Title: How to handle type widening (int→bigint) in DLT streaming tables without dropping the table

hnnhhnnh — Mon, 29 Dec 2025 04:11:04 GMT

Setup
Bronze source table (external to DLT, CDF & type widening enabled):

# Source table properties:
# delta.enableChangeDataFeed: "true"
# delta.enableDeletionVectors: "true"
# delta.enableTypeWidening: "true"
# delta.minReaderVersion: "3"
# delta.minWriterVersion: "7"

# Type widening applied:
spark.sql("ALTER TABLE cat_dev.poc.source_data ALTER COLUMN id TYPE BIGINT")
DLT Pipeline notebook:

import dlt
from pyspark.sql import functions as F

CATALOG = spark.conf.get("catalog", "test_catalog")
SOURCE_SCHEMA = "poc_schema_evolution"
SOURCE_TABLE = "source_data"

@Dlt.view
def bronze_source():
"""Read source data as CDF streaming"""
return (
spark.readStream
.format("delta")
.option("readChangeFeed", "true")
.table(f"{CATALOG}.{SOURCE_SCHEMA}.{SOURCE_TABLE}")
)

dlt.create_streaming_table(
name="silver_person",
comment="Silver person table - POC for schema evolution",
table_properties={
"delta.minReaderVersion": "3",
"delta.minWriterVersion": "7",
"pipelines.typeWideningAvailable": "true",
"pipelines.enableTypeWidening": "true",
"delta.columnMapping.mode": "name",
"delta.enableDeletionVectors": "true",
"delta.enableChangeDataFeed": "true",
"delta.autoOptimize.autoCompact": "true",
"delta.autoOptimize.optimizeWrite": "true",
"pipelines.reset.allowed": "false",
}
)

dlt.apply_changes(
target="silver_person",
source="bronze_source",
keys=["id"],
sequence_by=F.col("load_dts"),
stored_as_scd_type=1,
except_column_list=["_change_type", "_commit_version", "_commit_timestamp"],
)
Pipeline configuration (databricks.yml):

configuration:
catalog: ${var.catalog}
"spark.databricks.delta.streaming.skipChangeCommits": "true"
"pipelines.enableTypeWidening": "true"
"spark.databricks.delta.streaming.allowSourceColumnTypeChange": "true"
Verified Table Properties
Silver table (after creation):

delta.autoOptimize.autoCompact: "true"
delta.autoOptimize.optimizeWrite: "true"
delta.columnMapping.mode: "name"
delta.enableChangeDataFeed: "true"
delta.enableDeletionVectors: "true"
pipelines.enableTypeWidening: "true"
pipelines.typeWideningAvailable: "true"
pipelines.reset.allowed: "false"
Error
After several successful runs with INT id column, I widened the source table's id column from INT to BIGINT. The DLT pipeline fails with:

[CANNOT_UPDATE_TABLE_SCHEMA] Failed to merge the current and new schemas for table silver_person.
To proceed with this schema change, you can trigger a full refresh of this table.

[DELTA_FAILED_TO_MERGE_FIELDS] Failed to merge fields 'id' and 'id'
[DELTA_MERGE_INCOMPATIBLE_DATATYPE] Failed to merge incompatible data types IntegerType and LongType
Full refresh also fails with the same error (even with pipelines.reset.allowed temporarily set to true).

schemaTrackingLocation Tried in readStream ❌ Error: must be under DLT checkpoint
Questions
Is there a way to handle type widening (INT→BIGINT) in DLT streaming tables with apply_changes without dropping the target table?
Does type widening with schema evolution work for DLT streaming tables, or is it only supported for batch/merge operations?
Is schemaTrackingLocation supported in @Dlt.view, or only in regular Structured Streaming outside DLT?
What is the recommended pattern for handling upstream type changes in production DLT pipelines?

Re: Title: How to handle type widening (int→bigint) in DLT streaming tables without dropping the tab

mukul1409 — Mon, 29 Dec 2025 09:20:48 GMT

Hi @hnnhhnnh

DLT streaming tables that use apply changes do not support widening the data type of key columns such as changing an integer to a bigint after the table is created. Even though Delta and Unity Catalog support type widening in general, DLT streaming with apply changes requires a stable schema for key columns and will fail on such changes. This is why both incremental runs and full refresh fail. schemaTrackingLocation is not supported inside DLT views and is only available in standard Structured Streaming outside DLT. The recommended approach in production is to avoid type changes on key columns in streaming DLT pipelines, introduce a new column with the widened type and migrate downstream usage, or rebuild the target table when such a change is required.

topic Re: Title: How to handle type widening (int→bigint) in DLT streaming tables without dropping the tab in Data Engineering

Title: How to handle type widening (int→bigint) in DLT streaming tables without dropping the table

Re: Title: How to handle type widening (int→bigint) in DLT streaming tables without dropping the tab