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* [SPARK-12728][SQL] Integrates SQL generation with native viewCheng Lian2016-01-262-0/+23
| | | | | | | | | | | | | | | | | | | | | | | This PR is a follow-up of PR #10541. It integrates the newly introduced SQL generation feature with native view to make native view canonical. In this PR, a new SQL option `spark.sql.nativeView.canonical` is added. When this option and `spark.sql.nativeView` are both `true`, Spark SQL tries to handle `CREATE VIEW` DDL statements using SQL query strings generated from view definition logical plans. If we failed to map the plan to SQL, we fallback to the original native view approach. One important issue this PR fixes is that, now we can use CTE when defining a view. Originally, when native view is turned on, we wrap the view definition text with an extra `SELECT`. However, HiveQL parser doesn't allow CTE appearing as a subquery. Namely, something like this is disallowed: ```sql SELECT n FROM ( WITH w AS (SELECT 1 AS n) SELECT * FROM w ) v ``` This PR fixes this issue because the extra `SELECT` is no longer needed (also, CTE expressions are inlined as subqueries during analysis phase, thus there won't be CTE expressions in the generated SQL query string). Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #10733 from liancheng/spark-12728.integrate-sql-gen-with-native-view.
* [SPARK-12935][SQL] DataFrame API for Count-Min SketchCheng Lian2016-01-264-1/+149
| | | | | | | | This PR integrates Count-Min Sketch from spark-sketch into DataFrame. This version resorts to `RDD.aggregate` for building the sketch. A more performant UDAF version can be built in future follow-up PRs. Author: Cheng Lian <lian@databricks.com> Closes #10911 from liancheng/cms-df-api.
* [SPARK-12854][SQL] Implement complex types support in ColumnarBatchNong Li2016-01-268-69/+1562
| | | | | | | | | | | | | | | | | | | | | | | | | | This patch adds support for complex types for ColumnarBatch. ColumnarBatch supports structs and arrays. There is a simple mapping between the richer catalyst types to these two. Strings are treated as an array of bytes. ColumnarBatch will contain a column for each node of the schema. Non-complex schemas consists of just leaf nodes. Structs represent an internal node with one child for each field. Arrays are internal nodes with one child. Structs just contain nullability. Arrays contain offsets and lengths into the child array. This structure is able to handle arbitrary nesting. It has the key property that we maintain columnar throughout and that primitive types are only stored in the leaf nodes and contiguous across rows. For example, if the schema is ``` array<array<int>> ``` There are three columns in the schema. The internal nodes each have one children. The leaf node contains all the int data stored consecutively. As part of this, this patch adds append APIs in addition to the Put APIs (e.g. putLong(rowid, v) vs appendLong(v)). These APIs are necessary when the batch contains variable length elements. The vectors are not fixed length and will grow as necessary. This should make the usage a lot simpler for the writer. Author: Nong Li <nong@databricks.com> Closes #10820 from nongli/spark-12854.
* [SPARK-3369][CORE][STREAMING] Java mapPartitions Iterator->Iterable is ↵Sean Owen2016-01-262-16/+13
| | | | | | | | | | | | inconsistent with Scala's Iterator->Iterator Fix Java function API methods for flatMap and mapPartitions to require producing only an Iterator, not Iterable. Also fix DStream.flatMap to require a function producing TraversableOnce only, not Traversable. CC rxin pwendell for API change; tdas since it also touches streaming. Author: Sean Owen <sowen@cloudera.com> Closes #10413 from srowen/SPARK-3369.
* [SQL][MINOR] A few minor tweaks to CSV reader.Reynold Xin2016-01-262-14/+9
| | | | | | | | This pull request simply fixes a few minor coding style issues in csv, as I was reviewing the change post-hoc. Author: Reynold Xin <rxin@databricks.com> Closes #10919 from rxin/csv-minor.
* [SPARK-12879] [SQL] improve the unsafe row writing frameworkWenchen Fan2016-01-253-22/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | As we begin to use unsafe row writing framework(`BufferHolder` and `UnsafeRowWriter`) in more and more places(`UnsafeProjection`, `UnsafeRowParquetRecordReader`, `GenerateColumnAccessor`, etc.), we should add more doc to it and make it easier to use. This PR abstract the technique used in `UnsafeRowParquetRecordReader`: avoid unnecessary operatition as more as possible. For example, do not always point the row to the buffer at the end, we only need to update the size of row. If all fields are of primitive type, we can even save the row size updating. Then we can apply this technique to more places easily. a local benchmark shows `UnsafeProjection` is up to 1.7x faster after this PR: **old version** ``` Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate ------------------------------------------------------------------------------- single long 2616.04 102.61 1.00 X single nullable long 3032.54 88.52 0.86 X primitive types 9121.05 29.43 0.29 X nullable primitive types 12410.60 21.63 0.21 X ``` **new version** ``` Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate ------------------------------------------------------------------------------- single long 1533.34 175.07 1.00 X single nullable long 2306.73 116.37 0.66 X primitive types 8403.93 31.94 0.18 X nullable primitive types 12448.39 21.56 0.12 X ``` For single non-nullable long(the best case), we can have about 1.7x speed up. Even it's nullable, we can still have 1.3x speed up. For other cases, it's not such a boost as the saved operations only take a little proportion of the whole process. The benchmark code is included in this PR. Author: Wenchen Fan <wenchen@databricks.com> Closes #10809 from cloud-fan/unsafe-projection.
* [SPARK-12975][SQL] Throwing Exception when Bucketing Columns are part of ↵gatorsmile2016-01-251-0/+9
| | | | | | | | | | | | | | | | | | | | | | Partitioning Columns When users are using `partitionBy` and `bucketBy` at the same time, some bucketing columns might be part of partitioning columns. For example, ``` df.write .format(source) .partitionBy("i") .bucketBy(8, "i", "k") .saveAsTable("bucketed_table") ``` However, in the above case, adding column `i` into `bucketBy` is useless. It is just wasting extra CPU when reading or writing bucket tables. Thus, like Hive, we can issue an exception and let users do the change. Also added a test case for checking if the information of `sortBy` and `bucketBy` columns are correctly saved in the metastore table. Could you check if my understanding is correct? cloud-fan rxin marmbrus Thanks! Author: gatorsmile <gatorsmile@gmail.com> Closes #10891 from gatorsmile/commonKeysInPartitionByBucketBy.
* [SPARK-12901][SQL][HOT-FIX] Fix scala 2.11 compilation.Yin Huai2016-01-252-2/+2
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* [SPARK-12902] [SQL] visualization for generated operatorsDavies Liu2016-01-257-30/+98
| | | | | | | | | | | | | | This PR brings back visualization for generated operators, they looks like: ![sql](https://cloud.githubusercontent.com/assets/40902/12460920/0dc7956a-bf6b-11e5-9c3f-8389f452526e.png) ![stage](https://cloud.githubusercontent.com/assets/40902/12460923/11806ac4-bf6b-11e5-9c72-e84a62c5ea93.png) Note: SQL metrics are not supported right now, because they are very slow, will be supported once we have batch mode. Author: Davies Liu <davies@databricks.com> Closes #10828 from davies/viz_codegen.
* [SPARK-12901][SQL] Refactor options for JSON and CSV datasource (not case ↵hyukjinkwon2016-01-256-52/+40
| | | | | | | | | | | | | | | | | class and same format). https://issues.apache.org/jira/browse/SPARK-12901 This PR refactors the options in JSON and CSV datasources. In more details, 1. `JSONOptions` uses the same format as `CSVOptions`. 2. Not case classes. 3. `CSVRelation` that does not have to be serializable (it was `with Serializable` but I removed) Author: hyukjinkwon <gurwls223@gmail.com> Closes #10895 from HyukjinKwon/SPARK-12901.
* [SPARK-12624][PYSPARK] Checks row length when converting Java arrays to ↵Cheng Lian2016-01-241-1/+8
| | | | | | | | | | Python rows When actual row length doesn't conform to specified schema field length, we should give a better error message instead of throwing an unintuitive `ArrayOutOfBoundsException`. Author: Cheng Lian <lian@databricks.com> Closes #10886 from liancheng/spark-12624.
* [SPARK-12904][SQL] Strength reduction for integral and decimal literal ↵Reynold Xin2016-01-231-0/+1
| | | | | | | | | | comparisons This pull request implements strength reduction for comparing integral expressions and decimal literals, which is more common now because we switch to parsing fractional literals as decimal types (rather than doubles). I added the rules to the existing DecimalPrecision rule with some refactoring to simplify the control flow. I also moved DecimalPrecision rule into its own file due to the growing size. Author: Reynold Xin <rxin@databricks.com> Closes #10882 from rxin/SPARK-12904-1.
* [SPARK-12872][SQL] Support to specify the option for compression codec for ↵hyukjinkwon2016-01-225-29/+96
| | | | | | | | | | | | | | | JSON datasource https://issues.apache.org/jira/browse/SPARK-12872 This PR makes the JSON datasource can compress output by option instead of manually setting Hadoop configurations. For reflecting codec by names, it is similar with https://github.com/apache/spark/pull/10805. As `CSVCompressionCodecs` can be shared with other datasources, it became a separate class to share as `CompressionCodecs`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #10858 from HyukjinKwon/SPARK-12872.
* [SPARK-12959][SQL] Writing Bucketed Data with Disabled Bucketing in SQLConfgatorsmile2016-01-222-2/+2
| | | | | | | | | | | | When users turn off bucketing in SQLConf, we should issue some messages to tell users these operations will be converted to normal way. Also added a test case for this scenario and fixed the helper function. Do you think this PR is helpful when using bucket tables? cloud-fan Thank you! Author: gatorsmile <gatorsmile@gmail.com> Closes #10870 from gatorsmile/bucketTableWritingTestcases.
* [SPARK-12747][SQL] Use correct type name for Postgres JDBC's real arrayLiang-Chi Hsieh2016-01-212-0/+4
| | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-12747 Postgres JDBC driver uses "FLOAT4" or "FLOAT8" not "real". Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #10695 from viirya/fix-postgres-jdbc.
* [SPARK-12797] [SQL] Generated TungstenAggregate (without grouping keys)Davies Liu2016-01-205-12/+111
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | As discussed in #10786, the generated TungstenAggregate does not support imperative functions. For a query ``` sqlContext.range(10).filter("id > 1").groupBy().count() ``` The generated code will looks like: ``` /* 032 */ if (!initAgg0) { /* 033 */ initAgg0 = true; /* 034 */ /* 035 */ // initialize aggregation buffer /* 037 */ long bufValue2 = 0L; /* 038 */ /* 039 */ /* 040 */ // initialize Range /* 041 */ if (!range_initRange5) { /* 042 */ range_initRange5 = true; ... /* 071 */ } /* 072 */ /* 073 */ while (!range_overflow8 && range_number7 < range_partitionEnd6) { /* 074 */ long range_value9 = range_number7; /* 075 */ range_number7 += 1L; /* 076 */ if (range_number7 < range_value9 ^ 1L < 0) { /* 077 */ range_overflow8 = true; /* 078 */ } /* 079 */ /* 085 */ boolean primitive11 = false; /* 086 */ primitive11 = range_value9 > 1L; /* 087 */ if (!false && primitive11) { /* 092 */ // do aggregate and update aggregation buffer /* 099 */ long primitive17 = -1L; /* 100 */ primitive17 = bufValue2 + 1L; /* 101 */ bufValue2 = primitive17; /* 105 */ } /* 107 */ } /* 109 */ /* 110 */ // output the result /* 112 */ bufferHolder25.reset(); /* 114 */ rowWriter26.initialize(bufferHolder25, 1); /* 118 */ rowWriter26.write(0, bufValue2); /* 120 */ result24.pointTo(bufferHolder25.buffer, bufferHolder25.totalSize()); /* 121 */ currentRow = result24; /* 122 */ return; /* 124 */ } /* 125 */ ``` cc nongli Author: Davies Liu <davies@databricks.com> Closes #10840 from davies/gen_agg.
* [SPARK-12848][SQL] Change parsed decimal literal datatype from Double to DecimalHerman van Hovell2016-01-203-28/+38
| | | | | | | | | | | | | | The current parser turns a decimal literal, for example ```12.1```, into a Double. The problem with this approach is that we convert an exact literal into a non-exact ```Double```. The PR changes this behavior, a Decimal literal is now converted into an extact ```BigDecimal```. The behavior for scientific decimals, for example ```12.1e01```, is unchanged. This will be converted into a Double. This PR replaces the ```BigDecimal``` literal by a ```Double``` literal, because the ```BigDecimal``` is the default now. You can use the double literal by appending a 'D' to the value, for instance: ```3.141527D``` cc davies rxin Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #10796 from hvanhovell/SPARK-12848.
* [SPARK-12616][SQL] Making Logical Operator `Union` Support Arbitrary Number ↵gatorsmile2016-01-207-26/+33
| | | | | | | | | | | | | | of Children The existing `Union` logical operator only supports two children. Thus, adding a new logical operator `Unions` which can have arbitrary number of children to replace the existing one. `Union` logical plan is a binary node. However, a typical use case for union is to union a very large number of input sources (DataFrames, RDDs, or files). It is not uncommon to union hundreds of thousands of files. In this case, our optimizer can become very slow due to the large number of logical unions. We should change the Union logical plan to support an arbitrary number of children, and add a single rule in the optimizer to collapse all adjacent `Unions` into a single `Unions`. Note that this problem doesn't exist in physical plan, because the physical `Unions` already supports arbitrary number of children. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #10577 from gatorsmile/unionAllMultiChildren.
* [SPARK-12881] [SQL] subexpress elimination in mutable projectionDavies Liu2016-01-205-6/+22
| | | | | | Author: Davies Liu <davies@databricks.com> Closes #10814 from davies/mutable_subexpr.
* [SPARK-12871][SQL] Support to specify the option for compression codec.hyukjinkwon2016-01-193-2/+70
| | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-12871 This PR added an option to support to specify compression codec. This adds the option `codec` as an alias `compression` as filed in [SPARK-12668 ](https://issues.apache.org/jira/browse/SPARK-12668). Note that I did not add configurations for Hadoop 1.x as this `CsvRelation` is using Hadoop 2.x API and I guess it is going to drop Hadoop 1.x support. Author: hyukjinkwon <gurwls223@gmail.com> Closes #10805 from HyukjinKwon/SPARK-12420.
* [SPARK-12560][SQL] SqlTestUtils.stripSparkFilter needs to copy utf8stringsImran Rashid2016-01-191-1/+1
| | | | | | | | | | See https://issues.apache.org/jira/browse/SPARK-12560 This isn't causing any problems currently because the tests for string predicate pushdown are currently disabled. I ran into this while trying to turn them back on with a different version of parquet. Figure it was good to fix now in any case. Author: Imran Rashid <irashid@cloudera.com> Closes #10510 from squito/SPARK-12560.
* [SPARK-12867][SQL] Nullability of Intersect can be strictergatorsmile2016-01-191-0/+21
| | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-12867 When intersecting one nullable column with one non-nullable column, the result will not contain any null. Thus, we can make nullability of `intersect` stricter. liancheng Could you please check if the code changes are appropriate? Also added test cases to verify the results. Thanks! Author: gatorsmile <gatorsmile@gmail.com> Closes #10812 from gatorsmile/nullabilityIntersect.
* [SPARK-12887] Do not expose var's in TaskMetricsAndrew Or2016-01-192-3/+1
| | | | | | | | | | | | | | | | This is a step in implementing SPARK-10620, which migrates TaskMetrics to accumulators. TaskMetrics has a bunch of var's, some are fully public, some are `private[spark]`. This is bad coding style that makes it easy to accidentally overwrite previously set metrics. This has happened a few times in the past and caused bugs that were difficult to debug. Instead, we should have get-or-create semantics, which are more readily understandable. This makes sense in the case of TaskMetrics because these are just aggregated metrics that we want to collect throughout the task, so it doesn't matter who's incrementing them. Parent PR: #10717 Author: Andrew Or <andrew@databricks.com> Author: Josh Rosen <joshrosen@databricks.com> Author: andrewor14 <andrew@databricks.com> Closes #10815 from andrewor14/get-or-create-metrics.
* [SPARK-12870][SQL] better format bucket id in file nameWenchen Fan2016-01-193-6/+12
| | | | | | | | for normal parquet file without bucket, it's file name ends with a jobUUID which maybe all numbers and mistakeny regarded as bucket id. This PR improves the format of bucket id in file name by using a different seperator, `_`, so that the regex is more robust. Author: Wenchen Fan <wenchen@databricks.com> Closes #10799 from cloud-fan/fix-bucket.
* [SQL][MINOR] Fix one little mismatched comment according to the codes in ↵proflin2016-01-191-1/+1
| | | | | | | | interface.scala Author: proflin <proflin.me@gmail.com> Closes #10824 from proflin/master.
* [SPARK-12668][SQL] Providing aliases for CSV options to be similar to Pandas ↵hyukjinkwon2016-01-183-6/+20
| | | | | | | | | | | | | | | and R https://issues.apache.org/jira/browse/SPARK-12668 Spark CSV datasource has been being merged (filed in [SPARK-12420](https://issues.apache.org/jira/browse/SPARK-12420)). This is a quicky PR that simply renames several CSV options to similar Pandas and R. - Alias for delimiter ­-> sep - charset -­> encoding Author: hyukjinkwon <gurwls223@gmail.com> Closes #10800 from HyukjinKwon/SPARK-12668.
* [HOT][BUILD] Changed the import ordergatorsmile2016-01-182-2/+2
| | | | | | | | | | | | | This PR is to fix the master's build break. The following tests failed due to the import order issues in the master. https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49651/consoleFull https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49652/consoleFull https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49653/consoleFull Author: gatorsmile <gatorsmile@gmail.com> Closes #10823 from gatorsmile/importOrder.
* [SPARK-12700] [SQL] embed condition into SMJ and BroadcastHashJoinDavies Liu2016-01-186-72/+96
| | | | | | | | | | | | Currently SortMergeJoin and BroadcastHashJoin do not support condition, the need a followed Filter for that, the result projection to generate UnsafeRow could be very expensive if they generate lots of rows and could be filtered mostly by condition. This PR brings the support of condition for SortMergeJoin and BroadcastHashJoin, just like other outer joins do. This could improve the performance of Q72 by 7x (from 120s to 16.5s). Author: Davies Liu <davies@databricks.com> Closes #10653 from davies/filter_join.
* [SPARK-12889][SQL] Rename ParserDialect -> ParserInterface.Reynold Xin2016-01-183-5/+5
| | | | | | | | Based on discussions in #10801, I'm submitting a pull request to rename ParserDialect to ParserInterface. Author: Reynold Xin <rxin@databricks.com> Closes #10817 from rxin/SPARK-12889.
* [SPARK-12841][SQL] fix cast in filterWenchen Fan2016-01-182-7/+17
| | | | | | | | In SPARK-10743 we wrap cast with `UnresolvedAlias` to give `Cast` a better alias if possible. However, for cases like `filter`, the `UnresolvedAlias` can't be resolved and actually we don't need a better alias for this case. This PR move the cast wrapping logic to `Column.named` so that we will only do it when we need a alias name. Author: Wenchen Fan <wenchen@databricks.com> Closes #10781 from cloud-fan/bug.
* [SPARK-12855][SQL] Remove parser dialect developer APIReynold Xin2016-01-186-82/+8
| | | | | | | | This pull request removes the public developer parser API for external parsers. Given everything a parser depends on (e.g. logical plans and expressions) are internal and not stable, external parsers will break with every release of Spark. It is a bad idea to create the illusion that Spark actually supports pluggable parsers. In addition, this also reduces incentives for 3rd party projects to contribute parse improvements back to Spark. Author: Reynold Xin <rxin@databricks.com> Closes #10801 from rxin/SPARK-12855.
* [SPARK-12796] [SQL] Whole stage codegenDavies Liu2016-01-1618-29/+604
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This is the initial work for whole stage codegen, it support Projection/Filter/Range, we will continue work on this to support more physical operators. A micro benchmark show that a query with range, filter and projection could be 3X faster then before. It's turned on by default. For a tree that have at least two chained plans, a WholeStageCodegen will be inserted into it, for example, the following plan ``` Limit 10 +- Project [(id#5L + 1) AS (id + 1)#6L] +- Filter ((id#5L & 1) = 1) +- Range 0, 1, 4, 10, [id#5L] ``` will be translated into ``` Limit 10 +- WholeStageCodegen +- Project [(id#1L + 1) AS (id + 1)#2L] +- Filter ((id#1L & 1) = 1) +- Range 0, 1, 4, 10, [id#1L] ``` Here is the call graph to generate Java source for A and B (A support codegen, but B does not): ``` * WholeStageCodegen Plan A FakeInput Plan B * ========================================================================= * * -> execute() * | * doExecute() --------> produce() * | * doProduce() -------> produce() * | * doProduce() ---> execute() * | * consume() * doConsume() ------------| * | * doConsume() <----- consume() ``` A SparkPlan that support codegen need to implement doProduce() and doConsume(): ``` def doProduce(ctx: CodegenContext): (RDD[InternalRow], String) def doConsume(ctx: CodegenContext, child: SparkPlan, input: Seq[ExprCode]): String ``` Author: Davies Liu <davies@databricks.com> Closes #10735 from davies/whole2.
* [SPARK-12840] [SQL] Support passing arbitrary objects (not just expressions) ↵Davies Liu2016-01-151-2/+2
| | | | | | | | | | into code generated classes This is a refactor to support codegen for aggregation and broadcast join. Author: Davies Liu <davies@databricks.com> Closes #10777 from davies/rename2.
* [SPARK-12644][SQL] Update parquet reader to be vectorized.Nong Li2016-01-1511-53/+622
| | | | | | | | | | | | | | | | | This inlines a few of the Parquet decoders and adds vectorized APIs to support decoding in batch. There are a few particulars in the Parquet encodings that make this much more efficient. In particular, RLE encodings are very well suited for batch decoding. The Parquet 2.0 encodings are also very suited for this. This is a work in progress and does not affect the current execution. In subsequent patches, we will support more encodings and types before enabling this. Simple benchmarks indicate this can decode single ints about > 3x faster. Author: Nong Li <nong@databricks.com> Author: Nong <nongli@gmail.com> Closes #10593 from nongli/spark-12644.
* [SPARK-12649][SQL] support reading bucketed tableWenchen Fan2016-01-1513-19/+111
| | | | | | | | | | | | | | | This PR adds the support to read bucketed tables, and correctly populate `outputPartitioning`, so that we can avoid shuffle for some cases. TODO(follow-up PRs): * bucket pruning * avoid shuffle for bucketed table join when use any super-set of the bucketing key. (we should re-visit it after https://issues.apache.org/jira/browse/SPARK-12704 is fixed) * recognize hive bucketed table Author: Wenchen Fan <wenchen@databricks.com> Closes #10604 from cloud-fan/bucket-read.
* [SPARK-12833][HOT-FIX] Reset the locale after we set it.Yin Huai2016-01-151-4/+9
| | | | | | Author: Yin Huai <yhuai@databricks.com> Closes #10778 from yhuai/resetLocale.
* [SPARK-12575][SQL] Grammar parity with existing SQL parserHerman van Hovell2016-01-1511-74/+92
| | | | | | | | | | | | | | | | In this PR the new CatalystQl parser stack reaches grammar parity with the old Parser-Combinator based SQL Parser. This PR also replaces all uses of the old Parser, and removes it from the code base. Although the existing Hive and SQL parser dialects were mostly the same, some kinks had to be worked out: - The SQL Parser allowed syntax like ```APPROXIMATE(0.01) COUNT(DISTINCT a)```. In order to make this work we needed to hardcode approximate operators in the parser, or we would have to create an approximate expression. ```APPROXIMATE_COUNT_DISTINCT(a, 0.01)``` would also do the job and is much easier to maintain. So, this PR **removes** this keyword. - The old SQL Parser supports ```LIMIT``` clauses in nested queries. This is **not supported** anymore. See https://github.com/apache/spark/pull/10689 for the rationale for this. - Hive has a charset name char set literal combination it supports, for instance the following expression ```_ISO-8859-1 0x4341464562616265``` would yield this string: ```CAFEbabe```. Hive will only allow charset names to start with an underscore. This is quite annoying in spark because as soon as you use a tuple names will start with an underscore. In this PR we **remove** this feature from the parser. It would be quite easy to implement such a feature as an Expression later on. - Hive and the SQL Parser treat decimal literals differently. Hive will turn any decimal into a ```Double``` whereas the SQL Parser would convert a non-scientific decimal into a ```BigDecimal```, and would turn a scientific decimal into a Double. We follow Hive's behavior here. The new parser supports a big decimal literal, for instance: ```81923801.42BD```, which can be used when a big decimal is needed. cc rxin viirya marmbrus yhuai cloud-fan Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #10745 from hvanhovell/SPARK-12575-2.
* Fix typoJulien Baley2016-01-151-3/+3
| | | | | | | | disvoered => discovered Author: Julien Baley <julien.baley@gmail.com> Closes #10773 from julienbaley/patch-1.
* [SPARK-12833][HOT-FIX] Fix scala 2.11 compilation.Yin Huai2016-01-151-3/+3
| | | | | | | | Seems https://github.com/apache/spark/commit/5f83c6991c95616ecbc2878f8860c69b2826f56c breaks scala 2.11 compilation. Author: Yin Huai <yhuai@databricks.com> Closes #10774 from yhuai/fixScala211Compile.
* [SPARK-12833][SQL] Initial import of spark-csvHossein2016-01-1521-7/+1610
| | | | | | | | | | | CSV is the most common data format in the "small data" world. It is often the first format people want to try when they see Spark on a single node. Having to rely on a 3rd party component for this leads to poor user experience for new users. This PR merges the popular spark-csv data source package (https://github.com/databricks/spark-csv) with SparkSQL. This is a first PR to bring the functionality to spark 2.0 master. We will complete items outlines in the design document (see JIRA attachment) in follow up pull requests. Author: Hossein <hossein@databricks.com> Author: Reynold Xin <rxin@databricks.com> Closes #10766 from rxin/csv.
* [SPARK-12813][SQL] Eliminate serialization for back to back operationsMichael Armbrust2016-01-147-153/+210
| | | | | | | | | | | | | | | The goal of this PR is to eliminate unnecessary translations when there are back-to-back `MapPartitions` operations. In order to achieve this I also made the following simplifications: - Operators no longer have hold encoders, instead they have only the expressions that they need. The benefits here are twofold: the expressions are visible to transformations so go through the normal resolution/binding process. now that they are visible we can change them on a case by case basis. - Operators no longer have type parameters. Since the engine is responsible for its own type checking, having the types visible to the complier was an unnecessary complication. We still leverage the scala compiler in the companion factory when constructing a new operator, but after this the types are discarded. Deferred to a follow up PR: - Remove as much of the resolution/binding from Dataset/GroupedDataset as possible. We should still eagerly check resolution and throw an error though in the case of mismatches for an `as` operation. - Eliminate serializations in more cases by adding more cases to `EliminateSerialization` Author: Michael Armbrust <michael@databricks.com> Closes #10747 from marmbrus/encoderExpressions.
* [SPARK-12756][SQL] use hash expression in ExchangeWenchen Fan2016-01-137-41/+55
| | | | | | | | | | This PR makes bucketing and exchange share one common hash algorithm, so that we can guarantee the data distribution is same between shuffle and bucketed data source, which enables us to only shuffle one side when join a bucketed table and a normal one. This PR also fixes the tests that are broken by the new hash behaviour in shuffle. Author: Wenchen Fan <wenchen@databricks.com> Closes #10703 from cloud-fan/use-hash-expr-in-shuffle.
* [SPARK-12791][SQL] Simplify CaseWhen by breaking "branches" into ↵Reynold Xin2016-01-132-10/+11
| | | | | | | | | | | | "conditions" and "values" This pull request rewrites CaseWhen expression to break the single, monolithic "branches" field into a sequence of tuples (Seq[(condition, value)]) and an explicit optional elseValue field. Prior to this pull request, each even position in "branches" represents the condition for each branch, and each odd position represents the value for each branch. The use of them have been pretty confusing with a lot sliding windows or grouped(2) calls. Author: Reynold Xin <rxin@databricks.com> Closes #10734 from rxin/simplify-case.
* [SPARK-9297] [SQL] Add covar_pop and covar_sampLiang-Chi Hsieh2016-01-131-0/+40
| | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-9297 Add two aggregation functions: covar_pop and covar_samp. Author: Liang-Chi Hsieh <viirya@gmail.com> Author: Liang-Chi Hsieh <viirya@appier.com> Closes #10029 from viirya/covar-funcs.
* [SPARK-12692][BUILD][SQL] Scala style: Fix the style violation (Space before ↵Kousuke Saruta2016-01-125-16/+16
| | | | | | | | | | | ",") Fix the style violation (space before , and :). This PR is a followup for #10643 and rework of #10685 . Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #10732 from sarutak/SPARK-12692-followup-sql.
* [SPARK-12785][SQL] Add ColumnarBatch, an in memory columnar format for ↵Nong Li2016-01-126-0/+1463
| | | | | | | | | | | | | | | | | | | | | | | | | execution. There are many potential benefits of having an efficient in memory columnar format as an alternate to UnsafeRow. This patch introduces ColumnarBatch/ColumnarVector which starts this effort. The remaining implementation can be done as follow up patches. As stated in the in the JIRA, there are useful external components that operate on memory in a simple columnar format. ColumnarBatch would serve that purpose and could server as a zero-serialization/zero-copy exchange for this use case. This patch supports running the underlying data either on heap or off heap. On heap runs a bit faster but we would need offheap for zero-copy exchanges. Currently, this mode is hidden behind one interface (ColumnVector). This differs from Parquet or the existing columnar cache because this is *not* intended to be used as a storage format. The focus is entirely on CPU efficiency as we expect to only have 1 of these batches in memory per task. The layout of the values is just dense arrays of the value type. Author: Nong Li <nong@databricks.com> Author: Nong <nongli@gmail.com> Closes #10628 from nongli/spark-12635.
* [SPARK-12724] SQL generation support for persisted data source tablesCheng Lian2016-01-128-31/+27
| | | | | | | | This PR implements SQL generation support for persisted data source tables. A new field `metastoreTableIdentifier: Option[TableIdentifier]` is added to `LogicalRelation`. When a `LogicalRelation` representing a persisted data source relation is created, this field holds the database name and table name of the relation. Author: Cheng Lian <lian@databricks.com> Closes #10712 from liancheng/spark-12724-datasources-sql-gen.
* Revert "[SPARK-12692][BUILD][SQL] Scala style: Fix the style violation ↵Reynold Xin2016-01-1230-106/+101
| | | | | | (Space before "," or ":")" This reverts commit 8cfa218f4f1b05f4d076ec15dd0a033ad3e4500d.
* [SPARK-9843][SQL] Make catalyst optimizer pass pluggable at runtimeRobert Kruszewski2016-01-124-2/+46
| | | | | | | | Let me know whether you'd like to see it in other place Author: Robert Kruszewski <robertk@palantir.com> Closes #10210 from robert3005/feature/pluggable-optimizer.
* [SPARK-12692][BUILD][SQL] Scala style: Fix the style violation (Space before ↵Kousuke Saruta2016-01-1230-101/+106
| | | | | | | | | | | "," or ":") Fix the style violation (space before , and :). This PR is a followup for #10643. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #10718 from sarutak/SPARK-12692-followup-sql.
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