feat: adding script to read from lmdb #task

[raj-prince]

1. Creates two files:

2. Creation of data-set takes non-streaming path.

{"timestamp":{"seconds":1758478332,"nanos":191559913},"severity":"INFO","message":"File system has been successfully mounted."}
{"timestamp":{"seconds":1758478335,"nanos":361636957},"severity":"INFO","message":"Falling back to staged write for 'lmdb_test_env/lock.mdb'. Streaming write is limited to sequential writes on new/empty files."}
{"timestamp":{"seconds":1758478335,"nanos":891509953},"severity":"INFO","message":"Falling back to staged write for 'lmdb_test_env/data.mdb'. Streaming write is limited to sequential writes on new/empty files."}
{"timestamp":{"seconds":1758478336,"nanos":907035549},"severity":"INFO","message":"Falling back to staged write for 'lmdb_test_env/data.mdb'. Streaming write is limited to sequential writes on new/empty files."}

[gemini-code-assist]

Summary of Changes

Hello @raj-prince, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces new tooling designed to explore and analyze data access patterns within LMDB databases. It provides a Python script that can create an LMDB instance and then execute both simulated random-access reads, typical for machine learning batch processing, and efficient sequential reads. The inclusion of a requirements.txt simplifies environment setup, and an example analysis output file offers insights into the characteristics of random data access, likely serving as a foundational step for optimizing data loading strategies.

Highlights

• New LMDB Read Pattern Script: A new Python script, lmdb_read_pattern.py, has been added to demonstrate and analyze different data reading patterns from an LMDB database.
• LMDB Database Creation and Population: The script includes functionality to create an LMDB database, populating it with a specified number of samples (1000) of a defined size (128KB) for testing purposes.
• Random Read Simulation: A run_random_read function simulates a data loader's behavior by performing random access reads in batches, which is crucial for understanding performance in machine learning data pipelines.
• Sequential Read Implementation: A run_sequential_read function provides an efficient method for iterating and reading all entries sequentially from the LMDB database using a cursor.
• Dependency Management: A requirements.txt file has been added to explicitly list the necessary Python packages (numpy and lmdb) required to run the LMDB sample scripts.
• Read Pattern Analysis Output: An example output file, rand_read_pattern.txt, is included, which details the read ranges and summary statistics from a random read pattern analysis on an LMDB data file.

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[gemini-code-assist]

Code Review

This pull request introduces a script to demonstrate read patterns from an LMDB database. The script is a good starting point, but I have identified several areas for improvement. There is a critical issue with a hardcoded, user-specific path that hinders portability. I've also provided feedback to enhance code clarity, correctness, and robustness, including removing unused code, correcting misleading logs, ensuring proper resource management with context managers, and handling potential null values. Lastly, an unused dependency in requirements.txt should be removed.

[gemini-code-assist]

The database directory DB_DIR is hardcoded to a user-specific absolute path. This makes the script not portable and will cause it to fail on any other machine or in a different environment. It is highly recommended to use a relative path or make the path configurable, for example, through command-line arguments.

Suggested change

	DB_DIR = '/home/princer_google_com/gcs/lmdb_test_env'
	DB_DIR = 'lmdb_test_env'

[gemini-code-assist]

The log message here, and the comment on line 43, state that 10,000 random samples are being read. However, with NUM_BATCHES = 10 and BATCH_SIZE = 5, only 10 * 5 = 50 samples are actually read. This is misleading and should be corrected to reflect the actual number of operations.

Suggested change

	print(f"Total read time (10,000 random samples): {read_duration:.4f} seconds")
	print(f"Total read time ({NUM_BATCHES * BATCH_SIZE} random samples): {read_duration:.4f} seconds")

[gemini-code-assist]

The numpy module is imported but is not used anywhere in the script. Unused imports should be removed to keep the code clean and avoid confusion.

[gemini-code-assist]

The full_path variable is defined but never used in the script. It should be removed to improve code clarity.

[gemini-code-assist]

This env.close() call is not guaranteed to be executed if an error occurs earlier in the function. To ensure the LMDB environment is always closed properly, it's recommended to use lmdb.open as a context manager.

For example, you could change line 21 to with lmdb.open(DB_DIR, map_size=NUM_SAMPLES * SAMPLE_SIZE * 2) as env:, indent the following block (lines 23-33), and then remove this env.close() call.

[gemini-code-assist]

txn.get(key) will return None if the key is not found in the database. The subsequent call to value.split() would then raise an AttributeError. It is safer to add a check to ensure value is not None before attempting to process it.

Suggested change

	label, data = value.split(b'_', 1)
	batch_data.append((label, data))
	if value is not None:
	label, data = value.split(b'_', 1)
	batch_data.append((label, data))

[gemini-code-assist]

This env.close() call is not guaranteed to be executed if an error occurs earlier in the function. To ensure the LMDB environment is always closed properly, it's recommended to use lmdb.open as a context manager.

For example, you could change line 41 to with lmdb.open(DB_DIR, readonly=True, lock=False) as env:, indent the following block, and then remove this env.close() call.

[gemini-code-assist]

The break condition if count >= NUM_SAMPLES: is redundant. The cursor iterator (for key, value in cursor:) will naturally stop after iterating over all key-value pairs in the database. Since the database is created by create_lmdb_db with NUM_SAMPLES items, this check is unnecessary and can be removed for better clarity.

[gemini-code-assist]

This env.close() call is not guaranteed to be executed if an error occurs earlier in the function. To ensure the LMDB environment is always closed properly, it's recommended to use lmdb.open as a context manager.

For example, you could change line 79 to with lmdb.open(DB_DIR, readonly=True, lock=False) as env:, indent the following block, and then remove this env.close() call.

[gemini-code-assist]

The numpy dependency is listed here, but it is not actually used in the lmdb_read_pattern.py script (the corresponding import is unused). Unused dependencies should be removed to keep the project's requirements minimal.