce.
Currently pivot's signature looks like
```scala
scala.annotation.varargs
def pivot(pivotColumn: Column, values: Column*): GroupedData
scala.annotation.varargs
def pivot(pivotColumn: String, values: Any*): GroupedData
```
I think we can remove the one that takes "Column" types, since callers should always be passing in literals. It'd also be more clear if the values are not varargs, but rather Seq or java.util.List.
I also made similar changes for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#9929 from rxin/SPARK-11946.
we should pass in resolved encodera to logical `CoGroup` and bind them in physical `CoGroup`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9928 from cloud-fan/cogroup.
Based on feedback from Matei, this is more consistent with mapPartitions in Spark.
Also addresses some of the cleanups from a previous commit that renames the type variables.
Author: Reynold Xin <rxin@databricks.com>
Closes#9919 from rxin/SPARK-11933.
We should use `InternalRow.isNullAt` to check if the field is null before calling `InternalRow.getXXX`
Thanks gatorsmile who discovered this bug.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9904 from cloud-fan/null.
Can someone review my code to make sure I'm not missing anything? Thanks!
Author: Xiu Guo <xguo27@gmail.com>
Author: Xiu Guo <guoxi@us.ibm.com>
Closes#9612 from xguo27/SPARK-11628.
1. Renamed map to mapGroup, flatMap to flatMapGroup.
2. Renamed asKey -> keyAs.
3. Added more documentation.
4. Changed type parameter T to V on GroupedDataset.
5. Added since versions for all functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#9880 from rxin/SPARK-11899.
In this PR I delete a method that breaks type inference for aggregators (only in the REPL)
The error when this method is present is:
```
<console>:38: error: missing parameter type for expanded function ((x$2) => x$2._2)
ds.groupBy(_._1).agg(sum(_._2), sum(_._3)).collect()
```
Author: Michael Armbrust <michael@databricks.com>
Closes#9870 from marmbrus/dataset-repl-agg.
This mainly moves SqlNewHadoopRDD to the sql package. There is some state that is
shared between core and I've left that in core. This allows some other associated
minor cleanup.
Author: Nong Li <nong@databricks.com>
Closes#9845 from nongli/spark-11787.
Hive has since changed this behavior as well. https://issues.apache.org/jira/browse/HIVE-3454
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#9685 from nongli/spark-11724.
Apply the user supplied pathfilter while retrieving the files from fs.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9830 from dilipbiswal/spark-11544.
Fixes bug with grouping sets (including cube/rollup) where aggregates that included grouping expressions would return the wrong (null) result.
Also simplifies the analyzer rule a bit and leaves column pruning to the optimizer.
Added multiple unit tests to DataFrameAggregateSuite and verified it passes hive compatibility suite:
```
build/sbt -Phive -Dspark.hive.whitelist='groupby.*_grouping.*' 'test-only org.apache.spark.sql.hive.execution.HiveCompatibilitySuite'
```
This is an alternative to pr https://github.com/apache/spark/pull/9419 but I think its better as it simplifies the analyzer rule instead of adding another special case to it.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#9815 from aray/groupingset-agg-fix.
In addition, tightened visibility of a lot of classes in the columnar package from private[sql] to private[columnar].
Author: Reynold Xin <rxin@databricks.com>
Closes#9842 from rxin/SPARK-11858.
When handling self joins, the implementation did not consider the case insensitivity of HiveContext. It could cause an exception as shown in the JIRA:
```
TreeNodeException: Failed to copy node.
```
The fix is low risk. It avoids unnecessary attribute replacement. It should not affect the existing behavior of self joins. Also added the test case to cover this case.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9762 from gatorsmile/joinMakeCopy.
This patch adds an alternate to the Parquet RecordReader from the parquet-mr project
that is much faster for flat schemas. Instead of using the general converter mechanism
from parquet-mr, this directly uses the lower level APIs from parquet-columnar and a
customer RecordReader that directly assembles into UnsafeRows.
This is optionally disabled and only used for supported schemas.
Using the tpcds store sales table and doing a sum of increasingly more columns, the results
are:
For 1 Column:
Before: 11.3M rows/second
After: 18.2M rows/second
For 2 Columns:
Before: 7.2M rows/second
After: 11.2M rows/second
For 5 Columns:
Before: 2.9M rows/second
After: 4.5M rows/second
Author: Nong Li <nong@databricks.com>
Closes#9774 from nongli/parquet.
Also added some nicer error messages for incompatible types (private types and primitive types) for Kryo/Java encoder.
Author: Reynold Xin <rxin@databricks.com>
Closes#9823 from rxin/SPARK-11833.
This patch refactors the existing Kryo encoder expressions and adds support for Java serialization.
Author: Reynold Xin <rxin@databricks.com>
Closes#9802 from rxin/SPARK-11810.
Apply the user supplied pathfilter while retrieving the files from fs.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9652 from dilipbiswal/spark-11544.
return Double.NaN for mean/average when count == 0 for all numeric types that is converted to Double, Decimal type continue to return null.
Author: JihongMa <linlin200605@gmail.com>
Closes#9705 from JihongMA/SPARK-11720.
Currently, if the first SQLContext is not removed after stopping SparkContext, a SQLContext could set there forever. This patch make this more robust.
Author: Davies Liu <davies@databricks.com>
Closes#9706 from davies/clear_context.
we use `ExpressionEncoder.tuple` to build the result encoder, which assumes the input encoder should point to a struct type field if it’s non-flat.
However, our keyEncoder always point to a flat field/fields: `groupingAttributes`, we should combine them into a single `NamedExpression`.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9792 from cloud-fan/agg.
If user use primitive parameters in UDF, there is no way for him to do the null-check for primitive inputs, so we are assuming the primitive input is null-propagatable for this case and return null if the input is null.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9770 from cloud-fan/udf.
When we resolve the join operator, we may change the output of right side if self-join is detected. So in `Dataset.joinWith`, we should resolve the join operator first, and then get the left output and right output from it, instead of using `left.output` and `right.output` directly.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9806 from cloud-fan/self-join.
I also found a bug with self-joins returning incorrect results in the Dataset API. Two test cases attached and filed SPARK-11803.
Author: Reynold Xin <rxin@databricks.com>
Closes#9789 from rxin/SPARK-11802.
I also wrote a test case -- but unfortunately the test case is not working due to SPARK-11795.
Author: Reynold Xin <rxin@databricks.com>
Closes#9784 from rxin/SPARK-11503.
In the previous method, fields.toArray will cast java.util.List[StructField] into Array[Object] which can not cast into Array[StructField], thus when invoking this method will throw "java.lang.ClassCastException: [Ljava.lang.Object; cannot be cast to [Lorg.apache.spark.sql.types.StructField;"
I directly cast java.util.List[StructField] into Array[StructField] in this patch.
Author: mayuanwen <mayuanwen@qiyi.com>
Closes#9649 from jackieMaKing/Spark-11679.
During executing PromoteStrings rule, if one side of binaryComparison is StringType and the other side is not StringType, the current code will promote(cast) the StringType to DoubleType, and if the StringType doesn't contain the numbers, it will get null value. So if it is doing <=> (NULL-safe equal) with Null, it will not filter anything, caused the problem reported by this jira.
I proposal to the changes through this PR, can you review my code changes ?
This problem only happen for <=>, other operators works fine.
scala> val filteredDF = df.filter(df("column") > (new Column(Literal(null))))
filteredDF: org.apache.spark.sql.DataFrame = [column: string]
scala> filteredDF.show
+------+
|column|
+------+
+------+
scala> val filteredDF = df.filter(df("column") === (new Column(Literal(null))))
filteredDF: org.apache.spark.sql.DataFrame = [column: string]
scala> filteredDF.show
+------+
|column|
+------+
+------+
scala> df.registerTempTable("DF")
scala> sqlContext.sql("select * from DF where 'column' = NULL")
res27: org.apache.spark.sql.DataFrame = [column: string]
scala> res27.show
+------+
|column|
+------+
+------+
Author: Kevin Yu <qyu@us.ibm.com>
Closes#9720 from kevinyu98/working_on_spark-11447.
This patch adds an alias for current_timestamp (now function).
Also fixes SPARK-9196 to re-enable the test case for current_timestamp.
Author: Reynold Xin <rxin@databricks.com>
Closes#9753 from rxin/SPARK-11768.
Invocation of getters for type extending AnyVal returns default value (if field value is null) instead of throwing NPE. Please check comments for SPARK-11553 issue for more details.
Author: Bartlomiej Alberski <bartlomiej.alberski@allegrogroup.com>
Closes#9642 from alberskib/bugfix/SPARK-11553.
Parquet supports some JSON and BSON datatypes. They are represented as binary for BSON and string (UTF-8) for JSON internally.
I searched a bit and found Apache drill also supports both in this way, [link](https://drill.apache.org/docs/parquet-format/).
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#9658 from HyukjinKwon/SPARK-11692.
https://issues.apache.org/jira/browse/SPARK-11044
Spark writes a parquet file only with writer version1 ignoring the writer version given by user.
So, in this PR, it keeps the writer version if given or sets version1 as default.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: HyukjinKwon <gurwls223@gmail.com>
Closes#9060 from HyukjinKwon/SPARK-11044.
This patch adds the following options to the JSON data source, for dealing with non-standard JSON files:
* `allowComments` (default `false`): ignores Java/C++ style comment in JSON records
* `allowUnquotedFieldNames` (default `false`): allows unquoted JSON field names
* `allowSingleQuotes` (default `true`): allows single quotes in addition to double quotes
* `allowNumericLeadingZeros` (default `false`): allows leading zeros in numbers (e.g. 00012)
To avoid passing a lot of options throughout the json package, I introduced a new JSONOptions case class to define all JSON config options.
Also updated documentation to explain these options.
Scala
Python
Author: Reynold Xin <rxin@databricks.com>
Closes#9724 from rxin/SPARK-11745.
I didn't remove the old Sort operator, since we still use it in randomized tests. I moved it into test module and renamed it ReferenceSort.
Author: Reynold Xin <rxin@databricks.com>
Closes#9700 from rxin/SPARK-11734.
All the physical types are properly tested at `ParquetIOSuite` but logical type mapping is not being tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#9660 from HyukjinKwon/SPARK-11694.
* rename `AppendColumn` to `AppendColumns` to be consistent with the physical plan name.
* clean up stale comments.
* always pass in resolved encoder to `TypedColumn.withInputType`(test added)
* enable a mistakenly disabled java test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9688 from cloud-fan/follow.
https://issues.apache.org/jira/browse/SPARK-11678
The change of this PR is to pass root paths of table to the partition discovery logic. So, the process of partition discovery stops at those root paths instead of going all the way to the root path of the file system.
Author: Yin Huai <yhuai@databricks.com>
Closes#9651 from yhuai/SPARK-11678.
This PR adds a new method, `reduce`, to `GroupedDataset`, which allows similar operations to `reduceByKey` on a traditional `PairRDD`.
```scala
val ds = Seq("abc", "xyz", "hello").toDS()
ds.groupBy(_.length).reduce(_ + _).collect() // not actually commutative :P
res0: Array(3 -> "abcxyz", 5 -> "hello")
```
While implementing this method and its test cases several more deficiencies were found in our encoder handling. Specifically, in order to support positional resolution, named resolution and tuple composition, it is important to keep the unresolved encoder around and to use it when constructing new `Datasets` with the same object type but different output attributes. We now divide the encoder lifecycle into three phases (that mirror the lifecycle of standard expressions) and have checks at various boundaries:
- Unresoved Encoders: all users facing encoders (those constructed by implicits, static methods, or tuple composition) are unresolved, meaning they have only `UnresolvedAttributes` for named fields and `BoundReferences` for fields accessed by ordinal.
- Resolved Encoders: internal to a `[Grouped]Dataset` the encoder is resolved, meaning all input has been resolved to a specific `AttributeReference`. Any encoders that are placed into a logical plan for use in object construction should be resolved.
- BoundEncoder: Are constructed by physical plans, right before actual conversion from row -> object is performed.
It is left to future work to add explicit checks for resolution and provide good error messages when it fails. We might also consider enforcing the above constraints in the type system (i.e. `fromRow` only exists on a `ResolvedEncoder`), but we should probably wait before spending too much time on this.
Author: Michael Armbrust <michael@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9673 from marmbrus/pr/9628.
switched stddev support from DeclarativeAggregate to ImperativeAggregate.
Author: JihongMa <linlin200605@gmail.com>
Closes#9380 from JihongMA/SPARK-11420.
Parquet supports some unsigned datatypes. However, Since Spark does not support unsigned datatypes, it needs to emit an exception with a clear message rather then with the one saying illegal datatype.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#9646 from HyukjinKwon/SPARK-10113.
`to_unix_timestamp` is the deterministic version of `unix_timestamp`, as it accepts at least one parameters.
Since the behavior here is quite similar to `unix_timestamp`, I think the dataframe API is not necessary here.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#9347 from adrian-wang/to_unix_timestamp.
This adds a pivot method to the dataframe api.
Following the lead of cube and rollup this adds a Pivot operator that is translated into an Aggregate by the analyzer.
Currently the syntax is like:
~~courseSales.pivot(Seq($"year"), $"course", Seq("dotNET", "Java"), sum($"earnings"))~~
~~Would we be interested in the following syntax also/alternatively? and~~
courseSales.groupBy($"year").pivot($"course", "dotNET", "Java").agg(sum($"earnings"))
//or
courseSales.groupBy($"year").pivot($"course").agg(sum($"earnings"))
Later we can add it to `SQLParser`, but as Hive doesn't support it we cant add it there, right?
~~Also what would be the suggested Java friendly method signature for this?~~
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#7841 from aray/sql-pivot.
This patch modifies Spark's closure cleaner (and a few other places) to use ASM 5, which is necessary in order to support cleaning of closures that were compiled by Java 8.
In order to avoid ASM dependency conflicts, Spark excludes ASM from all of its dependencies and uses a shaded version of ASM 4 that comes from `reflectasm` (see [SPARK-782](https://issues.apache.org/jira/browse/SPARK-782) and #232). This patch updates Spark to use a shaded version of ASM 5.0.4 that was published by the Apache XBean project; the POM used to create the shaded artifact can be found at https://github.com/apache/geronimo-xbean/blob/xbean-4.4/xbean-asm5-shaded/pom.xml.
http://movingfulcrum.tumblr.com/post/80826553604/asm-framework-50-the-missing-migration-guide was a useful resource while upgrading the code to use the new ASM5 opcodes.
I also added a new regression tests in the `java8-tests` subproject; the existing tests were insufficient to catch this bug, which only affected Scala 2.11 user code which was compiled targeting Java 8.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9512 from JoshRosen/SPARK-6152.
We need to support custom classes like java beans and combine them into tuple, and it's very hard to do it with the TypeTag-based approach.
We should keep only the compose-based way to create tuple encoder.
This PR also move `Encoder` to `org.apache.spark.sql`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9567 from cloud-fan/java.
This patch adds the building blocks for codegening subexpr elimination and implements
it end to end for UnsafeProjection. The building blocks can be used to do the same thing
for other operators.
It introduces some utilities to compute common sub expressions. Expressions can be added to
this data structure. The expr and its children will be recursively matched against existing
expressions (ones previously added) and grouped into common groups. This is built using
the existing `semanticEquals`. It does not understand things like commutative or associative
expressions. This can be done as future work.
After building this data structure, the codegen process takes advantage of it by:
1. Generating a helper function in the generated class that computes the common
subexpression. This is done for all common subexpressions that have at least
two occurrences and the expression tree is sufficiently complex.
2. When generating the apply() function, if the helper function exists, call that
instead of regenerating the expression tree. Repeated calls to the helper function
shortcircuit the evaluation logic.
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9480 from nongli/spark-10371.
Currently the user facing api for typed aggregation has some limitations:
* the customized typed aggregation must be the first of aggregation list
* the customized typed aggregation can only use long as buffer type
* the customized typed aggregation can only use flat type as result type
This PR tries to remove these limitations.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9599 from cloud-fan/agg.
https://issues.apache.org/jira/browse/SPARK-9830
This PR contains the following main changes.
* Removing `AggregateExpression1`.
* Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`.
* Removing planner rule used to plan `Aggregate`.
* Linking `MultipleDistinctRewriter` to analyzer.
* Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`.
* Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`.
* Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved).
Author: Yin Huai <yhuai@databricks.com>
Closes#9556 from yhuai/removeAgg1.
This PR adds a new interface for user-defined aggregations, that can be used in `DataFrame` and `Dataset` operations to take all of the elements of a group and reduce them to a single value.
For example, the following aggregator extracts an `int` from a specific class and adds them up:
```scala
case class Data(i: Int)
val customSummer = new Aggregator[Data, Int, Int] {
def prepare(d: Data) = d.i
def reduce(l: Int, r: Int) = l + r
def present(r: Int) = r
}.toColumn()
val ds: Dataset[Data] = ...
val aggregated = ds.select(customSummer)
```
By using helper functions, users can make a generic `Aggregator` that works on any input type:
```scala
/** An `Aggregator` that adds up any numeric type returned by the given function. */
class SumOf[I, N : Numeric](f: I => N) extends Aggregator[I, N, N] with Serializable {
val numeric = implicitly[Numeric[N]]
override def zero: N = numeric.zero
override def reduce(b: N, a: I): N = numeric.plus(b, f(a))
override def present(reduction: N): N = reduction
}
def sum[I, N : Numeric : Encoder](f: I => N): TypedColumn[I, N] = new SumOf(f).toColumn
```
These aggregators can then be used alongside other built-in SQL aggregations.
```scala
val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
ds
.groupBy(_._1)
.agg(
sum(_._2), // The aggregator defined above.
expr("sum(_2)").as[Int], // A built-in dynatically typed aggregation.
count("*")) // A built-in statically typed aggregation.
.collect()
res0: ("a", 30, 30, 2L), ("b", 3, 3, 2L), ("c", 1, 1, 1L)
```
The current implementation focuses on integrating this into the typed API, but currently only supports running aggregations that return a single long value as explained in `TypedAggregateExpression`. This will be improved in a followup PR.
Author: Michael Armbrust <michael@databricks.com>
Closes#9555 from marmbrus/dataset-useragg.
Actually this was resolved by https://github.com/apache/spark/pull/8275.
But I found the JIRA issue for this is not marked as resolved since the PR above was made for another issue but the PR above resolved both.
I commented that this is resolved by the PR above; however, I opened this PR as I would like to just add
a little bit of corrections.
In the previous PR, I refactored the test by not reducing just collecting filters; however, this would not test properly `And` filter (which is not given to the tests). I unintentionally changed this from the original way (before being refactored).
In this PR, I just followed the original way to collect filters by reducing.
I would like to close this if this PR is inappropriate and somebody would like this deal with it in the separate PR related with this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#9554 from HyukjinKwon/SPARK-9557.
The reason is that:
1. For partitioned hive table, we will move the partitioned columns after data columns. (e.g. `<a: Int, b: Int>` partition by `a` will become `<b: Int, a: Int>`)
2. When append data to table, we use position to figure out how to match input columns to table's columns.
So when we append data to partitioned table, we will match wrong columns between input and table. A solution is reordering the input columns before match by position, like what we did for [`InsertIntoHadoopFsRelation`](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala#L101-L105)
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9408 from cloud-fan/append.
A few changes:
1. Removed fold, since it can be confusing for distributed collections.
2. Created specific interfaces for each Dataset function (e.g. MapFunction, ReduceFunction, MapPartitionsFunction)
3. Added more documentation and test cases.
The other thing I'm considering doing is to have a "collector" interface for FlatMapFunction and MapPartitionsFunction, similar to MapReduce's map function.
Author: Reynold Xin <rxin@databricks.com>
Closes#9531 from rxin/SPARK-11564.
This PR adds support for multiple column in a single count distinct aggregate to the new aggregation path.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9409 from hvanhovell/SPARK-11451.
This PR enables the Expand operator to process and produce Unsafe Rows.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9414 from hvanhovell/SPARK-11450.
https://issues.apache.org/jira/browse/SPARK-10116
This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`.
mengxr mkolod
Author: Imran Rashid <irashid@cloudera.com>
Closes#8314 from squito/SPARK-10116.
This PR adds test cases that test various column pruning and filter push-down cases.
Author: Cheng Lian <lian@databricks.com>
Closes#9468 from liancheng/spark-10978.follow-up.
JIRA: https://issues.apache.org/jira/browse/SPARK-9162
Currently ScalaUDF extends CodegenFallback and doesn't provide code generation implementation. This path implements code generation for ScalaUDF.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9270 from viirya/scalaudf-codegen.
This PR adds the ability to do typed SQL aggregations. We will likely also want to provide an interface to allow users to do aggregations on objects, but this is deferred to another PR.
```scala
val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
ds.groupBy(_._1).agg(sum("_2").as[Int]).collect()
res0: Array(("a", 30), ("b", 3), ("c", 1))
```
Author: Michael Armbrust <michael@databricks.com>
Closes#9499 from marmbrus/dataset-agg.
the main problem is: we interpret column name with special handling of `.` for DataFrame. This enables us to write something like `df("a.b")` to get the field `b` of `a`. However, we don't need this feature in `DataFrame.apply("*")` or `DataFrame.withColumnRenamed`. In these 2 cases, the column name is the final name already, we don't need extra process to interpret it.
The solution is simple, use `queryExecution.analyzed.output` to get resolved column directly, instead of using `DataFrame.resolve`.
close https://github.com/apache/spark/pull/8811
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9462 from cloud-fan/special-chars.
After aggregation, the dataset could be smaller than inputs, so it's better to do hash based aggregation for all inputs, then using sort based aggregation to merge them.
Author: Davies Liu <davies@databricks.com>
Closes#9383 from davies/fix_switch.
We have some aggregate function tests in both DataFrameAggregateSuite and SQLQuerySuite. The two have almost the same coverage and we should just remove the SQL one.
Author: Reynold Xin <rxin@databricks.com>
Closes#9475 from rxin/SPARK-11510.
1. Renamed localSort -> sortWithinPartitions to avoid ambiguity in "local"
2. distributeBy -> repartition to match the existing repartition.
Author: Reynold Xin <rxin@databricks.com>
Closes#9470 from rxin/SPARK-11504.
stddev is an alias for stddev_samp. variance should be consistent with stddev.
Also took the chance to remove internal Stddev and Variance, and only kept StddevSamp/StddevPop and VarianceSamp/VariancePop.
Author: Reynold Xin <rxin@databricks.com>
Closes#9449 from rxin/SPARK-11490.
depend on `caseSensitive` to do column name equality check, instead of just `==`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9410 from cloud-fan/partition.
We added a bunch of higher order statistics such as skewness and kurtosis to GroupedData. I don't think they are common enough to justify being listed, since users can always use the normal statistics aggregate functions.
That is to say, after this change, we won't support
```scala
df.groupBy("key").kurtosis("colA", "colB")
```
However, we will still support
```scala
df.groupBy("key").agg(kurtosis(col("colA")), kurtosis(col("colB")))
```
Author: Reynold Xin <rxin@databricks.com>
Closes#9446 from rxin/SPARK-11489.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9434 from cloud-fan/rdd2ds and squashes the following commits:
0892d72 [Wenchen Fan] support create Dataset from RDD
This PR adds a new method `unhandledFilters` to `BaseRelation`. Data sources which implement this method properly may avoid the overhead of defensive filtering done by Spark SQL.
Author: Cheng Lian <lian@databricks.com>
Closes#9399 from liancheng/spark-10978.unhandled-filters.
JIRA: https://issues.apache.org/jira/browse/SPARK-10304
This patch detects if the structure of partition directories is not valid.
The test cases are from #8547. Thanks zhzhan.
cc liancheng
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8840 from viirya/detect_invalid_part_dir.
This PR adds a new method `groupBy(cols: Column*)` to `Dataset` that allows users to group using column expressions instead of a lambda function. Since the return type of these expressions is not known at compile time, we just set the key type as a generic `Row`. If the user would like to work the key in a type-safe way, they can call `grouped.asKey[Type]`, which is also added in this PR.
```scala
val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
val grouped = ds.groupBy($"_1").asKey[String]
val agged = grouped.mapGroups { case (g, iter) =>
Iterator((g, iter.map(_._2).sum))
}
agged.collect()
res0: Array(("a", 30), ("b", 3), ("c", 1))
```
Author: Michael Armbrust <michael@databricks.com>
Closes#9359 from marmbrus/columnGroupBy and squashes the following commits:
bbcb03b [Michael Armbrust] Update DatasetSuite.scala
8fd2908 [Michael Armbrust] Update DatasetSuite.scala
0b0e2f8 [Michael Armbrust] [SPARK-11404] [SQL] Support for groupBy using column expressions
When we join 2 datasets, we will combine 2 encoders into a tupled one, and use it as the encoder for the jioned dataset. Assume both of the 2 encoders are flat, their `constructExpression`s both reference to the first element of input row. However, when we combine 2 encoders, the schema of input row changed, now the right encoder should reference to second element of input row. So we should rebind right encoder to let it know the new schema of input row before combine it.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9391 from cloud-fan/join and squashes the following commits:
846d3ab [Wenchen Fan] rebind right encoder when join 2 datasets
1. Supporting expanding structs in Projections. i.e.
"SELECT s.*" where s is a struct type.
This is fixed by allowing the expand function to handle structs in addition to tables.
2. Supporting expanding * inside aggregate functions of structs.
"SELECT max(struct(col1, structCol.*))"
This requires recursively expanding the expressions. In this case, it it the aggregate
expression "max(...)" and we need to recursively expand its children inputs.
Author: Nong Li <nongli@gmail.com>
Closes#9343 from nongli/spark-11329.
…ering.
For cached tables, we can just maintain the partitioning and ordering from the
source relation.
Author: Nong Li <nongli@gmail.com>
Closes#9404 from nongli/spark-5354.
DISTRIBUTE BY allows the user to hash partition the data by specified exprs. It also allows for
optioning sorting within each resulting partition. There is no required relationship between the
exprs for partitioning and sorting (i.e. one does not need to be a prefix of the other).
This patch adds to APIs to DataFrames which can be used together to provide this functionality:
1. distributeBy() which partitions the data frame into a specified number of partitions using the
partitioning exprs.
2. localSort() which sorts each partition using the provided sorting exprs.
To get the DISTRIBUTE BY functionality, the user simply does: df.distributeBy(...).localSort(...)
Author: Nong Li <nongli@gmail.com>
Closes#9364 from nongli/spark-11410.
This PR fixes two issues:
1. `PhysicalRDD.outputsUnsafeRows` is always `false`
Thus a `ConvertToUnsafe` operator is often required even if the underlying data source relation does output `UnsafeRow`.
1. Internal/external row conversion for `HadoopFsRelation` is kinda messy
Currently we're using `HadoopFsRelation.needConversion` and [dirty type erasure hacks][1] to indicate whether the relation outputs external row or internal row and apply external-to-internal conversion when necessary. Basically, all builtin `HadoopFsRelation` data sources, i.e. Parquet, JSON, ORC, and Text output `InternalRow`, while typical external `HadoopFsRelation` data sources, e.g. spark-avro and spark-csv, output `Row`.
This PR adds a `private[sql]` interface method `HadoopFsRelation.buildInternalScan`, which by default invokes `HadoopFsRelation.buildScan` and converts `Row`s to `UnsafeRow`s (which are also `InternalRow`s). All builtin `HadoopFsRelation` data sources override this method and directly output `UnsafeRow`s. In this way, now `HadoopFsRelation` always produces `UnsafeRow`s. Thus `PhysicalRDD.outputsUnsafeRows` can be properly set by checking whether the underlying data source is a `HadoopFsRelation`.
A remaining question is that, can we assume that all non-builtin `HadoopFsRelation` data sources output external rows? At least all well known ones do so. However it's possible that some users implemented their own `HadoopFsRelation` data sources that leverages `InternalRow` and thus all those unstable internal data representations. If this assumption is safe, we can deprecate `HadoopFsRelation.needConversion` and cleanup some more conversion code (like [here][2] and [here][3]).
This PR supersedes #9125.
Follow-ups:
1. Makes JSON and ORC data sources output `UnsafeRow` directly
1. Makes `HiveTableScan` output `UnsafeRow` directly
This is related to 1 since ORC data source shares the same `Writable` unwrapping code with `HiveTableScan`.
[1]: https://github.com/apache/spark/blob/v1.5.1/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala#L353
[2]: https://github.com/apache/spark/blob/v1.5.1/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala#L331-L335
[3]: https://github.com/apache/spark/blob/v1.5.1/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala#L630-L669
Author: Cheng Lian <lian@databricks.com>
Closes#9305 from liancheng/spark-11345.unsafe-hadoop-fs-relation.
Currently the empty line in json file will be parsed into Row with all null field values. But in json, "{}" represents a json object, empty line is supposed to be skipped.
Make a trivial change for this.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#9211 from zjffdu/SPARK-11226.
When we cogroup 2 `GroupedIterator`s in `CoGroupedIterator`, if the right side is smaller, we will consume right data and keep the left data unchanged. Then we call `hasNext` which will call `left.hasNext`. This will make `GroupedIterator` generate an extra group as the previous one has not been comsumed yet.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9346 from cloud-fan/cogroup and squashes the following commits:
9be67c8 [Wenchen Fan] SPARK-11393
When enabling mergedSchema and predicate filter, this fails since Parquet does not accept filters pushed down when the columns of the filters do not exist in the schema.
This is related with Parquet issue (https://issues.apache.org/jira/browse/PARQUET-389).
For now, it just simply disables predicate push down when using merged schema in this PR.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#9327 from HyukjinKwon/SPARK-11103.
This PR introduce a mechanism to call spill() on those SQL operators that support spilling (for example, BytesToBytesMap, UnsafeExternalSorter and ShuffleExternalSorter) if there is not enough memory for execution. The preserved first page is needed anymore, so removed.
Other Spillable objects in Spark core (ExternalSorter and AppendOnlyMap) are not included in this PR, but those could benefit from this (trigger others' spilling).
The PrepareRDD may be not needed anymore, could be removed in follow up PR.
The following script will fail with OOM before this PR, finished in 150 seconds with 2G heap (also works in 1.5 branch, with similar duration).
```python
sqlContext.setConf("spark.sql.shuffle.partitions", "1")
df = sqlContext.range(1<<25).selectExpr("id", "repeat(id, 2) as s")
df2 = df.select(df.id.alias('id2'), df.s.alias('s2'))
j = df.join(df2, df.id==df2.id2).groupBy(df.id).max("id", "id2")
j.explain()
print j.count()
```
For thread-safety, here what I'm got:
1) Without calling spill(), the operators should only be used by single thread, no safety problems.
2) spill() could be triggered in two cases, triggered by itself, or by other operators. we can check trigger == this in spill(), so it's still in the same thread, so safety problems.
3) if it's triggered by other operators (right now cache will not trigger spill()), we only spill the data into disk when it's in scanning stage (building is finished), so the in-memory sorter or memory pages are read-only, we only need to synchronize the iterator and change it.
4) During scanning, the iterator will only use one record in one page, we can't free this page, because the downstream is currently using it (used by UnsafeRow or other objects). In BytesToBytesMap, we just skip the current page, and dump all others into disk. In UnsafeExternalSorter, we keep the page that is used by current record (having the same baseObject), free it when loading the next record. In ShuffleExternalSorter, the spill() will not trigger during scanning.
5) In order to avoid deadlock, we didn't call acquireMemory during spill (so we reused the pointer array in InMemorySorter).
Author: Davies Liu <davies@databricks.com>
Closes#9241 from davies/force_spill.
Before this PR, user has to consume the iterator of one group before process next group, or we will get into infinite loops.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9330 from cloud-fan/group.
In some cases, we can broadcast the smaller relation in cartesian join, which improve the performance significantly.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#8652 from chenghao-intel/cartesian.
This PR adds a new operation `joinWith` to a `Dataset`, which returns a `Tuple` for each pair where a given `condition` evaluates to true.
```scala
case class ClassData(a: String, b: Int)
val ds1 = Seq(ClassData("a", 1), ClassData("b", 2)).toDS()
val ds2 = Seq(("a", 1), ("b", 2)).toDS()
> ds1.joinWith(ds2, $"_1" === $"a").collect()
res0: Array((ClassData("a", 1), ("a", 1)), (ClassData("b", 2), ("b", 2)))
```
This operation is similar to the relation `join` function with one important difference in the result schema. Since `joinWith` preserves objects present on either side of the join, the result schema is similarly nested into a tuple under the column names `_1` and `_2`.
This type of join can be useful both for preserving type-safety with the original object types as well as working with relational data where either side of the join has column names in common.
## Required Changes to Encoders
In the process of working on this patch, several deficiencies to the way that we were handling encoders were discovered. Specifically, it turned out to be very difficult to `rebind` the non-expression based encoders to extract the nested objects from the results of joins (and also typed selects that return tuples).
As a result the following changes were made.
- `ClassEncoder` has been renamed to `ExpressionEncoder` and has been improved to also handle primitive types. Additionally, it is now possible to take arbitrary expression encoders and rewrite them into a single encoder that returns a tuple.
- All internal operations on `Dataset`s now require an `ExpressionEncoder`. If the users tries to pass a non-`ExpressionEncoder` in, an error will be thrown. We can relax this requirement in the future by constructing a wrapper class that uses expressions to project the row to the expected schema, shielding the users code from the required remapping. This will give us a nice balance where we don't force user encoders to understand attribute references and binding, but still allow our native encoder to leverage runtime code generation to construct specific encoders for a given schema that avoid an extra remapping step.
- Additionally, the semantics for different types of objects are now better defined. As stated in the `ExpressionEncoder` scaladoc:
- Classes will have their sub fields extracted by name using `UnresolvedAttribute` expressions
and `UnresolvedExtractValue` expressions.
- Tuples will have their subfields extracted by position using `BoundReference` expressions.
- Primitives will have their values extracted from the first ordinal with a schema that defaults
to the name `value`.
- Finally, the binding lifecycle for `Encoders` has now been unified across the codebase. Encoders are now `resolved` to the appropriate schema in the constructor of `Dataset`. This process replaces an unresolved expressions with concrete `AttributeReference` expressions. Binding then happens on demand, when an encoder is going to be used to construct an object. This closely mirrors the lifecycle for standard expressions when executing normal SQL or `DataFrame` queries.
Author: Michael Armbrust <michael@databricks.com>
Closes#9300 from marmbrus/datasets-tuples.
When sampling and then filtering DataFrame, the SQL Optimizer will push down filter into sample and produce wrong result. This is due to the sampler is calculated based on the original scope rather than the scope after filtering.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9294 from yanboliang/spark-11303.
Currently, when a schema is inferred from a JSON file using sqlContext.read.json, the primitive object types are inferred as string, long, boolean, etc.
However, if the inferred type is too specific (JSON obviously does not enforce types itself), this can cause issues with merging dataframe schemas.
This pull request adds the option "primitivesAsString" to the JSON DataFrameReader which when true (defaults to false if not set) will infer all primitives as strings.
Below is an example usage of this new functionality.
```
val jsonDf = sqlContext.read.option("primitivesAsString", "true").json(sampleJsonFile)
scala> jsonDf.printSchema()
root
|-- bigInteger: string (nullable = true)
|-- boolean: string (nullable = true)
|-- double: string (nullable = true)
|-- integer: string (nullable = true)
|-- long: string (nullable = true)
|-- null: string (nullable = true)
|-- string: string (nullable = true)
```
Author: Stephen De Gennaro <stepheng@realitymine.com>
Closes#9249 from stephend-realitymine/stephend-primitives.
This patch refactors the MemoryManager class structure. After #9000, Spark had the following classes:
- MemoryManager
- StaticMemoryManager
- ExecutorMemoryManager
- TaskMemoryManager
- ShuffleMemoryManager
This is fairly confusing. To simplify things, this patch consolidates several of these classes:
- ShuffleMemoryManager and ExecutorMemoryManager were merged into MemoryManager.
- TaskMemoryManager is moved into Spark Core.
**Key changes and tasks**:
- [x] Merge ExecutorMemoryManager into MemoryManager.
- [x] Move pooling logic into Allocator.
- [x] Move TaskMemoryManager from `spark-unsafe` to `spark-core`.
- [x] Refactor the existing Tungsten TaskMemoryManager interactions so Tungsten code use only this and not both this and ShuffleMemoryManager.
- [x] Refactor non-Tungsten code to use the TaskMemoryManager instead of ShuffleMemoryManager.
- [x] Merge ShuffleMemoryManager into MemoryManager.
- [x] Move code
- [x] ~~Simplify 1/n calculation.~~ **Will defer to followup, since this needs more work.**
- [x] Port ShuffleMemoryManagerSuite tests.
- [x] Move classes from `unsafe` package to `memory` package.
- [ ] Figure out how to handle the hacky use of the memory managers in HashedRelation's broadcast variable construction.
- [x] Test porting and cleanup: several tests relied on mock functionality (such as `TestShuffleMemoryManager.markAsOutOfMemory`) which has been changed or broken during the memory manager consolidation
- [x] AbstractBytesToBytesMapSuite
- [x] UnsafeExternalSorterSuite
- [x] UnsafeFixedWidthAggregationMapSuite
- [x] UnsafeKVExternalSorterSuite
**Compatiblity notes**:
- This patch introduces breaking changes in `ExternalAppendOnlyMap`, which is marked as `DevloperAPI` (likely for legacy reasons): this class now cannot be used outside of a task.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9127 from JoshRosen/SPARK-10984.
This adds API for reading and writing text files, similar to SparkContext.textFile and RDD.saveAsTextFile.
```
SQLContext.read.text("/path/to/something.txt")
DataFrame.write.text("/path/to/write.txt")
```
Using the new Dataset API, this also supports
```
val ds: Dataset[String] = SQLContext.read.text("/path/to/something.txt").as[String]
```
Author: Reynold Xin <rxin@databricks.com>
Closes#9240 from rxin/SPARK-11274.
*This PR adds a new experimental API to Spark, tentitively named Datasets.*
A `Dataset` is a strongly-typed collection of objects that can be transformed in parallel using functional or relational operations. Example usage is as follows:
### Functional
```scala
> val ds: Dataset[Int] = Seq(1, 2, 3).toDS()
> ds.filter(_ % 1 == 0).collect()
res1: Array[Int] = Array(1, 2, 3)
```
### Relational
```scala
scala> ds.toDF().show()
+-----+
|value|
+-----+
| 1|
| 2|
| 3|
+-----+
> ds.select(expr("value + 1").as[Int]).collect()
res11: Array[Int] = Array(2, 3, 4)
```
## Comparison to RDDs
A `Dataset` differs from an `RDD` in the following ways:
- The creation of a `Dataset` requires the presence of an explicit `Encoder` that can be
used to serialize the object into a binary format. Encoders are also capable of mapping the
schema of a given object to the Spark SQL type system. In contrast, RDDs rely on runtime
reflection based serialization.
- Internally, a `Dataset` is represented by a Catalyst logical plan and the data is stored
in the encoded form. This representation allows for additional logical operations and
enables many operations (sorting, shuffling, etc.) to be performed without deserializing to
an object.
A `Dataset` can be converted to an `RDD` by calling the `.rdd` method.
## Comparison to DataFrames
A `Dataset` can be thought of as a specialized DataFrame, where the elements map to a specific
JVM object type, instead of to a generic `Row` container. A DataFrame can be transformed into
specific Dataset by calling `df.as[ElementType]`. Similarly you can transform a strongly-typed
`Dataset` to a generic DataFrame by calling `ds.toDF()`.
## Implementation Status and TODOs
This is a rough cut at the least controversial parts of the API. The primary purpose here is to get something committed so that we can better parallelize further work and get early feedback on the API. The following is being deferred to future PRs:
- Joins and Aggregations (prototype here f11f91e6f0)
- Support for Java
Additionally, the responsibility for binding an encoder to a given schema is currently done in a fairly ad-hoc fashion. This is an internal detail, and what we are doing today works for the cases we care about. However, as we add more APIs we'll probably need to do this in a more principled way (i.e. separate resolution from binding as we do in DataFrames).
## COMPATIBILITY NOTE
Long term we plan to make `DataFrame` extend `Dataset[Row]`. However,
making this change to che class hierarchy would break the function signatures for the existing
function operations (map, flatMap, etc). As such, this class should be considered a preview
of the final API. Changes will be made to the interface after Spark 1.6.
Author: Michael Armbrust <michael@databricks.com>
Closes#9190 from marmbrus/dataset-infra.
There's a lot of duplication between SortShuffleManager and UnsafeShuffleManager. Given that these now provide the same set of functionality, now that UnsafeShuffleManager supports large records, I think that we should replace SortShuffleManager's serialized shuffle implementation with UnsafeShuffleManager's and should merge the two managers together.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8829 from JoshRosen/consolidate-sort-shuffle-implementations.
This PR introduce a new feature to run SQL directly on files without create a table, for example:
```
select id from json.`path/to/json/files` as j
```
Author: Davies Liu <davies@databricks.com>
Closes#9173 from davies/source.
Due to PARQUET-251, `BINARY` columns in existing Parquet files may be written with corrupted statistics information. This information is used by filter push-down optimization. Since Spark 1.5 turns on Parquet filter push-down by default, we may end up with wrong query results. PARQUET-251 has been fixed in parquet-mr 1.8.1, but Spark 1.5 is still using 1.7.0.
This affects all Spark SQL data types that can be mapped to Parquet {{BINARY}}, namely:
- `StringType`
- `BinaryType`
- `DecimalType`
(But Spark SQL doesn't support pushing down filters involving `DecimalType` columns for now.)
To avoid wrong query results, we should disable filter push-down for columns of `StringType` and `BinaryType` until we upgrade to parquet-mr 1.8.
Author: Cheng Lian <lian@databricks.com>
Closes#9152 from liancheng/spark-11153.workaround-parquet-251.
(cherry picked from commit 0887e5e878)
Signed-off-by: Cheng Lian <lian@databricks.com>
Currently, we use CartesianProduct for join with null-safe-equal condition.
```
scala> sqlContext.sql("select * from t a join t b on (a.i <=> b.i)").explain
== Physical Plan ==
TungstenProject [i#2,j#3,i#7,j#8]
Filter (i#2 <=> i#7)
CartesianProduct
LocalTableScan [i#2,j#3], [[1,1]]
LocalTableScan [i#7,j#8], [[1,1]]
```
Actually, we can have an equal-join condition as `coalesce(i, default) = coalesce(b.i, default)`, then an partitioned join algorithm could be used.
After this PR, the plan will become:
```
>>> sqlContext.sql("select * from a join b ON a.id <=> b.id").explain()
TungstenProject [id#0L,id#1L]
Filter (id#0L <=> id#1L)
SortMergeJoin [coalesce(id#0L,0)], [coalesce(id#1L,0)]
TungstenSort [coalesce(id#0L,0) ASC], false, 0
TungstenExchange hashpartitioning(coalesce(id#0L,0),200)
ConvertToUnsafe
Scan PhysicalRDD[id#0L]
TungstenSort [coalesce(id#1L,0) ASC], false, 0
TungstenExchange hashpartitioning(coalesce(id#1L,0),200)
ConvertToUnsafe
Scan PhysicalRDD[id#1L]
```
Author: Davies Liu <davies@databricks.com>
Closes#9120 from davies/null_safe.
The purpose of this PR is to keep the unsafe format detail only inside the unsafe class itself, so when we use them(like use unsafe array in unsafe map, use unsafe array and map in columnar cache), we don't need to understand the format before use them.
change list:
* unsafe array's 4-bytes numElements header is now required(was optional), and become a part of unsafe array format.
* w.r.t the previous changing, the `sizeInBytes` of unsafe array now counts the 4-bytes header.
* unsafe map's format was `[numElements] [key array numBytes] [key array content(without numElements header)] [value array content(without numElements header)]` before, which is a little hacky as it makes unsafe array's header optional. I think saving 4 bytes is not a big deal, so the format is now: `[key array numBytes] [unsafe key array] [unsafe value array]`.
* w.r.t the previous changing, the `sizeInBytes` of unsafe map now counts both map's header and array's header.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9131 from cloud-fan/unsafe.
The unit test added in #9132 is flaky. This is a follow up PR to add `listenerBus.waitUntilEmpty` to fix it.
Author: zsxwing <zsxwing@gmail.com>
Closes#9163 from zsxwing/SPARK-11126-follow-up.
SQLListener adds all stage infos to `_stageIdToStageMetrics`, but only removes stage infos belonging to SQL executions. This PR fixed it by ignoring stages that don't belong to SQL executions.
Reported by Terry Hoo in https://www.mail-archive.com/userspark.apache.org/msg38810.html
Author: zsxwing <zsxwing@gmail.com>
Closes#9132 from zsxwing/SPARK-11126.
Make sure comma-separated paths get processed correcly in ResolvedDataSource for a HadoopFsRelationProvider
Author: Koert Kuipers <koert@tresata.com>
Closes#8416 from koertkuipers/feat-sql-comma-separated-paths.
In Spark SQL, the Exchange planner tries to avoid unnecessary sorts in cases where the data has already been sorted by a superset of the requested sorting columns. For instance, let's say that a query calls for an operator's input to be sorted by `a.asc` and the input happens to already be sorted by `[a.asc, b.asc]`. In this case, we do not need to re-sort the input. The converse, however, is not true: if the query calls for `[a.asc, b.asc]`, then `a.asc` alone will not satisfy the ordering requirements, requiring an additional sort to be planned by Exchange.
However, the current Exchange code gets this wrong and incorrectly skips sorting when the existing output ordering is a subset of the required ordering. This is simple to fix, however.
This bug was introduced in https://github.com/apache/spark/pull/7458, so it affects 1.5.0+.
This patch fixes the bug and significantly improves the unit test coverage of Exchange's sort-planning logic.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9140 from JoshRosen/SPARK-11135.
#9084 uncovered that many tests that test spilling don't actually spill. This is a follow-up patch to fix that to ensure our unit tests actually catch potential bugs in spilling. The size of this patch is inflated by the refactoring of `ExternalSorterSuite`, which had a lot of duplicate code and logic.
Author: Andrew Or <andrew@databricks.com>
Closes#9124 from andrewor14/spilling-tests.
This patch extends TungstenAggregate to support ImperativeAggregate functions. The existing TungstenAggregate operator only supported DeclarativeAggregate functions, which are defined in terms of Catalyst expressions and can be evaluated via generated projections. ImperativeAggregate functions, on the other hand, are evaluated by calling their `initialize`, `update`, `merge`, and `eval` methods.
The basic strategy here is similar to how SortBasedAggregate evaluates both types of aggregate functions: use a generated projection to evaluate the expression-based declarative aggregates with dummy placeholder expressions inserted in place of the imperative aggregate function output, then invoke the imperative aggregate functions and target them against the aggregation buffer. The bulk of the diff here consists of code that was copied and adapted from SortBasedAggregate, with some key changes to handle TungstenAggregate's sort fallback path.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9038 from JoshRosen/support-interpreted-in-tungsten-agg-final.
```scala
withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") {
withTempPath { dir =>
val path = s"${dir.getCanonicalPath}/part=1"
(1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(path)
// If the "part = 1" filter gets pushed down, this query will throw an exception since
// "part" is not a valid column in the actual Parquet file
checkAnswer(
sqlContext.read.parquet(path).filter("a > 0 and (part = 0 or a > 1)"),
(2 to 3).map(i => Row(i, i.toString, 1)))
}
}
```
We expect the result to be:
```
2,1
3,1
```
But got
```
1,1
2,1
3,1
```
Author: Cheng Hao <hao.cheng@intel.com>
Closes#8916 from chenghao-intel/partition_filter.
Right now, we have QualifiedTableName, TableIdentifier, and Seq[String] to represent table identifiers. We should only have one form and TableIdentifier is the best one because it provides methods to get table name, database name, return unquoted string, and return quoted string.
Author: Wenchen Fan <wenchen@databricks.com>
Author: Wenchen Fan <cloud0fan@163.com>
Closes#8453 from cloud-fan/table-name.
With this feature, we can track the query plan, time cost, exception during query execution for spark users.
Author: Wenchen Fan <cloud0fan@163.com>
Closes#9078 from cloud-fan/callback.
We should not stop resolving having when the having condtion is resolved, or something like `count(1)` will crash.
Author: Wenchen Fan <cloud0fan@163.com>
Closes#9105 from cloud-fan/having.
This patch unifies the memory management of the storage and execution regions such that either side can borrow memory from each other. When memory pressure arises, storage will be evicted in favor of execution. To avoid regressions in cases where storage is crucial, we dynamically allocate a fraction of space for storage that execution cannot evict. Several configurations are introduced:
- **spark.memory.fraction (default 0.75)**: fraction of the heap space used for execution and storage. The lower this is, the more frequently spills and cached data eviction occur. The purpose of this config is to set aside memory for internal metadata, user data structures, and imprecise size estimation in the case of sparse, unusually large records.
- **spark.memory.storageFraction (default 0.5)**: size of the storage region within the space set aside by `spark.memory.fraction`. Cached data may only be evicted if total storage exceeds this region.
- **spark.memory.useLegacyMode (default false)**: whether to use the memory management that existed in Spark 1.5 and before. This is mainly for backward compatibility.
For a detailed description of the design, see [SPARK-10000](https://issues.apache.org/jira/browse/SPARK-10000). This patch builds on top of the `MemoryManager` interface introduced in #9000.
Author: Andrew Or <andrew@databricks.com>
Closes#9084 from andrewor14/unified-memory-manager.
The SQLTab will be shared by multiple sessions.
If we create multiple independent SQLContexts (not using newSession()), will still see multiple SQLTabs in the Spark UI.
Author: Davies Liu <davies@databricks.com>
Closes#9048 from davies/sqlui.
This PR improve the unrolling and read of complex types in columnar cache:
1) Using UnsafeProjection to do serialization of complex types, so they will not be serialized three times (two for actualSize)
2) Copy the bytes from UnsafeRow/UnsafeArrayData to ByteBuffer directly, avoiding the immediate byte[]
3) Using the underlying array in ByteBuffer to create UTF8String/UnsafeRow/UnsafeArrayData without copy.
Combine these optimizations, we can reduce the unrolling time from 25s to 21s (20% less), reduce the scanning time from 3.5s to 2.5s (28% less).
```
df = sqlContext.read.parquet(path)
t = time.time()
df.cache()
df.count()
print 'unrolling', time.time() - t
for i in range(10):
t = time.time()
print df.select("*")._jdf.queryExecution().toRdd().count()
print time.time() - t
```
The schema is
```
root
|-- a: struct (nullable = true)
| |-- b: long (nullable = true)
| |-- c: string (nullable = true)
|-- d: array (nullable = true)
| |-- element: long (containsNull = true)
|-- e: map (nullable = true)
| |-- key: long
| |-- value: string (valueContainsNull = true)
```
Now the columnar cache depends on that UnsafeProjection support all the data types (including UDT), this PR also fix that.
Author: Davies Liu <davies@databricks.com>
Closes#9016 from davies/complex2.
