danny0405 commented on code in PR #17827: URL: https://github.com/apache/hudi/pull/17827#discussion_r3419523900
########## rfc/rfc-103/rfc-103.md: ########## @@ -0,0 +1,414 @@ + <!-- + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +--> +# RFC-103: Hudi LSM tree layout + +## Proposers + +- @zhangyue19921010 +- @xushiyan + +## Approvers + +- @danny0405 +- @vinothchandar + +## Status + +Main issue: https://github.com/apache/hudi/issues/14310 + +## Background + +LSM Trees (Log-Structured Merge-Trees) are data structures optimized for write-intensive workloads and are widely used in modern database systems such as LevelDB, RocksDB, Cassandra, etc. They offer higher write performance typically compared to traditional B+Tree structures. + +Systems like Paimon, adopt the LSM structure for data lake workloads as well, with a tiered merge (compaction) mechanism, they offer some valid tradeoffs in terms of: + +- Lower memory requirements to merge logs compared to hash merge algorithms(when the number of files to merge is small); efficient compaction +- Layout sorted by keys within each file group, that can be faster for point lookup + +## Goal + +This RFC proposes applying LSM-inspired principles (**sorted writes + N-way merges**) to improve the data organization protocol for Hudi tables, +and favoring native Parquet log file format over log format in Avro or embedded Parquet log block, to achieve improvements on the performance and stability of MOR compaction, +and point lookup efficiency. + +Comparing to Avro log format, using native Parquet log format further achieves read performance improvements on native predicate pushdown, stats pruning, and better compression. + +## Design Overview + + + +The core idea is to treat, **within each file group**: + +- **Log files** as **Level-0 (L0)** of an LSM tree +- The single **Base file** as **Level-1 (L1)** + +The file namings: + +- The base file naming retain unchanged. +- The data log file naming switches from `.<fileId>_<instant>.log.<version>_<writeToken>` to `<fileId>_<writeToken>_<instant>_<version>.parquet`: the file is not a hidden file now, + hidden file brings in adoptions issues in some engines(Apache Hive), exposing the log files make it more straight-forward(for debugging or for file scanning). The native file format + suffix is used for the similar purposes: easy adoption for query engines, easy to extend(to other formats like Lance). +- The delete log file naming: `<fileId>_<writeToken>_<instant>_<version>.delete.parquet` + +The reason not to add `.log` in the log file name: + +- regex match can already distinguish the existing files; +- with LSM tree, there is no strict boundaries for these files, the only difference is the layer the file belongs to and whether it is compacted/replaced + +To realize this layout: + +- Records inside **log and base files must be sorted by record key(s)** (**Core Feature 1**) +- Records can be optionally deduplicated before writing to any log file. (optional for query optimization) +- Existing services should implement **sorted merge-based compaction**: + - **log-compaction** handles **L0 compaction** + - **compaction table service** handles **L0 → L1 compaction** + - both use a **sorted merge algorithm** (**Core Feature 2**) + +## Considerations + +### Table configs + +The layout should be enforced by a table property, for e.g. `hoodie.table.storage.layout=default|lsm_tree` (default value: `default`, which is current base/log file organization). The layout applies to both COW and MOR table. + +### Engine-agnostic + +The layout should be engine-agnostic. Writer engines can make use of shared implementation and add specific logic or design to conform to the layout. + +For example, Flink writers use buffer sort, the Flink sink must flush sorted records into a single file to guarantee file-level ordering. + +### Write operations + +Write operations should remain semantically unchanged when the layout is enabled. + +In MOR tables, when **small file handling** occurs, inserts may be bin-packed into file slices without log files, creating a new base file, the **sorted write** needs to be applied. +A `SortedCreateHandle` is needed for writing base files, similar to `SortedMergeHandle`. + +For MOR tables, the most performant writer setup for LSM tree layout will be bucket index + bulk insert, which best utilizes sort-merging. The downside would be that small files may proliferate, which can be mitigated by doing log compaction. + +### Indexes + +Writer indexes should still function as is under this layout. Same for reader indexes. + +### Clustering + +Clustering can be supported by adopting a hybrid clustering strategy that keeps records sorted by record key(s) within each file group, while keep records collocated in file groups based on user-specified non-record key fields. + +## Core Feature 1: Sorted Write + +All writes are sorted. That is, within any written file (**base or log**), records are fully sorted by record key(s). + +All write operations and writer index types should be supported, as the layout is only about keeping records sorted in data files, which is orthogonal to the choice of write operation and index type. + +### Example: Flink Streaming Write Pipeline + + + +The write pipeline mainly consists of four core stages: + +- **Repartitioning** +- **Sorting** +- **Deduplication**(optional) +- **I/O** + +Optimizations: + +1. **Asynchronous processing architecture** + Introduce a **Disruptor ring buffer** within the sink operator to decouple production and consumption, significantly improving throughput and handling cases where the producer outpaces the consumer. + +2. **Efficient memory management** + Integrate Flink’s built-in **MemorySegmentPool** with **BinaryInMemorySortBuffer** to enable fine-grained memory control and efficient sorting, greatly reducing GC pressure and sorting overhead. + +## Core Feature 2: Sorted Merge Read / Compaction + +During read and compaction, merging can be performed as k-way merging: + +- Resulting **log files** contain fully sorted records +- Resulting **base files** contain fully sorted records +- File group reads reuse the same sorted merge logic, with **predicate pruning** applied when present + +Merging write handles and file group reader should activate the code path for using the merging algorithm when LSM tree layout is enabled for the table. + +### K-way merging algorithm + +To optimize the merging performance, we propose a statemachine-based loser-tree merging algorithm to perform k-way merging. + + + +This part assumes k pre-sorted input streams and implements high-throughput merge reading in SortMergeReaderLoserTreeStateMachine by combining two mechanisms: + +- A loser tree for efficient global winner selection. +- A state machine for continuous same-key consumption and transition control. + +#### 1. Design Goals + +- Minimize latency and memory overhead for multi-way sorted merge. +- Guarantee deterministic ordering for records with the same key. +- Support streaming merge semantics (including delete/upsert) without building large per-key buffers. + +#### 2. Core Structures + +- tree[]: loser-tree internal nodes, where tree[0] is the current champion leaf index. +- leaves[]: current head record of each input stream plus node state. +- firstSameKeyIndex: fast jump pointer to another contender with the same key. +- States: + - WINNER_WITH_NEW_KEY + - WINNER_WITH_SAME_KEY + - WINNER_POPPED + - LOSER_WITH_NEW_KEY + - LOSER_WITH_SAME_KEY + - LOSER_POPPED + +#### 3. Execution Flow + +1. Initialization + Read one record from each input stream, set initial state, and build the loser tree via adjust. +2. Winner/loser propagation + adjust only updates one root path (O(log k)). Comparison is key-based; if equal, sourceIndex is used as a deterministic tie-breaker. +3. Same-key linking + Equal-key comparisons mark losers as LOSER_WITH_SAME_KEY and record firstSameKeyIndex for fast same-key handoff. +4. Pop and advance + popWinner() marks current winner as popped and re-adjusts: + - If same-key contenders exist, it switches directly to WINNER_WITH_SAME_KEY. + - Otherwise, popAdvance() pulls the next record from that source and resumes normal competition. +5. Group merge output + The iterator repeatedly pops and merges all records of one key using mergeFunctionWrapper, then emits one merged result for that key. + +#### 4. Performance Characteristics + +- Time complexity: O(N log k) for N total records. +- Space complexity: O(k) (one active record per stream plus tree metadata). +- Benefits: + - Fewer redundant comparisons than naive merge approaches. + - No large same-key temporary list. + - Stable, reproducible merge order via deterministic tie-breaking. + +--- + +## Log format v2: native log file format + +### Current log format (v1) + +Current log format is organized as below (ref: [tech spec v8](https://hudi.apache.org/learn/tech-specs-1point0#log-format)): + +```text +#HUDI# (magic, 6 bytes) +Block Size (8 bytes) +Log Format Version (4 bytes) +Block Type (4 bytes) +Header Metadata (variable) +Content Length (8 bytes) +Content (variable) - data block, embedded Avro/Parquet/HFile binary data +Footer Metadata (variable) +Reverse Pointer (8 bytes) +``` + +These fields are encoded into a custom binary format and stored in log files with extension like `.log.<version>_<write_token>`. + +### Proposed log format v2 + +The proposed new log format leverages native file format's metadata layer to capture the metadata fields defined by Hudi log format, while keeping the content field (data block). Take parquet for example: + +```text +Row group 1 (data) +Row group 2 (data) +... +Footer + - Parquet schema + - Row group metadata + - key-value metadata <-- Hudi log format metadata goes in here +``` + +Hudi log format metadata can be stored as a single entry in the Parquet footer with key = `hudi.log.format.metadata` and value being a serialized map of the metadata entries: + +| Hudi log format metadata | +|:---------------------------------------------| +| log format version | +| block type | +| `INSTANT_TIME` | +| `TARGET_INSTANT_TIME` | +| `SCHEMA` | +| `COMMAND_BLOCK_TYPE` | +| `COMPACTED_BLOCK_TIMES` | +| `RECORD_POSITIONS` | +| `BLOCK_IDENTIFIER` | +| `IS_PARTIAL` | +| `BASE_FILE_INSTANT_TIME_OF_RECORD_POSITIONS` | + +When using native log format, the log file name uses a suffix like `.<native format>`, for e.g., `.parquet`. + +### Why native file format over embedded Parquet log blocks? + +An alternative approach is to keep the V1 log format structure and embed Parquet-encoded data as block content. However, the embedding approach has drawbacks compared to using native Parquet files: + +| Aspect | Embedded Parquet (V1) | Native Parquet (V2) | +|---------------------------|----------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------| +| **Parquet optimizations** | Vectorized reads, predicate pushdown, column pruning available after block location via InLineFileSystem | Available directly via standard Parquet read path | +| **Write model** | Designed for append (for HDFS, not for object storage) | Write-once model (aligns with object storage) | +| **Reading overhead** | Must read log block header first, then use InLineFileSystem abstraction with offset translation to access embedded content | Read Parquet footer for metadata, then direct file read (no InLineFileSystem) | +| **Tool compatibility** | Requires Hudi-specific readers | Any Parquet-compatible tool can read | +| **Compression** | Block-level only | Parquet's columnar encoding | +| **Schema storage** | Duplicated in header and content | Consolidated in Parquet footer | + +Using native log file format can also be extended to other file format, like [Lance](https://lance.org/format/file/) for example. The Hudi log format metadata can be stored in Lance file's [global buffer](https://lance.org/format/file/#external-buffers) to facilitate log file operations. + +### Block type handling + +**Data log**: The data file is a native Parquet file with `hudi.log.block_type` = `parquet_data`. The Parquet schema is the writer's table schema, including Hudi metadata columns (`_hoodie_commit_time`, `_hoodie_commit_seqno`, `_hoodie_record_key`, `_hoodie_partition_path`, `_hoodie_file_name`), followed by the user-defined data columns. +The schema is stored natively in the Parquet footer (no duplication with header metadata). The `hudi.log.format.metadata` footer entry carries the serialized metadata map as described above. + +**Delete log**: Store delete records as Parquet rows using a fixed delete schema. Set `hudi.log.block_type` = `delete`. The delete file is written in the same native Parquet format as data blocks — only the schema and block type differ. The `hudi.log.format.metadata` footer entry carries the same serialized metadata map. + +Delete block Parquet schema: + +```text +message hudi_delete_block { + required binary record_key (STRING); + // optional binary partition_path (STRING); // this is useless + optional binary ordering_val; +} +``` + +- `record_key`: the record key of the record to delete. +- `partition_path`: the partition path where the record resides(useless, will remove in the V2 format). +- `ordering_val`: the ordering value used for ordering records, stored as raw bytes in its native representation. The logical type is determined by the table's ordering field schema. This field is null when no ordering value is present. + +Note: Iceberg and Delta Lake use position-based deletion vectors. These are fundamentally different from Hudi's key-based delete blocks which identify records by key and carry ordering values for merge semantics. +However, since Hudi already tracks record positions via Roaring64NavigableMap bitmaps in log block metadata (`RECORD_POSITIONS`), +it may be possible to additionally emit position-based deletion vectors alongside delete blocks for cross-format interoperability. This is out of scope for this RFC but worth exploring in a follow-up. + +**Command blocks**: this is deprecated, not supported in the new layout. + +**CDC blocks**: Same structure as data blocks with CDC schema. Set `hudi.log.block_type` = `cdc`. + +### Compatibility + +1. **File naming**: Use `.parquet` as log file suffix, needs file name parsing support +2. **Writer changes**: In V2, writing produces a new `.parquet` log file. +3. **Reader changes**: Detect format via suffix (`.log` for V1, `_<version>.parquet` for V2) and apply the corresponding read path +4. **Merging**: The established merging semantics (ordering, deduplication, delete handling) apply regardless of format + +| Scenario | Behavior | +|:------------------------------------|:-------------------------------------------| +| V2 reader reading V1 files | Supported (detect by file suffix) | +| V1 reader reading V2 files | Not supported (expected for older readers) | +| V1 and V2 file groups in same table | Supported | + +Will introduce a new table version in the table config, and by default it writes logs as format v2. If the user still wants to write old log files, +they need to specify the `hoodie.write.table.version` explicitly. + +**Migration path**: After enabling V2, existing file groups starts writing V2 log files. New file groups created after enabling V2 use V2 format directly. + +### How native log format (v2) relates to LSM tree layout + +Using the native log file format does not depend on using LSM tree layout. Tables will unlock the benefits as described in this section regardless of using the default or LSM tree layout. + +## Hybrid Compaction Strategy + +Hudi currently supports two compaction types: + +- Full compaction (`WriteOperationType.COMPACT`): merge log files with the target base file within the same file group and produce a new base file +- Log compaction (`WriteOperationType.LOG_COMPACT`): merge multiple log files into a larger log file within the same file group + +The key limitation is the operational overhead: users have to schedule 2 types of compaction, configure separate strategies, and run different jobs to optimize the storage layout for MOR tables. + +In this RFC, we introduce a new hybrid compaction strategy that combines full compaction and log compaction. The compaction plan tracks individual file groups and determines which compaction type each file group needs. + +- Triggering: same as existing triggering strategy (`NUM_COMMITS`, `TIME_ELAPSED`, etc). +- Scheduling/Planning: for each file group, analyze its file storage pattern (base file size, log file size distribution, number of log files, etc), assign each compaction operation to a type of `FULL` (compaction) or `LOG` (log compaction). + - Save plan to timeline (`.compaction.requested`) +- Execution: read the plan, execute either full compaction or log compaction for each file group as per the plan. + - Upon finish, commit to timeline as `.commit`. + +### Compaction plan metadata changes + +Today, compaction type is decided at scheduling time by `WriteOperationType`, so a plan executes as either all FULL or all LOG. To support hybrid plans: + +- Add an `operationType` field to `HoodieCompactionOperation` (in `HoodieCompactionOperation.avsc`): + + ```json + { + "name": "operationType", + "type": ["null", "string"], + "default": null, + "doc": "FULL or LOG. null defaults to FULL for backward compatibility." + } + ``` + +- Bump `LATEST_COMPACTION_METADATA_VERSION` in `CompactionUtils` (`2` → `3`) +- Mark the plan as hybrid via `strategy.compactorClassName` (or an equivalent explicit plan marker) + +### Planning: per-file-group type assignment + +A new `HybridCompactionPlanGenerator` assigns `operationType` per operation based on file group analysis: + +| Condition | Assigned type | Rationale | +|:------------------------------------------------------------------|:--------------|:----------------------------------------------------------------| +| No base file, or base file below size threshold | `FULL` | Rewrite is cheap, produces optimal base file | +| Large base file + many small log files | `LOG` | Avoid rewriting large base; merge logs first | +| Total log size exceeds ratio threshold of base file (e.g., > 50%) | `FULL` | Full rewrite is worthwhile when logs are large relative to base | + +Thresholds can reuse existing compaction configs: `hoodie.compaction.logfile.size.threshold` (total log size) and `hoodie.compaction.logfile.num.threshold` (log file count). + +### Execution: per-operation dispatch + +Currently, `HoodieCompactor.compact(context, operationType, plan, ...)` dispatches the full plan to one path: `compact()` (via `HoodieMergeHandle`) or `logCompact()` (via `FileGroupReaderBasedAppendHandle`). Under this proposal, execution dispatches per operation by reading `operationType`: + +- `FULL` → existing `compact()` path (merge handle, produces new base file) +- `LOG` → existing `logCompact()` path (append handle, produces compacted log file) + +### Timeline and file system view + +Hybrid compaction uses the existing `COMPACTION_ACTION` timeline track (`.compaction.requested` / `.compaction.inflight`). No new timeline action type is introduced. + +`HoodieTableFileSystemView` currently keeps separate maps (`fgIdToPendingCompaction` and `fgIdToPendingLogCompaction`), each populated from its respective pending compaction filter on the timeline. Under this proposal, when reading a hybrid plan, the view inspects each operation's `operationType` and routes it accordingly: `FULL` to `fgIdToPendingCompaction`, `LOG` to `fgIdToPendingLogCompaction`. + +Under this proposed hybrid execution model, a `.commit` that includes LOG-type operations (producing log files instead of base files for some file groups) should not break readers. Hudi readers discover files by storage listing and file naming conventions, not by instant action type. The file system view, file group reader, cleaner, CDC extractor, and incremental query readers already handle file slices with or without base files. + +### Benefits + +- More optimized storage layout: hybrid compaction strategy gives more granular control of file sizes at file group level, for example, skewed file groups can receive more compaction, file group with large base file and many small log files prefer log compaction first +- Less operational overhead: 1 compaction strategy with 1 set of configs to manage scheduling and execution + +### How hybrid compaction strategy relates to LSM tree layout + +The hybrid compaction strategy does not depend on using LSM tree layout. + +- When LSM tree layout is enabled, the hybrid compaction will engage sorted-merge algorithm to perform log and base file merging or log file merging. +- When LSM tree layout is not enabled, the hybrid compaction will use hash-based merging (existing behavior). + +--- + +## Configuration + +The LSM tree layout can be enabled using a table config: + +```properties +# default value: "default" +hoodie.table.storage.layout=lsm_tree Review Comment: clarified in the new commit. -- This is an automated message from the Apache Git Service. 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