Seeking Guidance on HDF5 Dataset Chunking for Large-Scale Data

HDF5 Library
1

Hey guys…

I am new to working with HDF5 and am exploring its use for a project involving large-scale data storage and retrieval. My dataset consists of time-series data collected from multiple sensors, resulting in a few terabytes of data. I’m trying to optimize both the read and write performance.

I’ve read that chunking is a critical feature for such use cases, but I’m a bit confused about how to determine the optimal chunk size and layout for my dataset. Here are a few details about my setup:

  1. The data is stored in a 3D array format (time, sensor ID, and measurement type).
  2. The access pattern will primarily involve reading data for specific time intervals and subsets of sensors.
  3. Compression is also important to me, as I need to minimize storage requirements.

Could someone guide me on how to:

  1. Choose an appropriate chunk size based on my access patterns?
  2. Balance the trade-offs between compression, storage size, and performance?
  3. Test and evaluate the impact of different chunking configurations?

I also check this: https://forum.hdfgroup.org/t/reding-part-of-a-dataset-from-a-large-hdf5-file-on-a-remote-serversap-sac But I have not found any solution. Could anyone guide me about this? I had also appreciate any tips or references to resources that could help me better understand these concepts.

Thank you for your help!

2

Hi, @nehotiw771 !

I asked your questions to Google Gemini and Gemini returned the following h5py code:

import h5py
import numpy as np
import time

# Create a 3D dataset
data = np.random.rand(1000, 100, 5)

start_time = time.time()

# Create an HDF5 file with a specific chunk size and compression filter
with h5py.File('my_dataset.h5', 'w') as f:
    dset = f.create_dataset('data', data.shape, dtype=data.dtype,
                           chunks=(100, 10, 5), compression='gzip')
    dset[...] = data

# Read a specific time interval and sensor subset
with h5py.File('my_dataset.h5', 'r') as f:
    dset = f['data']
    subset = dset[100:200, 5:8, :]

end_time = time.time()

execution_time = end_time - start_time
print("Execution time:", execution_time, "seconds")

You can modify the above code with different dataset dimensions, chunk shapes, compression algorithm, and subset parameters that match the potential user’s access pattern.

Then, measure the performance on your target system of intended data users.
I recommend using Google Colab for running the above code for quick test.

Data producers often neglect the importance of data consumers.
Ideal HDF5 settings for data production is not necessarily good for data users.

Consulting is recommended if your project involves producing high quality scientific data products for large audience.

Regards,