before this PR, when users try to get an encoder for an un-supported class, they will only get a very simple error message like `Encoder for type xxx is not supported`.
After this PR, the error message become more friendly, for example:
```
No Encoder found for abc.xyz.NonEncodable
- array element class: "abc.xyz.NonEncodable"
- field (class: "scala.Array", name: "arrayField")
- root class: "abc.xyz.AnotherClass"
```
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9810 from cloud-fan/error-message.
JIRA: https://issues.apache.org/jira/browse/SPARK-11817
Instead of return None, we should truncate the fractional seconds to prevent inserting NULL.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9834 from viirya/truncate-fractional-sec.
DataSet APIs look great! However, I am lost when doing multiple level joins. For example,
```
val ds1 = Seq(("a", 1), ("b", 2)).toDS().as("a")
val ds2 = Seq(("a", 1), ("b", 2)).toDS().as("b")
val ds3 = Seq(("a", 1), ("b", 2)).toDS().as("c")
ds1.joinWith(ds2, $"a._2" === $"b._2").as("ab").joinWith(ds3, $"ab._1._2" === $"c._2").printSchema()
```
The printed schema is like
```
root
|-- _1: struct (nullable = true)
| |-- _1: struct (nullable = true)
| | |-- _1: string (nullable = true)
| | |-- _2: integer (nullable = true)
| |-- _2: struct (nullable = true)
| | |-- _1: string (nullable = true)
| | |-- _2: integer (nullable = true)
|-- _2: struct (nullable = true)
| |-- _1: string (nullable = true)
| |-- _2: integer (nullable = true)
```
Personally, I think we need the printSchema function. Sometimes, I do not know how to specify the column, especially when their data types are mixed. For example, if I want to write the following select for the above multi-level join, I have to know the schema:
```
newDS.select(expr("_1._2._2 + 1").as[Int]).collect()
```
marmbrus rxin cloud-fan Do you have the same feeling?
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9855 from gatorsmile/printSchemaDataSet.
This patch fixes an issue where the `spark.sql.TungstenAggregate.testFallbackStartsAt` SQLConf setting was not properly reset / cleared at the end of `TungstenAggregationQueryWithControlledFallbackSuite`. This ended up causing test failures in HiveCompatibilitySuite in Maven builds by causing spilling to occur way too frequently.
This configuration leak was inadvertently introduced during test cleanup in #9618.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9857 from JoshRosen/clear-fallback-prop-in-test-teardown.
Apply the user supplied pathfilter while retrieving the files from fs.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9830 from dilipbiswal/spark-11544.
This PR has the following optimization:
1) The greatest/least already does the null-check, so the `If` and `IsNull` are not necessary.
2) In greatest/least, it should initialize the result using the first child (removing one block).
3) For primitive types, the generated greater expression is too complicated (`a > b ? 1 : (a < b) ? -1 : 0) > 0`), should be as simple as `a > b`
Combine these optimization, this could improve the performance of `ss_max` query by 30%.
Author: Davies Liu <davies@databricks.com>
Closes#9846 from davies/improve_max.
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.
Fix a bug in DataFrameReader.table (table with schema name such as "db_name.table" doesn't work)
Use SqlParser.parseTableIdentifier to parse the table name before lookupRelation.
Author: Huaxin Gao <huaxing@oc0558782468.ibm.com>
Closes#9773 from huaxingao/spark-11778.
After some experiment, I found it's not convenient to have separate encoder builders: `FlatEncoder` and `ProductEncoder`. For example, when create encoders for `ScalaUDF`, we have no idea if the type `T` is flat or not. So I revert the splitting change in https://github.com/apache/spark/pull/9693, while still keeping the bug fixes and tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9726 from cloud-fan/follow.
When debugging DataSet API, I always need to print the logical and physical plans.
I am wondering if we should provide a simple API for EXPLAIN?
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9832 from gatorsmile/explainDS.
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.
The impact of this change is for a query that has a single distinct column and does not have any grouping expression like
`SELECT COUNT(DISTINCT a) FROM table`
The plan will be changed from
```
AGG-2 (count distinct)
Shuffle to a single reducer
Partial-AGG-2 (count distinct)
AGG-1 (grouping on a)
Shuffle by a
Partial-AGG-1 (grouping on 1)
```
to the following one (1.5 uses this)
```
AGG-2
AGG-1 (grouping on a)
Shuffle to a single reducer
Partial-AGG-1(grouping on a)
```
The first plan is more robust. However, to better benchmark the impact of this change, we should use 1.5's plan and use the conf of `spark.sql.specializeSingleDistinctAggPlanning` to control the plan.
Author: Yin Huai <yhuai@databricks.com>
Closes#9828 from yhuai/distinctRewriter.
We currently rely on the optimizer's constant folding to replace current_timestamp and current_date. However, this can still result in different values for different instances of current_timestamp/current_date if the optimizer is not running fast enough.
A better solution is to replace these functions in the analyzer in one shot.
Author: Reynold Xin <rxin@databricks.com>
Closes#9833 from rxin/SPARK-11849.
see HIVE-7975 and HIVE-12373
With changed semantic of setters in thrift objects in hive, setter should be called only after all parameters are set. It's not problem of current state but will be a problem in some day.
Author: navis.ryu <navis@apache.org>
Closes#9580 from navis/SPARK-11614.
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.
Before this PR there were two things that would blow up if you called `df.as[MyClass]` if `MyClass` was defined in the REPL:
- [x] Because `classForName` doesn't work on the munged names returned by `tpe.erasure.typeSymbol.asClass.fullName`
- [x] Because we don't have anything to pass into the constructor for the `$outer` pointer.
Note that this PR is just adding the infrastructure for working with inner classes in encoder and is not yet sufficient to make them work in the REPL. Currently, the implementation show in 95cec7d413 is causing a bug that breaks code gen due to some interaction between janino and the `ExecutorClassLoader`. This will be addressed in a follow-up PR.
Author: Michael Armbrust <michael@databricks.com>
Closes#9602 from marmbrus/dataset-replClasses.
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.
https://issues.apache.org/jira/browse/SPARK-11792
The main changes include:
* Renaming `SizeEstimation` to `KnownSizeEstimation`. Hopefully this new name has more information.
* Making `estimatedSize` return `Long` instead of `Option[Long]`.
* In `UnsaveHashedRelation`, `estimatedSize` will delegate the work to `SizeEstimator` if we have not created a `BytesToBytesMap`.
Since we will put `UnsaveHashedRelation` to `BlockManager`, it is generally good to let it provide a more accurate size estimation. Also, if we do not put `BytesToBytesMap` directly into `BlockerManager`, I feel it is not really necessary to make `BytesToBytesMap` extends `KnownSizeEstimation`.
Author: Yin Huai <yhuai@databricks.com>
Closes#9813 from yhuai/SPARK-11792-followup.
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.
Currently the size of cached batch in only controlled by `batchSize` (default value is 10000), which does not work well with the size of serialized columns (for example, complex types). The memory used to build the batch is not accounted, it's easy to OOM (especially after unified memory management).
This PR introduce a hard limit as 4M for total columns (up to 50 columns of uncompressed primitive columns).
This also change the way to grow buffer, double it each time, then trim it once finished.
cc liancheng
Author: Davies Liu <davies@databricks.com>
Closes#9760 from davies/cache_limit.
Based on the comment of cloud-fan in https://github.com/apache/spark/pull/9216, update the AttributeReference's hashCode function by including the hashCode of the other attributes including name, nullable and qualifiers.
Here, I am not 100% sure if we should include name in the hashCode calculation, since the original hashCode calculation does not include it.
marmbrus cloud-fan Please review if the changes are good.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9761 from gatorsmile/hashCodeNamedExpression.
This PR adds a new option `spark.sql.hive.thriftServer.singleSession` for disabling multi-session support in the Thrift server.
Note that this option is added as a Spark configuration (retrieved from `SparkConf`) rather than Spark SQL configuration (retrieved from `SQLConf`). This is because all SQL configurations are session-ized. Since multi-session support is by default on, no JDBC connection can modify global configurations like the newly added one.
Author: Cheng Lian <lian@databricks.com>
Closes#9740 from liancheng/spark-11089.single-session-option.
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.
According to discussion in PR #9664, the anonymous `HiveFunctionRegistry` in `HiveContext` can be removed now.
Author: Cheng Lian <lian@databricks.com>
Closes#9737 from liancheng/spark-11191.follow-up.
The randomly generated ArrayData used for the UDT `ExamplePoint` in `RowEncoderSuite` sometimes doesn't have enough elements. In this case, this test will fail. This patch is to fix it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9757 from viirya/fix-randomgenerated-udt.
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.
This fix is to change the equals method to check all of the specified fields for equality of AttributeReference.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9216 from gatorsmile/namedExpressEqual.
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.
These 2 are very similar, we can consolidate them into one.
Also add tests for it and fix a bug.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9729 from cloud-fan/tuple.
JIRA: https://issues.apache.org/jira/browse/SPARK-11743
RowEncoder doesn't support UserDefinedType now. We should add the support for it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9712 from viirya/rowencoder-udt.
code snippet to reproduce it:
```
TimeZone.setDefault(TimeZone.getTimeZone("Asia/Shanghai"))
val t = Timestamp.valueOf("1900-06-11 12:14:50.789")
val us = fromJavaTimestamp(t)
assert(getSeconds(us) === t.getSeconds)
```
it will be good to add a regression test for it, but the reproducing code need to change the default timezone, and even we change it back, the `lazy val defaultTimeZone` in `DataTimeUtils` is fixed.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9728 from cloud-fan/seconds.
