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@amindadgar amindadgar commented Jun 4, 2025

Summary by CodeRabbit

  • Refactor
    • Improved document loading by processing documents in parallel batches for faster and more reliable ingestion of large datasets.

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"""

Walkthrough

The load method in the MediawikiETL class was updated to process the input documents in parallel batches of 1000 using a ThreadPoolExecutor. Each batch is submitted concurrently to the ingestion pipeline, with logging for success and error handling to halt on exceptions.

Changes

File(s) Change Summary
hivemind_etl/mediawiki/etl.py Updated MediawikiETL.load to split documents into batches of 1000 and process them concurrently with error handling and logging.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant MediawikiETL
    participant ThreadPoolExecutor
    participant IngestionPipeline

    User->>MediawikiETL: load(documents)
    MediawikiETL->>ThreadPoolExecutor: submit batch ingestion tasks
    ThreadPoolExecutor->>IngestionPipeline: run_pipeline(batch)
    IngestionPipeline-->>ThreadPoolExecutor: result or exception
    ThreadPoolExecutor-->>MediawikiETL: future completion
    MediawikiETL->>MediawikiETL: log success or error
    MediawikiETL->>MediawikiETL: log total documents loaded
Loading

Poem

🐇 Batches hop in parallel streams,
Ingestion flows like swift moonbeams.
Logs keep watch, no error slips,
Threads dance fast with nimble tips.
A thousand docs, a joyful race—
Rabbits cheer this speedy pace! 🌟


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Reviewing files that changed from the base of the PR and between 0948b55 and d377801.

📒 Files selected for processing (1)
  • hivemind_etl/mediawiki/etl.py (2 hunks)
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Actionable comments posted: 1

🧹 Nitpick comments (2)
hivemind_etl/mediawiki/etl.py (2)

103-103: Consider making batch size configurable.

The hardcoded batch size of 1000 might not be optimal for all use cases. Consider making it configurable through the constructor or as a parameter.

For example, add a batch_size parameter to the constructor:

 def __init__(
     self,
     community_id: str,
     namespaces: list[int],
     platform_id: str,
     delete_dump_after_load: bool = True,
+    batch_size: int = 1000,
 ) -> None:

Then use self.batch_size in the load method.


104-106: Consider adding error handling for individual batches.

If one batch fails during ingestion, the entire process will stop. Consider adding error handling to log failures and continue with remaining batches, or at least provide better error context.

Example implementation:

 for i in range(0, len(documents), batch_size):
     batch_num = (i // batch_size) + 1
     total_batches = (len(documents) + batch_size - 1) // batch_size
     logging.info("Loading batch %d/%d into Qdrant!", batch_num, total_batches)
+    try:
         ingestion_pipeline.run_pipeline(documents[i : i + batch_size])
+    except Exception as e:
+        logging.error("Failed to load batch %d/%d: %s", batch_num, total_batches, e)
+        raise
🧰 Tools
🪛 Pylint (3.3.7)

[warning] 105-105: Use lazy % formatting in logging functions

(W1203)

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 9c76678 and 0948b55.

📒 Files selected for processing (1)
  • hivemind_etl/mediawiki/etl.py (1 hunks)
🧰 Additional context used
🪛 Pylint (3.3.7)
hivemind_etl/mediawiki/etl.py

[warning] 105-105: Use lazy % formatting in logging functions

(W1203)

⏰ Context from checks skipped due to timeout of 90000ms (1)
  • GitHub Check: ci / build-push / Build + Push Image

@amindadgar amindadgar merged commit 74b2348 into main Jun 4, 2025
3 checks passed
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2 participants