andormarkus-alcd opened a new issue, #1737:
URL: https://github.com/apache/iceberg-python/issues/1737
### Feature Request / Improvement
## Problem Statement
**I'm happy to submit a PR to implement this feature.**
PyIceberg currently provides functionality to add existing Parquet files to
an Iceberg table using `add_files()`, which is useful when files already exist
in a compatible format. However, the library lacks a convenient way to write
new Parquet files that are automatically compatible with the Iceberg table
format, specifically:
1. There's no straightforward API to write Parquet files that match an
Iceberg table's schema, partitioning, and other metadata requirements
2. Users currently need to implement complex logic to ensure schema
alignment, partition compatibility, etc.
3. This creates an unnecessary barrier for users wanting to write files that
can later be added to Iceberg tables without rewriting
## Use Case: High-Throughput Ingestion with AWS Lambda
We are currently using AWS Lambda functions to write to Iceberg tables. When
ingesting large volumes of files concurrently, we run into Lambda concurrency
limits because:
- The Parquet writing process is the most time-consuming part of the
operation
- The commit phase is relatively fast
By separating these operations (writing compatible Parquet files
independently, then committing via `add_files()` through a queue), we could
significantly increase our throughput. This would allow us to:
- Use Lambda functions to write compatible Parquet files in parallel
- Queue the much faster commit operations separately
- Use a second Lambda function to process these queued operations in bulk
(e.g., every minute), committing
multiple files at once rather than one by one
- Avoid concurrency limits that are currently bottlenecking our ingestion
pipeline
## Proposed Solution
Add a new API to PyIceberg that allows writing table-compatible Parquet
files. This could look something like:
```python
# Possible API design
writer = tbl.parquet_writer()
writer.write_dataframe(df) # No destination_path needed as table has
location info
# Or alternatively
tbl.write_parquet(df) # Writes to table's data location with appropriate
naming
# These files could then be added without rewriting
tbl.add_files() # Can discover compatible files in the table's data location
```
## The implementation would:
1. Automatically handle schema alignment
2. Apply correct partition transforms
3. Add appropriate metadata to ensure compatibility with add_files()
4. Set up Name Mapping appropriately
5. Generate files without field IDs in the Parquet metadata (as required by
add_files())
6. Use the table's location information to determine write paths
automatically
## Benefits
This would create a complete workflow for efficiently managing Iceberg
tables:
- Write compatible files
- Add them to tables without rewriting
- Perform normal maintenance operations
This new feature would simplify creating files that meet these requirements.
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