Output attributes of opposite side of `OuterJoin` should be nullable.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#1887 from ueshin/issues/SPARK-2965 and squashes the following commits:
bcb2d37 [Takuya UESHIN] Fix HashOuterJoin output nullabilities.
Author: chutium <teng.qiu@gmail.com>
Closes#1691 from chutium/SPARK-2700 and squashes the following commits:
b76ae8c [chutium] [SPARK-2700] [SQL] fixed styling issue
d75a8bd [chutium] [SPARK-2700] [SQL] Hidden files (such as .impala_insert_staging) should be filtered out by sqlContext.parquetFile
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.
Handle null in schemaRDD during converting them into Python.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1802 from davies/json and squashes the following commits:
88e6b1f [Davies Liu] handle null in schemaRDD()
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.
This PR aims to finalize accepted data value types in Python RDDs provided to Python `applySchema`.
JIRA: https://issues.apache.org/jira/browse/SPARK-2854
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1793 from yhuai/SPARK-2854 and squashes the following commits:
32f0708 [Yin Huai] LongType only accepts long values.
c2b23dd [Yin Huai] Do data type conversions based on the specified Spark SQL data type.
JIRA issue: [SPARK-2650](https://issues.apache.org/jira/browse/SPARK-2650)
Please refer to [comments](https://issues.apache.org/jira/browse/SPARK-2650?focusedCommentId=14084397&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14084397) of SPARK-2650 for some other details.
This PR adjusts the initial in-memory columnar buffer size to 1MB, same as the default value of Shark's `shark.column.partitionSize.mb` property when running in local mode. Will add Shark style partition size estimation in another PR.
Also, before this PR, `NullableColumnBuilder` copies the whole buffer to add the null positions section, and then `CompressibleColumnBuilder` copies and compresses the buffer again, even if compression is disabled (`PassThrough` compression scheme is used to disable compression). In this PR the first buffer copy is eliminated to reduce memory consumption.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1769 from liancheng/spark-2650 and squashes the following commits:
88a042e [Cheng Lian] Fixed method visibility and removed dead code
001f2e5 [Cheng Lian] Try fixing SPARK-2650 by adjusting initial buffer size and reducing memory allocation
Many users have reported being confused by the distinction between the `sql` and `hql` methods. Specifically, many users think that `sql(...)` cannot be used to read hive tables. In this PR I introduce a new configuration option `spark.sql.dialect` that picks which dialect with be used for parsing. For SQLContext this must be set to `sql`. In `HiveContext` it defaults to `hiveql` but can also be set to `sql`.
The `hql` and `hiveql` methods continue to act the same but are now marked as deprecated.
**This is a possibly breaking change for some users unless they set the dialect manually, though this is unlikely.**
For example: `hiveContex.sql("SELECT 1")` will now throw a parsing exception by default.
Author: Michael Armbrust <michael@databricks.com>
Closes#1746 from marmbrus/sqlLanguageConf and squashes the following commits:
ad375cc [Michael Armbrust] Merge remote-tracking branch 'apache/master' into sqlLanguageConf
20c43f8 [Michael Armbrust] override function instead of just setting the value
7e4ae93 [Michael Armbrust] Deprecate hql() method in favor of a config option, 'spark.sql.dialect'
There have been user complaints that the difference between `registerAsTable` and `saveAsTable` is too subtle. This PR addresses this by renaming `registerAsTable` to `registerTempTable`, which more clearly reflects what is happening. `registerAsTable` remains, but will cause a deprecation warning.
Author: Michael Armbrust <michael@databricks.com>
Closes#1743 from marmbrus/registerTempTable and squashes the following commits:
d031348 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
4dff086 [Michael Armbrust] Fix .java files too
89a2f12 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
0b7b71e [Michael Armbrust] Rename registerAsTable to registerTempTable
This patch adds the ability to register lambda functions written in Python, Java or Scala as UDFs for use in SQL or HiveQL.
Scala:
```scala
registerFunction("strLenScala", (_: String).length)
sql("SELECT strLenScala('test')")
```
Python:
```python
sqlCtx.registerFunction("strLenPython", lambda x: len(x), IntegerType())
sqlCtx.sql("SELECT strLenPython('test')")
```
Java:
```java
sqlContext.registerFunction("stringLengthJava", new UDF1<String, Integer>() {
Override
public Integer call(String str) throws Exception {
return str.length();
}
}, DataType.IntegerType);
sqlContext.sql("SELECT stringLengthJava('test')");
```
Author: Michael Armbrust <michael@databricks.com>
Closes#1063 from marmbrus/udfs and squashes the following commits:
9eda0fe [Michael Armbrust] newline
747c05e [Michael Armbrust] Add some scala UDF tests.
d92727d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into udfs
005d684 [Michael Armbrust] Fix naming and formatting.
d14dac8 [Michael Armbrust] Fix last line of autogened java files.
8135c48 [Michael Armbrust] Move UDF unit tests to pyspark.
40b0ffd [Michael Armbrust] Merge remote-tracking branch 'apache/master' into udfs
6a36890 [Michael Armbrust] Switch logging so that SQLContext can be serializable.
7a83101 [Michael Armbrust] Drop toString
795fd15 [Michael Armbrust] Try to avoid capturing SQLContext.
e54fb45 [Michael Armbrust] Docs and tests.
437cbe3 [Michael Armbrust] Update use of dataTypes, fix some python tests, address review comments.
01517d6 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udfs
8e6c932 [Michael Armbrust] WIP
3f96a52 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udfs
6237c8d [Michael Armbrust] WIP
2766f0b [Michael Armbrust] Move udfs support to SQL from hive. Add support for Java UDFs.
0f7d50c [Michael Armbrust] Draft of native Spark SQL UDFs for Scala and Python.
This also Closes#1701.
Author: GuoQiang Li <witgo@qq.com>
Closes#1208 from witgo/SPARK-1470 and squashes the following commits:
422646b [GuoQiang Li] Remove scalalogging-slf4j dependency
I think we will not generate the plan triggering this bug at this moment. But, let me explain it...
Right now, we are using `left.outputPartitioning` as the `outputPartitioning` of a `BroadcastHashJoin`. We may have a wrong physical plan for cases like...
```sql
SELECT l.key, count(*)
FROM (SELECT key, count(*) as cnt
FROM src
GROUP BY key) l // This is buildPlan
JOIN r // This is the streamedPlan
ON (l.cnt = r.value)
GROUP BY l.key
```
Let's say we have a `BroadcastHashJoin` on `l` and `r`. For this case, we will pick `l`'s `outputPartitioning` for the `outputPartitioning`of the `BroadcastHashJoin` on `l` and `r`. Also, because the last `GROUP BY` is using `l.key` as the key, we will not introduce an `Exchange` for this aggregation. However, `r`'s outputPartitioning may not match the required distribution of the last `GROUP BY` and we fail to group data correctly.
JIRA is being reindexed. I will create a JIRA ticket once it is back online.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1735 from yhuai/BroadcastHashJoin and squashes the following commits:
96d9cb3 [Yin Huai] Set outputPartitioning correctly.
Author: GuoQiang Li <witgo@qq.com>
Closes#1369 from witgo/SPARK-1470_new and squashes the following commits:
66a1641 [GuoQiang Li] IncompatibleResultTypeProblem
73a89ba [GuoQiang Li] Use the scala-logging wrapper instead of the directly sfl4j api.
We need to carefully set the ouputPartitioning of the HashOuterJoin Operator. Otherwise, we may not correctly handle nulls.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1721 from yhuai/SPARK-2212-BugFix and squashes the following commits:
ed5eef7 [Yin Huai] Correctly choosing outputPartitioning for the HashOuterJoin operator.
Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes.
This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance.
root
|-- field1: integer (nullable = true)
|-- field2: string (nullable = true)
|-- field3: struct (nullable = true)
| |-- field4: integer (nullable = true)
| |-- field5: array (nullable = true)
| | |-- element: integer (containsNull = false)
|-- field6: array (nullable = true)
| |-- element: struct (containsNull = false)
| | |-- field7: string (nullable = true)
Then we can access them by row.field3.field5[0] or row.field6[5].field7
It also will infer the schema in Python, convert Row/dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType.
You can use pyspark.sql.Row to convert unnamed structure into Row object, make the RDD can be inferable. Such as:
ctx.inferSchema(rdd.map(lambda x: Row(a=x[0], b=x[1]))
Or you could use Row to create a class just like namedtuple, for example:
Person = Row("name", "age")
ctx.inferSchema(rdd.map(lambda x: Person(*x)))
Also, you can call applySchema to apply an schema to a RDD of tuple/list and turn it into a SchemaRDD. The `schema` should be StructType, see the API docs for details.
schema = StructType([StructField("name, StringType, True),
StructType("age", IntegerType, True)])
ctx.applySchema(rdd, schema)
PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1598 from davies/nested and squashes the following commits:
f1d15b6 [Davies Liu] verify schema with the first few rows
8852aaf [Davies Liu] check type of schema
abe9e6e [Davies Liu] address comments
61b2292 [Davies Liu] add @deprecated to pythonToJavaMap
1e5b801 [Davies Liu] improve cache of classes
51aa135 [Davies Liu] use Row to infer schema
e9c0d5c [Davies Liu] remove string typed schema
353a3f2 [Davies Liu] fix code style
63de8f8 [Davies Liu] fix typo
c79ca67 [Davies Liu] fix serialization of nested data
6b258b5 [Davies Liu] fix pep8
9d8447c [Davies Liu] apply schema provided by string of names
f5df97f [Davies Liu] refactor, address comments
9d9af55 [Davies Liu] use arrry to applySchema and infer schema in Python
84679b3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into nested
0eaaf56 [Davies Liu] fix doc tests
b3559b4 [Davies Liu] use generated Row instead of namedtuple
c4ddc30 [Davies Liu] fix conflict between name of fields and variables
7f6f251 [Davies Liu] address all comments
d69d397 [Davies Liu] refactor
2cc2d45 [Davies Liu] refactor
182fb46 [Davies Liu] refactor
bc6e9e1 [Davies Liu] switch to new Schema API
547bf3e [Davies Liu] Merge branch 'master' into nested
a435b5a [Davies Liu] add docs and code refactor
2c8debc [Davies Liu] Merge branch 'master' into nested
644665a [Davies Liu] use tuple and namedtuple for schemardd
just a match forgot, found after SPARK-2710 , TimestampType can be used by a SchemaRDD generated from JDBC ResultSet
Author: chutium <teng.qiu@gmail.com>
Closes#1636 from chutium/SPARK-2729 and squashes the following commits:
71af77a [chutium] [SPARK-2729] [SQL] added Timestamp in NullableColumnAccessorSuite
39cf9f8 [chutium] [SPARK-2729] add Timestamp Type into ColumnBuilder TestSuite, ref. #1636
ab6ff97 [chutium] [SPARK-2729] Forgot to match Timestamp type in ColumnBuilder
This patch is to support the hash based outer join. Currently, outer join for big relations are resort to `BoradcastNestedLoopJoin`, which is super slow. This PR will create 2 hash tables for both relations in the same partition, which greatly reduce the table scans.
Here is the testing code that I used:
```
package org.apache.spark.sql.hive
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._
case class Record(key: String, value: String)
object JoinTablePrepare extends App {
import TestHive2._
val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"${i % 828193}", s"val_$i")))
runSqlHive("SHOW TABLES")
runSqlHive("DROP TABLE if exists a")
runSqlHive("DROP TABLE if exists b")
runSqlHive("DROP TABLE if exists result")
rdd.registerAsTable("records")
runSqlHive("""CREATE TABLE a (key STRING, value STRING)
| ROW FORMAT SERDE
| 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
| STORED AS RCFILE
""".stripMargin)
runSqlHive("""CREATE TABLE b (key STRING, value STRING)
| ROW FORMAT SERDE
| 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
| STORED AS RCFILE
""".stripMargin)
runSqlHive("""CREATE TABLE result (key STRING, value STRING)
| ROW FORMAT SERDE
| 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
| STORED AS RCFILE
""".stripMargin)
hql(s"""from records
| insert into table a
| select key, value
""".stripMargin)
hql(s"""from records
| insert into table b select key + 100000, value
""".stripMargin)
}
object JoinTablePerformanceTest extends App {
import TestHive2._
hql("SHOW TABLES")
hql("set spark.sql.shuffle.partitions=20")
val leftOuterJoin = "insert overwrite table result select a.key, b.value from a left outer join b on a.key=b.key"
val rightOuterJoin = "insert overwrite table result select a.key, b.value from a right outer join b on a.key=b.key"
val fullOuterJoin = "insert overwrite table result select a.key, b.value from a full outer join b on a.key=b.key"
val results = ("LeftOuterJoin", benchmark(leftOuterJoin)) :: ("LeftOuterJoin", benchmark(leftOuterJoin)) ::
("RightOuterJoin", benchmark(rightOuterJoin)) :: ("RightOuterJoin", benchmark(rightOuterJoin)) ::
("FullOuterJoin", benchmark(fullOuterJoin)) :: ("FullOuterJoin", benchmark(fullOuterJoin)) :: Nil
val explains = hql(s"explain $leftOuterJoin").collect ++ hql(s"explain $rightOuterJoin").collect ++ hql(s"explain $fullOuterJoin").collect
println(explains.mkString(",\n"))
results.foreach { case (prompt, result) => {
println(s"$prompt: took ${result._1} ms (${result._2} records)")
}
}
def benchmark(cmd: String) = {
val begin = System.currentTimeMillis()
val result = hql(cmd)
val end = System.currentTimeMillis()
val count = hql("select count(1) from result").collect.mkString("")
((end - begin), count)
}
}
```
And the result as shown below:
```
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#95,value#98]],
[ HashOuterJoin [key#95], [key#97], LeftOuter, None],
[ Exchange (HashPartitioning [key#95], 20)],
[ HiveTableScan [key#95], (MetastoreRelation default, a, None), None],
[ Exchange (HashPartitioning [key#97], 20)],
[ HiveTableScan [key#97,value#98], (MetastoreRelation default, b, None), None],
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#102,value#105]],
[ HashOuterJoin [key#102], [key#104], RightOuter, None],
[ Exchange (HashPartitioning [key#102], 20)],
[ HiveTableScan [key#102], (MetastoreRelation default, a, None), None],
[ Exchange (HashPartitioning [key#104], 20)],
[ HiveTableScan [key#104,value#105], (MetastoreRelation default, b, None), None],
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#109,value#112]],
[ HashOuterJoin [key#109], [key#111], FullOuter, None],
[ Exchange (HashPartitioning [key#109], 20)],
[ HiveTableScan [key#109], (MetastoreRelation default, a, None), None],
[ Exchange (HashPartitioning [key#111], 20)],
[ HiveTableScan [key#111,value#112], (MetastoreRelation default, b, None), None]
LeftOuterJoin: took 16072 ms ([3000000] records)
LeftOuterJoin: took 14394 ms ([3000000] records)
RightOuterJoin: took 14802 ms ([3000000] records)
RightOuterJoin: took 14747 ms ([3000000] records)
FullOuterJoin: took 17715 ms ([6000000] records)
FullOuterJoin: took 17629 ms ([6000000] records)
```
Without this PR, the benchmark will run seems never end.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#1147 from chenghao-intel/hash_based_outer_join and squashes the following commits:
65c599e [Cheng Hao] Fix issues with the community comments
72b1394 [Cheng Hao] Fix bug of stale value in joinedRow
55baef7 [Cheng Hao] Add HashOuterJoin
It is a follow-up PR of SPARK-2179 (https://issues.apache.org/jira/browse/SPARK-2179). It makes package names of data type APIs more consistent across languages (Scala: `org.apache.spark.sql`, Java: `org.apache.spark.sql.api.java`, Python: `pyspark.sql`).
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1712 from yhuai/javaDataType and squashes the following commits:
62eb705 [Yin Huai] Move package-info.
add4bcb [Yin Huai] Make the package names of data type classes consistent across languages by moving all Java data type classes to package sql.api.java.
Since we let users create Rows. It makes sense to accept mutable Maps as values of MapType columns.
JIRA: https://issues.apache.org/jira/browse/SPARK-2779
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1705 from yhuai/SPARK-2779 and squashes the following commits:
00d72fd [Yin Huai] Use scala.collection.Map.
This PR resolves the following two tickets:
- [SPARK-2531](https://issues.apache.org/jira/browse/SPARK-2531): BNLJ currently assumes the build side is the right relation. This patch refactors some of its logic to take into account a BuildSide properly.
- [SPARK-2436](https://issues.apache.org/jira/browse/SPARK-2436): building on top of the above, we simply use the physical size statistics (if available) of both relations, and make the smaller relation the build side in the planner.
Author: Zongheng Yang <zongheng.y@gmail.com>
Closes#1448 from concretevitamin/bnlj-buildSide and squashes the following commits:
1780351 [Zongheng Yang] Use size estimation to decide optimal build side of BNLJ.
68e6c5b [Zongheng Yang] Consolidate two adjacent pattern matchings.
96d312a [Zongheng Yang] Use a while loop instead of collection methods chaining.
4bc525e [Zongheng Yang] Make BroadcastNestedLoopJoin take a BuildSide.
Author: Michael Armbrust <michael@databricks.com>
Closes#1647 from marmbrus/parquetCase and squashes the following commits:
a1799b7 [Michael Armbrust] move comment
2a2a68b [Michael Armbrust] Merge remote-tracking branch 'apache/master' into parquetCase
bb35d5b [Michael Armbrust] Fix test case that produced an invalid plan.
e6870bf [Michael Armbrust] Better error message.
539a2e1 [Michael Armbrust] Resolve original attributes in ParquetTableScan
This adds a new ShuffleManager based on sorting, as described in https://issues.apache.org/jira/browse/SPARK-2045. The bulk of the code is in an ExternalSorter class that is similar to ExternalAppendOnlyMap, but sorts key-value pairs by partition ID and can be used to create a single sorted file with a map task's output. (Longer-term I think this can take on the remaining functionality in ExternalAppendOnlyMap and replace it so we don't have code duplication.)
The main TODOs still left are:
- [x] enabling ExternalSorter to merge across spilled files
- [x] with an Ordering
- [x] without an Ordering, using the keys' hash codes
- [x] adding more tests (e.g. a version of our shuffle suite that runs on this)
- [x] rebasing on top of the size-tracking refactoring in #1165 when that is merged
- [x] disabling spilling if spark.shuffle.spill is set to false
Despite this though, this seems to work pretty well (running successfully in cases where the hash shuffle would OOM, such as 1000 reduce tasks on executors with only 1G memory), and it seems to be comparable in speed or faster than hash-based shuffle (it will create much fewer files for the OS to keep track of). So I'm posting it to get some early feedback.
After these TODOs are done, I'd also like to enable ExternalSorter to sort data within each partition by a key as well, which will allow us to use it to implement external spilling in reduce tasks in `sortByKey`.
Author: Matei Zaharia <matei@databricks.com>
Closes#1499 from mateiz/sort-based-shuffle and squashes the following commits:
bd841f9 [Matei Zaharia] Various review comments
d1c137fd [Matei Zaharia] Various review comments
a611159 [Matei Zaharia] Compile fixes due to rebase
62c56c8 [Matei Zaharia] Fix ShuffledRDD sometimes not returning Tuple2s.
f617432 [Matei Zaharia] Fix a failing test (seems to be due to change in SizeTracker logic)
9464d5f [Matei Zaharia] Simplify code and fix conflicts after latest rebase
0174149 [Matei Zaharia] Add cleanup behavior and cleanup tests for sort-based shuffle
eb4ee0d [Matei Zaharia] Remove customizable element type in ShuffledRDD
fa2e8db [Matei Zaharia] Allow nextBatchStream to be called after we're done looking at all streams
a34b352 [Matei Zaharia] Fix tracking of indices within a partition in SpillReader, and add test
03e1006 [Matei Zaharia] Add a SortShuffleSuite that runs ShuffleSuite with sort-based shuffle
3c7ff1f [Matei Zaharia] Obey the spark.shuffle.spill setting in ExternalSorter
ad65fbd [Matei Zaharia] Rebase on top of Aaron's Sorter change, and use Sorter in our buffer
44d2a93 [Matei Zaharia] Use estimateSize instead of atGrowThreshold to test collection sizes
5686f71 [Matei Zaharia] Optimize merging phase for in-memory only data:
5461cbb [Matei Zaharia] Review comments and more tests (e.g. tests with 1 element per partition)
e9ad356 [Matei Zaharia] Update ContextCleanerSuite to make sure shuffle cleanup tests use hash shuffle (since they were written for it)
c72362a [Matei Zaharia] Added bug fix and test for when iterators are empty
de1fb40 [Matei Zaharia] Make trait SizeTrackingCollection private[spark]
4988d16 [Matei Zaharia] tweak
c1b7572 [Matei Zaharia] Small optimization
ba7db7f [Matei Zaharia] Handle null keys in hash-based comparator, and add tests for collisions
ef4e397 [Matei Zaharia] Support for partial aggregation even without an Ordering
4b7a5ce [Matei Zaharia] More tests, and ability to sort data if a total ordering is given
e1f84be [Matei Zaharia] Fix disk block manager test
5a40a1c [Matei Zaharia] More tests
614f1b4 [Matei Zaharia] Add spill metrics to map tasks
cc52caf [Matei Zaharia] Add more error handling and tests for error cases
bbf359d [Matei Zaharia] More work
3a56341 [Matei Zaharia] More partial work towards sort-based shuffle
7a0895d [Matei Zaharia] Some more partial work towards sort-based shuffle
b615476 [Matei Zaharia] Scaffolding for sort-based shuffle
The current PR contains the following changes:
* Expose `DataType`s in the sql package (internal details are private to sql).
* Users can create Rows.
* Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`.
* Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
* `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases).
* `JsonRDD` has been refactored to use changes introduced by this PR.
* Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`.
New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at
[sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext).
An example of using `applySchema` is shown below.
```scala
import org.apache.spark.sql._
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val schema =
StructType(
StructField("name", StringType, false) ::
StructField("age", IntegerType, true) :: Nil)
val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt))
val peopleSchemaRDD = sqlContext. applySchema(people, schema)
peopleSchemaRDD.printSchema
// root
// |-- name: string (nullable = false)
// |-- age: integer (nullable = true)
peopleSchemaRDD.registerAsTable("people")
sqlContext.sql("select name from people").collect.foreach(println)
```
I will add new contents to the SQL programming guide later.
JIRA: https://issues.apache.org/jira/browse/SPARK-2179
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1346 from yhuai/dataTypeAndSchema and squashes the following commits:
1d45977 [Yin Huai] Clean up.
a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c712fbf [Yin Huai] Converts types of values based on defined schema.
4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e5f8df5 [Yin Huai] Scaladoc.
122d1e7 [Yin Huai] Address comments.
03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2476ed0 [Yin Huai] Minor updates.
ab71f21 [Yin Huai] Format.
fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
bd40a33 [Yin Huai] Address comments.
991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala.
1cb35fe [Yin Huai] Add "valueContainsNull" to MapType.
3edb3ae [Yin Huai] Python doc.
692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
1d93395 [Yin Huai] Python APIs.
246da96 [Yin Huai] Add java data type APIs to javadoc index.
1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
d48fc7b [Yin Huai] Minor updates.
33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b9f3071 [Yin Huai] Java API for applySchema.
1c9f33c [Yin Huai] Java APIs for DataTypes and Row.
624765c [Yin Huai] Tests for applySchema.
aa92e84 [Yin Huai] Update data type tests.
8da1a17 [Yin Huai] Add Row.fromSeq.
9c99bc0 [Yin Huai] Several minor updates.
1d9c13a [Yin Huai] Update applySchema API.
85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e495e4e [Yin Huai] More comments.
42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc.
68525a2 [Yin Huai] Update JSON unit test.
3209108 [Yin Huai] Add unit tests.
dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false.
9168b83 [Yin Huai] Update comments.
fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType.
949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema.
7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema.
43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit.
0266761 [Yin Huai] Format
03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type.
3fa0df5 [Yin Huai] Provide easier ways to construct a StructType.
16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
Author: Michael Armbrust <michael@databricks.com>
Closes#1646 from marmbrus/nullDebug and squashes the following commits:
49050a8 [Michael Armbrust] Handle null values in debug()
Adds a new method for evaluating expressions using code that is generated though Scala reflection. This functionality is configured by the SQLConf option `spark.sql.codegen` and is currently turned off by default.
Evaluation can be done in several specialized ways:
- *Projection* - Given an input row, produce a new row from a set of expressions that define each column in terms of the input row. This can either produce a new Row object or perform the projection in-place on an existing Row (MutableProjection).
- *Ordering* - Compares two rows based on a list of `SortOrder` expressions
- *Condition* - Returns `true` or `false` given an input row.
For each of the above operations there is both a Generated and Interpreted version. When generation for a given expression type is undefined, the code generator falls back on calling the `eval` function of the expression class. Even without custom code, there is still a potential speed up, as loops are unrolled and code can still be inlined by JIT.
This PR also contains a new type of Aggregation operator, `GeneratedAggregate`, that performs aggregation by using generated `Projection` code. Currently the required expression rewriting only works for simple aggregations like `SUM` and `COUNT`. This functionality will be extended in a future PR.
This PR also performs several clean ups that simplified the implementation:
- The notion of `Binding` all expressions in a tree automatically before query execution has been removed. Instead it is the responsibly of an operator to provide the input schema when creating one of the specialized evaluators defined above. In cases when the standard eval method is going to be called, binding can still be done manually using `BindReferences`. There are a few reasons for this change: First, there were many operators where it just didn't work before. For example, operators with more than one child, and operators like aggregation that do significant rewriting of the expression. Second, the semantics of equality with `BoundReferences` are broken. Specifically, we have had a few bugs where partitioning breaks because of the binding.
- A copy of the current `SQLContext` is automatically propagated to all `SparkPlan` nodes by the query planner. Before this was done ad-hoc for the nodes that needed this. However, this required a lot of boilerplate as one had to always remember to make it `transient` and also had to modify the `otherCopyArgs`.
Author: Michael Armbrust <michael@databricks.com>
Closes#993 from marmbrus/newCodeGen and squashes the following commits:
96ef82c [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen
f34122d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen
67b1c48 [Michael Armbrust] Use conf variable in SQLConf object
4bdc42c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
41a40c9 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
de22aac [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
fed3634 [Michael Armbrust] Inspectors are not serializable.
ef8d42b [Michael Armbrust] comments
533fdfd [Michael Armbrust] More logging of expression rewriting for GeneratedAggregate.
3cd773e [Michael Armbrust] Allow codegen for Generate.
64b2ee1 [Michael Armbrust] Implement copy
3587460 [Michael Armbrust] Drop unused string builder function.
9cce346 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
1a61293 [Michael Armbrust] Address review comments.
0672e8a [Michael Armbrust] Address comments.
1ec2d6e [Michael Armbrust] Address comments
033abc6 [Michael Armbrust] off by default
4771fab [Michael Armbrust] Docs, more test coverage.
d30fee2 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
d2ad5c5 [Michael Armbrust] Refactor putting SQLContext into SparkPlan. Fix ordering, other test cases.
be2cd6b [Michael Armbrust] WIP: Remove old method for reference binding, more work on configuration.
bc88ecd [Michael Armbrust] Style
6cc97ca [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
4220f1e [Michael Armbrust] Better config, docs, etc.
ca6cc6b [Michael Armbrust] WIP
9d67d85 [Michael Armbrust] Fix hive planner
fc522d5 [Michael Armbrust] Hook generated aggregation in to the planner.
e742640 [Michael Armbrust] Remove unneeded changes and code.
675e679 [Michael Armbrust] Upgrade paradise.
0093376 [Michael Armbrust] Comment / indenting cleanup.
d81f998 [Michael Armbrust] include schema for binding.
0e889e8 [Michael Armbrust] Use typeOf instead tq
f623ffd [Michael Armbrust] Quiet logging from test suite.
efad14f [Michael Armbrust] Remove some half finished functions.
92e74a4 [Michael Armbrust] add overrides
a2b5408 [Michael Armbrust] WIP: Code generation with scala reflection.
Author: Michael Armbrust <michael@databricks.com>
Closes#1638 from marmbrus/cachedConfig and squashes the following commits:
2362082 [Michael Armbrust] Use SQLConf to configure in-memory columnar caching
The idea is that every Catalyst logical plan gets hold of a Statistics class, the usage of which provides useful estimations on various statistics. See the implementations of `MetastoreRelation`.
This patch also includes several usages of the estimation interface in the planner. For instance, we now use physical table sizes from the estimate interface to convert an equi-join to a broadcast join (when doing so is beneficial, as determined by a size threshold).
Finally, there are a couple minor accompanying changes including:
- Remove the not-in-use `BaseRelation`.
- Make SparkLogicalPlan take a `SQLContext` in the second param list.
Author: Zongheng Yang <zongheng.y@gmail.com>
Closes#1238 from concretevitamin/estimates and squashes the following commits:
329071d [Zongheng Yang] Address review comments; turn config name from string to field in SQLConf.
8663e84 [Zongheng Yang] Use BigInt for stat; for logical leaves, by default throw an exception.
2f2fb89 [Zongheng Yang] Fix statistics for SparkLogicalPlan.
9951305 [Zongheng Yang] Remove childrenStats.
16fc60a [Zongheng Yang] Avoid calling statistics on plans if auto join conversion is disabled.
8bd2816 [Zongheng Yang] Add a note on performance of statistics.
6e594b8 [Zongheng Yang] Get size info from metastore for MetastoreRelation.
01b7a3e [Zongheng Yang] Update scaladoc for a field and move it to @param section.
549061c [Zongheng Yang] Remove numTuples in Statistics for now.
729a8e2 [Zongheng Yang] Update docs to be more explicit.
573e644 [Zongheng Yang] Remove singleton SQLConf and move back `settings` to the trait.
2d99eb5 [Zongheng Yang] {Cleanup, use synchronized in, enrich} StatisticsSuite.
ca5b825 [Zongheng Yang] Inject SQLContext into SparkLogicalPlan, removing SQLConf mixin from it.
43d38a6 [Zongheng Yang] Revert optimization for BroadcastNestedLoopJoin (this fixes tests).
0ef9e5b [Zongheng Yang] Use multiplication instead of sum for default estimates.
4ef0d26 [Zongheng Yang] Make Statistics a case class.
3ba8f3e [Zongheng Yang] Add comment.
e5bcf5b [Zongheng Yang] Fix optimization conditions & update scala docs to explain.
7d9216a [Zongheng Yang] Apply estimation to planning ShuffleHashJoin & BroadcastNestedLoopJoin.
73cde01 [Zongheng Yang] Move SQLConf back. Assign default sizeInBytes to SparkLogicalPlan.
73412be [Zongheng Yang] Move SQLConf to Catalyst & add default val for sizeInBytes.
7a60ab7 [Zongheng Yang] s/Estimates/Statistics, s/cardinality/numTuples.
de3ae13 [Zongheng Yang] Add parquetAfter() properly in test.
dcff9bd [Zongheng Yang] Cleanups.
84301a4 [Zongheng Yang] Refactors.
5bf5586 [Zongheng Yang] Typo.
56a8e6e [Zongheng Yang] Prototype impl of estimations for Catalyst logical plans.
Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema().
In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1601 from davies/date and squashes the following commits:
f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list
c9d607a [Davies Liu] convert datetype for runtime
709d40d [Davies Liu] remove brackets
96db384 [Davies Liu] support datetime type for SchemaRDD
JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
Another try for #1399 & #1600. Those two PR breaks Jenkins builds because we made a separate profile `hive-thriftserver` in sub-project `assembly`, but the `hive-thriftserver` module is defined outside the `hive-thriftserver` profile. Thus every time a pull request that doesn't touch SQL code will also execute test suites defined in `hive-thriftserver`, but tests fail because related .class files are not included in the assembly jar.
In the most recent commit, module `hive-thriftserver` is moved into its own profile to fix this problem. All previous commits are squashed for clarity.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1620 from liancheng/jdbc-with-maven-fix and squashes the following commits:
629988e [Cheng Lian] Moved hive-thriftserver module definition into its own profile
ec3c7a7 [Cheng Lian] Cherry picked the Hive Thrift server
(This is a replacement of #1399, trying to fix potential `HiveThriftServer2` port collision between parallel builds. Please refer to [these comments](https://github.com/apache/spark/pull/1399#issuecomment-50212572) for details.)
JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
Merging the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).
Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1600 from liancheng/jdbc and squashes the following commits:
ac4618b [Cheng Lian] Uses random port for HiveThriftServer2 to avoid collision with parallel builds
090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
This reverts commit 06dc0d2c6b.
#1399 is making Jenkins fail. We should investigate and put this back after its passing tests.
Author: Michael Armbrust <michael@databricks.com>
Closes#1594 from marmbrus/revertJDBC and squashes the following commits:
59748da [Michael Armbrust] Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC server"
JIRA issue:
- Main: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
- Related: [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678)
Cherry picked the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).
(Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.)
TODO
- [x] Use `spark-submit` to launch the server, the CLI and beeline
- [x] Migration guideline draft for Shark users
----
Hit by a bug in `SparkSubmitArguments` while working on this PR: all application options that are recognized by `SparkSubmitArguments` are stolen as `SparkSubmit` options. For example:
```bash
$ spark-submit --class org.apache.hive.beeline.BeeLine spark-internal --help
```
This actually shows usage information of `SparkSubmit` rather than `BeeLine`.
~~Fixed this bug here since the `spark-internal` related stuff also touches `SparkSubmitArguments` and I'd like to avoid conflict.~~
**UPDATE** The bug mentioned above is now tracked by [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678). Decided to revert changes to this bug since it involves more subtle considerations and worth a separate PR.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1399 from liancheng/thriftserver and squashes the following commits:
090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
In JsonRDD.scalafy, we are using toMap/toList to convert a Java Map/List to a Scala one. These two operations are pretty expensive because they read elements from a Java Map/List and then load to a Scala Map/List. We can use Scala wrappers to wrap those Java collections instead of using toMap/toList.
I did a quick test to see the performance. I had a 2.9GB cached RDD[String] storing one JSON object per record (twitter dataset). My simple test program is attached below.
```scala
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext._
val jsonData = sc.textFile("...")
jsonData.cache.count
val jsonSchemaRDD = sqlContext.jsonRDD(jsonData)
jsonSchemaRDD.registerAsTable("jt")
sqlContext.sql("select count(*) from jt").collect
```
Stages for the schema inference and the table scan both had 48 tasks. These tasks were executed sequentially. For the current implementation, scanning the JSON dataset will materialize values of all fields of a record. The inferred schema of the dataset can be accessed at https://gist.github.com/yhuai/05fe8a57c638c6666f8d.
From the result, there was no significant difference on running `jsonRDD`. For the simple aggregation query, results are attached below.
```
Original:
Run 1: 26.1s
Run 2: 27.03s
Run 3: 27.035s
With this change:
Run 1: 21.086s
Run 2: 21.035s
Run 3: 21.029s
```
JIRA: https://issues.apache.org/jira/browse/SPARK-2603
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1504 from yhuai/removeToMapToList and squashes the following commits:
6831b77 [Yin Huai] Fix failed tests.
09b9bca [Yin Huai] Merge remote-tracking branch 'upstream/master' into removeToMapToList
d1abdb8 [Yin Huai] Remove unnecessary toMap and toList.
Author: Ian O Connell <ioconnell@twitter.com>
Closes#1377 from ianoc/feature/SPARK-2102 and squashes the following commits:
5498566 [Ian O Connell] Docs update suggested by Patrick
20e8555 [Ian O Connell] Slight style change
f92c294 [Ian O Connell] Add docs for new KryoSerializer option
f3735c8 [Ian O Connell] Add using a kryo resource pool for the SqlSerializer
4e5c342 [Ian O Connell] Register the SparkConf for kryo, it gets swept into serialization
665805a [Ian O Connell] Add a spark.kryo.registrationRequired option for configuring the Kryo Serializer
We need to use the analyzed attributes otherwise we end up with a tree that will never resolve.
Author: Michael Armbrust <michael@databricks.com>
Closes#1470 from marmbrus/fixApplySchema and squashes the following commits:
f968195 [Michael Armbrust] Use analyzed attributes when applying the schema.
4969015 [Michael Armbrust] Add test case.
JIRA: https://issues.apache.org/jira/browse/SPARK-2525.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1444 from yhuai/SPARK-2517 and squashes the following commits:
edbac3f [Yin Huai] Removed some compiler type erasure warnings.
JIRA issue: [SPARK-2119](https://issues.apache.org/jira/browse/SPARK-2119)
Essentially this PR fixed three issues to gain much better performance when reading large Parquet file off S3.
1. When reading the schema, fetching Parquet metadata from a part-file rather than the `_metadata` file
The `_metadata` file contains metadata of all row groups, and can be very large if there are many row groups. Since schema information and row group metadata are coupled within a single Thrift object, we have to read the whole `_metadata` to fetch the schema. On the other hand, schema is replicated among footers of all part-files, which are fairly small.
1. Only add the root directory of the Parquet file rather than all the part-files to input paths
HDFS API can automatically filter out all hidden files and underscore files (`_SUCCESS` & `_metadata`), there's no need to filter out all part-files and add them individually to input paths. What make it much worse is that, `FileInputFormat.listStatus()` calls `FileSystem.globStatus()` on each individual input path sequentially, each results a blocking remote S3 HTTP request.
1. Worked around [PARQUET-16](https://issues.apache.org/jira/browse/PARQUET-16)
Essentially PARQUET-16 is similar to the above issue, and results lots of sequential `FileSystem.getFileStatus()` calls, which are further translated into a bunch of remote S3 HTTP requests.
`FilteringParquetRowInputFormat` should be cleaned up once PARQUET-16 is fixed.
Below is the micro benchmark result. The dataset used is a S3 Parquet file consists of 3,793 partitions, about 110MB per partition in average. The benchmark is done with a 9-node AWS cluster.
- Creating a Parquet `SchemaRDD` (Parquet schema is fetched)
```scala
val tweets = parquetFile(uri)
```
- Before: 17.80s
- After: 8.61s
- Fetching partition information
```scala
tweets.getPartitions
```
- Before: 700.87s
- After: 21.47s
- Counting the whole file (both steps above are executed altogether)
```scala
parquetFile(uri).count()
```
- Before: ??? (haven't test yet)
- After: 53.26s
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1370 from liancheng/faster-parquet and squashes the following commits:
94a2821 [Cheng Lian] Added comments about schema consistency
d2c4417 [Cheng Lian] Worked around PARQUET-16 to improve Parquet performance
1c0d1b9 [Cheng Lian] Accelerated Parquet schema retrieving
5bd3d29 [Cheng Lian] Fixed Parquet log level
Author: Aaron Staple <aaron.staple@gmail.com>
Closes#1421 from staple/SPARK-2314 and squashes the following commits:
73e04dc [Aaron Staple] [SPARK-2314] Override collect and take in JavaSchemaRDD, forwarding to SchemaRDD implementations.
Author: Michael Armbrust <michael@databricks.com>
Closes#1414 from marmbrus/exprIdResolution and squashes the following commits:
97b47bc [Michael Armbrust] Attribute equality comparisons should be done by exprId.
Note that this commit changes the semantics when loading in data that was created with prior versions of Spark SQL. Before, we were writing out strings as Binary data without adding any other annotations. Thus, when data is read in from prior versions, data that was StringType will now become BinaryType. Users that need strings can CAST that column to a String. It was decided that while this breaks compatibility, it does make us compatible with other systems (Hive, Thrift, etc) and adds support for Binary data, so this is the right decision long term.
To support `BinaryType`, the following changes are needed:
- Make `StringType` use `OriginalType.UTF8`
- Add `BinaryType` using `PrimitiveTypeName.BINARY` without `OriginalType`
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#1373 from ueshin/issues/SPARK-2446 and squashes the following commits:
ecacb92 [Takuya UESHIN] Add BinaryType support to Parquet I/O.
616e04a [Takuya UESHIN] Make StringType use OriginalType.UTF8.
Reuse byte buffers when creating unique attributes for multiple instances of an InMemoryRelation in a single query plan.
Author: Michael Armbrust <michael@databricks.com>
Closes#1332 from marmbrus/doubleCache and squashes the following commits:
4a19609 [Michael Armbrust] Clean up concurrency story by calculating buffersn the constructor.
b39c931 [Michael Armbrust] Allocations are kind of a side effect.
f67eff7 [Michael Armbrust] Reusue same byte buffers when creating new instance of InMemoryRelation
Author: Michael Armbrust <michael@databricks.com>
Closes#1366 from marmbrus/partialDistinct and squashes the following commits:
12a31ab [Michael Armbrust] Add more efficient distinct operator.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#1355 from ueshin/issues/SPARK-2428 and squashes the following commits:
b6fa264 [Takuya UESHIN] Add except and intersect methods to SchemaRDD.
`RowWriteSupport` doesn't write empty `ArrayType` value, so the read value becomes `null`.
It should write empty `ArrayType` value as it is.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#1339 from ueshin/issues/SPARK-2415 and squashes the following commits:
32afc87 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-2415
2f05196 [Takuya UESHIN] Fix RowWriteSupport to handle empty ArrayType correctly.
Author: Reynold Xin <rxin@apache.org>
Closes#1334 from rxin/sqlConfThreadSafetuy and squashes the following commits:
c1e0a5a [Reynold Xin] Fixed the duplicate comment.
7614372 [Reynold Xin] [SPARK-2409] Make SQLConf thread safe.
Using Spark's take can result in an entire in-memory partition to be shipped in order to retrieve a single row.
Author: Michael Armbrust <michael@databricks.com>
Closes#1318 from marmbrus/takeLimit and squashes the following commits:
77289a5 [Michael Armbrust] Update scala doc
32f0674 [Michael Armbrust] Custom take implementation for LIMIT queries.
