训练LLM时处理数据的最佳实践

You are correct in your general observation, but let's clarify the concepts:

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1. Structured Data in LLM Training vs Traditional DL

• Traditional DL models (like those for tabular data, classical ML, or even some CV tasks) often use data that is highly structured:

  • Rows and columns (features), usually stored in CSV, Parquet, SQL, or Hive tables.
  • Examples: customer data, sensor logs, financial records, etc.

• LLMs (Large Language Models) are primarily trained on unstructured data:

  • Raw text, documents, code, web pages, etc.
  • The "structure" is usually just the text itself, sometimes with minimal metadata (source, timestamp, etc.).
  • Text datasets are often stored as plain text files, JSON, or binary formats optimized for sequential reading (e.g., TFRecord, Arrow, WebDataset, etc.).

Conclusion:

The proportion of structured data is much lower in LLM training compared to traditional DL tasks.

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2. Effectiveness of Hive Table Storage for LLM Training

• Hive tables are optimized for storing and querying structured data (columns, rows, SQL queries).
• For LLM training, your data is mostly unstructured text, and you typically need to stream large amounts of data efficiently for training.

Potential Issues with Hive for LLM:

• Inefficient for unstructured, large text blobs: Hive is not optimized for storing and reading massive unstructured text files.
• Slow data loading: Reading large text fields row-by-row from a distributed SQL store is much slower than streaming from flat files or specialized binary formats.
• Limited support for sharding and parallel streaming: Modern LLM training frameworks (like HuggingFace Datasets, PyTorch DataLoader, WebDataset, etc.) are optimized for file-based or object storage, not SQL/Hive.

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3. Best Practices for LLM Training Data Storage

• Use flat files (txt, jsonl, parquet, arrow) stored in distributed file systems (HDFS, S3, OSS, etc.).
• Sharding: Split large datasets into many smaller files for efficient parallel loading.
• Optimized formats: Consider formats like WebDataset, Arrow, or TFRecord for high-throughput streaming.

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Summary Table

Data Type	Typical Storage (Traditional DL)	Typical Storage (LLM)	Hive Table Effective?
Structured (tabular)	CSV, SQL, Hive, Parquet	Rarely used	Yes
Unstructured (text)	Rarely used	Flat files, jsonl, arrow	No (inefficient)

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Conclusion

• Yes, in LLM training, structured data is much less common than in traditional DL models.
• Storing LLM training data in Hive tables is generally less effective and not recommended for large-scale LLM training.
• Flat files and optimized streaming formats are the preferred choice for LLM data pipelines.

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If you have to use Hive for some reason (e.g., as a data source), it's best to export the data to flat files before training.