Read a large hdf5 file from url by chunk in python

user_5441914 0
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I have a 1.5 terabyte sized hdf5 file on an Amazon Simple Storage Service located at the link below. I don't have the disk space to save it nor do I have the memory to read it. Accordingly, I want to read it by chunk, process it, and discard the read part. I was hoping to use pandas' read_hdf to read it but it does not support urls. Neither does the h5py library it seems. Though it does mention a ros3 driver but I haven't been able to get it to work yet. I also tried the response to this question but the chunks cannot be read by h5py or I have not found a way yet. So I'm rather left with no idea on how to process this file. Does anyone have any idea how to do so? The link to the file is this:

https://oedi-data-lake.s3-us-west-2.amazonaws.com/building_synthetic_dataset/A_Synthetic_Building_Operation_Dataset.h5

pandas python-3.x hdf5 amazon-s3
2 Answers

After having this exact same issue, I believe I've cobbled together a working solution for this using fsspec:

import h5py
import fsspec

URL = "..." # Assuming a publicly accessible url

remote_f = fsspec.open(URL, mode="rb")
if hasattr(remote_f, "open"):
    remote_f = remote_f.open()

f = h5py.File(remote_f)
# Do regular hdf5 things...

I've confirmed, using your link above, that this does not read the data into memory, just as if it were a local file:

import h5py
import fsspec
URL = "https://oedi-data-lake.s3-us-west-2.amazonaws.com/building_synthetic_dataset/A_Synthetic_Building_Operation_Dataset.h5"
remote_f = fsspec.open(URL, mode="rb")
if hasattr(remote_f, "open"):
    remote_f = remote_f.open()
f = h5py.File(remote_f)
f.visititems(print)
# 1. README <HDF5 dataset "1. README": shape (), type "|O">
# 2. Resources <HDF5 group "/2. Resources" (2 members)>
# 2. Resources/2.1. Building Models <HDF5 group "/2. Resources/2.1. Building Models" (9 members)>
...
user_82333290

Try this:

import requests
from contextlib import closing
url = "https://oedi-data-lake.s3-us-west-2.amazonaws.com/building_synthetic_dataset/A_Synthetic_Building_Operation_Dataset.h5"
with closing(requests.get(url, stream=True)) as r:
    for row in r:
        # Handle each row here...
        print(row)

Result, first two rows:

b'\x89HDF\r\n\x1a\n\x00\x00\x00\x00\x00\x08\x08\x00\x04\x00\x10\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\xff\xff\xff\xff\xa1\x92\x14\x8d.\x01\x00\x00\xff\xff\xff\xff\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00`\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x88\x00\x00\x00\x00\x00\x00\x00\xa8\x02\x00\x00\x00\x00\x00\x00\x01\x00\x01\x00\x01\x00\x00\x00\x18\x00\x00\x00\x00\x00\x00\x00\x11\x00\x10\x00\x00\x00\x00\x00\x88\x00\x00\x00\x00\x00\x00\x00'
b'\xa8\x02\x00\x00\x00\x00\x00\x00TREE\x00\x00\x01\x00\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x000\x04\x00\x00\x00\x00\x00\x00(\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'

Line by line reading. No memory consumption.

user_171124230
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