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ColinLeeo
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May 26, 2026
Brings together batch decode infrastructure, multi-value aligned read, parallel page decode, columnar tablet write, and SIMD micro-optimizations from the long-lived `final` branch into a single review-ready change. This change is a code snapshot, not a replay of `final` commit history -- the upstream history was a long sequence of WIP commits that wasn't fit for review. Supersedes #749, #754, #774. Read path - Decoder base gains batch APIs (read_batch_int32/int64/float/double, skip_*); PLAIN, TS2DIFF, Gorilla decoders implement them. TS2DIFF has block-level peeking so time filters can skip blocks without decoding. Gorilla adds a raw-pointer GorillaBitReader that bypasses ByteStream overhead. - ChunkReader / AlignedChunkReader add *_DECODE_TV_BATCH methods that decode time + value into a TsBlock in one pass, applying batch time filters before append. - AlignedChunkReader supports a multi-value mode: one time chunk + N value chunks decoded in a single pass, sharing the decoded timestamps and filter mask. SingleDeviceTsBlockReader auto-detects same-device measurements via VectorMeasurementColumnContext. - Optional page-level parallel decompression via a DecodeThreadPool + BlockingQueue when ENABLE_THREADS is set. Page-plan classification (SKIP / FULL_PASS / BOUNDARY) lets a scatter-free memcpy fast path fire when every row passes and no column has nulls. Write path - ValuePageWriter gains write_batch / write_string_batch that take timestamp+value+nullness arrays directly, removing the per-value append loop. Tablet exposes set_timestamps / set_column_values / set_column_string_repeated / reset for bulk reuse and switches StringColumn to an Arrow-compatible offset+buffer layout. - TS2DIFFEncoder::flush now packs all deltas with a single pack_bits_msb + write_buf instead of per-value write_bits, falling back to the scalar path for the rare bit_width > 56 case. - Int64Statistic::update_batch (NEON-accelerated min/max/sum). Encoding / SIMD - TS2DIFF batch decode adds AVX2 helpers via SIMDe (already on develop) for both i32 and i64; scalar fallback unchanged. - PLAIN byte-swap path uses ARM NEON (vrev64q_u8 / vrev32q_u8) when available, falling back to __builtin_bswap. - CMakeLists adds ENABLE_SIMD and turns on -O3 -march=native -flto in Release builds. Allocator / ByteStream - ByteStream caches page_mask_ (= page_size - 1) so the hot path uses a bitmask instead of modulo; wrap_from rounds buffer sizes up to a power of two so the mask remains correct. total_size_ widened to uint64_t to support files > 4GB. - UncompressedCompressor now copies its output instead of aliasing caller buffers, letting callers free input safely. C wrapper / Arrow - Trimmed unused metadata-export surface (TsFileStatisticBase, TimeseriesMetadata, DeviceTimeseriesMetadataEntry, tag-filter handles) out of the public C API. Internal tag filtering is unaffected. - arrow_c.cc simplified: per-row offset handling for sliced variable-length arrays in place of the InvertArrowBitmap copy. Tests / benchmarks - New tsfile_reader_table_batch_test.cc covers the TsBlock batch read path. gorilla_codec_test.cc adds Int32/Int64/Float batch decode tests. examples/cpp_examples adds bench_read.cpp/.h and an examples/read_perf_compare/ target. - Removed cwrapper_metadata_test.cc and common/path.cc (Path bodies inlined into path.h; the C metadata API they covered is gone). Compatibility - All new C++ methods are additions; no existing C++ API was removed. - C wrapper headers lost the metadata export / tag filter symbols listed above -- downstream callers (Python wrapper in particular) will want a sanity check before merge. - cpp/third_party/ intentionally left at develop's state so the recent MSVC compatibility fixes (WITH_STATIC_CRT OFF, CMP0054 NEW, CMAKE_POLICY_VERSION_MINIMUM=3.5, _MSC_VER guards) are preserved. Verification - cmake configure + make -j on macOS arm64 (AppleClang, C++11) builds cleanly: libtsfile.2.2.1.dev.dylib and TsFile_Test both link, zero errors, only unused-lambda-capture warnings in pre-existing tests. - Full TsFile_Test run and downstream Python binding load are left as pre-merge checks. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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TsFile C++ Read Path Performance Optimization — Overview
Background
The current TsFile C++ read path uses row-by-row decoding with a row-oriented result set API. In full-scan and filtered query scenarios, throughput falls behind Parquet+Arrow. This optimization aims to make TsFile batch read throughput significantly exceed Parquet+Arrow while maintaining interface compatibility.
Summary of Optimizations
The optimizations span four layers:
1. Batch Decode Infrastructure
read_batch_int32/int64/float/doubleandskip_*batch interfaces to Decoder (PLAIN / TS2DIFF / Gorilla), processing 129 values per call instead of one virtual-dispatch per value.satisfy_batch_timebatch filter interface to Filter, evaluating an entire batch of timestamps at once.__builtin_bswap64/32(compiles to a single ARMREVinstruction) and skips theread_bufintermediate copy.2. Single-Column Batch Read Path
DECODE_TV_BATCHmethod in ChunkReader / AlignedChunkReader: decodes time + value in batches of 129 rows, applies batch filter, and writes results into TsBlock.get_next_tsblockto return TsBlock directly to the user.3. Multi-Value Column Merged Read
MultiAlignedTimeseriesIndexto allow a single AlignedChunkReader to hold 1 time column + N value columns simultaneously.VectorMeasurementColumnContextwraps a multi-value SSI; SingleDeviceTsBlockReader automatically detects and merges multiple measurements within the same device.SingleDeviceTsBlockReader::close()where multiple map entries pointed to the sameVectorMeasurementColumnContext.get_cur_page_header(previously sharedfile_data_value_buf_size_caused heap-buffer-overflow when columns had different page sizes).4. Parallel Decode + Batch Append Fast Path
DecodeThreadPoolfor page-level parallel decompression of N value columns (Snappy decompress in parallel).multi_DECODE_TV_BATCH, when all rows pass the filter and no column has nulls, the per-rowrow_appender.append()loop is bypassed — each column's decoded batch is written to the Vector buffer in a singlememcpy.Test Dataset
Benchmark Results
TAG_FILTER — filter by device id, read 100,000 rows × 4 value columns from a single device:
TIME_FILTER — filter by time range, read 333,333 rows × 4 value columns across all devices:
Phase Timing Breakdown (Post-Optimization)
Instrumented timing of each phase within
multi_DECODE_TV_BATCH:PR Plan
Split into 5 PRs, merged in dependency order:
PR 1 → 2 → 3 → 4 have sequential dependencies and must be merged in order. PR 5 has no dependencies and can be merged independently.
Correctness Verification