Unpersist a uncached RDD, will not raise exception, for example:
```
val data = Array(1, 2, 3, 4, 5)
val distData = sc.parallelize(data)
distData.unpersist(true)
```
But the `SchemaRDD` will raise exception if the `SchemaRDD` is not cached. Since `SchemaRDD` is the subclasses of the `RDD`, we should follow the same behavior.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3572 from chenghao-intel/try_uncache and squashes the following commits:
50a7a89 [Cheng Hao] SchemaRDD.unpersist() should not raise exception if it is not persisted
Author: Jacky Li <jacky.likun@huawei.com>
Closes#3585 from jackylk/remove and squashes the following commits:
045423d [Jacky Li] remove unnecessary import
val jsc = new org.apache.spark.api.java.JavaSparkContext(sc)
val jhc = new org.apache.spark.sql.hive.api.java.JavaHiveContext(jsc)
val nrdd = jhc.hql("select null from spark_test.for_test")
println(nrdd.schema)
Then the error is thrown as follows:
scala.MatchError: NullType (of class org.apache.spark.sql.catalyst.types.NullType$)
at org.apache.spark.sql.types.util.DataTypeConversions$.asJavaDataType(DataTypeConversions.scala:43)
Author: YanTangZhai <hakeemzhai@tencent.com>
Author: yantangzhai <tyz0303@163.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#3538 from YanTangZhai/MatchNullType and squashes the following commits:
e052dff [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
4b4bb34 [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
896c7b7 [yantangzhai] fix NullType MatchError in JavaSchemaRDD when sql has null
6e643f8 [YanTangZhai] Merge pull request #11 from apache/master
e249846 [YanTangZhai] Merge pull request #10 from apache/master
d26d982 [YanTangZhai] Merge pull request #9 from apache/master
76d4027 [YanTangZhai] Merge pull request #8 from apache/master
03b62b0 [YanTangZhai] Merge pull request #7 from apache/master
8a00106 [YanTangZhai] Merge pull request #6 from apache/master
cbcba66 [YanTangZhai] Merge pull request #3 from apache/master
cdef539 [YanTangZhai] Merge pull request #1 from apache/master
Spark SQL has embeded sqrt and abs but DSL doesn't support those functions.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#3401 from sarutak/dsl-missing-operator and squashes the following commits:
07700cf [Kousuke Saruta] Modified Literal(null, NullType) to Literal(null) in DslQuerySuite
8f366f8 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into dsl-missing-operator
1b88e2e [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into dsl-missing-operator
0396f89 [Kousuke Saruta] Added sqrt and abs to Spark SQL DSL
Supporting multi column support in countDistinct function like count(distinct c1,c2..) in Spark SQL
Author: ravipesala <ravindra.pesala@huawei.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#3511 from ravipesala/countdistinct and squashes the following commits:
cc4dbb1 [ravipesala] style
070e12a [ravipesala] Supporting multi column support in count(distinct c1,c2..) in Spark SQL
When we use ORDER BY clause, at first, attributes referenced by projection are resolved (1).
And then, attributes referenced at ORDER BY clause are resolved (2).
But when resolving attributes referenced at ORDER BY clause, the resolution result generated in (1) is discarded so for example, following query fails.
SELECT c1 + c2 FROM mytable ORDER BY c1;
The query above fails because when resolving the attribute reference 'c1', the resolution result of 'c2' is discarded.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#3363 from sarutak/SPARK-4487 and squashes the following commits:
fd314f3 [Kousuke Saruta] Fixed attribute resolution logic in Analyzer
6e60c20 [Kousuke Saruta] Fixed conflicts
cb5b7e9 [Kousuke Saruta] Added test case for SPARK-4487
282d529 [Kousuke Saruta] Fixed attributes reference resolution error
b6123e6 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into concat-feature
317b7fb [Kousuke Saruta] WIP
Executing sum distinct for empty table throws `java.lang.UnsupportedOperationException: empty.reduceLeft`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3184 from ueshin/issues/SPARK-4318 and squashes the following commits:
8168c42 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-4318
66fdb0a [Takuya UESHIN] Re-refine aggregate functions.
6186eb4 [Takuya UESHIN] Fix Sum of GeneratedAggregate.
d2975f6 [Takuya UESHIN] Refine Sum and Average of GeneratedAggregate.
1bba675 [Takuya UESHIN] Refine Sum, SumDistinct and Average functions.
917e533 [Takuya UESHIN] Use aggregate instead of groupBy().
1a5f874 [Takuya UESHIN] Add tests to be executed as non-partial aggregation.
a5a57d2 [Takuya UESHIN] Fix empty Average.
22799dc [Takuya UESHIN] Fix empty Sum and SumDistinct.
65b7dd2 [Takuya UESHIN] Fix empty sum distinct.
The relational operator '<=>' is not working in Spark SQL. Same works in Spark HiveQL
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3387 from ravipesala/<=> and squashes the following commits:
7198e90 [ravipesala] Supporting relational operator '<=>' in Spark SQL
This PR enables the Web UI storage tab to show the in-memory table name instead of the mysterious query plan string as the name of the in-memory columnar RDD.
Note that after #2501, a single columnar RDD can be shared by multiple in-memory tables, as long as their query results are the same. In this case, only the first cached table name is shown. For example:
```sql
CACHE TABLE first AS SELECT * FROM src;
CACHE TABLE second AS SELECT * FROM src;
```
The Web UI only shows "In-memory table first".
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Author: Cheng Lian <lian@databricks.com>
Closes#3383 from liancheng/columnar-rdd-name and squashes the following commits:
071907f [Cheng Lian] Fixes tests
12ddfa6 [Cheng Lian] Names in-memory columnar RDD with corresponding table name
For expressions like `10 < someVar`, we should create an `Operators.Gt` filter, but right now an `Operators.Lt` is created. This issue affects all inequality predicates with literals on the left hand side.
(This bug existed before #3317 and affects branch-1.1. #3338 was opened to backport this to branch-1.1.)
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Author: Cheng Lian <lian@databricks.com>
Closes#3334 from liancheng/fix-parquet-comp-filter and squashes the following commits:
0130897 [Cheng Lian] Fixes Parquet comparison filter generation
While reviewing PR #3083 and #3161, I noticed that Parquet record filter generation code can be simplified significantly according to the clue stated in [SPARK-4453](https://issues.apache.org/jira/browse/SPARK-4213). This PR addresses both SPARK-4453 and SPARK-4213 with this simplification.
While generating `ParquetTableScan` operator, we need to remove all Catalyst predicates that have already been pushed down to Parquet. Originally, we first generate the record filter, and then call `findExpression` to traverse the generated filter to find out all pushed down predicates [[1](64c6b9bad5/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala (L213-L228))]. In this way, we have to introduce the `CatalystFilter` class hierarchy to bind the Catalyst predicates together with their generated Parquet filter, and complicate the code base a lot.
The basic idea of this PR is that, we don't need `findExpression` after filter generation, because we already know a predicate can be pushed down if we can successfully generate its corresponding Parquet filter. SPARK-4213 is fixed by returning `None` for any unsupported predicate type.
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Author: Cheng Lian <lian@databricks.com>
Closes#3317 from liancheng/simplify-parquet-filters and squashes the following commits:
d6a9499 [Cheng Lian] Fixes import styling issue
43760e8 [Cheng Lian] Simplifies Parquet filter generation logic
Adds a new operator that uses Spark's `ExternalSort` class. It is off by default now, but we might consider making it the default if benchmarks show that it does not regress performance.
Author: Michael Armbrust <michael@databricks.com>
Closes#3268 from marmbrus/externalSort and squashes the following commits:
48b9726 [Michael Armbrust] comments
b98799d [Michael Armbrust] Add test
afd7562 [Michael Armbrust] Add support for external sort.
While resolving struct fields, the resulted `GetField` expression is wrapped with an `Alias` to make it a named expression. Assume `a` is a struct instance with a field `b`, then `"a.b"` will be resolved as `Alias(GetField(a, "b"), "b")`. Thus, for this following SQL query:
```sql
SELECT a.b + 1 FROM t GROUP BY a.b + 1
```
the grouping expression is
```scala
Add(GetField(a, "b"), Literal(1, IntegerType))
```
while the aggregation expression is
```scala
Add(Alias(GetField(a, "b"), "b"), Literal(1, IntegerType))
```
This mismatch makes the above SQL query fail during the both analysis and execution phases. This PR fixes this issue by removing the alias when substituting aggregation expressions.
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Author: Cheng Lian <lian@databricks.com>
Closes#3248 from liancheng/spark-4322 and squashes the following commits:
23a46ea [Cheng Lian] Code simplification
dd20a79 [Cheng Lian] Should only trim aliases around `GetField`s
7f46532 [Cheng Lian] Enables struct fields as sub expressions of grouping fields
When sort based shuffle and code gen are on we were trying to ship the code generated rows during a shuffle. This doesn't work because the classes don't exist on the other side. Instead we now copy into a generic row before shipping.
Author: Michael Armbrust <michael@databricks.com>
Closes#3263 from marmbrus/aggCodeGen and squashes the following commits:
f6ba8cf [Michael Armbrust] fix and test
This is more uniform with the rest of SQL configuration and allows it to be turned on and off without restarting the SparkContext. In this PR I also turn off filter pushdown by default due to a number of outstanding issues (in particular SPARK-4258). When those are fixed we should turn it back on by default.
Author: Michael Armbrust <michael@databricks.com>
Closes#3258 from marmbrus/parquetFilters and squashes the following commits:
5655bfe [Michael Armbrust] Remove extra line.
15e9a98 [Michael Armbrust] Enable filters for tests
75afd39 [Michael Armbrust] Fix comments
78fa02d [Michael Armbrust] off by default
e7f9e16 [Michael Armbrust] First draft of correctly configuring parquet filter pushdown
This PR adds two features to the data sources API:
- Support for pushing down `IN` filters
- The ability for relations to optionally provide information about their `sizeInBytes`.
Author: Michael Armbrust <michael@databricks.com>
Closes#3260 from marmbrus/sourcesImprovements and squashes the following commits:
9a5e171 [Michael Armbrust] Use method instead of configuration directly
99c0e6b [Michael Armbrust] Add support for sizeInBytes.
416f167 [Michael Armbrust] Support for IN in data sources API.
2a04ab3 [Michael Armbrust] Simplify implementation of InSet.
This implement the feature davies mentioned in https://github.com/apache/spark/pull/2901#discussion-diff-19313312
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#3012 from adrian-wang/iso8601 and squashes the following commits:
50df6e7 [Daoyuan Wang] json data timestamp ISO8601 support
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3185 from ueshin/issues/SPARK-4319 and squashes the following commits:
a44a38e [Takuya UESHIN] Enable an ignored test "null count".
Following description is quoted from JIRA:
When I issue a hql query against a HiveContext where my predicate uses a column of string type with one of LT, LTE, GT, or GTE operator, I get the following error:
scala.MatchError: StringType (of class org.apache.spark.sql.catalyst.types.StringType$)
Looking at the code in org.apache.spark.sql.parquet.ParquetFilters, StringType is absent from the corresponding functions for creating these filters.
To reproduce, in a Hive 0.13.1 shell, I created the following table (at a specified DB):
create table sparkbug (
id int,
event string
) stored as parquet;
Insert some sample data:
insert into table sparkbug select 1, '2011-06-18' from <some table> limit 1;
insert into table sparkbug select 2, '2012-01-01' from <some table> limit 1;
Launch a spark shell and create a HiveContext to the metastore where the table above is located.
import org.apache.spark.sql._
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.hive.HiveContext
val hc = new HiveContext(sc)
hc.setConf("spark.sql.shuffle.partitions", "10")
hc.setConf("spark.sql.hive.convertMetastoreParquet", "true")
hc.setConf("spark.sql.parquet.compression.codec", "snappy")
import hc._
hc.hql("select * from <db>.sparkbug where event >= '2011-12-01'")
A scala.MatchError will appear in the output.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#3083 from sarutak/SPARK-4213 and squashes the following commits:
4ab6e56 [Kousuke Saruta] WIP
b6890c6 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-4213
9a1fae7 [Kousuke Saruta] Fixed ParquetFilters so that compare Strings
Author: Michael Armbrust <michael@databricks.com>
Closes#3077 from marmbrus/udfsWithUdts and squashes the following commits:
34b5f27 [Michael Armbrust] style
504adef [Michael Armbrust] Convert arguments to Scala UDFs
This feature is based on an offline discussion with mengxr, hopefully can be useful for the new MLlib pipeline API.
For the following test snippet
```scala
case class KeyValue(key: Int, value: String)
val testData = sc.parallelize(1 to 10).map(i => KeyValue(i, i.toString)).toSchemaRDD
def foo(a: Int, b: String) => a.toString + b
```
the newly introduced DSL enables the following syntax
```scala
import org.apache.spark.sql.catalyst.dsl._
testData.select(Star(None), foo.call('key, 'value) as 'result)
```
which is equivalent to
```scala
testData.registerTempTable("testData")
sqlContext.registerFunction("foo", foo)
sql("SELECT *, foo(key, value) AS result FROM testData")
```
Author: Cheng Lian <lian@databricks.com>
Closes#3067 from liancheng/udf-dsl and squashes the following commits:
f132818 [Cheng Lian] Adds DSL support for Scala UDF
Queries which has 'not like' is not working spark sql.
sql("SELECT * FROM records where value not like 'val%'")
same query works in Spark HiveQL
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3075 from ravipesala/SPARK-4207 and squashes the following commits:
35c11e7 [ravipesala] Supported 'not like' syntax in sql
This PR adds User-Defined Types (UDTs) to SQL. It is a precursor to using SchemaRDD as a Dataset for the new MLlib API. Currently, the UDT API is private since there is incomplete support (e.g., no Java or Python support yet).
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#3063 from marmbrus/udts and squashes the following commits:
7ccfc0d [Michael Armbrust] remove println
46a3aee [Michael Armbrust] Slightly easier to read test output.
6cc434d [Michael Armbrust] Recursively convert rows.
e369b91 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udts
15c10a6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into sql-udt2
f3c72fe [Joseph K. Bradley] Fixing merge
e13cd8a [Joseph K. Bradley] Removed Vector UDTs
5817b2b [Joseph K. Bradley] style edits
30ce5b2 [Joseph K. Bradley] updates based on code review
d063380 [Joseph K. Bradley] Cleaned up Java UDT Suite, and added warning about element ordering when creating schema from Java Bean
a571bb6 [Joseph K. Bradley] Removed old UDT code (registry and Java UDTs). Cleaned up other code. Extended JavaUserDefinedTypeSuite
6fddc1c [Joseph K. Bradley] Made MyLabeledPoint into a Java Bean
20630bc [Joseph K. Bradley] fixed scalastyle
fa86b20 [Joseph K. Bradley] Removed Java UserDefinedType, and made UDTs private[spark] for now
8de957c [Joseph K. Bradley] Modified UserDefinedType to store Java class of user type so that registerUDT takes only the udt argument.
8b242ea [Joseph K. Bradley] Fixed merge error after last merge. Note: Last merge commit also removed SQL UDT examples from mllib.
7f29656 [Joseph K. Bradley] Moved udt case to top of all matches. Small cleanups
b028675 [Xiangrui Meng] allow any type in UDT
4500d8a [Xiangrui Meng] update example code
87264a5 [Xiangrui Meng] remove debug code
3143ac3 [Xiangrui Meng] remove unnecessary changes
cfbc321 [Xiangrui Meng] support UDT in parquet
db16139 [Joseph K. Bradley] Added more doc for UserDefinedType. Removed unused code in Suite
759af7a [Joseph K. Bradley] Added more doc to UserDefineType
63626a4 [Joseph K. Bradley] Updated ScalaReflectionsSuite per @marmbrus suggestions
51e5282 [Joseph K. Bradley] fixed 1 test
f025035 [Joseph K. Bradley] Cleanups before PR. Added new tests
85872f6 [Michael Armbrust] Allow schema calculation to be lazy, but ensure its available on executors.
dff99d6 [Joseph K. Bradley] Added UDTs for Vectors in MLlib, plus DatasetExample using the UDTs
cd60cb4 [Joseph K. Bradley] Trying to get other SQL tests to run
34a5831 [Joseph K. Bradley] Added MLlib dependency on SQL.
e1f7b9c [Joseph K. Bradley] blah
2f40c02 [Joseph K. Bradley] renamed UDT types
3579035 [Joseph K. Bradley] udt annotation now working
b226b9e [Joseph K. Bradley] Changing UDT to annotation
fea04af [Joseph K. Bradley] more cleanups
964b32e [Joseph K. Bradley] some cleanups
893ee4c [Joseph K. Bradley] udt finallly working
50f9726 [Joseph K. Bradley] udts
04303c9 [Joseph K. Bradley] udts
39f8707 [Joseph K. Bradley] removed old udt suite
273ac96 [Joseph K. Bradley] basic UDT is working, but deserialization has yet to be done
8bebf24 [Joseph K. Bradley] commented out convertRowToScala for debugging
53de70f [Joseph K. Bradley] more udts...
982c035 [Joseph K. Bradley] still working on UDTs
19b2f60 [Joseph K. Bradley] still working on UDTs
0eaeb81 [Joseph K. Bradley] Still working on UDTs
105c5a3 [Joseph K. Bradley] Adding UserDefinedType to SQL, not done yet.
This PR adds an API for unregistering temporary tables. If a temporary table has been cached before, it's unpersisted as well.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#3039 from liancheng/unregister-temp-table and squashes the following commits:
54ae99f [Cheng Lian] Fixes Scala styling issue
1948c14 [Cheng Lian] Removes the unpersist argument
aca41d3 [Cheng Lian] Ensures thread safety
7d4fb2b [Cheng Lian] Adds unregisterTempTable API
JIRA: https://issues.apache.org/jira/browse/SPARK-4185.
This PR also has the fix of #3052.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#3056 from yhuai/SPARK-4185 and squashes the following commits:
ed3a5a8 [Yin Huai] Correctly handle type conflicts between structs and primitive types in an array.
`NoopColumnStats` was once used for binary, boolean and complex data types. This `ColumnStats` doesn't return properly shaped column statistics and causes caching failure if a table contains columns of the aforementioned types.
This PR adds `BooleanColumnStats`, `BinaryColumnStats` and `GenericColumnStats`, used for boolean, binary and all complex data types respectively. In addition, `NoopColumnStats` returns properly shaped column statistics containing null count and row count, but this class is now used for testing purpose only.
Author: Cheng Lian <lian@databricks.com>
Closes#3059 from liancheng/spark-4182 and squashes the following commits:
b398cfd [Cheng Lian] Fixes failed test case
fb3ee85 [Cheng Lian] Fixes SPARK-4182
This PR introduces a new set of APIs to Spark SQL to allow other developers to add support for reading data from new sources in `org.apache.spark.sql.sources`.
New sources must implement the interface `BaseRelation`, which is responsible for describing the schema of the data. BaseRelations have three `Scan` subclasses, which are responsible for producing an RDD containing row objects. The [various Scan interfaces](https://github.com/marmbrus/spark/blob/foreign/sql/core/src/main/scala/org/apache/spark/sql/sources/package.scala#L50) allow for optimizations such as column pruning and filter push down, when the underlying data source can handle these operations.
By implementing a class that inherits from RelationProvider these data sources can be accessed using using pure SQL. I've used the functionality to update the JSON support so it can now be used in this way as follows:
```sql
CREATE TEMPORARY TABLE jsonTableSQL
USING org.apache.spark.sql.json
OPTIONS (
path '/home/michael/data.json'
)
```
Further example usage can be found in the test cases: https://github.com/marmbrus/spark/tree/foreign/sql/core/src/test/scala/org/apache/spark/sql/sources
There is also a library that uses this new API to read avro data available here:
https://github.com/marmbrus/sql-avro
Author: Michael Armbrust <michael@databricks.com>
Closes#2475 from marmbrus/foreign and squashes the following commits:
1ed6010 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign
ab2c31f [Michael Armbrust] fix test
1d41bb5 [Michael Armbrust] unify argument names
5b47901 [Michael Armbrust] Remove sealed, more filter types
fab154a [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign
e3e690e [Michael Armbrust] Add hook for extraStrategies
a70d602 [Michael Armbrust] Fix style, more tests, FilteredSuite => PrunedFilteredSuite
70da6d9 [Michael Armbrust] Modify API to ease binary compatibility and interop with Java
7d948ae [Michael Armbrust] Fix equality of AttributeReference.
5545491 [Michael Armbrust] Address comments
5031ac3 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign
22963ef [Michael Armbrust] package objects compile wierdly...
b069146 [Michael Armbrust] traits => abstract classes
34f836a [Michael Armbrust] Make @DeveloperApi
0d74bcf [Michael Armbrust] Add documention on object life cycle
3e06776 [Michael Armbrust] remove line wraps
de3b68c [Michael Armbrust] Remove empty file
360cb30 [Michael Armbrust] style and java api
2957875 [Michael Armbrust] add override
0fd3a07 [Michael Armbrust] Draft of data sources API
- Adds optional precision and scale to Spark SQL's decimal type, which behave similarly to those in Hive 13 (https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf)
- Replaces our internal representation of decimals with a Decimal class that can store small values in a mutable Long, saving memory in this situation and letting some operations happen directly on Longs
This is still marked WIP because there are a few TODOs, but I'll remove that tag when done.
Author: Matei Zaharia <matei@databricks.com>
Closes#2983 from mateiz/decimal-1 and squashes the following commits:
35e6b02 [Matei Zaharia] Fix issues after merge
227f24a [Matei Zaharia] Review comments
31f915e [Matei Zaharia] Implement Davies's suggestions in Python
eb84820 [Matei Zaharia] Support reading/writing decimals as fixed-length binary in Parquet
4dc6bae [Matei Zaharia] Fix decimal support in PySpark
d1d9d68 [Matei Zaharia] Fix compile error and test issues after rebase
b28933d [Matei Zaharia] Support decimal precision/scale in Hive metastore
2118c0d [Matei Zaharia] Some test and bug fixes
81db9cb [Matei Zaharia] Added mutable Decimal that will be more efficient for small precisions
7af0c3b [Matei Zaharia] Add optional precision and scale to DecimalType, but use Unlimited for now
ec0a947 [Matei Zaharia] Make the result of AVG on Decimals be Decimal, not Double
Add `metadata: Metadata` to `StructField` to store extra information of columns. `Metadata` is a simple wrapper over `Map[String, Any]` with value types restricted to Boolean, Long, Double, String, Metadata, and arrays of those types. SerDe is via JSON.
Metadata is preserved through simple operations like `SELECT`.
marmbrus liancheng
Author: Xiangrui Meng <meng@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#2701 from mengxr/structfield-metadata and squashes the following commits:
dedda56 [Xiangrui Meng] merge remote
5ef930a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
c35203f [Xiangrui Meng] Merge pull request #1 from marmbrus/pr/2701
886b85c [Michael Armbrust] Expose Metadata and MetadataBuilder through the public scala and java packages.
589f314 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
1e2abcf [Xiangrui Meng] change default value of metadata to None in python
611d3c2 [Xiangrui Meng] move metadata from Expr to NamedExpr
ddfcfad [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
a438440 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
4266f4d [Xiangrui Meng] add StructField.toString back for backward compatibility
3f49aab [Xiangrui Meng] remove StructField.toString
24a9f80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
473a7c5 [Xiangrui Meng] merge master
c9d7301 [Xiangrui Meng] organize imports
1fcbf13 [Xiangrui Meng] change metadata type in StructField for Scala/Java
60cc131 [Xiangrui Meng] add doc and header
60614c7 [Xiangrui Meng] add metadata
e42c452 [Xiangrui Meng] merge master
93518fb [Xiangrui Meng] support metadata in python
905bb89 [Xiangrui Meng] java conversions
618e349 [Xiangrui Meng] make tests work in scala
61b8e0f [Xiangrui Meng] merge master
7e5a322 [Xiangrui Meng] do not output metadata in StructField.toString
c41a664 [Xiangrui Meng] merge master
d8af0ed [Xiangrui Meng] move tests to SQLQuerySuite
67fdebb [Xiangrui Meng] add test on join
d65072e [Xiangrui Meng] remove Map.empty
367d237 [Xiangrui Meng] add test
c194d5e [Xiangrui Meng] add metadata field to StructField and Attribute
if the query contains "not between" does not work like.
SELECT * FROM src where key not between 10 and 20'
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3017 from ravipesala/SPARK-4154 and squashes the following commits:
65fc89e [ravipesala] Handled admin comments
32e6d42 [ravipesala] 'not between' is not working
The parquet-mr project has introduced a new filter api (https://github.com/apache/incubator-parquet-mr/pull/4), along with several fixes . It can also eliminate entire RowGroups depending on certain statistics like min/max
We can leverage that to further improve performance of queries with filters.
Also filter2 api introduces ability to create custom filters. We can create a custom filter for the optimized In clause (InSet) , so that elimination happens in the ParquetRecordReader itself
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#2841 from saucam/master and squashes the following commits:
8282ba0 [Yash Datta] SPARK-3968: fix scala code style and add some more tests for filtering on optional columns
515df1c [Yash Datta] SPARK-3968: Add a test case for filter pushdown on optional column
5f4530e [Yash Datta] SPARK-3968: Fix scala code style
f304667 [Yash Datta] SPARK-3968: Using task metadata strategy for row group filtering
ec53e92 [Yash Datta] SPARK-3968: No push down should result in case we are unable to create a record filter
48163c3 [Yash Datta] SPARK-3968: Code cleanup
cc7b596 [Yash Datta] SPARK-3968: 1. Fix RowGroupFiltering not working 2. Use the serialization/deserialization from Parquet library for filter pushdown
caed851 [Yash Datta] Revert "SPARK-3968: Not pushing the filters in case of OPTIONAL columns" since filtering on optional columns is now supported in filter2 api
49703c9 [Yash Datta] SPARK-3968: Not pushing the filters in case of OPTIONAL columns
9d09741 [Yash Datta] SPARK-3968: Change parquet filter pushdown to use filter2 api of parquet-mr
Right now it works for only 2 tables like below query.
sql("SELECT * FROM records1 as a,records2 as b where a.key=b.key ")
But it does not work for more than 2 tables like below query
sql("SELECT * FROM records1 as a,records2 as b,records3 as c where a.key=b.key and a.key=c.key").
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2987 from ravipesala/multijoin and squashes the following commits:
429b005 [ravipesala] Support multiple joins
In JavaSqlContext, we need to let java program use big decimal, timestamp, date types.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#2850 from adrian-wang/javacontext and squashes the following commits:
4c4292c [Daoyuan Wang] change underlying type of JavaSchemaRDD as scala
bb0508f [Daoyuan Wang] add test cases
3c58b0d [Daoyuan Wang] add 3 types for java SQL context
Currently, `CTAS` (Create Table As Select) doesn't support specifying the `SerDe` in HQL. This PR will pass down the `ASTNode` into the physical operator `execution.CreateTableAsSelect`, which will extract the `CreateTableDesc` object via Hive `SemanticAnalyzer`. In the meantime, I also update the `HiveMetastoreCatalog.createTable` to optionally support the `CreateTableDesc` for table creation.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#2570 from chenghao-intel/ctas_serde and squashes the following commits:
e011ef5 [Cheng Hao] shim for both 0.12 & 0.13.1
cfb3662 [Cheng Hao] revert to hive 0.12
c8a547d [Cheng Hao] Support SerDe properties within CTAS
Add json and python api for date type.
By using Pickle, `java.sql.Date` was serialized as calendar, and recognized in python as `datetime.datetime`.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#2901 from adrian-wang/spark3988 and squashes the following commits:
c51a24d [Daoyuan Wang] convert datetime to date
5670626 [Daoyuan Wang] minor line combine
f760d8e [Daoyuan Wang] fix indent
444f100 [Daoyuan Wang] fix a typo
1d74448 [Daoyuan Wang] fix scala style
8d7dd22 [Daoyuan Wang] add json and python api for date type
Currently there is no support of Bitwise & , | in Spark HiveQl and Spark SQL as well. So this PR support the same.
I am closing https://github.com/apache/spark/pull/2926 as it has conflicts to merge. And also added support for Bitwise AND(&), OR(|) ,XOR(^), NOT(~) And I handled all review comments in that PR
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2961 from ravipesala/SPARK-3814-NEW4 and squashes the following commits:
a391c7a [ravipesala] Rebase with master
`schemaRDD2` is not tested because `schemaRDD1` is registered again.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#2869 from yhuai/JavaApplySchemaSuite and squashes the following commits:
95fe894 [Yin Huai] Correct variable name.
PR #2860 refines in-memory table statistics and enables broader broadcasted hash join optimization for in-memory tables. This makes `JoinSuite` fail when some test suite caches test table `testData` and gets executed before `JoinSuite`. Because expected `ShuffledHashJoin`s are optimized to `BroadcastedHashJoin` according to collected in-memory table statistics.
This PR fixes this issue by clearing the cache before testing join operator selection. A separate test case is also added to test broadcasted hash join operator selection.
Author: Cheng Lian <lian@databricks.com>
Closes#2960 from liancheng/fix-join-suite and squashes the following commits:
715b2de [Cheng Lian] Fixes caching related JoinSuite failure
SqlParser fails to parse -9223372036854775808 (Long.MinValue) so we cannot write queries such like as follows.
SELECT value FROM someTable WHERE value > -9223372036854775808
Additionally, because of the wrong syntax definition, we cannot apply unary minus only to literal. So, we cannot write such expressions.
-(value1 + value2) // Parenthesized expressions
-column // Columns
-MAX(column) // Functions
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2816 from sarutak/spark-sql-dsl-improvement2 and squashes the following commits:
32a5005 [Kousuke Saruta] Remove test setting for thriftserver
c2bab5e [Kousuke Saruta] Fixed SPARK-3959 and SPARK-3960
Supporting special chars in column names by using back ticks. Closed https://github.com/apache/spark/pull/2804 and created this PR as it has merge conflicts
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2927 from ravipesala/SPARK-3483-NEW and squashes the following commits:
f6329f3 [ravipesala] Rebased with master
Please refer to added tests for cases that can trigger the bug.
JIRA: https://issues.apache.org/jira/browse/SPARK-4068
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#2918 from yhuai/SPARK-4068 and squashes the following commits:
d360eae [Yin Huai] Handle nulls when building key paths from elements of an array.
Please check https://issues.apache.org/jira/browse/SPARK-4052 for cases triggering this bug.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#2899 from yhuai/SPARK-4052 and squashes the following commits:
1188f70 [Yin Huai] Address liancheng's comments.
b6712be [Yin Huai] Use scala.collection.Map instead of Predef.Map (scala.collection.immutable.Map).
This PR refines in-memory columnar table statistics:
1. adds 2 more statistics for in-memory table columns: `count` and `sizeInBytes`
1. adds filter pushdown support for `IS NULL` and `IS NOT NULL`.
1. caches and propagates statistics in `InMemoryRelation` once the underlying cached RDD is materialized.
Statistics are collected to driver side with an accumulator.
This PR also fixes SPARK-3914 by properly propagating in-memory statistics.
Author: Cheng Lian <lian@databricks.com>
Closes#2860 from liancheng/propagates-in-mem-stats and squashes the following commits:
0cc5271 [Cheng Lian] Restricts visibility of o.a.s.s.c.p.l.Statistics
c5ff904 [Cheng Lian] Fixes test table name conflict
a8c818d [Cheng Lian] Refines tests
1d01074 [Cheng Lian] Bug fix: shouldn't call STRING.actualSize on null string value
7dc6a34 [Cheng Lian] Adds more in-memory table statistics and propagates them properly
Previously cached data was found by `sameResult` plan matching on optimized plans. This technique however fails to locate the cached data when a temporary table with a projection is queried with a further reduced projection. The failure is due to the fact that optimization will collapse the projections, producing a plan that no longer produces the sameResult as the cached data (though the cached data still subsumes the desired data). For example consider the following previously failing test case.
```scala
sql("CACHE TABLE tempTable AS SELECT key FROM testData")
assertCached(sql("SELECT COUNT(*) FROM tempTable"))
```
In this PR I change the matching to occur after analysis instead of optimization, so that in the case of temporary tables, the plans will always match. I think this should work generally, however, this error does raise questions about the need to do more thorough subsumption checking when locating cached data.
Another question is what sort of semantics we want to provide when uncaching data from temporary tables. For example consider the following sequence of commands:
```scala
testData.select('key).registerTempTable("tempTable1")
testData.select('key).registerTempTable("tempTable2")
cacheTable("tempTable1")
// This obviously works.
assertCached(sql("SELECT COUNT(*) FROM tempTable1"))
// It seems good that this works ...
assertCached(sql("SELECT COUNT(*) FROM tempTable2"))
// ... but is this valid?
uncacheTable("tempTable2")
// Should this still be cached?
assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0)
```
Author: Michael Armbrust <michael@databricks.com>
Closes#2912 from marmbrus/cachingBug and squashes the following commits:
9c822d4 [Michael Armbrust] remove commented out code
5c72fb7 [Michael Armbrust] Add a test case / question about uncaching semantics.
63a23e4 [Michael Armbrust] Perform caching on analyzed instead of optimized plan.
03f1cfe [Michael Armbrust] Clean-up / add tests to SameResult suite.
Author: Michael Armbrust <michael@databricks.com>
Closes#2658 from marmbrus/nestedAggs and squashes the following commits:
862b763 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into nestedAggs
3234521 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into nestedAggs
8b06fdc [Michael Armbrust] possible fix for grouping on nested fields
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2767 from liancheng/multi-join and squashes the following commits:
9dc0d18 [Cheng Lian] Adds multiple join support for SQLContext
Adds some functions that were very useful when trying to track down the bug from #2656. This change also changes the tree output for query plans to include the `'` prefix to unresolved nodes and `!` prefix to nodes that refer to non-existent attributes.
Author: Michael Armbrust <michael@databricks.com>
Closes#2657 from marmbrus/debugging and squashes the following commits:
654b926 [Michael Armbrust] Clean-up, add tests
763af15 [Michael Armbrust] Add typeChecking debugging functions
8c69303 [Michael Armbrust] Add inputSet, references to QueryPlan. Improve tree string with a prefix to denote invalid or unresolved nodes.
fbeab54 [Michael Armbrust] Better toString, factories for AttributeSet.
This PR adds a new rule `CheckAggregation` to the analyzer to provide better error message for non-aggregate attributes with aggregation.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2774 from liancheng/non-aggregate-attr and squashes the following commits:
5246004 [Cheng Lian] Passes test suites
bf1878d [Cheng Lian] Adds checks for non-aggregate attributes with aggregation
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#2344 from adrian-wang/date and squashes the following commits:
f15074a [Daoyuan Wang] remove outdated lines
2038085 [Daoyuan Wang] update return type
00fe81f [Daoyuan Wang] address lian cheng's comments
0df6ea1 [Daoyuan Wang] rebase and remove simple string
bb1b1ef [Daoyuan Wang] remove failing test
aa96735 [Daoyuan Wang] not cast for same type compare
30bf48b [Daoyuan Wang] resolve rebase conflict
617d1a8 [Daoyuan Wang] add date_udf case to white list
c37e848 [Daoyuan Wang] comment update
5429212 [Daoyuan Wang] change to long
f8f219f [Daoyuan Wang] revise according to Cheng Hao
0e0a4f5 [Daoyuan Wang] minor format
4ddcb92 [Daoyuan Wang] add java api for date
0e3110e [Daoyuan Wang] try to fix timezone issue
17fda35 [Daoyuan Wang] set test list
2dfbb5b [Daoyuan Wang] support date type
Author: Reynold Xin <rxin@apache.org>
Closes#2727 from rxin/SPARK-3861-broadcast-hash-2 and squashes the following commits:
9c7b1a2 [Reynold Xin] Revert "Reuse CompactBuffer in UniqueKeyHashedRelation."
97626a1 [Reynold Xin] Reuse CompactBuffer in UniqueKeyHashedRelation.
7fcffb5 [Reynold Xin] Make UniqueKeyHashedRelation private[joins].
18eb214 [Reynold Xin] Merge branch 'SPARK-3861-broadcast-hash' into SPARK-3861-broadcast-hash-1
4b9d0c9 [Reynold Xin] UniqueKeyHashedRelation.get should return null if the value is null.
e0ebdd1 [Reynold Xin] Added a test case.
90b58c0 [Reynold Xin] [SPARK-3861] Avoid rebuilding hash tables on each partition
0c0082b [Reynold Xin] Fix line length.
cbc664c [Reynold Xin] Rename join -> joins package.
a070d44 [Reynold Xin] Fix line length in HashJoin
a39be8c [Reynold Xin] [SPARK-3857] Create a join package for various join operators.
Using `MEMORY_AND_DISK` as default storage level for in-memory table caching. Due to the in-memory columnar representation, recomputing an in-memory cached table partitions can be very expensive.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2686 from liancheng/spark-3824 and squashes the following commits:
35d2ed0 [Cheng Lian] Removes extra space
1ab7967 [Cheng Lian] Reduces test data size to fit DiskStore.getBytes()
ba565f0 [Cheng Lian] Maks CachedBatch serializable
07f0204 [Cheng Lian] Sets in-memory table default storage level to MEMORY_AND_DISK
This PR is a follow up of #2590, and tries to introduce a top level SQL parser entry point for all SQL dialects supported by Spark SQL.
A top level parser `SparkSQLParser` is introduced to handle the syntaxes that all SQL dialects should recognize (e.g. `CACHE TABLE`, `UNCACHE TABLE` and `SET`, etc.). For all the syntaxes this parser doesn't recognize directly, it fallbacks to a specified function that tries to parse arbitrary input to a `LogicalPlan`. This function is typically another parser combinator like `SqlParser`. DDL syntaxes introduced in #2475 can be moved to here.
The `ExtendedHiveQlParser` now only handle Hive specific extensions.
Also took the chance to refactor/reformat `SqlParser` for better readability.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2698 from liancheng/gen-sql-parser and squashes the following commits:
ceada76 [Cheng Lian] Minor styling fixes
9738934 [Cheng Lian] Minor refactoring, removes optional trailing ";" in the parser
bb2ab12 [Cheng Lian] SET property value can be empty string
ce8860b [Cheng Lian] Passes test suites
e86968e [Cheng Lian] Removes debugging code
8bcace5 [Cheng Lian] Replaces digit.+ to rep1(digit) (Scala style checking doesn't like it)
d15d54f [Cheng Lian] Unifies SQL and HiveQL parsers
"case when" conditional function is already supported in Spark SQL but there is no support in SqlParser. So added parser support to it.
Author : ravipesala ravindra.pesalahuawei.com
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2678 from ravipesala/SPARK-3813 and squashes the following commits:
70c75a7 [ravipesala] Fixed styles
713ea84 [ravipesala] Updated as per admin comments
709684f [ravipesala] Changed parser to support case when function.
The alias parameter is being ignored, which makes it more difficult to specify a qualifier for Generator expressions.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#2721 from NathanHowell/SPARK-3858 and squashes the following commits:
8aa0f43 [Nathan Howell] [SPARK-3858][SQL] Pass the generator alias into logical plan node
This PR aims to provide a way to skip/query corrupt JSON records. To do so, we introduce an internal column to hold corrupt records (the default name is `_corrupt_record`. This name can be changed by setting the value of `spark.sql.columnNameOfCorruptRecord`). When there is a parsing error, we will put the corrupt record in its unparsed format to the internal column. Users can skip/query this column through SQL.
* To query those corrupt records
```
-- For Hive parser
SELECT `_corrupt_record`
FROM jsonTable
WHERE `_corrupt_record` IS NOT NULL
-- For our SQL parser
SELECT _corrupt_record
FROM jsonTable
WHERE _corrupt_record IS NOT NULL
```
* To skip corrupt records and query regular records
```
-- For Hive parser
SELECT field1, field2
FROM jsonTable
WHERE `_corrupt_record` IS NULL
-- For our SQL parser
SELECT field1, field2
FROM jsonTable
WHERE _corrupt_record IS NULL
```
Generally, it is not recommended to change the name of the internal column. If the name has to be changed to avoid possible name conflicts, you can use `sqlContext.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, <new column name>)` or `sqlContext.sql(SET spark.sql.columnNameOfCorruptRecord=<new column name>)`.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#2680 from yhuai/corruptJsonRecord and squashes the following commits:
4c9828e [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord
309616a [Yin Huai] Change the default name of corrupt record to "_corrupt_record".
b4a3632 [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord
9375ae9 [Yin Huai] Set the column name of corrupt json record back to the default one after the unit test.
ee584c0 [Yin Huai] Provide a way to query corrupt json records as unparsed strings.
In JSONRDD.scala, add 'case TimestampType' in the enforceCorrectType function and a toTimestamp function.
Author: Mike Timper <mike@aurorafeint.com>
Closes#2720 from mtimper/master and squashes the following commits:
9386ab8 [Mike Timper] Fix and tests for SPARK-3853
Author: Reynold Xin <rxin@apache.org>
Closes#2719 from rxin/sql-join-break and squashes the following commits:
0c0082b [Reynold Xin] Fix line length.
cbc664c [Reynold Xin] Rename join -> joins package.
a070d44 [Reynold Xin] Fix line length in HashJoin
a39be8c [Reynold Xin] [SPARK-3857] Create a join package for various join operators.
This PR uses JSON instead of `toString` to serialize `DataType`s. The latter is not only hard to parse but also flaky in many cases.
Since we already write schema information to Parquet metadata in the old style, we have to reserve the old `DataType` parser and ensure downward compatibility. The old parser is now renamed to `CaseClassStringParser` and moved into `object DataType`.
JoshRosen davies Please help review PySpark related changes, thanks!
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2563 from liancheng/datatype-to-json and squashes the following commits:
fc92eb3 [Cheng Lian] Reverts debugging code, simplifies primitive type JSON representation
438c75f [Cheng Lian] Refactors PySpark DataType JSON SerDe per comments
6b6387b [Cheng Lian] Removes debugging code
6a3ee3a [Cheng Lian] Addresses per review comments
dc158b5 [Cheng Lian] Addresses PEP8 issues
99ab4ee [Cheng Lian] Adds compatibility est case for Parquet type conversion
a983a6c [Cheng Lian] Adds PySpark support
f608c6e [Cheng Lian] De/serializes DataType objects from/to JSON
If we write the filter which is always FALSE like
SELECT * from person WHERE FALSE;
200 tasks will run. I think, 1 task is enough.
And current optimizer cannot optimize the case NOT is duplicated like
SELECT * from person WHERE NOT ( NOT (age > 30));
The filter rule above should be simplified
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2692 from sarutak/SPARK-3831 and squashes the following commits:
25f3e20 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3831
23c750c [Kousuke Saruta] Improved unsupported predicate test case
a11b9f3 [Kousuke Saruta] Modified NOT predicate test case in PartitionBatchPruningSuite
8ea872b [Kousuke Saruta] Fixed the number of tasks when the data of LocalRelation is empty.
Although lazy caching for in-memory table seems consistent with the `RDD.cache()` API, it's relatively confusing for users who mainly work with SQL and not familiar with Spark internals. The `CACHE TABLE t; SELECT COUNT(*) FROM t;` pattern is also commonly seen just to ensure predictable performance.
This PR makes both the `CACHE TABLE t [AS SELECT ...]` statement and the `SQLContext.cacheTable()` API eager by default, and adds a new `CACHE LAZY TABLE t [AS SELECT ...]` syntax to provide lazy in-memory table caching.
Also, took the chance to make some refactoring: `CacheCommand` and `CacheTableAsSelectCommand` are now merged and renamed to `CacheTableCommand` since the former is strictly a special case of the latter. A new `UncacheTableCommand` is added for the `UNCACHE TABLE t` statement.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2513 from liancheng/eager-caching and squashes the following commits:
fe92287 [Cheng Lian] Makes table caching eager by default and adds syntax for lazy caching
_Also addresses: SPARK-1671, SPARK-1379 and SPARK-3641_
This PR introduces a new trait, `CacheManger`, which replaces the previous temporary table based caching system. Instead of creating a temporary table that shadows an existing table with and equivalent cached representation, the cached manager maintains a separate list of logical plans and their cached data. After optimization, this list is searched for any matching plan fragments. When a matching plan fragment is found it is replaced with the cached data.
There are several advantages to this approach:
- Calling .cache() on a SchemaRDD now works as you would expect, and uses the more efficient columnar representation.
- Its now possible to provide a list of temporary tables, without having to decide if a given table is actually just a cached persistent table. (To be done in a follow-up PR)
- In some cases it is possible that cached data will be used, even if a cached table was not explicitly requested. This is because we now look at the logical structure instead of the table name.
- We now correctly invalidate when data is inserted into a hive table.
Author: Michael Armbrust <michael@databricks.com>
Closes#2501 from marmbrus/caching and squashes the following commits:
63fbc2c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into caching.
0ea889e [Michael Armbrust] Address comments.
1e23287 [Michael Armbrust] Add support for cache invalidation for hive inserts.
65ed04a [Michael Armbrust] fix tests.
bdf9a3f [Michael Armbrust] Merge remote-tracking branch 'origin/master' into caching
b4b77f2 [Michael Armbrust] Address comments
6923c9d [Michael Armbrust] More comments / tests
80f26ac [Michael Armbrust] First draft of improved semantics for Spark SQL caching.
The following code gives error.
```
sqlContext.registerFunction("len", (s: String) => s.length)
sqlContext.sql("select len(foo) as a, count(1) from t1 group by len(foo)").collect()
```
Because SQl parser creates the aliases to the functions in grouping expressions with generated alias names. So if user gives the alias names to the functions inside projection then it does not match the generated alias name of grouping expression.
This kind of queries are working in Hive.
So the fix I have given that if user provides alias to the function in projection then don't generate alias in grouping expression,use the same alias.
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2511 from ravipesala/SPARK-3371 and squashes the following commits:
9fb973f [ravipesala] Removed aliases to grouping expressions.
f8ace79 [ravipesala] Fixed the testcase issue
bad2fd0 [ravipesala] SPARK-3371 : Fixed Renaming a function expression with group by gives error
BinaryType is derived from NativeType and added Ordering support.
Author: Venkata Ramana G <ramana.gollamudihuawei.com>
Author: Venkata Ramana Gollamudi <ramana.gollamudi@huawei.com>
Closes#2617 from gvramana/binarytype_sort and squashes the following commits:
1cf26f3 [Venkata Ramana Gollamudi] Supported Sorting of BinaryType
Thread names are useful for correlating failures.
Author: Reynold Xin <rxin@apache.org>
Closes#2600 from rxin/log4j and squashes the following commits:
83ffe88 [Reynold Xin] [SPARK-3748] Log thread name in unit test logs
This will allow us to take advantage of things like the spark.defaults file.
Author: Michael Armbrust <michael@databricks.com>
Closes#2493 from marmbrus/copySparkConf and squashes the following commits:
0bd1377 [Michael Armbrust] Copy SQL configuration from SparkConf when a SQLContext is created.
It returns null metadata from parquet if querying on empty parquet file while calculating splits.So added null check and returns the empty splits.
Author : ravipesala ravindra.pesalahuawei.com
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2456 from ravipesala/SPARK-3536 and squashes the following commits:
1e81a50 [ravipesala] Fixed the issue when querying on empty parquet file.
**This PR introduces a subtle change in semantics for HiveContext when using the results in Python or Scala. Specifically, while resolution remains case insensitive, it is now case preserving.**
_This PR is a follow up to #2293 (and to a lesser extent #2262#2334)._
In #2293 the catalog was changed to store analyzed logical plans instead of unresolved ones. While this change fixed the reported bug (which was caused by yet another instance of us forgetting to put in a `LowerCaseSchema` operator) it had the consequence of breaking assumptions made by `MultiInstanceRelation`. Specifically, we can't replace swap out leaf operators in a tree without rewriting changed expression ids (which happens when you self join the same RDD that has been registered as a temp table).
In this PR, I instead remove the need to insert `LowerCaseSchema` operators at all, by moving the concern of matching up identifiers completely into analysis. Doing so allows the test cases from both #2293 and #2262 to pass at the same time (and likely fixes a slew of other "unknown unknown" bugs).
While it is rolled back in this PR, storing the analyzed plan might actually be a good idea. For instance, it is kind of confusing if you register a temporary table, change the case sensitivity of resolution and now you can't query that table anymore. This can be addressed in a follow up PR.
Follow-ups:
- Configurable case sensitivity
- Consider storing analyzed plans for temp tables
Author: Michael Armbrust <michael@databricks.com>
Closes#2382 from marmbrus/lowercase and squashes the following commits:
c21171e [Michael Armbrust] Ensure the resolver is used for field lookups and ensure that case insensitive resolution is still case preserving.
d4320f1 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into lowercase
2de881e [Michael Armbrust] Address comments.
219805a [Michael Armbrust] style
5b93711 [Michael Armbrust] Replace LowerCaseSchema with Resolver.
This helps to replace shuffled hash joins with broadcast hash joins in some cases.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2468 from liancheng/more-stats and squashes the following commits:
32687dc [Cheng Lian] Moved the test case to PlannerSuite
5595a91 [Cheng Lian] Removes debugging code
73faf69 [Cheng Lian] Test case for auto choosing broadcast hash join
f30fe1d [Cheng Lian] Adds sizeInBytes estimation for Limit when all output types are native types
This feature allows user to add cache table from the select query.
Example : ```CACHE TABLE testCacheTable AS SELECT * FROM TEST_TABLE```
Spark takes this type of SQL as command and it does lazy caching just like ```SQLContext.cacheTable```, ```CACHE TABLE <name>``` does.
It can be executed from both SQLContext and HiveContext.
Recreated the pull request after rebasing with master.And fixed all the comments raised in previous pull requests.
https://github.com/apache/spark/pull/2381https://github.com/apache/spark/pull/2390
Author : ravipesala ravindra.pesalahuawei.com
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2397 from ravipesala/SPARK-2594 and squashes the following commits:
a5f0beb [ravipesala] Simplified the code as per Admin comment.
8059cd2 [ravipesala] Changed the behaviour from eager caching to lazy caching.
d6e469d [ravipesala] Code review comments by Admin are handled.
c18aa38 [ravipesala] Merge remote-tracking branch 'remotes/ravipesala/Add-Cache-table-as' into SPARK-2594
394d5ca [ravipesala] Changed style
fb1759b [ravipesala] Updated as per Admin comments
8c9993c [ravipesala] Changed the style
d8b37b2 [ravipesala] Updated as per the comments by Admin
bc0bffc [ravipesala] Merge remote-tracking branch 'ravipesala/Add-Cache-table-as' into Add-Cache-table-as
e3265d0 [ravipesala] Updated the code as per the comments by Admin in pull request.
724b9db [ravipesala] Changed style
aaf5b59 [ravipesala] Added comment
dc33895 [ravipesala] Updated parser to support add cache table command
b5276b2 [ravipesala] Updated parser to support add cache table command
eebc0c1 [ravipesala] Add CACHE TABLE <name> AS SELECT ...
6758f80 [ravipesala] Changed style
7459ce3 [ravipesala] Added comment
13c8e27 [ravipesala] Updated parser to support add cache table command
4e858d8 [ravipesala] Updated parser to support add cache table command
b803fc8 [ravipesala] Add CACHE TABLE <name> AS SELECT ...
This PR aims to support reading top level JSON arrays and take every element in such an array as a row (an empty array will not generate a row).
JIRA: https://issues.apache.org/jira/browse/SPARK-3308
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#2400 from yhuai/SPARK-3308 and squashes the following commits:
990077a [Yin Huai] Handle top level JSON arrays.
Author: Michael Armbrust <michael@databricks.com>
Closes#2164 from marmbrus/shufflePartitions and squashes the following commits:
0da1e8c [Michael Armbrust] test hax
ef2d985 [Michael Armbrust] more test hacks.
2dabae3 [Michael Armbrust] more test fixes
0bdbf21 [Michael Armbrust] Make parquet tests less order dependent
b42eeab [Michael Armbrust] increase test parallelism
80453d5 [Michael Armbrust] Decrease partitions when testing
This is a major refactoring of the in-memory columnar storage implementation, aims to eliminate boxing costs from critical paths (building/accessing column buffers) as much as possible. The basic idea is to refactor all major interfaces into a row-based form and use them together with `SpecificMutableRow`. The difficult part is how to adapt all compression schemes, esp. `RunLengthEncoding` and `DictionaryEncoding`, to this design. Since in-memory compression is disabled by default for now, and this PR should be strictly better than before no matter in-memory compression is enabled or not, maybe I'll finish that part in another PR.
**UPDATE** This PR also took the chance to optimize `HiveTableScan` by
1. leveraging `SpecificMutableRow` to avoid boxing cost, and
1. building specific `Writable` unwrapper functions a head of time to avoid per row pattern matching and branching costs.
TODO
- [x] Benchmark
- [ ] ~~Eliminate boxing costs in `RunLengthEncoding`~~ (left to future PRs)
- [ ] ~~Eliminate boxing costs in `DictionaryEncoding` (seems not easy to do without specializing `DictionaryEncoding` for every supported column type)~~ (left to future PRs)
## Micro benchmark
The benchmark uses a 10 million line CSV table consists of bytes, shorts, integers, longs, floats and doubles, measures the time to build the in-memory version of this table, and the time to scan the whole in-memory table.
Benchmark code can be found [here](https://gist.github.com/liancheng/fe70a148de82e77bd2c8#file-hivetablescanbenchmark-scala). Script used to generate the input table can be found [here](https://gist.github.com/liancheng/fe70a148de82e77bd2c8#file-tablegen-scala).
Speedup:
- Hive table scanning + column buffer building: **18.74%**
The original benchmark uses 1K as in-memory batch size, when increased to 10K, it can be 28.32% faster.
- In-memory table scanning: **7.95%**
Before:
| Building | Scanning
------- | -------- | --------
1 | 16472 | 525
2 | 16168 | 530
3 | 16386 | 529
4 | 16184 | 538
5 | 16209 | 521
Average | 16283.8 | 528.6
After:
| Building | Scanning
------- | -------- | --------
1 | 13124 | 458
2 | 13260 | 529
3 | 12981 | 463
4 | 13214 | 483
5 | 13583 | 500
Average | 13232.4 | 486.6
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2327 from liancheng/prevent-boxing/unboxing and squashes the following commits:
4419fe4 [Cheng Lian] Addressing comments
e5d2cf2 [Cheng Lian] Bug fix: should call setNullAt when field value is null to avoid NPE
8b8552b [Cheng Lian] Only checks for partition batch pruning flag once
489f97b [Cheng Lian] Bug fix: TableReader.fillObject uses wrong ordinals
97bbc4e [Cheng Lian] Optimizes hive.TableReader by by providing specific Writable unwrappers a head of time
3dc1f94 [Cheng Lian] Minor changes to eliminate row object creation
5b39cb9 [Cheng Lian] Lowers log level of compression scheme details
f2a7890 [Cheng Lian] Use SpecificMutableRow in InMemoryColumnarTableScan to avoid boxing
9cf30b0 [Cheng Lian] Added row based ColumnType.append/extract
456c366 [Cheng Lian] Made compression decoder row based
edac3cd [Cheng Lian] Makes ColumnAccessor.extractSingle row based
8216936 [Cheng Lian] Removes boxing cost in IntDelta and LongDelta by providing specialized implementations
b70d519 [Cheng Lian] Made some in-memory columnar storage interfaces row-based
This PR aims to correctly handle JSON arrays in the type of `ArrayType(...(ArrayType(StructType)))`.
JIRA: https://issues.apache.org/jira/browse/SPARK-3390.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#2364 from yhuai/SPARK-3390 and squashes the following commits:
46db418 [Yin Huai] Handle JSON arrays in the type of ArrayType(...(ArrayType(StructType))).
LogicalPlan contains a ‘resolved’ attribute indicating that all of its execution requirements have been resolved. This attribute is not checked before query execution. The analyzer contains a step to check that all Expressions are resolved, but this is not equivalent to checking all LogicalPlans. In particular, the Union plan’s implementation of ‘resolved’ verifies that the types of its children’s columns are compatible. Because the analyzer does not check that a Union plan is resolved, it is possible to execute a Union plan that outputs different types in the same column. See SPARK-2781 for an example.
This patch adds two checks to the analyzer’s CheckResolution rule. First, each logical plan is checked to see if it is not resolved despite its children being resolved. This allows the ‘problem’ unresolved plan to be included in the TreeNodeException for reporting. Then as a backstop the root plan is checked to see if it is resolved, which recursively checks that the entire plan tree is resolved. Note that the resolved attribute is implemented recursively, and this patch also explicitly checks the resolved attribute on each logical plan in the tree. I assume the query plan trees will not be large enough for this redundant checking to meaningfully impact performance.
Because this patch starts validating that LogicalPlans are resolved before execution, I had to fix some cases where unresolved plans were passing through the analyzer as part of the implementation of the hive query system. In particular, HiveContext applies the CreateTables and PreInsertionCasts, and ExtractPythonUdfs rules manually after the analyzer runs. I moved these rules to the analyzer stage (for hive queries only), in the process completing a code TODO indicating the rules should be moved to the analyzer.
It’s worth noting that moving the CreateTables rule means introducing an analyzer rule with a significant side effect - in this case the side effect is creating a hive table. The rule will only attempt to create a table once even if its batch is executed multiple times, because it converts the InsertIntoCreatedTable plan it matches against into an InsertIntoTable. Additionally, these hive rules must be added to the Resolution batch rather than as a separate batch because hive rules rules may be needed to resolve non-root nodes, leaving the root to be resolved on a subsequent batch iteration. For example, the hive compatibility test auto_smb_mapjoin_14, and others, make use of a query plan where the root is a Union and its children are each a hive InsertIntoTable.
Mixing the custom hive rules with standard analyzer rules initially resulted in an additional failure because of policy differences between spark sql and hive when casting a boolean to a string. Hive casts booleans to strings as “true” / “false” while spark sql casts booleans to strings as “1” / “0” (causing the cast1.q test to fail). This behavior is a result of the BooleanCasts rule in HiveTypeCoercion.scala, and from looking at the implementation of BooleanCasts I think converting to to “1”/“0” is potentially a programming mistake. (If the BooleanCasts rule is disabled, casting produces “true”/“false” instead.) I believe “true” / “false” should be the behavior for spark sql - I changed the behavior so bools are converted to “true”/“false” to be consistent with hive, and none of the existing spark tests failed.
Finally, in some initial testing with hive it appears that an implicit type coercion of boolean to string results in a lowercase string, e.g. CONCAT( TRUE, “” ) -> “true” while an explicit cast produces an all caps string, e.g. CAST( TRUE AS STRING ) -> “TRUE”. The change I’ve made just converts to lowercase strings in all cases. I believe it is at least more correct than the existing spark sql implementation where all Cast expressions become “1” / “0”.
Author: Aaron Staple <aaron.staple@gmail.com>
Closes#1706 from staple/SPARK-2781 and squashes the following commits:
32683c4 [Aaron Staple] Fix compilation failure due to merge.
7c77fda [Aaron Staple] Move ExtractPythonUdfs to Analyzer's extendedRules in HiveContext.
d49bfb3 [Aaron Staple] Address review comments.
915b690 [Aaron Staple] Fix merge issue causing compilation failure.
701dcd2 [Aaron Staple] [SPARK-2781][SQL] Check resolution of LogicalPlans in Analyzer.
First let me write down the current `projections` grammar of spark sql:
expression : orExpression
orExpression : andExpression {"or" andExpression}
andExpression : comparisonExpression {"and" comparisonExpression}
comparisonExpression : termExpression | termExpression "=" termExpression | termExpression ">" termExpression | ...
termExpression : productExpression {"+"|"-" productExpression}
productExpression : baseExpression {"*"|"/"|"%" baseExpression}
baseExpression : expression "[" expression "]" | ... | ident | ...
ident : identChar {identChar | digit} | delimiters | ...
identChar : letter | "_" | "."
delimiters : "," | ";" | "(" | ")" | "[" | "]" | ...
projection : expression [["AS"] ident]
projections : projection { "," projection}
For something like `a.b.c[1]`, it will be parsed as:
<img src="http://img51.imgspice.com/i/03008/4iltjsnqgmtt_t.jpg" border=0>
But for something like `a[1].b`, the current grammar can't parse it correctly.
A simple solution is written in `ParquetQuerySuite#NestedSqlParser`, changed grammars are:
delimiters : "." | "," | ";" | "(" | ")" | "[" | "]" | ...
identChar : letter | "_"
baseExpression : expression "[" expression "]" | expression "." ident | ... | ident | ...
This works well, but can't cover some corner case like `select t.a.b from table as t`:
<img src="http://img51.imgspice.com/i/03008/v2iau3hoxoxg_t.jpg" border=0>
`t.a.b` parsed as `GetField(GetField(UnResolved("t"), "a"), "b")` instead of `GetField(UnResolved("t.a"), "b")` using this new grammar.
However, we can't resolve `t` as it's not a filed, but the whole table.(if we could do this, then `select t from table as t` is legal, which is unexpected)
My solution is:
dotExpressionHeader : ident "." ident
baseExpression : expression "[" expression "]" | expression "." ident | ... | dotExpressionHeader | ident | ...
I passed all test cases under sql locally and add a more complex case.
"arrayOfStruct.field1 to access all values of field1" is not supported yet. Since this PR has changed a lot of code, I will open another PR for it.
I'm not familiar with the latter optimize phase, please correct me if I missed something.
Author: Wenchen Fan <cloud0fan@163.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#2230 from cloud-fan/dot and squashes the following commits:
e1a8898 [Wenchen Fan] remove support for arbitrary nested arrays
ee8a724 [Wenchen Fan] rollback LogicalPlan, support dot operation on nested array type
a58df40 [Michael Armbrust] add regression test for doubly nested data
16bc4c6 [Wenchen Fan] some enhance
95d733f [Wenchen Fan] split long line
dc31698 [Wenchen Fan] SPARK-2096 Correctly parse dot notations
This resolves https://issues.apache.org/jira/browse/SPARK-3395
Author: Eric Liang <ekl@google.com>
Closes#2266 from ericl/spark-3395 and squashes the following commits:
7f2b6f0 [Eric Liang] add regression test
05bd1e4 [Eric Liang] in the dsl, create a new schema instance in each applySchema
`SpecificMutableRow.update` doesn't check for null, and breaks existing `MutableRow` contract.
The tricky part here is that for performance considerations, the `update` method of all subclasses of `MutableValue` doesn't check for null and sets the null bit to false.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2325 from liancheng/check-for-null and squashes the following commits:
9366c44 [Cheng Lian] Check for null in SpecificMutableRow.update
Add support for the mathematical function"ABS" and the analytic function "last" to return a subset of the rows satisfying a query within spark sql. Test-cases included.
Author: xinyunh <xinyun.huang@huawei.com>
Author: bomeng <golf8lover>
Closes#2099 from xinyunh/sqlTest and squashes the following commits:
71d15e7 [xinyunh] remove POWER part
8843643 [xinyunh] fix the code style issue
39f0309 [bomeng] Modify the code of POWER and ABS. Move them to the file arithmetic
ff8e51e [bomeng] add abs() function support
7f6980a [xinyunh] fix the bug in 'Last' component
b3df91b [xinyunh] add 'Last' component
Unit test failed due to can not resolve the attribute references. Temporally disable this test case for a quick fixing, otherwise it will block the others.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#2334 from chenghao-intel/unit_test_failure and squashes the following commits:
661f784 [Cheng Hao] temporally disable the failed test case
This patch improves the SQLParser by adding support for BETWEEN conditions
Author: William Benton <willb@redhat.com>
Closes#2295 from willb/sql-between and squashes the following commits:
0016d30 [William Benton] Implement BETWEEN for SQLParser
This resolves https://issues.apache.org/jira/browse/SPARK-3349
Author: Eric Liang <ekl@google.com>
Closes#2262 from ericl/spark-3349 and squashes the following commits:
3e1b05c [Eric Liang] add regression test
ac32723 [Eric Liang] make limit/takeOrdered output SinglePartition
This PR is based on #1883 authored by marmbrus. Key differences:
1. Batch pruning instead of partition pruning
When #1883 was authored, batched column buffer building (#1880) hadn't been introduced. This PR combines these two and provide partition batch level pruning, which leads to smaller memory footprints and can generally skip more elements. The cost is that the pruning predicates are evaluated more frequently (partition number multiplies batch number per partition).
1. More filters are supported
Filter predicates consist of `=`, `<`, `<=`, `>`, `>=` and their conjunctions and disjunctions are supported.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2188 from liancheng/in-mem-batch-pruning and squashes the following commits:
68cf019 [Cheng Lian] Marked sqlContext as @transient
4254f6c [Cheng Lian] Enables in-memory partition pruning in PartitionBatchPruningSuite
3784105 [Cheng Lian] Overrides InMemoryColumnarTableScan.sqlContext
d2a1d66 [Cheng Lian] Disables in-memory partition pruning by default
062c315 [Cheng Lian] HiveCompatibilitySuite code cleanup
16b77bf [Cheng Lian] Fixed pruning predication conjunctions and disjunctions
16195c5 [Cheng Lian] Enabled both disjunction and conjunction
89950d0 [Cheng Lian] Worked around Scala style check
9c167f6 [Cheng Lian] Minor code cleanup
3c4d5c7 [Cheng Lian] Minor code cleanup
ea59ee5 [Cheng Lian] Renamed PartitionSkippingSuite to PartitionBatchPruningSuite
fc517d0 [Cheng Lian] More test cases
1868c18 [Cheng Lian] Code cleanup, bugfix, and adding tests
cb76da4 [Cheng Lian] Added more predicate filters, fixed table scan stats for testing purposes
385474a [Cheng Lian] Merge branch 'inMemStats' into in-mem-batch-pruning
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2213 from liancheng/spark-3320 and squashes the following commits:
45a0139 [Cheng Lian] Fixed typo in InMemoryColumnarQuerySuite
f67067d [Cheng Lian] Fixed SPARK-3320
If you have a table with TIMESTAMP column, that column can't be used in WHERE clause properly - it is not evaluated properly. [More](https://issues.apache.org/jira/browse/SPARK-3173)
Motivation: http://www.aproint.com/aggregation-with-spark-sql/
- [x] modify SqlParser so it supports casting to TIMESTAMP (workaround for item 2)
- [x] the string literal should be converted into Timestamp if the column is Timestamp.
Author: Zdenek Farana <zdenek.farana@gmail.com>
Author: Zdenek Farana <zdenek.farana@aproint.com>
Closes#2084 from byF/SPARK-3173 and squashes the following commits:
442b59d [Zdenek Farana] Fixed test merge conflict
2dbf4f6 [Zdenek Farana] Merge remote-tracking branch 'origin/SPARK-3173' into SPARK-3173
65b6215 [Zdenek Farana] Fixed timezone sensitivity in the test
47b27b4 [Zdenek Farana] Now works in the case of "StringLiteral=TimestampColumn"
96a661b [Zdenek Farana] Code style change
491dfcf [Zdenek Farana] Added test cases for SPARK-3173
4446b1e [Zdenek Farana] A string literal is casted into Timestamp when the column is Timestamp.
59af397 [Zdenek Farana] Added a new TIMESTAMP keyword; CAST to TIMESTAMP now can be used in SQL expression.
This PR adds a native implementation for SQL SQRT() and thus avoids delegating this function to Hive.
Author: William Benton <willb@redhat.com>
Closes#1750 from willb/spark-2813 and squashes the following commits:
22c8a79 [William Benton] Fixed missed newline from rebase
d673861 [William Benton] Added string coercions for SQRT and associated test case
e125df4 [William Benton] Added ExpressionEvaluationSuite test cases for SQRT
7b84bcd [William Benton] SQL SQRT now properly returns NULL for NULL inputs
8256971 [William Benton] added SQRT test to SqlQuerySuite
504d2e5 [William Benton] Added native SQRT implementation
We need to convert the case classes into Rows.
Author: Michael Armbrust <michael@databricks.com>
Closes#2133 from marmbrus/structUdfs and squashes the following commits:
189722f [Michael Armbrust] Merge remote-tracking branch 'origin/master' into structUdfs
8e29b1c [Michael Armbrust] Use existing function
d8d0b76 [Michael Armbrust] Fix udfs that return structs
Author: Michael Armbrust <michael@databricks.com>
Closes#2147 from marmbrus/inMemDefaultSize and squashes the following commits:
5390360 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into inMemDefaultSize
14204d3 [Michael Armbrust] Set the context before creating SparkLogicalPlans.
8da4414 [Michael Armbrust] Make sure we throw errors when leaf nodes fail to provide statistcs
18ce029 [Michael Armbrust] Ensure in-memory tables don't always broadcast.
```if (!fs.getFileStatus(path).isDir) throw Exception``` make no sense after this commit #1370
be careful if someone is working on SPARK-2551, make sure the new change passes test case ```test("Read a parquet file instead of a directory")```
Author: chutium <teng.qiu@gmail.com>
Closes#2044 from chutium/parquet-singlefile and squashes the following commits:
4ae477f [chutium] [SPARK-3138][SQL] sqlContext.parquetFile should be able to take a single file as parameter
JIRA:
- https://issues.apache.org/jira/browse/SPARK-3036
- https://issues.apache.org/jira/browse/SPARK-3037
Currently this uses the following Parquet schema for `MapType` when `valueContainsNull` is `true`:
```
message root {
optional group a (MAP) {
repeated group map (MAP_KEY_VALUE) {
required int32 key;
optional int32 value;
}
}
}
```
for `ArrayType` when `containsNull` is `true`:
```
message root {
optional group a (LIST) {
repeated group bag {
optional int32 array;
}
}
}
```
We have to think about compatibilities with older version of Spark or Hive or others I mentioned in the JIRA issues.
Notice:
This PR is based on #1963 and #1889.
Please check them first.
/cc marmbrus, yhuai
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#2032 from ueshin/issues/SPARK-3036_3037 and squashes the following commits:
4e8e9e7 [Takuya UESHIN] Add ArrayType containing null value support to Parquet.
013c2ca [Takuya UESHIN] Add MapType containing null value support to Parquet.
62989de [Takuya UESHIN] Merge branch 'issues/SPARK-2969' into issues/SPARK-3036_3037
8e38b53 [Takuya UESHIN] Merge branch 'issues/SPARK-3063' into issues/SPARK-3036_3037
Currently `ExistingRdd.convertToCatalyst` doesn't convert `Map` value.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#1963 from ueshin/issues/SPARK-3063 and squashes the following commits:
3ba41f2 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-3063
4d7bae2 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-3063
9321379 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-3063
d8a900a [Takuya UESHIN] Make ExistingRdd.convertToCatalyst be able to convert Map value.
Make `ScalaReflection` be able to handle like:
- `Seq[Int]` as `ArrayType(IntegerType, containsNull = false)`
- `Seq[java.lang.Integer]` as `ArrayType(IntegerType, containsNull = true)`
- `Map[Int, Long]` as `MapType(IntegerType, LongType, valueContainsNull = false)`
- `Map[Int, java.lang.Long]` as `MapType(IntegerType, LongType, valueContainsNull = true)`
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#1889 from ueshin/issues/SPARK-2969 and squashes the following commits:
24f1c5c [Takuya UESHIN] Change the default value of ArrayType.containsNull to true in Python API.
79f5b65 [Takuya UESHIN] Change the default value of ArrayType.containsNull to true in Java API.
7cd1a7a [Takuya UESHIN] Fix json test failures.
2cfb862 [Takuya UESHIN] Change the default value of ArrayType.containsNull to true.
2f38e61 [Takuya UESHIN] Revert the default value of MapTypes.valueContainsNull.
9fa02f5 [Takuya UESHIN] Fix a test failure.
1a9a96b [Takuya UESHIN] Modify ScalaReflection to handle ArrayType.containsNull and MapType.valueContainsNull.
There are 4 different compression codec available for ```ParquetOutputFormat```
in Spark SQL, it was set as a hard-coded value in ```ParquetRelation.defaultCompression```
original discuss:
https://github.com/apache/spark/pull/195#discussion-diff-11002083
i added a new config property in SQLConf to allow user to change this compression codec, and i used similar short names syntax as described in SPARK-2953 #1873 (https://github.com/apache/spark/pull/1873/files#diff-0)
btw, which codec should we use as default? it was set to GZIP (https://github.com/apache/spark/pull/195/files#diff-4), but i think maybe we should change this to SNAPPY, since SNAPPY is already the default codec for shuffling in spark-core (SPARK-2469, #1415), and parquet-mr supports Snappy codec natively (e440108de5).
Author: chutium <teng.qiu@gmail.com>
Closes#2039 from chutium/parquet-compression and squashes the following commits:
2f44964 [chutium] [SPARK-3131][SQL] parquet compression default codec set to snappy, also in test suite
e578e21 [chutium] [SPARK-3131][SQL] compression codec config property name and default codec set to snappy
21235dc [chutium] [SPARK-3131][SQL] Allow user to set parquet compression codec for writing ParquetFile in SQLContext
This PR adds a new conf flag `spark.sql.parquet.binaryAsString`. When it is `true`, if there is no parquet metadata file available to provide the schema of the data, we will always treat binary fields stored in parquet as string fields. This conf is used to provide a way to read string fields generated without UTF8 decoration.
JIRA: https://issues.apache.org/jira/browse/SPARK-2927
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1855 from yhuai/parquetBinaryAsString and squashes the following commits:
689ffa9 [Yin Huai] Add missing "=".
80827de [Yin Huai] Unit test.
1765ca4 [Yin Huai] Use .toBoolean.
9d3f199 [Yin Huai] Merge remote-tracking branch 'upstream/master' into parquetBinaryAsString
5d436a1 [Yin Huai] The initial support of adding a conf to treat binary columns stored in Parquet as string columns.
Author: Michael Armbrust <michael@databricks.com>
Closes#1863 from marmbrus/parquetPredicates and squashes the following commits:
10ad202 [Michael Armbrust] left <=> right
f249158 [Michael Armbrust] quiet parquet tests.
802da5b [Michael Armbrust] Add test case.
eab2eda [Michael Armbrust] Fix parquet predicate push down bug
Author: Michael Armbrust <michael@databricks.com>
Closes#1880 from marmbrus/columnBatches and squashes the following commits:
0649987 [Michael Armbrust] add test
4756fad [Michael Armbrust] fix compilation
2314532 [Michael Armbrust] Build column buffers in smaller batches
JIRA: https://issues.apache.org/jira/browse/SPARK-2908
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1840 from yhuai/SPARK-2908 and squashes the following commits:
86e833e [Yin Huai] Update test.
cb11759 [Yin Huai] nullTypeToStringType should check columns with the type of array of structs.
Author: Reynold Xin <rxin@apache.org>
Closes#1794 from rxin/sql-conf and squashes the following commits:
3ac11ef [Reynold Xin] getAllConfs return an immutable Map instead of an Array.
4b19d6c [Reynold Xin] Tighten the visibility of various SQLConf methods and renamed setter/getters.
