Data Storage Formats (Text, Arrow, Parquet)

Large language models rely on massive text corpora, which must be stored efficiently and accessed rapidly during training. These corpora typically undergo scraping, cleaning, and preprocessing. Choosing the right storage format is a foundational decision that impacts storage costs, I/O performance, and the overall efficiency of your data loading pipeline. While simple formats are easy to start with, specialized formats become increasingly beneficial as datasets scale into terabytes or petabytes.

Let's examine the common choices: plain text files, Apache Arrow, and Apache Parquet.

Plain Text Formats (e.g., .txt, .jsonl)

The most straightforward approach is often storing text data in plain text files. Each line might represent a document, or you might use formats like JSON Lines (.jsonl), where each line is a valid JSON object representing a data record (e.g., containing text and metadata).

Pros:

• Simplicity: Easy to create, inspect, and debug manually. Standard text processing tools (grep, sed, awk) work directly.
• Universality: Readable by virtually any programming language or tool without special libraries.

Cons:

• Inefficiency: Text parsing (especially complex JSON) can be CPU-intensive during data loading.
• Compression: Standard compression like Gzip or Zstandard can be applied, but columnar formats often achieve better compression ratios due to data similarity within columns.
• No Schema/Typing: The format itself doesn't enforce data types or structure, leading to potential errors downstream if parsing assumptions are violated.
• Slow Random Access: Accessing specific records requires scanning and parsing from the beginning or maintaining separate indexes.

A typical .jsonl file might look like this:

{
    "text": "The quick brown fox jumps over the lazy dog.",
    "source": "wikipedia",
    "id": "doc_001"
}
{
    "text": "Large language models require immense amounts of text data.",
    "source": "common_crawl",
    "id": "cc_abc"
}
{
    "text": "...",
    "source": "...",
    "id": "..."
}

Reading this in Python is simple, but potentially slow for very large files, especially if deserializing complex JSON:

import json
import gzip

def read_jsonl_gz(filepath):
    """Reads records from a gzipped JSON Lines file."""
    with gzip.open(filepath, 'rt', encoding='utf-8') as f:
        for line in f:
            try:
                yield json.loads(line)
            except json.JSONDecodeError:
                # Handle or log malformed lines
                print(f"Skipping malformed line: {line.strip()}")

# Usage example
# dataloader would iterate through this generator
# data_generator = read_jsonl_gz("my_large_dataset.jsonl.gz")
# for record in data_generator:
#    process(record['text'])

While acceptable for smaller datasets or initial prototyping, the CPU overhead of parsing text line-by-line becomes a bottleneck when feeding multiple GPUs training an LLM that consumes data at high speeds.

Apache Arrow

Apache Arrow is an in-memory columnar data format standard. It's designed for analytical query performance and efficient data interchange between systems and languages with minimal serialization/deserialization overhead (often zero-copy reads).

Concepts:

• Columnar Layout: Data is organized by column, not by row. All values for a specific field (e.g., the 'text' field) are stored contiguously in memory.
• Zero-Copy Reads: Processes can often access Arrow data structures in memory without copying them, significantly speeding up data access, especially in inter-process communication (like between a data loading process and the main training process).
• Language Agnostic: Official libraries exist for many languages (Python, Java, C++, Rust, etc.), making it excellent for pipelines involving different technologies.
• Rich Data Types: Supports a comprehensive set of data types, including nested structures, timestamps, and numerical types.

Pros:

• Fast Reads/Scans: Columnar layout is cache-friendly and allows for vectorized operations (using SIMD instructions), making sequential reads very fast.
• Efficient Interoperability: Ideal for passing data between processes (e.g., using shared memory) or between libraries (e.g., Pandas DataFrames can be converted to/from Arrow tables almost instantly).
• Memory Efficiency: Often more memory-efficient than Python object representations for the same data.

Cons:

• In-Memory Focus: While Arrow has a file format (.arrow or .feather), it's primarily optimized for in-memory representation. Parquet is generally preferred for persistent, compressed storage on disk.
• Write Overhead: Constructing Arrow arrays can sometimes have higher overhead than writing plain text.
• Less Human-Readable: Binary format, requires specific tools or libraries to inspect.

Arrow is heavily used under the hood by libraries like Hugging Face datasets. When you load or map datasets using datasets, it often uses Arrow tables internally for caching and fast access.

import pyarrow as pa
import pyarrow.feather as feather
import time

# Example: Creating and writing an Arrow Table
# Assume 'data' is a list of dictionaries like [{'text': '...', 'id': '...'}, ...]
# Convert Python objects to Arrow Arrays
texts = pa.array([d['text'] for d in data], type=pa.string())
ids = pa.array([d['id'] for d in data], type=pa.string())

# Create an Arrow Table
table = pa.Table.from_arrays([texts, ids], names=['text', 'id'])

# Write to Feather (Arrow file format)
feather.write_feather(table, 'my_dataset.arrow')

# Reading is typically very fast
start_time = time.time()
read_table = feather.read_table('my_dataset.arrow')
end_time = time.time()
print(f"Arrow read time: {end_time - start_time:.4f} seconds")

# Accessing a column is efficient
text_column = read_table['text']
# print(text_column[0].as_py()) # Access the first text entry

The real power comes when multiple stages of your pipeline use Arrow, avoiding costly serialization steps. A data preprocessing job running on Spark might output Arrow files directly to cloud storage, which are then read efficiently by a PyTorch DataLoader using pyarrow.

Apache Parquet

Apache Parquet is a widely adopted columnar storage format optimized for large-scale data warehousing and analytics, but also highly effective for storing LLM datasets on disk.

Concepts:

• Columnar On-Disk: Like Arrow, it stores data column by column, but it's designed for persistence on disk (e.g., HDFS, S3, local filesystem).
• Compression & Encoding: Offers excellent compression ratios by applying encodings (like dictionary encoding, run-length encoding) suitable for each column's data type and characteristics, followed by general-purpose compression (Snappy, Gzip, Zstd, Brotli).
• Schema Evolution: Supports schema evolution, allowing you to add columns later without rewriting old data.
• Predicate Pushdown: Storage systems can often filter rows based on query predicates directly within the Parquet file before loading data into memory (though this is more relevant for analytical queries than sequential training reads).

Pros:

• Storage Efficiency: Achieves significant disk space savings compared to text formats, reducing storage costs and network transfer times.
• Fast Sequential Scans: Efficiently reads columns needed for training, skipping irrelevant ones. Reading only the 'text' column is much faster than reading entire rows if other metadata exists.
• Ecosystem Integration: Excellent support in big data frameworks (Spark, Dask, Presto, Hive) and Python libraries (pandas, pyarrow).

Cons:

• Write Complexity: Writing Parquet files involves more computational overhead than plain text due to encoding and compression.
• Not Human-Readable: Binary format requiring specific tools.
• Row-Level Operations: Less efficient for operations requiring access to entire rows at once compared to row-based formats (though often still faster overall due to I/O savings).

Parquet is often the format of choice for the final, processed dataset that will be repeatedly read during training.

import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd # Often used as an intermediary

# Example using PyArrow directly (similar to Arrow Feather example)
# Assume 'table' is a pyarrow.Table as created in the Arrow example

# Writing a Parquet file (with Snappy compression by default)
pq.write_table(table, 'my_dataset.parquet', compression='snappy')

# Reading the Parquet file
start_time = time.time()
# Can read specific columns only, reducing I/O
read_parquet_table = pq.read_table('my_dataset.parquet', columns=['text'])
end_time = time.time()
print(
    f"Parquet read time (text column only): "
    f"{end_time - start_time:.4f} seconds"
)

# Example using Pandas (common workflow)
# df = pd.DataFrame(data) # data is list of dicts
# df.to_parquet('my_dataset_pd.parquet', engine='pyarrow', compression='snappy')
# read_df = pd.read_parquet('my_dataset_pd.parquet', engine='pyarrow', columns=['text'])

Comparison and Recommendations

Here's a summary of the trade-offs:

Feature	Text (.txt, .jsonl)	Apache Arrow	Apache Parquet
Type	Row-based	Columnar (In-Memory)	Columnar (On-Disk)
Primary Use	Simplicity, Debugging	Fast In-Memory Analytics, IPC	Efficient Storage, Analytics
Human Readable	Yes	No	No
Compression	External (Gzip, Zstd)	Limited (IPC often raw)	Excellent (Built-in)
Read Speed	Slow (Parsing Overhead)	Very Fast (Zero-Copy)	Fast (Columnar Scan)
Write Speed	Fast	Moderate	Moderate-Slow (Encoding)
CPU Usage (Read)	High (Parsing)	Low	Low-Moderate (Decoding)
Random Access	Poor	Moderate (In-Memory)	Moderate (Row Groups)
Ecosystem	Universal	Growing (Pandas, Spark)	Excellent (Big Data)

Comparison of common data storage formats for large text datasets.

Recommendations for LLM Datasets:

1. Intermediate Processing: Use Apache Arrow for efficient data transfer between stages of a complex preprocessing pipeline, especially if using tools like Spark or Dask, or passing data between Python processes. Libraries like Hugging Face datasets leverage Arrow heavily for caching and memory mapping.
2. Final Storage for Training: Store the final, large, cleaned dataset as Apache Parquet files on a distributed filesystem (like S3 or HDFS). Its compression significantly reduces storage costs, and its columnar nature allows efficient reading of only the necessary columns (usually just the text tokens) during training, maximizing I/O throughput. Use efficient compression like Snappy or Zstandard.
3. Small Datasets / Debugging: Plain text or JSON Lines can be acceptable for smaller experiments or when human readability is essential for debugging specific data points.

Choosing Parquet for your large-scale training data stored on cloud storage or HDFS, potentially read via libraries that use Arrow for in-memory representation (like datasets or custom DataLoaders using pyarrow.parquet), provides an efficient foundation for feeding data into your distributed training jobs. This minimizes I/O bottlenecks and storage footprint, allowing you to focus compute resources on the model training itself.