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Description
SynapseML version
com.microsoft.azure:synapseml_2.12:0.11.4-spark3.3
System information
- Language version (e.g. python 3.8, scala 2.12): python 3.9
- Spark Version (e.g. 3.2.3): 3.3.2
- Spark Platform (e.g. Synapse, Databricks): Databricks
Describe the problem
I have a for-loop lightgbm fit job for rolling back validation;
The job failed on multi-node cluster with log error Connection Refused
, and after checked the failed tasks, the executor failed with detail error message java.lang.ArrayIndexOutOfBoundsException
and caused the Connection Refused
error;
Meanwhile the job can run on single-node cluster without any issue.
The dataframe sent to model is around 48,000, with partition as below
Partition 0 has 19000 records
Partition 1 has 18000 records
Partition 2 has 7000 records
Partition 3 has 4000 records
And the issue cannot be fixed by df.repartition(5)
.

Code to reproduce issue
max_base_date = '2024-09-01'
tmp_train_df = train_merged_df.where(sf.col('base_date')<max_base_date).cache()
tmp_actual_df = actual_merged_df.where(sf.col('base_date')<max_base_date).cache()
model.fit(tmp_train_df, tmp_actual_df)
Other info / logs
No response
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