feat: integrate MemWAL with deterministic sharding in daft-lance#23
Merged
Conversation
added 2 commits
June 5, 2026 14:33
Integrates Lance's log-structured ingestion framework (MemWAL) with daft-lance to enable high-throughput parallel writes. When enabled, each write task shards records deterministically by writing to a unique Memory Write-Ahead Log region. Post-write, a distributed compaction phase commits WAL records to standard Copy-on-Write (COW) fragments. Co-Authored-By: Beinan Wang <beinanwang@microsoft.com>
Addresses linting/styling issues reported by CI pre-commit checks: - Removes unused WriteResult import in test_mem_wal_writes.py - Re-formats multi-line arrays and parameters using black/ruff style guidelines - Formats uv.lock metadata Co-Authored-By: Beinan Wang <beinanwang@microsoft.com>
rchowell
approved these changes
Jun 5, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
Integrates Lance's log-structured ingestion framework (MemWAL) with
daft-lanceto enable high-throughput parallel writes.Key changes:
use_mem_wal: bool = Falseandcompact_after_write: bool = Trueflags toLanceDataSink.uuid.uuid4()) to each task / micropartition write. This allows concurrent writers (e.g. distributed Ray workers) to write without OCC manifest lock contention.compact_files_internalfromdaft_lance.lance_compactionso that freshly written MemWAL data is immediately visible.tests/io/lancedb/test_mem_wal_writes.pycovering creation, appending, sharding, compaction flags, schema preservation, and COW fallbacks.Test plan
tests/io/lancedb/test_mem_wal_writes.pypass.daft-lancetest suite runs and passes cleanly with zero regressions.🤖 Generated with Claude Code