Is there an existing issue for this?
Current Behavior
In Databricks DBR13.3 cluster. This code will return the interpolated column with no problem.
# this table has id, day, ts (timestamp) and signal columns
input_table = spark.table("my_input_table")
assert input_table.columns == ["id", "day", "ts", "signal"], "cols are not the same"
transformed_data = TSDF(input_table, ts_col="ts", partition_cols=["id", "day"])
interpolated = (
transformed_data.resample(freq="5 minutes", func="mean")
.interpolate(method="linear")
.df
)
interpolated.columns == ["id", "day", "ts", "signal"], "cols are not the same"
Expected Behavior
When upgrading to DBR14 I would expect there are no columns dropped and the interpolated dataframe has the same columns as the input one
Steps To Reproduce
- Set up a compute cluster with DBR13 LTS and one with DBR 14 LTS
- have an input table with similar columns as above, perharps even just one partition column
- Run the code above to see the difference between the two enviroments
Cloud
AWS
Version
dbl-tempo==0.1.27
Relevant log output
No response
Is there an existing issue for this?
Current Behavior
In Databricks DBR13.3 cluster. This code will return the interpolated column with no problem.
Expected Behavior
When upgrading to DBR14 I would expect there are no columns dropped and the interpolated dataframe has the same columns as the input one
Steps To Reproduce
Cloud
AWS
Version
dbl-tempo==0.1.27Relevant log output
No response