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feat: add plan_splits function for distributed compute #5863
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- Add FilteredReadPlan struct using RowAddrTreeMap for row selection - Add get_or_create_plan API for lazy plan computation via OnceCell - Support providing pre-computed plan to FilteredReadExec::try_new - Centralize plan creation in get_or_create_plan_impl - Make RowAddrSelection public in lance-core
- Add FilteredReadInternalPlan (private) using BTreeMap<u32, Vec<Range<u64>>> for efficient local execution without bitmap conversion - Keep FilteredReadPlan (public) using RowAddrTreeMap for distributed execution - Local path: plan_scan() → internal plan → ScopedFragmentRead (zero conversions) - External API: get_or_create_plan() converts internal → external once - with_plan() converts external → internal for distributed workers - Add bitmap_to_ranges() utility in lance-core for efficient bitmap conversion - Use BTreeMap for rows to maintain deterministic fragment order 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Codecov Report❌ Patch coverage is
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Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
…c implementations in the future Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
Signed-off-by: Daniel Rammer <hamersaw@protonmail.com>
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Adding a
plan_splitsfunction to the Scanner to facilitate a single solution for partitioning Lance dataset for efficient distributed compute. This function (1) filters the dataset (using index looking / delete vectors) producing a mapping of fragment IDs to valid row ranges and (2) bin packs these fragment rows ranges into "splits" that target a configurable partition size (in rows count or bytes).Perhaps the two most important aspects to align on are:
(1) Using a
Splitsenum that returns different "split types". In the case of a filterable query this returns aVec<FilteredReadPlan>where each can be fed to the newexecute_filtered_read_planfunction to read (without incurring traditional read overhead through index lookup , deletion vector application, etc). In every other case (currently nearest / vector search) we return a list of Fragments (existing Spark partition method). This is meant to be a sane default that will be improved upon in the future. For example, we may want to partition these search types based on index files rather than fragment-level boundaries, etc.(2) Removing the current
FilteredReadPlanand making the existingFilteredReadInternalPlanthe default. The differentiating factor between these is the latter stores row ranges (ie.Range<u64>) and the former a bitmap of row indexes. IIUC the intuition is that for network transfer a bitmap will be more efficient so we should use that user-facing. IMO we use row ranges in our internal APIs and so the bitmap is ONLY useful if we are network transfering "splits" AND and bitmap representation is smaller. You can do a simple calculation to understand if the serialization will be smaller (ex.# ranges * 2 * bytes per range index <> # of rows / 8). So rather than forcing the bitmap conversion on this API, we can quickly identify in the serialization logic if it makes sense to use bitmap or row range and perform that logic inline. Additionally, this is something we can punt on for now and just work with row ranges until it becomes a problem.