Bug Description
Docs claim that fab table schema has full support for Lakehouse tables:
https://microsoft.github.io/fabric-cli/commands/tables/#:~:text=.-,Lakehouse,-(full%20support)
However, the table schema command can fail for a valid Delta table when the original Delta transaction-log JSON file containing the metaData action has already been removed by Delta log retention/checkpointing.
I have two tables in the same Lakehouse:
table_a — fab table schema works correctly.
table_b — fab table schema returns InvalidDeltaTable.
Both tables are valid Delta tables and are accessible from Fabric/Spark.
The relevant difference appears to be their _delta_log history:
- The working table still contains the initial
00000000000000000000.json file, which contains a metaData object with schemaString object inside.
- The failing table has older JSON transaction logs removed and the currently retained JSON files start around version
00000000000000000260.json. None of the retained JSON files contain a metaData object. The older table state is represented through Delta checkpoint files.
Based on the current Fabric CLI implementation, fab table schema scans JSON files inside _delta_log and searches for a metaData.schemaString. It does not appear to reconstruct the Delta metadata from checkpoint Parquet files. As a result, a valid Delta table can incorrectly produce an InvalidDeltaTable error.
fabric-cli version
1.6.1
Python version
3.11.9
Operating System
Windows
CLI Mode
Command line mode
Authentication Method
Interactive browser login
Steps to Reproduce
- Create a Lakehouse containing a Delta table.
- Allow the table to accumulate enough Delta transaction history for older JSON log files containing the original
metaData action to be removed according to Delta log retention/checkpointing.
- Verify that the table remains a valid Delta table and can be queried normally from Fabric/Spark.
- Verify that the remaining
_delta_log/*.json files do not contain a metaData action, while checkpoint files exist.
- Run:
fab table schema testworkspace.Workspace/testlakehouse.Lakehouse/Tables/dbo/problem_table
The command returns:
[InvalidDeltaTable] Failed to extract the table schema. Please ensure the path points to a valid Delta table
For comparison, running the same command against another table in the same Lakehouse whose _delta_log still contains the original metadata JSON works correctly:
fab table schema testworkspace.Workspace/testlakehouse.Lakehouse/Tables/dbo/working_table
Expected Behavior
fab table schema should return the schema for any valid supported Delta table regardless of whether the original JSON transaction-log file containing the metaData action is still retained.
Delta tables remain valid after checkpointing and transaction-log retention, so schema extraction should work from the current Delta snapshot rather than depend on historical JSON files being present.
Actual Behavior
fab table schema returns:
[InvalidDeltaTable] Failed to extract the table schema. Please ensure the path points to a valid Delta table
even though the table itself is a valid Delta table.
The command succeeds for another table in the same Lakehouse where a retained _delta_log/*.json file still contains a metaData object.
Additional Context
Looking at the current fabric-cli implementation of table schema, the command appears to:
- Open
<table>/_delta_log.
- Enumerate
.json transaction-log files.
- Read those JSON files in reverse order.
- Search for a
metaData action.
- Extract
metaData.schemaString.
- Return
InvalidDeltaTable if no such JSON action is found.
This approach does not seem to account for Delta checkpoints.
In the failing table, the oldest retained JSON transaction-log file is approximately:
00000000000000000260.json
and none of the currently retained JSON files contain a metaData action.
The working table still has:
00000000000000000000.json
which contains the expected metaData.schemaString.
This makes the behavior dependent on Delta transaction-log retention/history rather than on whether the table is actually a valid Delta table.
Possible Solution
Instead of deriving the schema only by searching retained _delta_log/*.json files for a metaData action, fab table schema could reconstruct the current Delta snapshot, including metadata from checkpoint Parquet files.
Alternatively, the implementation could use a Delta-aware API/library or another Fabric metadata endpoint capable of returning the current table schema independently of transaction-log retention.
The InvalidDeltaTable error should also ideally be reserved for genuinely invalid/non-Delta tables. If schema extraction fails because the required metadata is unavailable through the current CLI implementation, a more specific error would make troubleshooting easier.
Bug Description
Docs claim that
fab table schemahas full support for Lakehouse tables:https://microsoft.github.io/fabric-cli/commands/tables/#:~:text=.-,Lakehouse,-(full%20support)
However, the
table schemacommand can fail for a valid Delta table when the original Delta transaction-log JSON file containing themetaDataaction has already been removed by Delta log retention/checkpointing.I have two tables in the same Lakehouse:
table_a—fab table schemaworks correctly.table_b—fab table schemareturnsInvalidDeltaTable.Both tables are valid Delta tables and are accessible from Fabric/Spark.
The relevant difference appears to be their
_delta_loghistory:00000000000000000000.jsonfile, which contains ametaDataobject withschemaStringobject inside.00000000000000000260.json. None of the retained JSON files contain ametaDataobject. The older table state is represented through Delta checkpoint files.Based on the current Fabric CLI implementation,
fab table schemascans JSON files inside_delta_logand searches for ametaData.schemaString. It does not appear to reconstruct the Delta metadata from checkpoint Parquet files. As a result, a valid Delta table can incorrectly produce anInvalidDeltaTableerror.fabric-cli version
1.6.1
Python version
3.11.9
Operating System
Windows
CLI Mode
Command line mode
Authentication Method
Interactive browser login
Steps to Reproduce
metaDataaction to be removed according to Delta log retention/checkpointing._delta_log/*.jsonfiles do not contain ametaDataaction, while checkpoint files exist.The command returns:
For comparison, running the same command against another table in the same Lakehouse whose
_delta_logstill contains the original metadata JSON works correctly:Expected Behavior
fab table schemashould return the schema for any valid supported Delta table regardless of whether the original JSON transaction-log file containing themetaDataaction is still retained.Delta tables remain valid after checkpointing and transaction-log retention, so schema extraction should work from the current Delta snapshot rather than depend on historical JSON files being present.
Actual Behavior
fab table schemareturns:even though the table itself is a valid Delta table.
The command succeeds for another table in the same Lakehouse where a retained
_delta_log/*.jsonfile still contains ametaDataobject.Additional Context
Looking at the current
fabric-cliimplementation oftable schema, the command appears to:<table>/_delta_log..jsontransaction-log files.metaDataaction.metaData.schemaString.InvalidDeltaTableif no such JSON action is found.This approach does not seem to account for Delta checkpoints.
In the failing table, the oldest retained JSON transaction-log file is approximately:
and none of the currently retained JSON files contain a
metaDataaction.The working table still has:
which contains the expected
metaData.schemaString.This makes the behavior dependent on Delta transaction-log retention/history rather than on whether the table is actually a valid Delta table.
Possible Solution
Instead of deriving the schema only by searching retained
_delta_log/*.jsonfiles for ametaDataaction,fab table schemacould reconstruct the current Delta snapshot, including metadata from checkpoint Parquet files.Alternatively, the implementation could use a Delta-aware API/library or another Fabric metadata endpoint capable of returning the current table schema independently of transaction-log retention.
The
InvalidDeltaTableerror should also ideally be reserved for genuinely invalid/non-Delta tables. If schema extraction fails because the required metadata is unavailable through the current CLI implementation, a more specific error would make troubleshooting easier.