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spark-instrumented-optimizer/project/MimaExcludes.scala
Michael Armbrust cd1d4110cf [SPARK-6908] [SQL] Use isolated Hive client 
This PR switches Spark SQL's Hive support to use the isolated hive client interface introduced by #5851, instead of directly interacting with the client.  By using this isolated client we can now allow users to dynamically configure the version of Hive that they are connecting to by setting `spark.sql.hive.metastore.version` without the need recompile.  This also greatly reduces the surface area for our interaction with the hive libraries, hopefully making it easier to support other versions in the future.

Jars for the desired hive version can be configured using `spark.sql.hive.metastore.jars`, which accepts the following options:
 - a colon-separated list of jar files or directories for hive and hadoop.
 - `builtin` - attempt to discover the jars that were used to load Spark SQL and use those. This
            option is only valid when using the execution version of Hive.
 - `maven` - download the correct version of hive on demand from maven.

By default, `builtin` is used for Hive 13.

This PR also removes the test step for building against Hive 12, as this will no longer be required to talk to Hive 12 metastores.  However, the full removal of the Shim is deferred until a later PR.

Remaining TODOs:
 - Remove the Hive Shims and inline code for Hive 13.
 - Several HiveCompatibility tests are not yet passing.
  - `nullformatCTAS` - As detailed below, we now are handling CTAS parsing ourselves instead of hacking into the Hive semantic analyzer.  However, we currently only handle the common cases and not things like CTAS where the null format is specified.
  - `combine1` now leaks state about compression somehow, breaking all subsequent tests.  As such we currently add it to the blacklist
  - `part_inherit_tbl_props` and `part_inherit_tbl_props_with_star` do not work anymore.  We are correctly propagating the information
  - "load_dyn_part14.*" - These tests pass when run on their own, but fail when run with all other tests.  It seems our `RESET` mechanism may not be as robust as it used to be?

Other required changes:
 -  `CreateTableAsSelect` no longer carries parts of the HiveQL AST with it through the query execution pipeline.  Instead, we parse CTAS during the HiveQL conversion and construct a `HiveTable`.  The full parsing here is not yet complete as detailed above in the remaining TODOs.  Since the operator is Hive specific, it is moved to the hive package.
 - `Command` is simplified to be a trait that simply acts as a marker for a LogicalPlan that should be eagerly evaluated.

Author: Michael Armbrust <michael@databricks.com>

Closes #5876 from marmbrus/useIsolatedClient and squashes the following commits:

258d000 [Michael Armbrust] really really correct path handling
e56fd4a [Michael Armbrust] getAbsolutePath
5a259f5 [Michael Armbrust] fix typos
81bb366 [Michael Armbrust] comments from vanzin
5f3945e [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
4b5cd41 [Michael Armbrust] yin's comments
f5de7de [Michael Armbrust] cleanup
11e9c72 [Michael Armbrust] better coverage in versions suite
7e8f010 [Michael Armbrust] better error messages and jar handling
e7b3941 [Michael Armbrust] more permisive checking for function registration
da91ba7 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
5fe5894 [Michael Armbrust] fix serialization suite
81711c4 [Michael Armbrust] Initial support for running without maven
1d8ae44 [Michael Armbrust] fix final tests?
1c50813 [Michael Armbrust] more comments
a3bee70 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
a6f5df1 [Michael Armbrust] style
ab07f7e [Michael Armbrust] WIP
4d8bf02 [Michael Armbrust] Remove hive 12 compilation
8843a25 [Michael Armbrust] [SPARK-6908] [SQL] Use isolated Hive client
2015-05-07 19:36:24 -07:00

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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
import com.typesafe.tools.mima.core._
import com.typesafe.tools.mima.core.ProblemFilters._
/**
* Additional excludes for checking of Spark's binary compatibility.
*
* The Mima build will automatically exclude @DeveloperApi and @Experimental classes. This acts
* as an official audit of cases where we excluded other classes. Please use the narrowest
* possible exclude here. MIMA will usually tell you what exclude to use, e.g.:
*
* ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.rdd.RDD.take")
*
* It is also possible to exclude Spark classes and packages. This should be used sparingly:
*
* MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
*/
object MimaExcludes {
def excludes(version: String) =
version match {
case v if v.startsWith("1.4") =>
Seq(
MimaBuild.excludeSparkPackage("deploy"),
MimaBuild.excludeSparkPackage("ml"),
// SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff"),
// These are needed if checking against the sbt build, since they are part of
// the maven-generated artifacts in 1.3.
excludePackage("org.spark-project.jetty"),
MimaBuild.excludeSparkPackage("unused"),
ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional"),
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.rdd.JdbcRDD.compute"),
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"),
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"),
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorActor")
) ++ Seq(
// SPARK-4655 - Making Stage an Abstract class broke binary compatility even though
// the stage class is defined as private[spark]
ProblemFilters.exclude[AbstractClassProblem]("org.apache.spark.scheduler.Stage")
) ++ Seq(
// SPARK-6510 Add a Graph#minus method acting as Set#difference
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus")
) ++ Seq(
// SPARK-6492 Fix deadlock in SparkContext.stop()
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" +
"apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK")
)++ Seq(
// SPARK-6693 add tostring with max lines and width for matrix
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Matrix.toString")
)++ Seq(
// SPARK-6703 Add getOrCreate method to SparkContext
ProblemFilters.exclude[IncompatibleResultTypeProblem]
("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext")
)++ Seq(
// SPARK-7090 Introduce LDAOptimizer to LDA to further improve extensibility
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.mllib.clustering.LDA$EMOptimizer")
) ++ Seq(
// SPARK-6756 add toSparse, toDense, numActives, numNonzeros, and compressed to Vector
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Vector.compressed"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Vector.toDense"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Vector.numNonzeros"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Vector.toSparse"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Vector.numActives")
) ++ Seq(
// Execution should never be included as its always internal.
MimaBuild.excludeSparkPackage("sql.execution"),
// This `protected[sql]` method was removed in 1.3.1
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.sql.SQLContext.checkAnalysis"),
// This `private[sql]` class was removed in 1.4.0:
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.sql.execution.AddExchange"),
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.sql.execution.AddExchange$"),
// These test support classes were moved out of src/main and into src/test:
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.sql.parquet.ParquetTestData"),
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.sql.parquet.ParquetTestData$"),
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.sql.parquet.TestGroupWriteSupport")
)
case v if v.startsWith("1.3") =>
Seq(
MimaBuild.excludeSparkPackage("deploy"),
MimaBuild.excludeSparkPackage("ml"),
// These are needed if checking against the sbt build, since they are part of
// the maven-generated artifacts in the 1.2 build.
MimaBuild.excludeSparkPackage("unused"),
ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional")
) ++ Seq(
// SPARK-2321
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.SparkStageInfoImpl.this"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.SparkStageInfo.submissionTime")
) ++ Seq(
// SPARK-4614
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Matrices.randn"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Matrices.rand")
) ++ Seq(
// SPARK-5321
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.SparseMatrix.transposeMultiply"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Matrix.transpose"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.DenseMatrix.transposeMultiply"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Matrix." +
"org$apache$spark$mllib$linalg$Matrix$_setter_$isTransposed_="),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Matrix.isTransposed"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.linalg.Matrix.foreachActive")
) ++ Seq(
// SPARK-5540
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.solveLeastSquares"),
// SPARK-5536
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateBlock")
) ++ Seq(
// SPARK-3325
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.streaming.api.java.JavaDStreamLike.print"),
// SPARK-2757
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.streaming.flume.sink.SparkAvroCallbackHandler." +
"removeAndGetProcessor")
) ++ Seq(
// SPARK-5123 (SparkSQL data type change) - alpha component only
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.ml.feature.HashingTF.outputDataType"),
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.ml.feature.Tokenizer.outputDataType"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.ml.feature.Tokenizer.validateInputType"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.ml.classification.LogisticRegressionModel.validateAndTransformSchema"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.ml.classification.LogisticRegression.validateAndTransformSchema")
) ++ Seq(
// SPARK-4014
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.TaskContext.taskAttemptId"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.TaskContext.attemptNumber")
) ++ Seq(
// SPARK-5166 Spark SQL API stabilization
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Transformer.transform"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Estimator.fit"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Transformer.transform"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Pipeline.fit"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.PipelineModel.transform"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Estimator.fit"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Evaluator.evaluate"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Evaluator.evaluate"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidator.fit"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidatorModel.transform"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScaler.fit"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScalerModel.transform"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionModel.transform"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegression.fit"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.evaluation.BinaryClassificationEvaluator.evaluate")
) ++ Seq(
// SPARK-5270
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.isEmpty")
) ++ Seq(
// SPARK-5430
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.treeReduce"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.treeAggregate")
) ++ Seq(
// SPARK-5297 Java FileStream do not work with custom key/values
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.streaming.api.java.JavaStreamingContext.fileStream")
) ++ Seq(
// SPARK-5315 Spark Streaming Java API returns Scala DStream
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.streaming.api.java.JavaDStreamLike.reduceByWindow")
) ++ Seq(
// SPARK-5461 Graph should have isCheckpointed, getCheckpointFiles methods
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.graphx.Graph.getCheckpointFiles"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.graphx.Graph.isCheckpointed")
) ++ Seq(
// SPARK-4789 Standardize ML Prediction APIs
ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.linalg.VectorUDT"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.serialize"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.sqlType")
) ++ Seq(
// SPARK-5814
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$wrapDoubleArray"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$fillFullMatrix"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$iterations"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$computeYtY"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeLinkRDDs"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$alpha"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$randomFactor"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeInLinkBlock"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$dspr"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$lambda"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$implicitPrefs"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$rank")
) ++ Seq(
// SPARK-4682
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.RealClock"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.Clock"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.TestClock")
) ++ Seq(
// SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff")
)
case v if v.startsWith("1.2") =>
Seq(
MimaBuild.excludeSparkPackage("deploy"),
MimaBuild.excludeSparkPackage("graphx")
) ++
MimaBuild.excludeSparkClass("mllib.linalg.Matrix") ++
MimaBuild.excludeSparkClass("mllib.linalg.Vector") ++
Seq(
ProblemFilters.exclude[IncompatibleTemplateDefProblem](
"org.apache.spark.scheduler.TaskLocation"),
// Added normL1 and normL2 to trait MultivariateStatisticalSummary
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL1"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL2"),
// MapStatus should be private[spark]
ProblemFilters.exclude[IncompatibleTemplateDefProblem](
"org.apache.spark.scheduler.MapStatus"),
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.network.netty.PathResolver"),
ProblemFilters.exclude[MissingClassProblem](
"org.apache.spark.network.netty.client.BlockClientListener"),
// TaskContext was promoted to Abstract class
ProblemFilters.exclude[AbstractClassProblem](
"org.apache.spark.TaskContext"),
ProblemFilters.exclude[IncompatibleTemplateDefProblem](
"org.apache.spark.util.collection.SortDataFormat")
) ++ Seq(
// Adding new methods to the JavaRDDLike trait:
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.takeAsync"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.foreachPartitionAsync"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.countAsync"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.collectAsync")
) ++ Seq(
// SPARK-3822
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.SparkContext.org$apache$spark$SparkContext$$createTaskScheduler")
) ++ Seq(
// SPARK-1209
ProblemFilters.exclude[MissingClassProblem](
"org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil"),
ProblemFilters.exclude[MissingClassProblem](
"org.apache.hadoop.mapred.SparkHadoopMapRedUtil"),
ProblemFilters.exclude[MissingTypesProblem](
"org.apache.spark.rdd.PairRDDFunctions")
) ++ Seq(
// SPARK-4062
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.streaming.kafka.KafkaReceiver#MessageHandler.this")
)
case v if v.startsWith("1.1") =>
Seq(
MimaBuild.excludeSparkPackage("deploy"),
MimaBuild.excludeSparkPackage("graphx")
) ++
Seq(
// Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
// Should probably mark this as Experimental
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
// We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
// for countApproxDistinct* functions, which does not work in Java. We later removed
// them, and use the following to tell Mima to not care about them.
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.storage.DiskStore.getValues"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.storage.MemoryStore.Entry")
) ++
Seq(
// Serializer interface change. See SPARK-3045.
ProblemFilters.exclude[IncompatibleTemplateDefProblem](
"org.apache.spark.serializer.DeserializationStream"),
ProblemFilters.exclude[IncompatibleTemplateDefProblem](
"org.apache.spark.serializer.Serializer"),
ProblemFilters.exclude[IncompatibleTemplateDefProblem](
"org.apache.spark.serializer.SerializationStream"),
ProblemFilters.exclude[IncompatibleTemplateDefProblem](
"org.apache.spark.serializer.SerializerInstance")
)++
Seq(
// Renamed putValues -> putArray + putIterator
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.storage.MemoryStore.putValues"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.storage.DiskStore.putValues"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.storage.TachyonStore.putValues")
) ++
Seq(
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.streaming.flume.FlumeReceiver.this"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.streaming.kafka.KafkaUtils.createStream"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.streaming.kafka.KafkaReceiver.this")
) ++
Seq( // Ignore some private methods in ALS.
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
"org.apache.spark.mllib.recommendation.ALS.this"),
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
) ++
MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
MimaBuild.excludeSparkClass("storage.Values") ++
MimaBuild.excludeSparkClass("storage.Entry") ++
MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
// Class was missing "@DeveloperApi" annotation in 1.0.
MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
Seq(
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.mllib.tree.impurity.Gini.calculate"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
ProblemFilters.exclude[IncompatibleMethTypeProblem](
"org.apache.spark.mllib.tree.impurity.Variance.calculate")
) ++
Seq( // Package-private classes removed in SPARK-2341
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser$"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser$"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser$")
) ++
Seq( // package-private classes removed in MLlib
ProblemFilters.exclude[MissingMethodProblem](
"org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.org$apache$spark$mllib$regression$GeneralizedLinearAlgorithm$$prependOne")
) ++
Seq( // new Vector methods in MLlib (binary compatible assuming users do not implement Vector)
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Vector.copy")
) ++
Seq( // synthetic methods generated in LabeledPoint
ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.regression.LabeledPoint$"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.regression.LabeledPoint.apply"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.regression.LabeledPoint.toString")
) ++
Seq ( // Scala 2.11 compatibility fix
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.StreamingContext.<init>$default$2")
)
case v if v.startsWith("1.0") =>
Seq(
MimaBuild.excludeSparkPackage("api.java"),
MimaBuild.excludeSparkPackage("mllib"),
MimaBuild.excludeSparkPackage("streaming")
) ++
MimaBuild.excludeSparkClass("rdd.ClassTags") ++
MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
case _ => Seq()
}
}
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