the-other-tim-brown commented on code in PR #18013:
URL: https://github.com/apache/hudi/pull/18013#discussion_r2771797220
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rfc/rfc-100/rfc-100.md:
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@@ -63,153 +63,129 @@ column that already exists.
### Building on Existing Foundation
-This RFC leverages two key foundation pieces:
+This RFC leverages foundation pieces:
-1. **RFC-80 Column Groups**: Provides the mechanism to split file groups
across different column groups, enabling efficient storage of different data
types within the same logical file group.
-
-2. **RFC-99 BLOB Types**: Introduces BINARY and LARGE_BINARY types to the Hudi
type system, providing the type foundation for unstructured data storage.
+1**RFC-99 BLOB Types**: Introduces BLOB types to the Hudi type system,
providing the type foundation for unstructured data storage.
## Requirements
Below are the high-level requirements for this feature.
1. Users must be able to define tables with a mix of structured (current
types) and unstructured (blob type)
columns
-2. Records are distributed across file groups like regular Hudi storage layout
into file groups. But within each
- file group, structured and unstructured columns are split into different
column groups. This way the table can
- also scalably grow in terms of number of columns.
-3. Unstructured data can be stored inline (e.g small images right inside the
column group) or out-of-line (e.g
- pointer to a multi-GB video file someplace). This decision should be made
dynamically during write/storage time.
+2. Unstructured data can be stored inline (e.g small images right inside the
file) or out-of-line (e.g
+ pointer to a multi-GB video file someplace). Out-of-line references can
also include a position within the file which
+ allows multiple blobs to be stored within a single file to reduce the
number of files in storage.
+ This decision should be made dynamically during write/storage time.
3. All table life-cycle operations and table services work seamlessly across
both column types.for e.g cleaning
- the file slices should reclaim both inline and out-of-line blob data.
Clustering should be able re-organize
- records across file groups or even redistribute columns across column
groups within the same file group.
-4. Storage should support different column group distributions i.e different
membership of columns
- across column groups, across file groups, to ensure users or table services
can flexibly reconfigure all this as
- table grows, without re-writing all of the data.
-5. Hudi should expose controls at the writer level, to control whether new
columns are written to new column
- groups or expand an existing column group within a file group.
+ the file slices should reclaim out-of-line blob data when the reference is
managed by Hudi.
+ Clustering should be able re-organize records across file groups or even
repack blobs if required.
+4. Hudi should expose controls at the writer level, to control whether to
store blobs inline or out-of-line
+ based on size thresholds.
+5. Query engines like Spark should be able to read the unstructured data and
materialize the values lazily to reduce memory pressure during shuffles.
## High-Level Design
-The design introduces a hybrid storage model where each file group can contain
multiple column groups with different file formats optimized for their data
types. Structured columns continue using
-Parquet format, while unstructured columns can use specialized formats like
Lance or optimized Parquet configurations or HFile for random-access.
-
-### 1. Mixed Base File Format Support
-
-**Per-Column Group Format Selection**: Each column group within a file group
can use different base file formats:
-- **Structured Column Groups**: Continue using Parquet with standard
optimizations
-- **Unstructured Column Groups**: Use Lance format for vector/embedding data
or specially configured Parquet for BLOB storage
-
-**Format Configuration**: File format is determined at column group creation
time based on (per the current RFC-80).
-But, ideally all these configurations should be automatic and Hudi should
auto-generate colum group names and mappings.
-
-
-```sql
-CREATE TABLE multimedia_catalog (
- id BIGINT,
- product_name STRING,
- category STRING,
- image BINARY,
- video LARGE_BINARY,
- embeddings ARRAY<FLOAT>
-) USING HUDI
-TBLPROPERTIES (
- 'hoodie.table.type' = 'MERGE_ON_READ',
- 'hoodie.bucket.index.hash.field' = 'id',
- 'hoodie.columngroup.structured' = 'id,product_name,category;id',
- 'hoodie.columngroup.images' = 'id,image;id',
- 'hoodie.columngroup.videos' = 'id,video;id',
- 'hoodie.columngroup.ml' = 'id,embeddings;id',
- 'hoodie.columngroup.images.format' = 'parquet',
- 'hoodie.columngroup.videos.format' = 'lance',
- 'hoodie.columngroup.ml.format' = 'hfile'
-)
-```
-
-### 2. Dynamic Inline/Out-of-Line Storage
+The design introduces an abstraction that allows inline and out-of-line
storage of byte arrays that work seamlessly for the end user. Structured
columns continue using
+Parquet format, while unstructured data can use specialized formats like Lance
or optimized Parquet configurations or HFile for random-access.
-**Storage Decision Logic**: During write time, Hudi determines storage
strategy based on:
-- **Inline Storage**: BLOB data < 1MB stored directly in the column group
file, to avoid excessive cloud storage API calls.
-- **Out-of-Line Storage**: Large BLOB data stored in dedicated object store
locations with pointers in the main file, to avoid write amplification during
updates.
+### 1. Storage Abstraction
+We will add a Blob type to the HoodieSchema that encapsulates both inline and
out-of-line storage strategies. This will allow the user to use a mix of
storage strategies seamlessly.
Review Comment:
Yes, it will be different. I am wondering if we want to include a schema
evolution option to evolve binary fields to blobs when users upgrade their
tables.
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