Fix file chunks list creation

[clostao]

PR Type

Bug fix

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Description

• Fixed the file chunks list creation by filtering nodes to include only those with decodable data.
• Corrected the totalChunks calculation to reflect the actual number of valid chunks.
• Updated the chunks array to map only the filtered nodes, ensuring accurate metadata.

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Changes walkthrough 📝

	Relevant files
Bug fix	file.ts Fix and optimize file chunks list creation logic                  packages/auto-drive/src/metadata/offchain/file.ts Added imports for IPLDNodeData and MetadataType. Filtered nodes to include only those with decodable data. Corrected the calculation of totalChunks based on filtered nodes. Updated chunks mapping to use filtered nodes. +7/-3

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[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Error Handling Ensure that the decode operation in the filter for chunks does not throw an error or handle potential exceptions that might arise from corrupt or unexpected data. Data Integrity Verify that the filtering and mapping logic correctly reflects the intended metadata structure and that no valid data is inadvertently filtered out.

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Score
Possible bug	Add error handling to the data decoding process to enhance robustness Ensure that the IPLDNodeData.decode(n.Data).data check in the filter function is robust against potential decoding failures or unexpected data formats. Consider adding error handling or validation to prevent runtime errors. packages/auto-drive/src/metadata/offchain/file.ts [24-26] const chunks = Array.from(dag.nodes.values()).filter( - (n) => n.Data && IPLDNodeData.decode(n.Data).data, + (n) => n.Data && safelyDecodeIPLDNodeData(n.Data)?.data, ) Suggestion importance[1-10]: 8 Why: Adding error handling to the data decoding process is a significant improvement that can prevent runtime errors due to unexpected data formats or decoding failures, enhancing the robustness of the code.	8
Possible issue	Ensure proper handling of missing data scenarios Verify that the chunk.Data?.length ?? 0 fallback to 0 is intentional and appropriate. If Data is expected to be non-null in valid cases, consider throwing an error or logging a warning when Data is missing. packages/auto-drive/src/metadata/offchain/file.ts [37] -size: chunk.Data?.length ?? 0, +size: chunk.Data?.length ?? logMissingDataError(), Suggestion importance[1-10]: 7 Why: The suggestion to handle missing data scenarios by logging an error or warning is valuable for debugging and ensuring data integrity, though it assumes that missing data is an exceptional case.	7
Enhancement	Improve variable naming for clarity Consider using a more descriptive variable name than chunks for the filtered list of nodes to reflect that these nodes contain valid data. packages/auto-drive/src/metadata/offchain/file.ts [24-26] -const chunks = Array.from(dag.nodes.values()).filter( +const validDataNodes = Array.from(dag.nodes.values()).filter( (n) => n.Data && IPLDNodeData.decode(n.Data).data, ) Suggestion importance[1-10]: 6 Why: Using a more descriptive variable name like validDataNodes improves code readability and clarity, making it easier to understand that the nodes contain valid data.	6
Performance	Optimize performance by caching decoded data To improve performance, consider caching the results of IPLDNodeData.decode(n.Data) if it is computationally expensive and used multiple times for the same node data. packages/auto-drive/src/metadata/offchain/file.ts [24-26] +const decodedDataCache = new Map(); const chunks = Array.from(dag.nodes.values()).filter( - (n) => n.Data && IPLDNodeData.decode(n.Data).data, + (n) => n.Data && getOrDecodeData(n, decodedDataCache).data, ) Suggestion importance[1-10]: 5 Why: Caching decoded data can improve performance if the decoding process is computationally expensive and the same data is used multiple times, though the actual performance gain depends on the specific use case.	5