61b427d4b1
After the following patches, the main (Scala) API is now usable for Java users directly. https://github.com/apache/spark/pull/4056 https://github.com/apache/spark/pull/4054 https://github.com/apache/spark/pull/4049 https://github.com/apache/spark/pull/4030 https://github.com/apache/spark/pull/3965 https://github.com/apache/spark/pull/3958 Author: Reynold Xin <rxin@databricks.com> Closes #4065 from rxin/sql-java-api and squashes the following commits: b1fd860 [Reynold Xin] Fix Mima 6d86578 [Reynold Xin] Ok one more attempt in fixing Python... e8f1455 [Reynold Xin] Fix Python again... 3e53f91 [Reynold Xin] Fixed Python. 83735da [Reynold Xin] Fix BigDecimal test. e9f1de3 [Reynold Xin] Use scala BigDecimal. 500d2c4 [Reynold Xin] Fix Decimal. ba3bfa2 [Reynold Xin] Updated javadoc for RowFactory. c4ae1c5 [Reynold Xin] [SPARK-5193][SQL] Remove Spark SQL Java-specific API.
278 lines
16 KiB
Scala
278 lines
16 KiB
Scala
/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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import com.typesafe.tools.mima.core._
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/**
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* Additional excludes for checking of Spark's binary compatibility.
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*
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* The Mima build will automatically exclude @DeveloperApi and @Experimental classes. This acts
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* as an official audit of cases where we excluded other classes. Please use the narrowest
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* possible exclude here. MIMA will usually tell you what exclude to use, e.g.:
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*
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* ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.rdd.RDD.take")
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*
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* It is also possible to exclude Spark classes and packages. This should be used sparingly:
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*
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* MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
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*/
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object MimaExcludes {
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def excludes(version: String) =
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version match {
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case v if v.startsWith("1.3") =>
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Seq(
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MimaBuild.excludeSparkPackage("deploy"),
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// These are needed if checking against the sbt build, since they are part of
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// the maven-generated artifacts in the 1.2 build.
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MimaBuild.excludeSparkPackage("unused"),
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ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional")
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) ++ Seq(
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// SPARK-2321
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.SparkStageInfoImpl.this"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.SparkStageInfo.submissionTime")
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) ++ Seq(
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// SPARK-4614
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.linalg.Matrices.randn"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.linalg.Matrices.rand")
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) ++ Seq(
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// SPARK-3325
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.streaming.api.java.JavaDStreamLike.print"),
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// SPARK-2757
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ProblemFilters.exclude[IncompatibleResultTypeProblem](
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"org.apache.spark.streaming.flume.sink.SparkAvroCallbackHandler." +
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"removeAndGetProcessor")
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) ++ Seq(
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// SPARK-5123 (SparkSQL data type change) - alpha component only
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ProblemFilters.exclude[IncompatibleResultTypeProblem](
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"org.apache.spark.ml.feature.HashingTF.outputDataType"),
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ProblemFilters.exclude[IncompatibleResultTypeProblem](
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"org.apache.spark.ml.feature.Tokenizer.outputDataType"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.ml.feature.Tokenizer.validateInputType"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.ml.classification.LogisticRegressionModel.validateAndTransformSchema"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.ml.classification.LogisticRegression.validateAndTransformSchema")
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) ++ Seq(
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// SPARK-4014
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.TaskContext.taskAttemptId"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.TaskContext.attemptNumber")
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) ++ Seq(
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// SPARK-5166 Spark SQL API stabilization
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ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Transformer.transform"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Estimator.fit")
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)
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case v if v.startsWith("1.2") =>
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Seq(
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MimaBuild.excludeSparkPackage("deploy"),
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MimaBuild.excludeSparkPackage("graphx")
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) ++
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MimaBuild.excludeSparkClass("mllib.linalg.Matrix") ++
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MimaBuild.excludeSparkClass("mllib.linalg.Vector") ++
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Seq(
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ProblemFilters.exclude[IncompatibleTemplateDefProblem](
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"org.apache.spark.scheduler.TaskLocation"),
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// Added normL1 and normL2 to trait MultivariateStatisticalSummary
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL1"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL2"),
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// MapStatus should be private[spark]
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ProblemFilters.exclude[IncompatibleTemplateDefProblem](
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"org.apache.spark.scheduler.MapStatus"),
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ProblemFilters.exclude[MissingClassProblem](
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"org.apache.spark.network.netty.PathResolver"),
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ProblemFilters.exclude[MissingClassProblem](
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"org.apache.spark.network.netty.client.BlockClientListener"),
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// TaskContext was promoted to Abstract class
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ProblemFilters.exclude[AbstractClassProblem](
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"org.apache.spark.TaskContext"),
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ProblemFilters.exclude[IncompatibleTemplateDefProblem](
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"org.apache.spark.util.collection.SortDataFormat")
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) ++ Seq(
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// Adding new methods to the JavaRDDLike trait:
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDDLike.takeAsync"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDDLike.foreachPartitionAsync"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDDLike.countAsync"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDDLike.collectAsync")
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) ++ Seq(
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// SPARK-3822
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ProblemFilters.exclude[IncompatibleResultTypeProblem](
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"org.apache.spark.SparkContext.org$apache$spark$SparkContext$$createTaskScheduler")
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) ++ Seq(
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// SPARK-1209
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ProblemFilters.exclude[MissingClassProblem](
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"org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil"),
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ProblemFilters.exclude[MissingClassProblem](
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"org.apache.hadoop.mapred.SparkHadoopMapRedUtil"),
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ProblemFilters.exclude[MissingTypesProblem](
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"org.apache.spark.rdd.PairRDDFunctions")
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) ++ Seq(
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// SPARK-4062
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.streaming.kafka.KafkaReceiver#MessageHandler.this")
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)
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case v if v.startsWith("1.1") =>
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Seq(
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MimaBuild.excludeSparkPackage("deploy"),
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MimaBuild.excludeSparkPackage("graphx")
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) ++
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Seq(
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// Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
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ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
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// Should probably mark this as Experimental
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
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// We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
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// for countApproxDistinct* functions, which does not work in Java. We later removed
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// them, and use the following to tell Mima to not care about them.
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ProblemFilters.exclude[IncompatibleResultTypeProblem](
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"org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
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ProblemFilters.exclude[IncompatibleResultTypeProblem](
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"org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.storage.DiskStore.getValues"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.storage.MemoryStore.Entry")
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) ++
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Seq(
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// Serializer interface change. See SPARK-3045.
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ProblemFilters.exclude[IncompatibleTemplateDefProblem](
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"org.apache.spark.serializer.DeserializationStream"),
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ProblemFilters.exclude[IncompatibleTemplateDefProblem](
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"org.apache.spark.serializer.Serializer"),
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ProblemFilters.exclude[IncompatibleTemplateDefProblem](
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"org.apache.spark.serializer.SerializationStream"),
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ProblemFilters.exclude[IncompatibleTemplateDefProblem](
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"org.apache.spark.serializer.SerializerInstance")
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)++
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Seq(
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// Renamed putValues -> putArray + putIterator
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.storage.MemoryStore.putValues"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.storage.DiskStore.putValues"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.storage.TachyonStore.putValues")
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) ++
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Seq(
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.streaming.flume.FlumeReceiver.this"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.streaming.kafka.KafkaUtils.createStream"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.streaming.kafka.KafkaReceiver.this")
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) ++
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Seq( // Ignore some private methods in ALS.
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
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ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
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"org.apache.spark.mllib.recommendation.ALS.this"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
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) ++
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MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
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MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
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MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
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MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
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MimaBuild.excludeSparkClass("storage.Values") ++
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MimaBuild.excludeSparkClass("storage.Entry") ++
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MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
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// Class was missing "@DeveloperApi" annotation in 1.0.
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MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
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Seq(
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.mllib.tree.impurity.Gini.calculate"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem](
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"org.apache.spark.mllib.tree.impurity.Variance.calculate")
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) ++
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Seq( // Package-private classes removed in SPARK-2341
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ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser"),
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ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser$"),
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ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser"),
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ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser$"),
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ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser"),
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ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser$")
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) ++
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Seq( // package-private classes removed in MLlib
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.org$apache$spark$mllib$regression$GeneralizedLinearAlgorithm$$prependOne")
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) ++
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Seq( // new Vector methods in MLlib (binary compatible assuming users do not implement Vector)
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ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Vector.copy")
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) ++
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Seq( // synthetic methods generated in LabeledPoint
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ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.regression.LabeledPoint$"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.regression.LabeledPoint.apply"),
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ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.regression.LabeledPoint.toString")
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) ++
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Seq ( // Scala 2.11 compatibility fix
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ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.StreamingContext.<init>$default$2")
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)
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case v if v.startsWith("1.0") =>
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Seq(
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MimaBuild.excludeSparkPackage("api.java"),
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MimaBuild.excludeSparkPackage("mllib"),
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MimaBuild.excludeSparkPackage("streaming")
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) ++
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MimaBuild.excludeSparkClass("rdd.ClassTags") ++
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MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
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MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
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MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
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MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
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MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
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MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
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MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
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MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
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MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
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MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
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MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
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MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
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case _ => Seq()
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}
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}
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