org.apache.spark

Logging

trait Logging extends AnyRef

:: DeveloperApi :: Utility trait for classes that want to log data. Creates a SLF4J logger for the class and allows logging messages at different levels using methods that only evaluate parameters lazily if the log level is enabled.

NOTE: DO NOT USE this class outside of Spark. It is intended as an internal utility. This will likely be changed or removed in future releases.

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@DeveloperApi()
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Known Subclasses
ALS, ActorHelper, AddFile, AddJar, Aggregate, AnalyzeTable, AsyncRDDActions, Bagel, BatchPythonEvaluation, BinaryClassificationMetrics, Broadcast, BroadcastHashJoin, BroadcastNestedLoopJoin, CacheTableCommand, CartesianProduct, CoGroupedRDD, ConstantInputDStream, CreateTableAsSelect, CrossValidator, CrossValidatorModel, DStream, DataValidators, DecisionTree, DecisionTree, DescribeCommand, DescribeHiveTableCommand, Distinct, DoubleRDDFunctions, DropTable, EdgeRDD, EdgeRDDImpl, Estimator, EvaluatePython, Except, Exchange, ExecutedCommand, ExistingRdd, ExplainCommand, ExternalAppendOnlyMap, ExternalSort, Filter, GeneralizedLinearAlgorithm, Generate, GeneratedAggregate, GradientBoostedTrees, GradientBoostedTrees, GradientDescent, GradientDescent, GraphLoader, HadoopRDD, HashOuterJoin, HashingTF, HistoryServer, HistoryServer, HiveContext, HiveTableScan, InputDStream, InputFormatInfo, InsertIntoHiveTable, InsertIntoParquetTable, Intersect, JdbcRDD, JobLogger, JobProgressListener, KMeans, KryoSerializer, LBFGS, LBFGS, LassoWithSGD, LeftSemiJoinBNL, LeftSemiJoinHash, Limit, LinearRegressionWithSGD, LocalHiveContext, LogicalRDD, LogisticRegression, LogisticRegressionModel, LogisticRegressionWithLBFGS, LogisticRegressionWithSGD, MatrixFactorizationModel, Model, NaiveBayes, NativeCommand, NettyBlockRpcServer, NettyBlockTransferService, NewHadoopRDD, NioBlockTransferService, Node, OrderedRDDFunctions, OutputFaker, PageRank, PairRDDFunctions, ParquetRelation2, ParquetTableScan, PartitionPruningRDD, PhysicalRDD, Pipeline, PipelineModel, PipelineStage, Pregel, Project, RDD, RandomForest, RankingMetrics, ReceiverInputDStream, RegressionMetrics, RidgeRegressionWithSGD, RowMatrix, RunnableCommand, SQLContext, SVMWithSGD, Sample, SchemaRDD, ScriptTransformation, SequenceFileRDDFunctions, SetCommand, ShimFileSinkDesc, ShuffledHashJoin, ShuffledRDD, Sort, SparkConf, SparkContext, SparkContext, SparkCuratorUtil, SparkEnv, SparkEnv, SparkHadoopUtil, SparkLogicalPlan, SparkPlan, StandardScaler, StandardScaler, StandardScalerModel, StatsReportListener, StreamingContext, StreamingContext, StreamingKMeans, StreamingKMeansModel, StreamingLinearAlgorithm, StreamingLinearRegressionWithSGD, TakeOrdered, TestHive, TestHiveContext, TestSQLContext, Tokenizer, Transformer, UncacheTableCommand, Union, UnionRDD, VertexRDD, VertexRDDImpl, Word2Vec, ZooKeeperPersistenceEngine
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  13. final def isInstanceOf[T0]: Boolean

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  14. def isTraceEnabled(): Boolean

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  15. def log: Logger

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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  17. def logDebug(msg: ⇒ String): Unit

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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logError(msg: ⇒ String): Unit

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  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logInfo(msg: ⇒ String): Unit

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  22. def logName: String

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  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  24. def logTrace(msg: ⇒ String): Unit

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  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logWarning(msg: ⇒ String): Unit

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