...arquetFile accept hadoop glob pattern in path.
Author: Thu Kyaw <trk007@gmail.com>
Closes#3407 from tkyaw/master and squashes the following commits:
19115ad [Thu Kyaw] Merge https://github.com/apache/spark
ceded32 [Thu Kyaw] [SPARK-3928][SQL] Support wildcard matches on Parquet files.
d322c28 [Thu Kyaw] [SPARK-3928][SQL] Support wildcard matches on Parquet files.
ce677c6 [Thu Kyaw] [SPARK-3928][SQL] Support wildcard matches on Parquet files.
Add support for `GROUPING SETS`, `ROLLUP`, `CUBE` and the the virtual column `GROUPING__ID`.
More details on how to use the `GROUPING SETS" can be found at: https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation,+Cube,+Grouping+and+Rolluphttps://issues.apache.org/jira/secure/attachment/12676811/grouping_set.pdf
The generic idea of the implementations are :
1 Replace the `ROLLUP`, `CUBE` with `GROUPING SETS`
2 Explode each of the input row, and then feed them to `Aggregate`
* Each grouping set are represented as the bit mask for the `GroupBy Expression List`, for each bit, `1` means the expression is selected, otherwise `0` (left is the lower bit, and right is the higher bit in the `GroupBy Expression List`)
* Several of projections are constructed according to the grouping sets, and within each projection(Seq[Expression), we replace those expressions with `Literal(null)` if it's not selected in the grouping set (based on the bit mask)
* Output Schema of `Explode` is `child.output :+ grouping__id`
* GroupBy Expressions of `Aggregate` is `GroupBy Expression List :+ grouping__id`
* Keep the `Aggregation expressions` the same for the `Aggregate`
The expressions substitutions happen in Logic Plan analyzing, so we will benefit from the Logical Plan optimization (e.g. expression constant folding, and map side aggregation etc.), Only an `Explosive` operator added for Physical Plan, which will explode the rows according the pre-set projections.
A known issue will be done in the follow up PR:
* Optimization `ColumnPruning` is not supported yet for `Explosive` node.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#1567 from chenghao-intel/grouping_sets and squashes the following commits:
fe65fcc [Cheng Hao] Remove the extra space
3547056 [Cheng Hao] Add more doc and Simplify the Expand
a7c869d [Cheng Hao] update code as feedbacks
d23c672 [Cheng Hao] Add GroupingExpression to replace the Seq[Expression]
414b165 [Cheng Hao] revert the unnecessary changes
ec276c6 [Cheng Hao] Support Rollup/Cube/GroupingSets
```
TestSQLContext.sparkContext.parallelize(
"""{"ip":"27.31.100.29","headers":{"Host":"1.abc.com","Charset":"UTF-8"}}""" ::
"""{"ip":"27.31.100.29","headers":{}}""" ::
"""{"ip":"27.31.100.29","headers":""}""" :: Nil)
```
As empty string (the "headers") will be considered as String in the beginning (in line 2 and 3), it ignores the real nested data type (struct type "headers" in line 1), and also take the line 1 (the "headers") as String Type, which is not our expected.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3708 from chenghao-intel/json and squashes the following commits:
e7a72e9 [Cheng Hao] add more concise unit test
853de51 [Cheng Hao] NullType instead of StringType when sampling against empty string or null value
Predicates like `a = NULL` and `a < NULL` can't be pushed down since Parquet `Lt`, `LtEq`, `Gt`, `GtEq` doesn't accept null value. Note that `Eq` and `NotEq` can only be used with `null` to represent predicates like `a IS NULL` and `a IS NOT NULL`.
However, normally this issue doesn't cause NPE because any value compared to `NULL` results `NULL`, and Spark SQL automatically optimizes out `NULL` predicate in the `SimplifyFilters` rule. Only testing code that intentionally disables the optimizer may trigger this issue. (That's why this issue is not marked as blocker and I do **NOT** think we need to backport this to branch-1.1
This PR restricts `Lt`, `LtEq`, `Gt` and `GtEq` to non-null values only, and only uses `Eq` with null value to pushdown `IsNull` and `IsNotNull`. Also, added support for Parquet `NotEq` filter for completeness and (tiny) performance gain, it's also used to pushdown `IsNotNull`.
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Author: Cheng Lian <lian@databricks.com>
Closes#3367 from liancheng/filters-with-null and squashes the following commits:
cc41281 [Cheng Lian] Fixes several styling issues
de7de28 [Cheng Lian] Adds stricter rules for Parquet filters with null
Add `sort by` support for both DSL & SqlParser.
This PR is relevant with #3386, either one merged, will cause the other rebased.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3481 from chenghao-intel/sortby and squashes the following commits:
041004f [Cheng Hao] Add sort by for DSL & SimpleSqlParser
Using lowercase for ```options``` key to make it case-insensitive, then we should use lower case to get value from parameters.
So flowing cmd work
```
create temporary table normal_parquet
USING org.apache.spark.sql.parquet
OPTIONS (
PATH '/xxx/data'
)
```
Author: scwf <wangfei1@huawei.com>
Author: wangfei <wangfei1@huawei.com>
Closes#3470 from scwf/ddl-ulcase and squashes the following commits:
ae78509 [scwf] address comments
8f4f585 [wangfei] address comments
3c132ef [scwf] minor fix
a0fc20b [scwf] Merge branch 'master' of https://github.com/apache/spark into ddl-ulcase
4f86401 [scwf] adding CaseInsensitiveMap
e244e8d [wangfei] using lower case in json
e0cb017 [wangfei] make options in-casesensitive
This PR brings support of using StructType(and other hashable types) as key in MapType.
Author: Davies Liu <davies@databricks.com>
Closes#3714 from davies/fix_struct_in_map and squashes the following commits:
68585d7 [Davies Liu] fix primitive types in MapType
9601534 [Davies Liu] support StructType as key in MapType
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3595 from chenghao-intel/udf0 and squashes the following commits:
a858973 [Cheng Hao] Add 0 arguments support for udf
This PR provides a set Parquet testing API (see trait `ParquetTest`) that enables developers to write more concise test cases. A new set of Parquet test suites built upon this API are added and aim to replace the old `ParquetQuerySuite`. To avoid potential merge conflicts, old testing code are not removed yet. The following classes can be safely removed after most Parquet related PRs are handled:
- `ParquetQuerySuite`
- `ParquetTestData`
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Author: Cheng Lian <lian@databricks.com>
Closes#3644 from liancheng/parquet-tests and squashes the following commits:
800e745 [Cheng Lian] Enforces ordering of test output
3bb8731 [Cheng Lian] Refactors HiveParquetSuite
aa2cb2e [Cheng Lian] Decouples ParquetTest and TestSQLContext
7b43a68 [Cheng Lian] Updates ParquetTest Scaladoc
7f07af0 [Cheng Lian] Adds a new set of Parquet test suites
In BroadcastHashJoin, currently it is using a hard coded value (5 minutes) to wait for the execution and broadcast of the small table.
In my opinion, it should be a configurable value since broadcast may exceed 5 minutes in some case, like in a busy/congested network environment.
Author: Jacky Li <jacky.likun@huawei.com>
Closes#3133 from jackylk/timeout-config and squashes the following commits:
733ac08 [Jacky Li] add spark.sql.broadcastTimeout in SQLConf.scala
557acd4 [Jacky Li] switch to sqlContext.getConf
81a5e20 [Jacky Li] make wait time configurable in BroadcastHashJoin
In `HashOuterJoin.scala`, spark read data from both side of join operation before zip them together. It is a waste for memory. We are trying to read data from only one side, put them into a hashmap, and then generate the `JoinedRow` with data from other side one by one.
Currently, we could only do this optimization for `left outer join` and `right outer join`. For `full outer join`, we will do something in another issue.
for
table test_csv contains 1 million records
table dim_csv contains 10 thousand records
SQL:
`select * from test_csv a left outer join dim_csv b on a.key = b.key`
the result is:
master:
```
CSV: 12671 ms
CSV: 9021 ms
CSV: 9200 ms
Current Mem Usage:787788984
```
after patch：
```
CSV: 10382 ms
CSV: 7543 ms
CSV: 7469 ms
Current Mem Usage:208145728
```
Author: tianyi <tianyi@asiainfo-linkage.com>
Author: tianyi <tianyi.asiainfo@gmail.com>
Closes#3375 from tianyi/SPARK-4483 and squashes the following commits:
72a8aec [tianyi] avoid having mutable state stored inside of the task
99c5c97 [tianyi] performance optimization
d2f94d7 [tianyi] fix bug: missing output when the join-key is null.
2be45d1 [tianyi] fix spell bug
1f2c6f1 [tianyi] remove commented codes
a676de6 [tianyi] optimize some codes
9e7d5b5 [tianyi] remove commented old codes
838707d [tianyi] Optimization about reduce memory costs during the HashOuterJoin
The problem is `codegenEnabled` is `val`, but it uses a `val` `sqlContext`, which can be override by subclasses. Here is a simple example to show this issue.
```Scala
scala> :paste
// Entering paste mode (ctrl-D to finish)
abstract class Foo {
protected val sqlContext = "Foo"
val codegenEnabled: Boolean = {
println(sqlContext) // it will call subclass's `sqlContext` which has not yet been initialized.
if (sqlContext != null) {
true
} else {
false
}
}
}
class Bar extends Foo {
override val sqlContext = "Bar"
}
println(new Bar().codegenEnabled)
// Exiting paste mode, now interpreting.
null
false
defined class Foo
defined class Bar
```
We should make `sqlContext` `final` to prevent subclasses from overriding it incorrectly.
Author: zsxwing <zsxwing@gmail.com>
Closes#3660 from zsxwing/SPARK-4812 and squashes the following commits:
1cbb623 [zsxwing] Make `sqlContext` final to prevent subclasses from overriding it incorrectly
Author: jerryshao <saisai.shao@intel.com>
Closes#3698 from jerryshao/SPARK-4847 and squashes the following commits:
4741130 [jerryshao] Make later added extraStrategies effect when calling strategies
When I use Parquet File as a output file using ParquetOutputFormat#getDefaultWorkFile, the file name is not zero padded while RDD#saveAsText does zero padding.
Author: Sasaki Toru <sasakitoa@nttdata.co.jp>
Closes#3602 from sasakitoa/parquet-zeroPadding and squashes the following commits:
6b0e58f [Sasaki Toru] Merge branch 'master' of git://github.com/apache/spark into parquet-zeroPadding
20dc79d [Sasaki Toru] Fixed the name of Parquet File generated by AppendingParquetOutputFormat
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#3630 from jackylk/remove and squashes the following commits:
150e7e0 [Jacky Li] remove unnecessary import
Author: Michael Armbrust <michael@databricks.com>
Closes#3613 from marmbrus/parquetPartitionPruning and squashes the following commits:
4f138f8 [Michael Armbrust] Use catalyst for partition pruning in newParquet.
This is a very small fix that catches one specific exception and returns an empty table. #3441 will address this in a more principled way.
Author: Michael Armbrust <michael@databricks.com>
Closes#3586 from marmbrus/fixEmptyParquet and squashes the following commits:
2781d9f [Michael Armbrust] Handle empty lists for newParquet
04dd376 [Michael Armbrust] Avoid exception when reading empty parquet data through Hive
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
Author: baishuo <vc_java@hotmail.com>
Closes#3526 from baishuo/master-trycatch and squashes the following commits:
d446e14 [baishuo] correct the code style
b36bf96 [baishuo] correct the code style
ae0e447 [baishuo] add finally to avoid resource leak
A very small nit update.
Author: Reynold Xin <rxin@databricks.com>
Closes#3552 from rxin/license-header and squashes the following commits:
df8d1a4 [Reynold Xin] Indent license header properly for interfaces.scala.
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3516 from ravipesala/ddl_doc and squashes the following commits:
d101fdf [ravipesala] Style issues fixed
d2238cd [ravipesala] Corrected documentation
group tab is missing for scaladoc
Author: Jacky Li <jacky.likun@gmail.com>
Closes#3458 from jackylk/patch-7 and squashes the following commits:
0121a70 [Jacky Li] add @group tab in limit() and count()
Re-implement the Python broadcast using file:
1) serialize the python object using cPickle, write into disks.
2) Create a wrapper in JVM (for the dumped file), it read data from during serialization
3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors
4) During deserialization, writing the data into disk.
5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access.
It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor).
Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2):
name | 1.1 | 1.2 with this patch | improvement
---------|--------|---------|--------
python-broadcast-w-bytes | 25.20 | 9.33 | 170.13% |
python-broadcast-w-set | 4.13 | 4.50 | -8.35% |
Testing with 100 tasks (16 CPUs):
name | 1.1 | 1.2 with this patch | improvement
---------|--------|---------|--------
python-broadcast-w-bytes | 38.16 | 8.40 | 353.98%
python-broadcast-w-set | 23.29 | 9.59 | 142.80%
Author: Davies Liu <davies@databricks.com>
Closes#3417 from davies/pybroadcast and squashes the following commits:
50a58e0 [Davies Liu] address comments
b98de1d [Davies Liu] disable gc while unpickle
e5ee6b9 [Davies Liu] support large string
09303b8 [Davies Liu] read all data into memory
dde02dd [Davies Liu] improve performance of python broadcast
This PR is a workaround for SPARK-4479. Two changes are introduced: when merge sort is bypassed in `ExternalSorter`,
1. also bypass RDD elements buffering as buffering is the reason that `MutableRow` backed row objects must be copied, and
2. avoids defensive copies in `Exchange` operator
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Author: Cheng Lian <lian@databricks.com>
Closes#3422 from liancheng/avoids-defensive-copies and squashes the following commits:
591f2e9 [Cheng Lian] Passes all shuffle suites
0c3c91e [Cheng Lian] Fixes shuffle write metrics when merge sort is bypassed
ed5df3c [Cheng Lian] Fixes styling changes
f75089b [Cheng Lian] Avoids unnecessary defensive copies when sort based shuffle is on
Goals:
- Support for accessing parquet using SQL but not requiring Hive (thus allowing support of parquet tables with decimal columns)
- Support for folder based partitioning with automatic discovery of available partitions
- Caching of file metadata
See scaladoc of `ParquetRelation2` for more details.
Author: Michael Armbrust <michael@databricks.com>
Closes#3269 from marmbrus/newParquet and squashes the following commits:
1dd75f1 [Michael Armbrust] Pass all paths for FileInputFormat at once.
645768b [Michael Armbrust] Review comments.
abd8e2f [Michael Armbrust] Alternative implementation of parquet based on the datasources API.
938019e [Michael Armbrust] Add an experimental interface to data sources that exposes catalyst expressions.
e9d2641 [Michael Armbrust] logging / formatting improvements.
Sample code in the description of SchemaRDD.where is not correct
Author: Jacky Li <jacky.likun@gmail.com>
Closes#3344 from jackylk/patch-6 and squashes the following commits:
62cd126 [Jacky Li] [SQL] fix function description mistake
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.
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
This patch will bring support for broadcasting objects larger than 2G.
pickle, zlib, FrameSerializer and Array[Byte] all can not support objects larger than 2G, so this patch introduce LargeObjectSerializer to serialize broadcast objects, the object will be serialized and compressed into small chunks, it also change the type of Broadcast[Array[Byte]]] into Broadcast[Array[Array[Byte]]]].
Testing for support broadcast objects larger than 2G is slow and memory hungry, so this is tested manually, could be added into SparkPerf.
Author: Davies Liu <davies@databricks.com>
Author: Davies Liu <davies.liu@gmail.com>
Closes#2659 from davies/huge and squashes the following commits:
7b57a14 [Davies Liu] add more tests for broadcast
28acff9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
a2f6a02 [Davies Liu] bug fix
4820613 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
5875c73 [Davies Liu] address comments
10a349b [Davies Liu] address comments
0c33016 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
6182c8f [Davies Liu] Merge branch 'master' into huge
d94b68f [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
2514848 [Davies Liu] address comments
fda395b [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
1c2d928 [Davies Liu] fix scala style
091b107 [Davies Liu] broadcast objects larger than 2G
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.
The Spark ParquetRelation.scala code makes the assumption that the parquet.Log class has already been loaded. If ParquetRelation.enableLogForwarding executes prior to the parquet.Log class being loaded then the code in enableLogForwarding has no affect.
ParquetRelation.scala attempts to override the parquet logger but, at least currently (and if your application simply reads a parquet file before it does anything else with Parquet), the parquet.Log class hasn't been loaded yet. Therefore the code in ParquetRelation.enableLogForwarding has no affect. If you look at the code in parquet.Log there's a static initializer that needs to be called prior to enableLogForwarding or whatever enableLogForwarding does gets undone by this static initializer.
The "fix" would be to force the static initializer to get called in parquet.Log as part of enableForwardLogging.
Author: Jim Carroll <jim@dontcallme.com>
Closes#3271 from jimfcarroll/parquet-logging and squashes the following commits:
37bdff7 [Jim Carroll] Fix Spark's control of Parquet logging.
Since parquet library has been updated , we no longer need to filter the records returned from parquet library for null records , as now the library skips those :
from parquet-hadoop/src/main/java/parquet/hadoop/InternalParquetRecordReader.java
public boolean nextKeyValue() throws IOException, InterruptedException {
boolean recordFound = false;
while (!recordFound) {
// no more records left
if (current >= total)
{ return false; }
try {
checkRead();
currentValue = recordReader.read();
current ++;
if (recordReader.shouldSkipCurrentRecord())
{
// this record is being filtered via the filter2 package
if (DEBUG) LOG.debug("skipping record");
continue;
}
if (currentValue == null)
{
// only happens with FilteredRecordReader at end of block current = totalCountLoadedSoFar;
if (DEBUG) LOG.debug("filtered record reader reached end of block");
continue;
}
recordFound = true;
if (DEBUG) LOG.debug("read value: " + currentValue);
} catch (RuntimeException e)
{ throw new ParquetDecodingException(format("Can not read value at %d in block %d in file %s", current, currentBlock, file), e); }
}
return true;
}
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#3229 from saucam/remove_filter and squashes the following commits:
8909ae9 [Yash Datta] SPARK-4365: Remove unnecessary filter call on records returned from parquet library
If you profile the writing of a Parquet file, the single worst time consuming call inside of org.apache.spark.sql.parquet.MutableRowWriteSupport.write is actually in the scala.collection.AbstractSequence.size call. This is because the size call actually ends up COUNTING the elements in a scala.collection.LinearSeqOptimized.length ("optimized?").
This doesn't need to be done. "size" is called repeatedly where needed rather than called once at the top of the method and stored in a 'val'.
Author: Jim Carroll <jim@dontcallme.com>
Closes#3254 from jimfcarroll/parquet-perf and squashes the following commits:
30cc0b5 [Jim Carroll] Improve performance when writing Parquet files.
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.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3139 from chenghao-intel/comparison_test and squashes the following commits:
f5d7146 [Cheng Hao] avoid exception in printing the codegen enabled
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
it generates warnings at compile time marmbrus
Author: Xiangrui Meng <meng@databricks.com>
Closes#3192 from mengxr/dtc-decimal and squashes the following commits:
955e9fb [Xiangrui Meng] remove a decimal case branch that has no effect
andrewor14 Another try at SPARK-1209, to address https://github.com/apache/spark/pull/2814#issuecomment-61197619
I successfully tested with `mvn -Dhadoop.version=1.0.4 -DskipTests clean package; mvn -Dhadoop.version=1.0.4 test` I assume that is what failed Jenkins last time. I also tried `-Dhadoop.version1.2.1` and `-Phadoop-2.4 -Pyarn -Phive` for more coverage.
So this is why the class was put in `org.apache.hadoop` to begin with, I assume. One option is to leave this as-is for now and move it only when Hadoop 1.0.x support goes away.
This is the other option, which adds a call to force the constructor to be public at run-time. It's probably less surprising than putting Spark code in `org.apache.hadoop`, but, does involve reflection. A `SecurityManager` might forbid this, but it would forbid a lot of stuff Spark does. This would also only affect Hadoop 1.0.x it seems.
Author: Sean Owen <sowen@cloudera.com>
Closes#3048 from srowen/SPARK-1209 and squashes the following commits:
0d48f4b [Sean Owen] For Hadoop 1.0.x, make certain constructors public, which were public in later versions
466e179 [Sean Owen] Disable MIMA warnings resulting from moving the class -- this was also part of the PairRDDFunctions type hierarchy though?
eb61820 [Sean Owen] Move SparkHadoopMapRedUtil / SparkHadoopMapReduceUtil from org.apache.hadoop to org.apache.spark
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
marmbrus
Author: Xiangrui Meng <meng@databricks.com>
Closes#3125 from mengxr/SPARK-4262 and squashes the following commits:
307695e [Xiangrui Meng] add .schemaRDD to JavaSchemaRDD
This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1.
Author: Davies Liu <davies@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Josh Rosen <joshrosen@databricks.com>
Closes#2920 from davies/fix_autobatch and squashes the following commits:
e544ef9 [Davies Liu] revert unrelated change
6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
1d557fc [Davies Liu] fix tests
8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
76abdce [Davies Liu] clean up
53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
b4292ce [Davies Liu] fix bug in master
d79744c [Davies Liu] recover hive tests
be37ece [Davies Liu] refactor
eb3938d [Davies Liu] refactor serializer in scala
8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
