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[BUG] java.net.ConnectException: Connection refused (Connection refused) #2310

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3 of 19 tasks
user673 opened this issue Nov 11, 2024 · 2 comments
Open
3 of 19 tasks

[BUG] java.net.ConnectException: Connection refused (Connection refused) #2310

user673 opened this issue Nov 11, 2024 · 2 comments
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bug triage

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@user673
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user673 commented Nov 11, 2024

SynapseML version

com.microsoft.azure:synapseml_2.12:1.0.5

System information

  • Python 3.10.12
  • Apache Spark 3.5.0 (14.3 LTS databricks cluster)
  • Databricks

Describe the problem

When training an LgbmRegressor on my dataset, I encountered an error. Here are some approaches I found to address it:

  1. Using repartition(1) or a small dataset with a single partition: This approach can resolve the issue in smaller datasets, but it is unsuitable for larger ones. Placing all data in one partition can increase computational time significantly, making it inefficient.

  2. Utilizing a single-node cluster on Databricks: This method is similar to the first, as it limits data handling to a single node, which again isn’t practical for larger datasets.

  3. Setting a custom maxCatThreshold parameter of LgbmRegressor: This involves defining maxCatThreshold as half the maximum unique values across all categorical columns. While this solution works well in many cases, the error can still occur in some situations.

  4. Adjusting maxCatThreshold to the full maximum unique values on categorical columns, with additional repartitioning: This approach involves setting maxCatThreshold to the maximum count of unique values in categorical columns and applying a repartition on the training data to align with the original number of partitions, as shown here:

    train_data = train_data.repartition(train_data.rdd.getNumPartitions())

adjusting maxCatThreshold shown here:

   unique_counts = [
      (col, df.select(col).distinct().count())
      for col in old_categorical_column_names
  ]
    # Find the column with the maximum number of unique values
    max_unique_col = max(unique_counts, key=lambda x: x[1])
    value_maxCatThreshold = max_unique_col[1]

This solution has been the most reliable so far, though I’m uncertain why it works and am concerned that the issue may reappear.

Code to reproduce issue

ʼʼʼ
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
import random
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml import Pipeline
from synapse.ml.lightgbm import LightGBMRegressor
from pyspark.sql.types import StringType

# Initialize Spark Session
spark = SparkSession.builder \
    .appName("DatasetGeneration") \
    .getOrCreate()

# Number of rows and columns
n_rows = 3000000  # 3 million rows

# Create column lists
all_columns = [f'col_{i}' for i in range(1, 27)]
categorical_columns = [f'col_{i}' for i in range(1, 11)]
numerical_columns = [f'col_{i}' for i in range(11, 27)]

# Define categories for categorical columns (Custom defined numbers of unique values in cat columns to match my original dataset)
categories1 = ['A']
categories2 = ['A', 'B']
categories3 = ['A', 'B']
categories4 = ['A', 'B', 'C', 'D', 'E']
categories5 = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W']
categories6 = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R']
categories7 = ['A']
categories8 = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X']
categories9 = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'A1', 'B1', 'C1', 'D1', 'E1', 'F1']
cat5 = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
categories10 = [elem + str(i) for elem in cat5 for i in range(10)] + ['B101']

category_dict = {
    'col_1': categories1,
    'col_2': categories2,
    'col_3': categories3,
    'col_4': categories4,
    'col_5': categories5,
    'col_6': categories6,
    'col_7': categories7,
    'col_8': categories8,
    'col_9': categories9,
    'col_10': categories10
}

# Function to return a random category for a given column
def random_category(column_name):
    return random.choice(category_dict[column_name])

# Generate random categorical values for each column using UDFs
for col in categorical_columns:
    spark.udf.register(f"{col}_udf", lambda: random_category(col), StringType())

# Create DataFrame with random values
df = spark.range(n_rows).select(
    *[F.rand().alias(col) for col in numerical_columns],  # Numerical columns with random floats
    *[F.expr(f"{col}_udf()").alias(col) for col in categorical_columns]  # Apply UDF for categorical columns
)

# Transforming dataset to modeling
old_categorical_column_names = categorical_columns = [f'col_{i}' for i in range(1, 11)]
#col_12 is target
numerical_features = [f'col_{i}' for i in range(11, 27)]
numerical_features = list(set(numerical_features) - set(['col_12']))
new_categorical_column_names = [
    categorical_column_name + "_indexed"
    for categorical_column_name in old_categorical_column_names
]

category_indexer = StringIndexer(
    inputCols=old_categorical_column_names,
    outputCols=new_categorical_column_names,
    handleInvalid="keep",
)

feature_assembler = VectorAssembler(
    inputCols=new_categorical_column_names + numerical_features,
    outputCol="features",
    handleInvalid="keep",
)

transforming_pipeline = Pipeline(stages=[category_indexer, feature_assembler])


fitted_transforming_pipeline = transforming_pipeline.fit(df)
train_data_transformed = fitted_transforming_pipeline.transform(df)

# Model training
model = LightGBMRegressor(
            # Handle categorical features
            categoricalSlotNames=new_categorical_column_names,
            featuresCol="features",
            labelCol='col_12',
            predictionCol='pred',
            seed=0,
            verbosity=3,
            )
model.fit(train_data_transformed)

Other info / logs

Py4JJavaError: An error occurred while calling o820.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 856.0 failed 4 times, most recent failure: Lost task 5.3 in stage 856.0 (TID 1708) (10.139.64.15 executor 3): java.net.ConnectException: Connection refused (Connection refused)
at java.net.PlainSocketImpl.socketConnect(Native Method)
at java.net.AbstractPlainSocketImpl.doConnect(AbstractPlainSocketImpl.java:350)
at java.net.AbstractPlainSocketImpl.connectToAddress(AbstractPlainSocketImpl.java:206)
at java.net.AbstractPlainSocketImpl.connect(AbstractPlainSocketImpl.java:188)
at java.net.SocksSocketImpl.connect(SocksSocketImpl.java:392)
at java.net.Socket.connect(Socket.java:613)
at java.net.Socket.connect(Socket.java:561)
at java.net.Socket.(Socket.java:457)
at java.net.Socket.(Socket.java:234)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.getNetworkTopologyInfoFromDriver(NetworkManager.scala:133)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.$anonfun$getGlobalNetworkInfo$2(NetworkManager.scala:120)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:24)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.$anonfun$getGlobalNetworkInfo$1(NetworkManager.scala:115)
at com.microsoft.azure.synapse.ml.core.env.StreamUtilities$.using(StreamUtilities.scala:28)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.getGlobalNetworkInfo(NetworkManager.scala:111)
at com.microsoft.azure.synapse.ml.lightgbm.BasePartitionTask.initialize(BasePartitionTask.scala:197)
at com.microsoft.azure.synapse.ml.lightgbm.BasePartitionTask.mapPartitionTask(BasePartitionTask.scala:132)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.$anonfun$executePartitionTasks$1(LightGBMBase.scala:615)
at org.apache.spark.sql.execution.MapPartitionsExec.$anonfun$doExecute$3(objects.scala:226)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:933)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:933)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.$anonfun$computeOrReadCheckpoint$1(RDD.scala:409)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:406)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:373)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.$anonfun$computeOrReadCheckpoint$1(RDD.scala:409)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:406)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:373)
at org.apache.spark.scheduler.ResultTask.$anonfun$runTask$3(ResultTask.scala:82)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.ResultTask.$anonfun$runTask$1(ResultTask.scala:82)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:201)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:186)
at org.apache.spark.scheduler.Task.$anonfun$run$5(Task.scala:151)
at com.[REDACTED]bricks.unity.EmptyHandle$.runWithAndClose(UCSHandle.scala:134)
at org.apache.spark.scheduler.Task.$anonfun$run$1(Task.scala:145)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$9(Executor.scala:958)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:105)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:961)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:853)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:750)

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:3872)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:3794)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:3781)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:3781)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1659)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1644)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1644)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:4118)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:4030)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:4018)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:54)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$runJob$1(DAGScheduler.scala:1321)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at com.[REDACTED]bricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:94)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:1309)
at org.apache.spark.SparkContext.runJobInternal(SparkContext.scala:3070)
at org.apache.spark.sql.execution.collect.Collector.$anonfun$runSparkJobs$1(Collector.scala:303)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at com.[REDACTED]bricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:94)
at org.apache.spark.sql.execution.collect.Collector.runSparkJobs(Collector.scala:299)
at org.apache.spark.sql.execution.collect.Collector.$anonfun$collect$1(Collector.scala:384)
at com.[REDACTED]bricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:94)
at org.apache.spark.sql.execution.collect.Collector.collect(Collector.scala:381)
at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:122)
at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:131)
at org.apache.spark.sql.execution.qrc.InternalRowFormat$.collect(cachedSparkResults.scala:94)
at org.apache.spark.sql.execution.qrc.InternalRowFormat$.collect(cachedSparkResults.scala:90)
at org.apache.spark.sql.execution.qrc.InternalRowFormat$.collect(cachedSparkResults.scala:78)
at org.apache.spark.sql.execution.qrc.ResultCacheManager.$anonfun$computeResult$1(ResultCacheManager.scala:549)
at com.[REDACTED]bricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:94)
at org.apache.spark.sql.execution.qrc.ResultCacheManager.collectResult$1(ResultCacheManager.scala:540)
at org.apache.spark.sql.execution.qrc.ResultCacheManager.computeResult(ResultCacheManager.scala:557)
at org.apache.spark.sql.execution.qrc.ResultCacheManager.$anonfun$getOrComputeResultInternal$1(ResultCacheManager.scala:400)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.execution.qrc.ResultCacheManager.getOrComputeResultInternal(ResultCacheManager.scala:400)
at org.apache.spark.sql.execution.qrc.ResultCacheManager.getOrComputeResult(ResultCacheManager.scala:318)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeCollectResult$1(SparkPlan.scala:558)
at com.[REDACTED]bricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:94)
at org.apache.spark.sql.execution.SparkPlan.executeCollectResult(SparkPlan.scala:555)
at org.apache.spark.sql.Dataset.collectResult(Dataset.scala:3780)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:4736)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:3747)
at org.apache.spark.sql.Dataset.$anonfun$withAction$3(Dataset.scala:4727)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:1103)
at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:4725)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$9(SQLExecution.scala:392)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:700)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$1(SQLExecution.scala:277)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:1175)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId0(SQLExecution.scala:164)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:637)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:4725)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:3747)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.executePartitionTasks(LightGBMBase.scala:623)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.executeTraining(LightGBMBase.scala:598)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.trainOneDataBatch(LightGBMBase.scala:446)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.$anonfun$train$2(LightGBMBase.scala:62)
at com.microsoft.azure.synapse.ml.logging.SynapseMLLogging.logVerb(SynapseMLLogging.scala:163)
at com.microsoft.azure.synapse.ml.logging.SynapseMLLogging.logVerb$(SynapseMLLogging.scala:160)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMRegressor.logVerb(LightGBMRegressor.scala:39)
at com.microsoft.azure.synapse.ml.logging.SynapseMLLogging.logFit(SynapseMLLogging.scala:153)
at com.microsoft.azure.synapse.ml.logging.SynapseMLLogging.logFit$(SynapseMLLogging.scala:152)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMRegressor.logFit(LightGBMRegressor.scala:39)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.train(LightGBMBase.scala:64)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.train$(LightGBMBase.scala:36)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMRegressor.train(LightGBMRegressor.scala:39)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMRegressor.train(LightGBMRegressor.scala:39)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:114)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:397)
at py4j.Gateway.invoke(Gateway.java:306)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:199)
at py4j.ClientServerConnection.run(ClientServerConnection.java:119)
at java.lang.Thread.run(Thread.java:750)
Caused by: java.net.ConnectException: Connection refused (Connection refused)
at java.net.PlainSocketImpl.socketConnect(Native Method)
at java.net.AbstractPlainSocketImpl.doConnect(AbstractPlainSocketImpl.java:350)
at java.net.AbstractPlainSocketImpl.connectToAddress(AbstractPlainSocketImpl.java:206)
at java.net.AbstractPlainSocketImpl.connect(AbstractPlainSocketImpl.java:188)
at java.net.SocksSocketImpl.connect(SocksSocketImpl.java:392)
at java.net.Socket.connect(Socket.java:613)
at java.net.Socket.connect(Socket.java:561)
at java.net.Socket.(Socket.java:457)
at java.net.Socket.(Socket.java:234)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.getNetworkTopologyInfoFromDriver(NetworkManager.scala:133)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.$anonfun$getGlobalNetworkInfo$2(NetworkManager.scala:120)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:24)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.core.utils.FaultToleranceUtils$.retryWithTimeout(FaultToleranceUtils.scala:29)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.$anonfun$getGlobalNetworkInfo$1(NetworkManager.scala:115)
at com.microsoft.azure.synapse.ml.core.env.StreamUtilities$.using(StreamUtilities.scala:28)
at com.microsoft.azure.synapse.ml.lightgbm.NetworkManager$.getGlobalNetworkInfo(NetworkManager.scala:111)
at com.microsoft.azure.synapse.ml.lightgbm.BasePartitionTask.initialize(BasePartitionTask.scala:197)
at com.microsoft.azure.synapse.ml.lightgbm.BasePartitionTask.mapPartitionTask(BasePartitionTask.scala:132)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMBase.$anonfun$executePartitionTasks$1(LightGBMBase.scala:615)
at org.apache.spark.sql.execution.MapPartitionsExec.$anonfun$doExecute$3(objects.scala:226)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:933)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:933)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.$anonfun$computeOrReadCheckpoint$1(RDD.scala:409)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:406)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:373)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.$anonfun$computeOrReadCheckpoint$1(RDD.scala:409)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:406)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:373)
at org.apache.spark.scheduler.ResultTask.$anonfun$runTask$3(ResultTask.scala:82)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.ResultTask.$anonfun$runTask$1(ResultTask.scala:82)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:201)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:186)
at org.apache.spark.scheduler.Task.$anonfun$run$5(Task.scala:151)
at com.[REDACTED]bricks.unity.EmptyHandle$.runWithAndClose(UCSHandle.scala:134)
at org.apache.spark.scheduler.Task.$anonfun$run$1(Task.scala:145)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$9(Executor.scala:958)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:105)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:961)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at com.[REDACTED]bricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:853)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
File , line 2
----> 2 model.fit(train_data_transformed)

What component(s) does this bug affect?

  • area/cognitive: Cognitive project
  • area/core: Core project
  • area/deep-learning: DeepLearning project
  • area/lightgbm: Lightgbm project
  • area/opencv: Opencv project
  • area/vw: VW project
  • area/website: Website
  • area/build: Project build system
  • area/notebooks: Samples under notebooks folder
  • area/docker: Docker usage
  • area/models: models related issue

What language(s) does this bug affect?

  • language/scala: Scala source code
  • language/python: Pyspark APIs
  • language/r: R APIs
  • language/csharp: .NET APIs
  • language/new: Proposals for new client languages

What integration(s) does this bug affect?

  • integrations/synapse: Azure Synapse integrations
  • integrations/azureml: Azure ML integrations
  • integrations/databricks: Databricks integrations
@user673 user673 added the bug label Nov 11, 2024
@avtokit2700
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The same mistake.

@Yohahaha
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We found same issue.

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@avtokit2700 @Yohahaha @user673