Add simple resolver #4

marmbrus · 2015-06-18T20:50:49Z

JoshRosen · 2015-06-18T20:52:43Z

sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala

+   *                     the physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s.
+   */
+  protected def checkAnswer[A <: Product : TypeTag](


I guess this version isn't needed anymore / won't be used, so we might as well remove it.

Yeah, thats probably reasonable, though I'm fine either way since you already did a nice job of extracting all the real work into the SparkPlanTest object. I guess in most cases you probably will want to give names instead of using the defaults though.

JoshRosen · 2015-06-18T21:37:56Z

Thanks for the changes; I'll pull this into my PR and keep iterating.

Add simple resolver

…l operators This patch introduces `SparkPlanTest`, a base class for unit tests of SparkPlan physical operators. This is analogous to Spark SQL's existing `QueryTest`, which does something similar for end-to-end tests with actual queries. These helper methods provide nicer error output when tests fail and help developers to avoid writing lots of boilerplate in order to execute manually constructed physical plans. Author: Josh Rosen <joshrosen@databricks.com> Author: Josh Rosen <rosenville@gmail.com> Author: Michael Armbrust <michael@databricks.com> Closes apache#6885 from JoshRosen/spark-plan-test and squashes the following commits: f8ce275 [Josh Rosen] Fix some IntelliJ inspections and delete some dead code 84214be [Josh Rosen] Add an extra column which isn't part of the sort ae1896b [Josh Rosen] Provide implicits automatically a80f9b0 [Josh Rosen] Merge pull request #4 from marmbrus/pr/6885 d9ab1e4 [Michael Armbrust] Add simple resolver c60a44d [Josh Rosen] Manually bind references 996332a [Josh Rosen] Add types so that tests compile a46144a [Josh Rosen] WIP

…l operators This patch introduces `SparkPlanTest`, a base class for unit tests of SparkPlan physical operators. This is analogous to Spark SQL's existing `QueryTest`, which does something similar for end-to-end tests with actual queries. These helper methods provide nicer error output when tests fail and help developers to avoid writing lots of boilerplate in order to execute manually constructed physical plans. Author: Josh Rosen <joshrosen@databricks.com> Author: Josh Rosen <rosenville@gmail.com> Author: Michael Armbrust <michael@databricks.com> Closes apache#6885 from JoshRosen/spark-plan-test and squashes the following commits: f8ce275 [Josh Rosen] Fix some IntelliJ inspections and delete some dead code 84214be [Josh Rosen] Add an extra column which isn't part of the sort ae1896b [Josh Rosen] Provide implicits automatically a80f9b0 [Josh Rosen] Merge pull request #4 from marmbrus/pr/6885 d9ab1e4 [Michael Armbrust] Add simple resolver c60a44d [Josh Rosen] Manually bind references 996332a [Josh Rosen] Add types so that tests compile a46144a [Josh Rosen] WIP (cherry picked from commit 207a98c) Signed-off-by: Michael Armbrust <michael@databricks.com>

Documentation: * Added ml-guide.md, linked from mllib-guide.md * Updated mllib-guide.md with small section pointing to ml-guide.md Examples: * CrossValidatorExample * SimpleParamsExample * (I copied these + the SimpleTextClassificationPipeline example into the ml-guide.md) Bug fixes: * PipelineModel: did not use ParamMaps correctly * UnaryTransformer: issues with TypeTag serialization (Thanks to mengxr for that fix!) CC: mengxr shivaram etrain Documentation for Pipelines: I know the docs are not complete, but the goal is to have enough to let interested people get started using spark.ml and to add more docs once the package is more established/complete. Author: Joseph K. Bradley <joseph@databricks.com> Author: jkbradley <joseph.kurata.bradley@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes apache#3588 from jkbradley/ml-package-docs and squashes the following commits: d393b5c [Joseph K. Bradley] fixed bug in Pipeline (typo from last commit). updated examples for CV and Params for spark.ml c38469c [Joseph K. Bradley] Updated ml-guide with CV examples 99f88c2 [Joseph K. Bradley] Fixed bug in PipelineModel.transform* with usage of params. Updated CrossValidatorExample to use more training examples so it is less likely to get a 0-size fold. ea34dc6 [jkbradley] Merge pull request #4 from mengxr/ml-package-docs 3b83ec0 [Xiangrui Meng] replace TypeTag with explicit datatype 41ad9b1 [Joseph K. Bradley] Added examples for spark.ml: SimpleParamsExample + Java version, CrossValidatorExample + Java version. CrossValidatorExample not working yet. Added programming guide for spark.ml, but need to add CrossValidatorExample to it once CrossValidatorExample works. (cherry picked from commit 469a6e5) Signed-off-by: Xiangrui Meng <meng@databricks.com>

…leRow when nested data and partitioned table This PR adapts anselmevignon's apache#4697 to master and branch-1.3. Please refer to PR description of apache#4697 for details.  [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/4792)  Author: Cheng Lian <lian@databricks.com> Author: Cheng Lian <liancheng@users.noreply.github.com> Author: Yin Huai <yhuai@databricks.com> Closes apache#4792 from liancheng/spark-5775 and squashes the following commits: 538f506 [Cheng Lian] Addresses comments cee55cf [Cheng Lian] Merge pull request #4 from yhuai/spark-5775-yin b0b74fb [Yin Huai] Remove runtime pattern matching. ca6e038 [Cheng Lian] Fixes SPARK-5775 (cherry picked from commit e6003f0) Signed-off-by: Cheng Lian <lian@databricks.com>

…ve path. when i run cmd like that sc.addFile("../test.txt"), it did not work and throwed an exception: java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:../test.txt at org.apache.hadoop.fs.Path.initialize(Path.java:206) at org.apache.hadoop.fs.Path.<init>(Path.java:172) ........ ....... Caused by: java.net.URISyntaxException: Relative path in absolute URI: file:../test.txt at java.net.URI.checkPath(URI.java:1804) at java.net.URI.<init>(URI.java:752) at org.apache.hadoop.fs.Path.initialize(Path.java:203) Author: DoingDone9 <799203320@qq.com> Closes apache#4993 from DoingDone9/relativePath and squashes the following commits: ee375cd [DoingDone9] Update SparkContextSuite.scala d594e16 [DoingDone9] Update SparkContext.scala 0ff3fa8 [DoingDone9] test for add file dced8eb [DoingDone9] Update SparkContext.scala e4a13fe [DoingDone9] getCanonicalPath 161cae3 [DoingDone9] Merge pull request #4 from apache/master c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master (cherry picked from commit 00e730b) Signed-off-by: Sean Owen <sowen@cloudera.com>

…, because this will make some UDAF can not work. spark avoid old inteface of hive, then some udaf can not work like "org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage" Author: DoingDone9 <799203320@qq.com> Closes apache#5131 from DoingDone9/udaf and squashes the following commits: 9de08d0 [DoingDone9] Update HiveUdfSuite.scala 49c62dc [DoingDone9] Update hiveUdfs.scala 98b134f [DoingDone9] Merge pull request #5 from apache/master 161cae3 [DoingDone9] Merge pull request #4 from apache/master c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master (cherry picked from commit 968408b) Signed-off-by: Michael Armbrust <michael@databricks.com>

…l` in IF/CASEWHEN ## What changes were proposed in this pull request? Currently, `SimplifyConditionals` handles `true` and `false` to optimize branches. This PR improves `SimplifyConditionals` to take advantage of `null` conditions for `if` and `CaseWhen` expressions, too. **Before** ``` scala> sql("SELECT IF(null, 1, 0)").explain() == Physical Plan == WholeStageCodegen : +- Project [if (null) 1 else 0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4] : +- INPUT +- Scan OneRowRelation[] scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain() == Physical Plan == WholeStageCodegen : +- Project [CASE WHEN null THEN 1 ELSE 2 END AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#14] : +- INPUT +- Scan OneRowRelation[] ``` **After** ``` scala> sql("SELECT IF(null, 1, 0)").explain() == Physical Plan == WholeStageCodegen : +- Project [0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4] : +- INPUT +- Scan OneRowRelation[] scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain() == Physical Plan == WholeStageCodegen : +- Project [2 AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#4] : +- INPUT +- Scan OneRowRelation[] ``` **Hive** ``` hive> select if(null,1,2); OK 2 hive> select case when cast(null as boolean) then 1 else 2 end; OK 2 ``` ## How was this patch tested? Pass the Jenkins tests (including new extended test cases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes apache#12122 from dongjoon-hyun/SPARK-14338.

…pressions ## What changes were proposed in this pull request? This PR changes the direction of expression transformation in the DecimalPrecision rule. Previously, the expressions were transformed down, which led to incorrect result types when decimal expressions had other decimal expressions as their operands. The root cause of this issue was in visiting outer nodes before their children. Consider the example below: ``` val inputSchema = StructType(StructField("col", DecimalType(26, 6)) :: Nil) val sc = spark.sparkContext val rdd = sc.parallelize(1 to 2).map(_ => Row(BigDecimal(12))) val df = spark.createDataFrame(rdd, inputSchema) // Works correctly since no nested decimal expression is involved // Expected result type: (26, 6) * (26, 6) = (38, 12) df.select($"col" * $"col").explain(true) df.select($"col" * $"col").printSchema() // Gives a wrong result since there is a nested decimal expression that should be visited first // Expected result type: ((26, 6) * (26, 6)) * (26, 6) = (38, 12) * (26, 6) = (38, 18) df.select($"col" * $"col" * $"col").explain(true) df.select($"col" * $"col" * $"col").printSchema() ``` The example above gives the following output: ``` // Correct result without sub-expressions == Parsed Logical Plan == 'Project [('col * 'col) AS (col * col)#4] +- LogicalRDD [col#1] == Analyzed Logical Plan == (col * col): decimal(38,12) Project [CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS (col * col)#4] +- LogicalRDD [col#1] == Optimized Logical Plan == Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4] +- LogicalRDD [col#1] == Physical Plan == *Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4] +- Scan ExistingRDD[col#1] // Schema root |-- (col * col): decimal(38,12) (nullable = true) // Incorrect result with sub-expressions == Parsed Logical Plan == 'Project [(('col * 'col) * 'col) AS ((col * col) * col)apache#11] +- LogicalRDD [col#1] == Analyzed Logical Plan == ((col * col) * col): decimal(38,12) Project [CheckOverflow((promote_precision(cast(CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS ((col * col) * col)apache#11] +- LogicalRDD [col#1] == Optimized Logical Plan == Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)apache#11] +- LogicalRDD [col#1] == Physical Plan == *Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)apache#11] +- Scan ExistingRDD[col#1] // Schema root |-- ((col * col) * col): decimal(38,12) (nullable = true) ``` ## How was this patch tested? This PR was tested with available unit tests. Moreover, there are tests to cover previously failing scenarios. Author: aokolnychyi <anton.okolnychyi@sap.com> Closes apache#18583 from aokolnychyi/spark-21332.

…ver)QueryTestSuite ### What changes were proposed in this pull request? This PR adds 2 changes regarding exception handling in `SQLQueryTestSuite` and `ThriftServerQueryTestSuite` - fixes an expected output sorting issue in `ThriftServerQueryTestSuite` as if there is an exception then there is no need for sort - introduces common exception handling in those 2 suites with a new `handleExceptions` method ### Why are the changes needed? Currently `ThriftServerQueryTestSuite` passes on master, but it fails on one of my PRs (apache#23531) with this error (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/111651/testReport/org.apache.spark.sql.hive.thriftserver/ThriftServerQueryTestSuite/sql_3/): ``` org.scalatest.exceptions.TestFailedException: Expected " [Recursion level limit 100 reached but query has not exhausted, try increasing spark.sql.cte.recursion.level.limit org.apache.spark.SparkException] ", but got " [org.apache.spark.SparkException Recursion level limit 100 reached but query has not exhausted, try increasing spark.sql.cte.recursion.level.limit] " Result did not match for query #4 WITH RECURSIVE r(level) AS ( VALUES (0) UNION ALL SELECT level + 1 FROM r ) SELECT * FROM r ``` The unexpected reversed order of expected output (error message comes first, then the exception class) is due to this line: https://github.com/apache/spark/pull/26028/files#diff-b3ea3021602a88056e52bf83d8782de8L146. It should not sort the expected output if there was an error during execution. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Existing UTs. Closes apache#26028 from peter-toth/SPARK-29359-better-exception-handling. Authored-by: Peter Toth <peter.toth@gmail.com> Signed-off-by: Yuming Wang <wgyumg@gmail.com>

### What changes were proposed in this pull request? This PR introduces sasl retry count in RetryingBlockTransferor. ### Why are the changes needed? Previously a boolean variable, saslTimeoutSeen, was used. However, the boolean variable wouldn't cover the following scenario: 1. SaslTimeoutException 2. IOException 3. SaslTimeoutException 4. IOException Even though IOException at #2 is retried (resulting in increment of retryCount), the retryCount would be cleared at step #4. Since the intention of saslTimeoutSeen is to undo the increment due to retrying SaslTimeoutException, we should keep a counter for SaslTimeoutException retries and subtract the value of this counter from retryCount. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? New test is added, courtesy of Mridul. Closes apache#39611 from tedyu/sasl-cnt. Authored-by: Ted Yu <yuzhihong@gmail.com> Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>

…edExpression() ### What changes were proposed in this pull request? In `EquivalentExpressions.addExpr()`, add a guard `supportedExpression()` to make it consistent with `addExprTree()` and `getExprState()`. ### Why are the changes needed? This fixes a regression caused by apache#39010 which added the `supportedExpression()` to `addExprTree()` and `getExprState()` but not `addExpr()`. One example of a use case affected by the inconsistency is the `PhysicalAggregation` pattern in physical planning. There, it calls `addExpr()` to deduplicate the aggregate expressions, and then calls `getExprState()` to deduplicate the result expressions. Guarding inconsistently will cause the aggregate and result expressions go out of sync, eventually resulting in query execution error (or whole-stage codegen error). ### Does this PR introduce _any_ user-facing change? This fixes a regression affecting Spark 3.3.2+, where it may manifest as an error running aggregate operators with higher-order functions. Example running the SQL command: ```sql select max(transform(array(id), x -> x)), max(transform(array(id), x -> x)) from range(2) ``` example error message before the fix: ``` java.lang.IllegalStateException: Couldn't find max(transform(array(id#0L), lambdafunction(lambda x#2L, lambda x#2L, false)))#4 in [max(transform(array(id#0L), lambdafunction(lambda x#1L, lambda x#1L, false)))#3] ``` after the fix this error is gone. ### How was this patch tested? Added new test cases to `SubexpressionEliminationSuite` for the immediate issue, and to `DataFrameAggregateSuite` for an example of user-visible symptom. Closes apache#40473 from rednaxelafx/spark-42851. Authored-by: Kris Mok <kris.mok@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>

### What changes were proposed in this pull request? In the `Window` node, both `partitionSpec` and `orderSpec` must be orderable, but the current type check only verifies `orderSpec` is orderable. This can cause an error in later optimizing phases. Given a query: ``` with t as (select id, map(id, id) as m from range(0, 10)) select rank() over (partition by m order by id) from t ``` Before the PR, it fails with an `INTERNAL_ERROR`: ``` org.apache.spark.SparkException: [INTERNAL_ERROR] grouping/join/window partition keys cannot be map type. SQLSTATE: XX000 at org.apache.spark.SparkException$.internalError(SparkException.scala:92) at org.apache.spark.SparkException$.internalError(SparkException.scala:96) at org.apache.spark.sql.catalyst.optimizer.NormalizeFloatingNumbers$.needNormalize(NormalizeFloatingNumbers.scala:103) at org.apache.spark.sql.catalyst.optimizer.NormalizeFloatingNumbers$.org$apache$spark$sql$catalyst$optimizer$NormalizeFloatingNumbers$$needNormalize(NormalizeFloatingNumbers.scala:94) ... ``` After the PR, it fails with a `EXPRESSION_TYPE_IS_NOT_ORDERABLE`, which is expected: ``` org.apache.spark.sql.catalyst.ExtendedAnalysisException: [EXPRESSION_TYPE_IS_NOT_ORDERABLE] Column expression "m" cannot be sorted because its type "MAP<BIGINT, BIGINT>" is not orderable. SQLSTATE: 42822; line 2 pos 53; Project [RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4] +- Project [id#1L, m#0, RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4, RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4] +- Window [rank(id#1L) windowspecdefinition(m#0, id#1L ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4], [m#0], [id#1L ASC NULLS FIRST] +- Project [id#1L, m#0] +- SubqueryAlias t +- SubqueryAlias t +- Project [id#1L, map(id#1L, id#1L) AS m#0] +- Range (0, 10, step=1, splits=None) at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:52) ... ``` ### How was this patch tested? Unit test. Closes apache#45730 from chenhao-db/SPARK-47572. Authored-by: Chenhao Li <chenhao.li@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>

…edExpression() ### What changes were proposed in this pull request? In `EquivalentExpressions.addExpr()`, add a guard `supportedExpression()` to make it consistent with `addExprTree()` and `getExprState()`. ### Why are the changes needed? This fixes a regression caused by apache#39010 which added the `supportedExpression()` to `addExprTree()` and `getExprState()` but not `addExpr()`. One example of a use case affected by the inconsistency is the `PhysicalAggregation` pattern in physical planning. There, it calls `addExpr()` to deduplicate the aggregate expressions, and then calls `getExprState()` to deduplicate the result expressions. Guarding inconsistently will cause the aggregate and result expressions go out of sync, eventually resulting in query execution error (or whole-stage codegen error). ### Does this PR introduce _any_ user-facing change? This fixes a regression affecting Spark 3.3.2+, where it may manifest as an error running aggregate operators with higher-order functions. Example running the SQL command: ```sql select max(transform(array(id), x -> x)), max(transform(array(id), x -> x)) from range(2) ``` example error message before the fix: ``` java.lang.IllegalStateException: Couldn't find max(transform(array(id#0L), lambdafunction(lambda x#2L, lambda x#2L, false)))#4 in [max(transform(array(id#0L), lambdafunction(lambda x#1L, lambda x#1L, false)))#3] ``` after the fix this error is gone. ### How was this patch tested? Added new test cases to `SubexpressionEliminationSuite` for the immediate issue, and to `DataFrameAggregateSuite` for an example of user-visible symptom. Closes apache#40473 from rednaxelafx/spark-42851. Authored-by: Kris Mok <kris.mok@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> (cherry picked from commit ef0a76e) Signed-off-by: Wenchen Fan <wenchen@databricks.com>

… throw internal error ### What changes were proposed in this pull request? This PR fixes the error messages and classes when Python UDFs are used in higher order functions. ### Why are the changes needed? To show the proper user-facing exceptions with error classes. ### Does this PR introduce _any_ user-facing change? Yes, previously it threw internal error such as: ```python from pyspark.sql.functions import transform, udf, col, array spark.range(1).select(transform(array("id"), lambda x: udf(lambda y: y)(x))).collect() ``` Before: ``` py4j.protocol.Py4JJavaError: An error occurred while calling o74.collectToPython. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 15 in stage 0.0 failed 1 times, most recent failure: Lost task 15.0 in stage 0.0 (TID 15) (ip-192-168-123-103.ap-northeast-2.compute.internal executor driver): org.apache.spark.SparkException: [INTERNAL_ERROR] Cannot evaluate expression: <lambda>(lambda x_0#3L)#2 SQLSTATE: XX000 at org.apache.spark.SparkException$.internalError(SparkException.scala:92) at org.apache.spark.SparkException$.internalError(SparkException.scala:96) ``` After: ``` pyspark.errors.exceptions.captured.AnalysisException: [INVALID_LAMBDA_FUNCTION_CALL.UNEVALUABLE] Invalid lambda function call. Python UDFs should be used in a lambda function at a higher order function. However, "<lambda>(lambda x_0#3L)" was a Python UDF. SQLSTATE: 42K0D; Project [transform(array(id#0L), lambdafunction(<lambda>(lambda x_0#3L)#2, lambda x_0#3L, false)) AS transform(array(id), lambdafunction(<lambda>(lambda x_0#3L), namedlambdavariable()))#4] +- Range (0, 1, step=1, splits=Some(16)) ``` ### How was this patch tested? Unittest was added ### Was this patch authored or co-authored using generative AI tooling? No. Closes apache#47079 from HyukjinKwon/SPARK-48706. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Kent Yao <yao@apache.org>

Add simple resolver

d9ab1e4

JoshRosen reviewed Jun 18, 2015
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JoshRosen added a commit that referenced this pull request Jun 18, 2015

Merge pull request #4 from marmbrus/pr/6885

a80f9b0

Add simple resolver

JoshRosen merged commit a80f9b0 into JoshRosen:spark-plan-test Jun 18, 2015

marmbrus deleted the pr/6885 branch August 3, 2015 22:55

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Add simple resolver #4

Add simple resolver #4

marmbrus commented Jun 18, 2015

JoshRosen Jun 18, 2015

marmbrus Jun 18, 2015

JoshRosen commented Jun 18, 2015

Add simple resolver #4

Add simple resolver #4

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