what is the depth with which schema evolution works while merging?
Automatic schema evolution does not work while merging in the following case.
import json
d1 = {'a':'b','b':{'c':{'1':1}}}
d2 = {'a':'s','b':{'c':{'1':2,'2':2}}}
d3 = {'a':'v','b':{'c':{'1':4}}}
df1 = spark.read.json(spark.sparkContext.parallelize([json.dumps(d1)]))
#passes
df1.write.saveAsTable('test_table4',format='delta',mode='overwrite', path=f"hdfs://hdmaster:9000/dest/test_table4")
df2 = spark.read.json(spark.sparkContext.parallelize([json.dumps(d2)]))
df2.createOrReplaceTempView('updates')
query = """
MERGE INTO test_table4 existing_records
USING updates updates
ON existing_records.a=updates.a
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
"""
spark.sql("set spark.databricks.delta.schema.autoMerge.enabled=true")
spark.sql(query) #passes
df3 = spark.read.json(spark.sparkContext.parallelize([json.dumps(d3)]))
df3.createOrReplaceTempView('updates')
query = """
MERGE INTO test_table4 existing_records
USING updates updates
ON existing_records.a=updates.a
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
"""
spark.sql("set spark.databricks.delta.schema.autoMerge.enabled=true")
spark.sql(query) #FAILS #FAILS
This looks like failing when depth is more than 2 and incoming df has columns missing.
Is this intentionally like this?
This is handled perfectly with option("mergeSchema", "true") if want to append. But I want to UPSERT the data. But Merge is not able to handle this schema change
Using Delta Lake version 0.8.0