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# Elastic Streaming Dataloader ## Motivation Training large models on LanceDB tables today requires loading the entire dataset into memory or writing bespoke batching logic. This PR introduces `StreamingDataset`, a PyTorch `IterableDataset` that streams directly from a LanceDB table with two hard guarantees that are difficult to achieve together: **elastic determinism** and **resumability**. ## Goals ### Elastic determinism The dataset partitions the table into a fixed number of *splits* (controlled by `num_splits`, `shuffle_seed`, and `epoch`). Samples are yielded by round-robining over splits one sample per split per cycle. Because the split structure is fixed, the set of samples that makes up each global training step is identical regardless of `world_size` or `num_workers`. You can scale your cluster up or down between runs and the model sees the same data in the same order — no re-sharding, no gradient variance from topology changes. ### Resumability `state_dict()` / `load_state_dict()` capture how many samples each split has consumed. Because all splits are the same size and the round-robin design keeps them in lockstep, the state reduces to a single scalar (`samples_consumed_per_split`) that is topology-independent. A checkpoint saved with 8 GPUs can resume correctly on 4 GPUs or 16 GPUs without any adjustment. ### PyTorch `IterableDataset` / streaming `StreamingDataset` implements the standard PyTorch `IterableDataset` interface, so it drops into any existing `DataLoader` pipeline without modification. Data is fetched lazily from Lance in chunks — only the rows needed for the current batch are ever in memory. Compared to the map dataset this takes more work from pytorch and puts it into the dataset itself (e.g. shuffling, filtering, etc.). We do this because we cannot achieve things like elastic determinism or prefiltering otherwise. ### Multi-worker support DataLoader workers are automatically assigned contiguous sub-blocks of splits (the rank's splits are divided evenly across workers). Each worker is independent: no shared state, no inter-process coordination. The only constraint is that `num_splits` must be divisible by `world_size * num_workers`. That being said, multi-worker is highly discouraged as it relies on multiprocessing which is inefficient. Still, we want to support it. ### Filters as prefilters Filters are applied at *permutation-build time* via `PermutationBuilder.filter()`, not re-evaluated on every fetch. The filtered row IDs are stored in the permutation table so that subsequent reads see only the matching rows. This allows us to avoid loading rows that don't match the filter (which is the default pytorch behavior) ### Prefetching Two parameters control the I/O pipeline: - `read_batch_size` (default 64) — number of rows fetched per `take_offsets` call. Larger values amortise per-request overhead, which is critical on object storage where a single round-trip can cost ~100 ms. - `prefetch_batches` (default 4) — number of batches prefetched in parallel per split via a `ThreadPoolExecutor`. While the model processes the current batch, the next several batches are already in flight, hiding storage latency behind compute. If set correctly then you can get good performance even with num_workers=0 (unless you are bottlenecked on transform). ### Transform parallelism The underlying `Permutation` API supports a `with_transform()` callback for decoding, augmentation, and format conversion. Unfortunately, this is not parallelized. Pytorch typically parallelizes this with num_workers which is multiprocessing which is highly inefficient. For simple transforms we should be able to utilize multithreading and Rust based UDFs. For complex python UDFs we could have a dedicated multiprocessing pipeline for just the transform. Or we could just utilize multithreading. In both cases we would exclude the I/O stage from the multiprocessing because that ends up being very memory hungry and inefficient. --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
255 lines
7.4 KiB
Rust
255 lines
7.4 KiB
Rust
// SPDX-License-Identifier: Apache-2.0
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// SPDX-FileCopyrightText: Copyright The LanceDB Authors
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use std::sync::{Arc, Mutex};
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use crate::{error::NapiErrorExt, table::Table};
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use lancedb::dataloader::{
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permutation::builder::{PermutationBuilder as LancePermutationBuilder, ShuffleStrategy},
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permutation::split::{SplitSizes, SplitStrategy},
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};
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use napi_derive::napi;
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#[napi(object)]
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pub struct SplitRandomOptions {
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pub ratios: Option<Vec<f64>>,
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pub counts: Option<Vec<i64>>,
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pub fixed: Option<i64>,
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pub seed: Option<i64>,
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pub clump_size: Option<i64>,
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pub split_names: Option<Vec<String>>,
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}
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#[napi(object)]
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pub struct SplitHashOptions {
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pub columns: Vec<String>,
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pub split_weights: Vec<i64>,
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pub discard_weight: Option<i64>,
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pub split_names: Option<Vec<String>>,
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}
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#[napi(object)]
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pub struct SplitSequentialOptions {
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pub ratios: Option<Vec<f64>>,
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pub counts: Option<Vec<i64>>,
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pub fixed: Option<i64>,
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pub split_names: Option<Vec<String>>,
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}
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#[napi(object)]
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pub struct SplitCalculatedOptions {
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pub calculation: String,
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pub split_names: Option<Vec<String>>,
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}
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#[napi(object)]
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pub struct ShuffleOptions {
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pub seed: Option<i64>,
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pub clump_size: Option<i64>,
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}
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pub struct PermutationBuilderState {
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pub builder: Option<LancePermutationBuilder>,
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}
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#[napi]
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pub struct PermutationBuilder {
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state: Arc<Mutex<PermutationBuilderState>>,
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}
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impl PermutationBuilder {
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pub fn new(builder: LancePermutationBuilder) -> Self {
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Self {
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state: Arc::new(Mutex::new(PermutationBuilderState {
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builder: Some(builder),
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})),
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}
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}
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}
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impl PermutationBuilder {
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fn modify(
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&self,
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func: impl FnOnce(LancePermutationBuilder) -> LancePermutationBuilder,
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) -> napi::Result<Self> {
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let mut state = self.state.lock().unwrap();
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let builder = state
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.builder
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.take()
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.ok_or_else(|| napi::Error::from_reason("Builder already consumed"))?;
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state.builder = Some(func(builder));
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Ok(Self {
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state: self.state.clone(),
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})
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}
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}
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#[napi]
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impl PermutationBuilder {
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#[napi]
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pub fn persist(
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&self,
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connection: &crate::connection::Connection,
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table_name: String,
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) -> napi::Result<Self> {
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let database = connection.database()?;
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self.modify(|builder| builder.persist(database, table_name))
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}
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/// Configure random splits
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#[napi]
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pub fn split_random(&self, options: SplitRandomOptions) -> napi::Result<Self> {
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// Check that exactly one split type is provided
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let split_args_count = [
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options.ratios.is_some(),
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options.counts.is_some(),
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options.fixed.is_some(),
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]
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.iter()
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.filter(|&&x| x)
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.count();
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if split_args_count != 1 {
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return Err(napi::Error::from_reason(
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"Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
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));
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}
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let sizes = if let Some(ratios) = options.ratios {
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SplitSizes::Percentages(ratios)
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} else if let Some(counts) = options.counts {
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SplitSizes::Counts(counts.into_iter().map(|c| c as u64).collect())
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} else if let Some(fixed) = options.fixed {
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SplitSizes::Fixed(fixed as u64)
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} else {
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unreachable!("One of the split arguments must be provided");
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};
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let seed = options.seed.map(|s| s as u64);
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let clump_size = options.clump_size.map(|c| c as u64);
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self.modify(|builder| {
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builder.with_split_strategy(
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SplitStrategy::Random {
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seed,
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sizes,
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clump_size,
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},
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options.split_names.clone(),
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)
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})
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}
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/// Configure hash-based splits
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#[napi]
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pub fn split_hash(&self, options: SplitHashOptions) -> napi::Result<Self> {
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let split_weights = options
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.split_weights
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.into_iter()
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.map(|w| w as u64)
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.collect();
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let discard_weight = options.discard_weight.unwrap_or(0) as u64;
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self.modify(move |builder| {
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builder.with_split_strategy(
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SplitStrategy::Hash {
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columns: options.columns,
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split_weights,
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discard_weight,
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},
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options.split_names,
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)
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})
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}
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/// Configure sequential splits
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#[napi]
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pub fn split_sequential(&self, options: SplitSequentialOptions) -> napi::Result<Self> {
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// Check that exactly one split type is provided
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let split_args_count = [
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options.ratios.is_some(),
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options.counts.is_some(),
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options.fixed.is_some(),
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]
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.iter()
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.filter(|&&x| x)
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.count();
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if split_args_count != 1 {
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return Err(napi::Error::from_reason(
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"Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
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));
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}
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let sizes = if let Some(ratios) = options.ratios {
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SplitSizes::Percentages(ratios)
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} else if let Some(counts) = options.counts {
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SplitSizes::Counts(counts.into_iter().map(|c| c as u64).collect())
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} else if let Some(fixed) = options.fixed {
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SplitSizes::Fixed(fixed as u64)
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} else {
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unreachable!("One of the split arguments must be provided");
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};
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self.modify(move |builder| {
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builder.with_split_strategy(SplitStrategy::Sequential { sizes }, options.split_names)
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})
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}
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/// Configure calculated splits
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#[napi]
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pub fn split_calculated(&self, options: SplitCalculatedOptions) -> napi::Result<Self> {
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self.modify(move |builder| {
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builder.with_split_strategy(
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SplitStrategy::Calculated {
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calculation: options.calculation,
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},
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options.split_names,
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)
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})
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}
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/// Configure shuffling
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#[napi]
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pub fn shuffle(&self, options: ShuffleOptions) -> napi::Result<Self> {
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let seed = options.seed.map(|s| s as u64);
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let clump_size = options.clump_size.map(|c| c as u64);
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self.modify(|builder| {
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builder.with_shuffle_strategy(ShuffleStrategy::Random { seed, clump_size })
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})
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}
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/// Configure filtering
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#[napi]
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pub fn filter(&self, filter: String) -> napi::Result<Self> {
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self.modify(|builder| builder.with_filter(filter))
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}
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/// Execute the permutation builder and create the table
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#[napi]
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pub async fn execute(&self) -> napi::Result<Table> {
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let builder = {
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let mut state = self.state.lock().unwrap();
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state
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.builder
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.take()
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.ok_or_else(|| napi::Error::from_reason("Builder already consumed"))?
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};
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let table = builder.build().await.default_error()?;
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Ok(Table::new(table))
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}
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}
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/// Create a permutation builder for the given table
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#[napi]
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pub fn permutation_builder(table: &crate::table::Table) -> napi::Result<PermutationBuilder> {
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use lancedb::dataloader::permutation::builder::PermutationBuilder as LancePermutationBuilder;
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let inner_table = table.inner_ref()?.clone();
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let inner_builder = LancePermutationBuilder::new(inner_table);
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Ok(PermutationBuilder::new(inner_builder))
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}
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