xiarixiaoyao commented on code in PR #7080: URL: https://github.com/apache/hudi/pull/7080#discussion_r1018740543
########## rfc/rfc-64/rfc-64.md: ########## @@ -0,0 +1,509 @@ +<!-- + 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-64: New Hudi Table Spec API for Query Integrations + +## Proposers + +- @codope +- @alexeykudinkin + +## Approvers + +- @xiarixiao +- @danny0405 +- @vinothchandar +- @prasanna +- @xushiyan + +## Status + +JIRA: [HUDI-4141](https://issues.apache.org/jira/browse/HUDI-4141) + +## Abstract + +In this RFC we propose new set of higher-level Table Spec APIs that would allow +us to up-level our current integration model with new Query Engines, enabling +faster turnaround for such integrations. + +## Background + +[WIP] Plan + +- [x] High-level capabilities API will be supporting +- [ ] High-level overview of integration points of the query engines +- [ ] Deep-dive into existing Spark/Flink/Trino integration points + +The figure below shows the read path in Spark and Presto/Trino query engines. At +a high level, integration with these engines require providing of the following capabilities: + +1. **Listing**: this stage requires enumerating of all of the data files w/in a table representing particular +snapshot of its state, that will be subsequently scanned to fetch the date requested by the target query. All query engines +expect output of this stage in the form of balanced out file "splits" that way allowing to even out any skew in +file sizes. This stage is where various _pruning_ techniques such as partition-pruning, file-level column +statistics filtering are applied. + +2. **Reading**: this stage transforms every file-split (identified in a previous stage) into an iterator over +the records persisted in it. This stage is where actual data shaping to suit the needs of particular query takes +place: records are projected into a schema expected by the query, corresponding filters are pushed down to +reduce amount of data fetched from storage, schema evolution is handled, delayed operations are reconciled (merging/deleting) + + + +## Implementation + +[WIP] Plan + +- [x] Components & API pseudo-code +- [x] Diagram show-casing data flow and integration points +- [ ] Example integration walk-through + +With this RFC, we aim to achieve following: + + - Up-level our current integration model with Query Engines, abstracting away Hudi's lower-level components, + instead providing simple and eloquent abstractions providing necessary capabilities (for listing, reading) + + - Make sure Hudi's APIs are high-level enough to be useful in providing programmatic access to the data + for users willing to access it directly + +To achieve that, we propose to introduce two tiers of APIs: + +1. **Mid-level** (engine-friendly): these APIs will be abstracting away Hudi's internals, behind + simple and familiar concepts and components such as file splits, iterators, + statistics, etc. + +2. **High-level** (user-friendly): these APIs will provide programmatic access to Hudi's capabilities + bypassing query engine interfaces (like SQL, Spark DS, etc) + +Following classic [layer-cake architecture](https://cs.uwaterloo.ca/~m2nagapp/courses/CS446/1195/Arch_Design_Activity/Layered.pdf) +would allow us to abstract away complexity of the lower levels components and APIs from +Query Engines (on the *querying* side) as well as from the end users: + + - Mid-level components will internally leverage Hudi's core building blocks (such as +`FileSystemView`, `FileIndex`, `MergedLogRecordScanner`, `FileReader` etc), while exposing APIs +providing capabilities expected by the Query Engines (listed above) + + - High-level components will be stacked on top of mid-level ones and can be used directly by users to +read and write to tables. + +These APIs will be built bottoms-up, initially focusing on the mid-level +components, then higher-level ones. Once the mid-level components are ready, we could +demonstrate their utility by integrating with one of the query engines. + +In the initial phase of this project we will be focusing the effort on the read-side of the integration, +shifting focus to the write-side in the subsequent phase. + +### Components & API + +Open Qs + +1. ~~Some of the models defined here do already exist and might be representing + lower-level components. Do we expose them in a higher level APIs or do we + bifurcate and hide them as internal impl details (for ex, `HoodieFileGroup` + vs `HoodieInternalFileGroup`)?~~ + 1. *We'll be re-using existing models abstracting them behind projected interfaces + exposing only necessary functionality, hiding away complexity of the whole impl* +2. ~~What about Expressions? Wrapping is going to be hard because we need to + analyze expression structure which is not going to be possible if we wrap.~~ + 1. We will need to introduce our own Expression hierarchy supporting a *superset* + of the commonly used expressions. While this requires quite some heavy-lifting + initially this unfortunately is the only option, provided that we need to + have deep introspections into these expression tress (in the analyses we run + w/in Column Stats Index, Partition Pruning, etc) + 2. However, we won't be implementing an Expression Execution Engine: for execution + we will continue to rely on execution engine machinery simply relaying from + our own representation into the engine-specific one +3. Do we need to implement Catalog backend? We’d be fine with just the session + catalog for now. + +#### Models + +We define following models as core building blocks of the new mid-/high-level APIs/ + +**TableId** is used to identify individual table (for ex, w/in catalog) + +```java +class TableId { + final String databaseName; + final String tableName; +} +``` + +**TableMeta** captures basic meta information of the table + +```java +class TableDef { + final TableId tableId; + + // Table's base path + final String basePath; + // Table's latest schema + final InternalSchema schema; + // Table's type (COW, MOR) + final HoodieTableType tableType; + + // ... +} +``` + +##### File Group/Slice + +In Hudi's glossary, following terms could be observed frequently: + + - File Group: collection of snapshots (or versions) of the group of records, where individual snapshot + is called *file slice* + - File Slice: individual snapshot(s) of the group of records + +Physically file slice is usually represented as + +- For COW: a *base file*, representing a single snapshot (version) of the group of records, + co-located in a single columnar-based file) + +- For MOR: a *base file* and corresponding delta-log files. For MOR, unlike COW, *file slice* actually +constitutes collection of snapshots (versions) of the group of records where: + - Base file represents initial snapshot/version of the group of records + - Delta-log files encode updated versions of the records stored in the base file + +```java +// NOTE: This model is the same as an existing one (ref'd here for notational purposes) +class HoodieFileSlice { + // Id of the file-group this slice belongs to + HoodieFileGroupId fileGroupId; + // Base file's path + String baseFilePath; + // Ordered list of the delta-log files + TreeSet<HoodieLogFile> logFilesPaths; + // ... +} +``` + +NOTE: In case of COW set of records within the File Group wouldn't change. + +```java +// NOTE: This model is the same as an existing one (ref'd here for notational purposes) +class HoodieFileGroup { + // Id of the file-group + HoodieFileGroupId id; + // Projection of the timeline of committed actions affecting file-slices in this group + HoodieTimeline timeline; + // File-slices ordered by corresponding instant they've been committed at + TreeMap<HoodieInstant, FileSlice> fileSlices; + // ... +} +``` + +**PartitionDescriptor** describes particular partition within the dataset. + +```java +class PartitionDescriptor { + // Relative partition path within the dataset + String partitionPath; + // List of values for corresponding partition columns (partition values) + List<Object> partitionColumnValues; +} +``` + +**PartitionSnapshot** represents a state of a single table's partition at any +particular instant T, comprised of the file-slices that are the _latest_ ( +current) within their corresponding file-groups at the instant T. + +NOTE: Non-partitioned tables assumed to have 1 partition enclosing the whole +table. + +```java +class PartitionSnapshot { + PartitionDescriptor descriptor; + // (Latest) Instant T as of which partition's state is represented + HoodieInstant latestInstant; + // File-slices current at the instant T + List<HoodieFileSlice> fileSlices; +} +``` + +**PartitionIncrementalSnapshot** represents a state of a single table's +partition at a particular instant T2, while only considering commits that +occurred in the table after T1 (ie within the timeline T1 -> T2) +comprised of the file-slices that are the _latest_ (current) within their +corresponding file-groups at instant T2. + +NOTE: `PartitionSnapshot` is a special case of `PartitionIncrementalSnapshot` where +T1 = 0. + +```java +class PartitionIncrementalSnapshot { + PartitionDescriptor descriptor; + // Timeline starting at T1 and ending at T2, completed actions thereof + // represent state of the partition + HoodieTimeline timeline; + // File-slices current at the instant T_y + List<HoodieFileSlice> fileSlices; +} +``` + +**ReadingMode** represents the query type. + +```java +enum ReadingMode { + // Representing state of the table at a particular instant T + SNAPSHOT, + // (MOR only) Representing state of the table at a particular instant T, + // reading exclusively base-files (ie, ignoring any delta-logs if present) + SNAPSHOT_READ_OPTIMIZED, + // Representing state of the table at a particular instant T_y assuming, + // as an initial instant Tx (snapshot is a specification of this mode, where T_x = 0) + INCREMENTAL, + // Representing state of the table as a stream of CRUD operations on + // individual records + CDC +} +``` + +**Expression** (**Predicate**) + Review Comment: only use for dataSkipping/partition prune ？ we donnot think we should use those predicate for engine scan action。 -- This is an automated message from the Apache Git Service. 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