Fastest save and load options for a numpy array

Question

I have a script that generates two-dimensional numpy arrays with dtype=float and shape on the order of (1e3, 1e6). Right now I'm using np.save and np.load to perform IO operations with the arrays. However, these functions take several seconds for each array. Are there faster methods for saving and loading the entire arrays (i.e., without making assumptions about their contents and reducing them)? I'm open to converting the arrays to another type before saving as long as the data are retained exactly.

Answer 1

According to my experience, np.save()&np.load() is the fastest solution when trasfering data between hard disk and memory so far. I've heavily relied my data loading on database and HDFS system before I realized this conclusion. My tests shows that: The database data loading(from hard disk to memory) bandwidth could be around 50 MBps(Byets/Second), but the np.load() bandwidth is almost same as my hard disk maximum bandwidth: 2GBps(Byets/Second). Both test environments use the simplest data structure.

And I don't think it's a problem to use several seconds to loading an array with shape: (1e3, 1e6). E.g. Your array shape is (1000, 1000000), its data type is float128, then the pure data size is (128/8)*1000*1,000,000=16,000,000,000=16GBytes and if it takes 4 seconds, Then your data loading bandwidth is 16GBytes/4Seconds = 4GBps. SATA3 maximum bandwidth is 600MBps=0.6GBps, your data loading bandwidth is already 6 times of it, your data loading performance almost could compete with DDR's maximum bandwidth, what else do you want?

So my final conclusion is:

Don't use python's Pickle, don't use any database, don't use any big data system to store your data into hard disk, if you could use np.save() and np.load(). These two functions are the fastest solution to transfer data between harddisk and memory so far.

I've also tested the HDF5 , and found that it's mush slower than np.load() and np.save(), so use np.save()&np.load() if you've enough DDR memory in your platfrom.

Answer 2

For really big arrays, I've heard about several solutions, and they mostly on being lazy on the I/O :

• NumPy.memmap, maps big arrays to binary form
  • Pros :
    • No dependency other than Numpy
    • Transparent replacement of ndarray (Any class accepting ndarray accepts memmap )
  • Cons :
    • Chunks of your array are limited to 2.5G
    • Still limited by Numpy throughput

• Use Python bindings for HDF5, a bigdata-ready file format, like PyTables or h5py

  • Pros :
    • Format supports compression, indexing, and other super nice features
    • Apparently the ultimate PetaByte-large file format
  • Cons :
    • Learning curve of having a hierarchical format ?
    • Have to define what your performance needs are (see later)

• Python's pickling system (out of the race, mentioned for Pythonicity rather than speed)

  • Pros:
    • It's Pythonic ! (haha)
    • Supports all sorts of objects
  • Cons:
    • Probably slower than others (because aimed at any objects not arrays)

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Numpy.memmap

From the docs of NumPy.memmap :

Create a memory-map to an array stored in a binary file on disk.

Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory

The memmap object can be used anywhere an ndarray is accepted. Given any memmap fp , isinstance(fp, numpy.ndarray) returns True.

---

HDF5 arrays

From the h5py doc

Lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want.

The format supports compression of data in various ways (more bits loaded for same I/O read), but this means that the data becomes less easy to query individually, but in your case (purely loading / dumping arrays) it might be efficient

Answer 3

Here is a comparison with PyTables.

I cannot get up to (int(1e3), int(1e6) due to memory restrictions. Therefore, I used a smaller array:

data = np.random.random((int(1e3), int(1e5)))

NumPy save:

%timeit np.save('array.npy', data)
1 loops, best of 3: 4.26 s per loop

NumPy load:

%timeit data2 = np.load('array.npy')
1 loops, best of 3: 3.43 s per loop

PyTables writing:

%%timeit
with tables.open_file('array.tbl', 'w') as h5_file:
    h5_file.create_array('/', 'data', data)

1 loops, best of 3: 4.16 s per loop

PyTables reading:

 %%timeit
 with tables.open_file('array.tbl', 'r') as h5_file:
      data2 = h5_file.root.data.read()

 1 loops, best of 3: 3.51 s per loop

The numbers are very similar. So no real gain wit PyTables here. But we are pretty close to the maximum writing and reading rate of my SSD.

Writing:

Maximum write speed: 241.6 MB/s
PyTables write speed: 183.4 MB/s

Reading:

Maximum read speed: 250.2
PyTables read speed: 217.4

Compression does not really help due to the randomness of the data:

%%timeit
FILTERS = tables.Filters(complib='blosc', complevel=5)
with tables.open_file('array.tbl', mode='w', filters=FILTERS) as h5_file:
    h5_file.create_carray('/', 'data', obj=data)
1 loops, best of 3: 4.08 s per loop

Reading of the compressed data becomes a bit slower:

%%timeit
with tables.open_file('array.tbl', 'r') as h5_file:
    data2 = h5_file.root.data.read()

1 loops, best of 3: 4.01 s per loop

This is different for regular data:

 reg_data = np.ones((int(1e3), int(1e5)))

Writing is significantly faster:

%%timeit
FILTERS = tables.Filters(complib='blosc', complevel=5)
with tables.open_file('array.tbl', mode='w', filters=FILTERS) as h5_file:
    h5_file.create_carray('/', 'reg_data', obj=reg_data)

1 loops, best of 3: 849 ms per loop

The same holds true for reading:

%%timeit
with tables.open_file('array.tbl', 'r') as h5_file:
    reg_data2 = h5_file.root.reg_data.read()

1 loops, best of 3: 1.7 s per loop

Conclusion: The more regular your data the faster it should get using PyTables.