Upsert operation migration and verification#48
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fix: handle edge in schema adapter for single missing field
fix: use provided sessionState in method argument
fix: scan time was always 0 for merge metrics
Co-authored-by: adampolomski <10196659+adampolomski@users.noreply.github.com>
Co-authored-by: adampolomski <10196659+adampolomski@users.noreply.github.com>
…dability-metrics Refactor upsert.rs for improved readability, idiomatic Rust, and meaningful metrics
This reverts commit 9a5a201.
… detection Instead of caching the conflicts DataFrame to work around DataFusion's Dictionary encoding schema mismatch, implement manual join logic: - Collect target DataFrame with join keys + file paths (small result) - Collect distinct source join keys (small result) - Perform join in memory using HashSet for efficient lookup - Extract file paths that have matching keys This avoids materializing large DataFrames while still handling the schema inconsistency by working entirely in memory on small, already-collected data. Memory impact: Only materializes join keys + file paths (one row per conflicting file), not full row data. Much more efficient than caching full DataFrames. Co-authored-by: adampolomski <10196659+adampolomski@users.noreply.github.com>
The previous approach incorrectly materialized the entire target DataFrame which could be billions of rows. The corrected approach: 1. Keeps target_df and source lazy (not materialized) 2. Performs inner join in DataFusion (lazy operation) 3. Selects only minimal columns (join keys + file path, not full rows) 4. Collects ONLY the join result which is small (only conflicting rows) Memory footprint: For a table with billions of rows but only thousands of conflicts, we materialize only thousands of rows with minimal columns, not billions of full rows. The join result is inherently small because it contains only rows where join keys match between source and target (actual conflicts). Co-authored-by: adampolomski <10196659+adampolomski@users.noreply.github.com>
…rics - Changed extract_conflicting_filenames to extract_conflicts_dataframe to return a DataFrame - Added extract_file_paths_from_conflicts to extract file paths from the cached DataFrame - Cache the conflicts DataFrame for reuse in multiple places - Added num_conflicting_records field to UpsertMetrics - Count and report conflicting records in metrics - Updated tests to verify num_conflicting_records metric Co-authored-by: adampolomski <10196659+adampolomski@users.noreply.github.com>
Co-authored-by: adampolomski <10196659+adampolomski@users.noreply.github.com>
Co-authored-by: adampolomski <10196659+adampolomski@users.noreply.github.com>
…sert' into copilot/extract-dataframe-for-upsert # Conflicts: # crates/core/src/operations/upsert.rs
…or-upsert Extract conflicts DataFrame and add conflicting records metric to upsert
…or-upsert Copilot/extract dataframe for upsert
Upsert performance and monitoring improvements
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[WIP] Update migration for upsert operation
Upsert operation migration and verification
Jan 2, 2026
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Description
Verified the upsert operation migration to this branch is complete. The operation provides high-performance upsert functionality using optimized DataFrame joins and partition filtering.
Implementation highlights:
DeltaOps::upsert(source, join_keys)API for conflict-based updatesTest coverage:
Related Issue(s)
N/A - Migration verification
Documentation
See
crates/core/src/operations/upsert.rsfor implementation details and inline documentation.✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.