# Serialising the Stars¶

Noodles lets you run jobs remotely and store/retrieve results in case of duplicate jobs or reruns. These features rely on the serialisation (and not unimportant, reconstruction) of all objects that are passed between scheduled functions. Serialisation refers to the process of turning any object into a stream of bytes from which we can reconstruct a functionally identical object. “Easy enough!” you might think, just use pickle.

[1]:

from noodles.tutorial import display_text
import pickle

function = pickle.dumps(str.upper)
message = pickle.dumps("Hello, Wold!")

display_text("function: " + str(function))
display_text("message: " + str(message))

function: b'\x80\x03cbuiltins\ngetattr\nq\x00cbuiltins\nstr\nq\x01X\x0 … q\x04.'
message: b'\x80\x03X\x0c\x00\x00\x00Hello, Wold!q\x00.'
[2]:

pickle.loads(function)(pickle.loads(message))

[2]:

'HELLO, WOLD!'


However pickle cannot serialise all objects … “Use dill!” you say; still the pickle/dill method of serializing is rather indiscriminate. Some of our objects may contain runtime data we can’t or don’t want to store, coroutines, threads, locks, open files, you name it. We work with a Sqlite3 database to store our data. An application might store gigabytes of numerical data. We don’t want those binary blobs in our database, rather to store them externally in a HDF5 file.

There are many cases where a more fine-grained control of serialisation is in order. The bottom line being, that there is no silver bullet solution. Here we show some examples on how to customize the Noodles serialisation mechanism.

## The registry¶

Noodles keeps a registry of Serialiser objects that know exactly how to serialise and reconstruct objects. This registry is specified to the backend when we call the one of the run functions. To make the serialisation registry visible to remote parties it is important that the registry can be imported. This is why it has to be a function of zero arguments (a thunk) returning the actual registry object.

def registry():
return Registry(...)

run(workflow,
db_file='project-cache.db',
registry=registry)


The registry that should always be included is noodles.serial.base. This registry knows how to serialise basic Python dictionaries, lists, tuples, sets, strings, bytes, slices and all objects that are internal to Noodles. Special care is taken with objects that have a __name__ attached and can be imported using the __module__.__name__ combination.

Registries can be composed using the + operator. For instance, suppose we want to use pickle as a default option for objects that are not in noodles.serial.base:

[3]:

import noodles

def registry():
return noodles.serial.pickle() \
+ noodles.serial.base()

reg = registry()


Let’s see what is made of our objects!

[4]:

display_text(reg.to_json([
"These data are JSON compatible!", 0, 1.3, None,
{"dictionaries": "too!"}], indent=2))

[
  "These data are JSON compatible!",
  0,
  1.3,
  null,
  {
    "dictionaries": "too!"
  }
]

Great! JSON compatible data stays the same. Now try an object that JSON doesn’t know about.

[5]:

display_text(reg.to_json({1, 2, 3}, indent=2), [1])

{
  "_noodles": "0.3.0",
  "type": "<object>",
  "class": "builtins.set",
  "data": [
    1,
    2,
    3
  ]
}

Objects are encoded as a dictionary containing a '_noodles' key. So what will happen if we serialise an object the registry cannot possibly know about? Next we define a little astronomical class describing a star in the Morgan-Keenan classification scheme.

[6]:

class Star(object):
"""Morgan-Keenan stellar classification."""
def __init__(self, spectral_type, number, luminocity_class):
assert spectral_type in "OBAFGKM"
assert number in range(10)

self.spectral_type = spectral_type
self.number = number
self.luminocity_class = luminocity_class

rigel = Star('B', 8, 'Ia')
display_text(reg.to_json(rigel, indent=2), [4], max_width=60)

{
  "_noodles": "0.3.0",
  "type": "<object>",
  "class": "__main__.Star",
  "data": "gANjX19tYWluX18KU3RhcgpxACmBcQF9cQIoWA0 … EHdWIu"
}

The registry obviously doesn’t know about Stars, so it falls back to serialisation using pickle. The pickled data is further encoded using base64. This solution won’t work if some of your data cannot be pickled. Also, if you’re sensitive to aesthetics, the pickled output doesn’t look very nice.

## serialize and construct¶

One way to take control of the serialisation of your objects is to add the __serialize__ and __construct__ methods.

[7]:

class Star(object):
"""Morgan-Keenan stellar classification."""
def __init__(self, spectral_type, number, luminocity_class):
assert spectral_type in "OBAFGKM"
assert number in range(10)

self.spectral_type = spectral_type
self.number = number
self.luminocity_class = luminocity_class

def __str__(self):
return f'{self.spectral_type}{self.number}{self.luminocity_class}'

def __repr__(self):
return f'Star.from_string(\'{str(self)}\')'

@staticmethod
def from_string(string):
"""Construct a new Star from a string describing the stellar type."""
return Star(string[0], int(string[1]), string[2:])

def __serialize__(self, pack):
return pack(str(self))

@classmethod
def __construct__(cls, data):
return Star.from_string(data)


The class became quite a bit bigger. However, the __str__, __repr__ and from_string methods are part of an interface you’d normally implement to make your class more useful.

[8]:

sun = Star('G', 2, 'V')
print("The Sun is a", sun, "type star.")

The Sun is a G2V type star.

[9]:

encoded_star = reg.to_json(sun, indent=2)
display_text(encoded_star, [4])

{
  "_noodles": "0.3.0",
  "type": "<automagic>",
  "class": "__main__.Star",
  "data": "G2V"
}

The __serialize__ method takes one argument (besides self). The argument pack is a function that creates the data record with all handles attached. The reason for this construct is that it takes keyword arguments for special cases.

def pack(data, ref=None, files=None):
pass

• The ref argument, if given as True, will make sure that this object will not get reconstructed unnecessarily. One instance where this is incredibly useful, is if the object is a gigabytes large Numpy array.
• The files argument, when given, should be a list of filenames. This makes sure Noodles knows about the involvement of external files.

The data passed to pack maybe of any type, as long as the serialisation registry knows how to serialise it.

The __construct__ method must be a class method. The data argument it is given can be expected to be identical to the data passed to the pack function at serialisation.

[10]:

decoded_star = reg.from_json(encoded_star)
display_text(repr(decoded_star))

Star.from_string('G2V')

## Writing a Serialiser class (example with large data)¶

Often, the class that needs serialising is not from your own package. In that case we need to write a specialised Serialiser class. For this purpose it may be nice to see how to serialise a Numpy array. This code is already in Noodles; we will look at a trimmed down version.

Given a NumPy array, we need to do two things:

• Generate a token by which to identify the array; we will use a SHA-256 hash to do this.
• Store the array effeciently; the HDF5 fileformat is perfectly suited.

### SHA-256¶

We need to hash the combination of datatype, array shape and the binary data:

[11]:

import numpy
import hashlib
import base64

def array_sha256(a):
"""Create a SHA256 hash from a Numpy array."""
dtype = str(a.dtype).encode()
shape = numpy.array(a.shape)
sha = hashlib.sha256()
sha.update(dtype)
sha.update(shape)
sha.update(a.tobytes())
return base64.urlsafe_b64encode(sha.digest()).decode()


Is this useable for large data? Let’s see how this scales (code to generate this plot is below):

So on my laptop, hashing an array of ~1 GB takes a little over three seconds, and it scales almost perfectly linear. Next we define the storage routine (and a loading routine, but that’s a oneliner).

[12]:

import h5py

def save_array_to_hdf5(filename, lock, array):
"""Save an array to a HDF5 file, using the SHA-256 of the array
data as path within the HDF5. The lock is needed to prevent
hdf5_path = array_sha256(array)
with lock, h5py.File(filename) as hdf5_file:
if not hdf5_path in hdf5_file:
dataset = hdf5_file.create_dataset(
hdf5_path, shape=array.shape, dtype=array.dtype)
dataset[...] = array
hdf5_file.close()

return hdf5_path


And put it all together in a class derived from SerArray.

[13]:

import filelock
from noodles.serial import Serialiser, Registry

class SerArray(Serialiser):
"""Serialises Numpy array to HDF5 file."""
def __init__(self, filename, lockfile):
super().__init__(numpy.ndarray)
self.filename = filename
self.lock = filelock.FileLock(lockfile)

def encode(self, obj, pack):
key = save_array_to_hdf5(self.filename, self.lock, obj)
return pack({
"filename": self.filename,
"hdf5_path": key,
}, files=[self.filename], ref=True)

def decode(self, cls, data):
with self.lock, h5py.File(self.filename) as hdf5_file:
return hdf5_file[data["hdf5_path"]].value


We have to insert the serialiser into a new registry.

[14]:

!rm -f tutorial.h5  # remove from previous run

[15]:

import noodles
from noodles.tutorial import display_text

def registry():
return Registry(
parent=noodles.serial.base(),
types={
numpy.ndarray: SerArray('tutorial.h5', 'tutorial.lock')
})

reg = registry()


Now we can serialise our first Numpy array!

[16]:

encoded_array = reg.to_json(numpy.arange(10), host='localhost', indent=2)
display_text(encoded_array, [6])

{
  "_noodles": "0.3.0",
  "type": "<object>",
  "class": "numpy.ndarray",
  "data": {
    "filename": "tutorial.h5",
    "hdf5_path": "4Z8kdMg-CbjgTKKYlz6b-_-Tsda5VAJL44OheRB10mU="
  },
  "ref": true,
  "host": "localhost",
  "files": [
    "tutorial.h5"
  ]
}

Now, we should be able to read back the data directly from the HDF5.

[17]:

with h5py.File('tutorial.h5') as f:
result = f['4Z8kdMg-CbjgTKKYlz6b-_-Tsda5VAJL44OheRB10mU='].value
print(result)

[0 1 2 3 4 5 6 7 8 9]


We have set the ref property to True, we can now read back the serialised object without dereferencing. This will result in a placeholder object containing only the encoded data:

[18]:

ref = reg.from_json(encoded_array)
display_text(ref)
display_text(vars(ref), max_width=60)

<noodles.serial.registry.RefObject object at 0x7f96002bc2b0>
{'rec': {'_noodles': '0.3.0', 'type': '<object>',  … .h5']}}

If we want to retrieve the data we should run from_json with deref=True:

[19]:

display_text(reg.from_json(encoded_array, deref=True))

[0 1 2 3 4 5 6 7 8 9]

## Appendix A: better parsing¶

If you’re interested in doing a bit better in parsing generic expressions into objects, take a look at pyparsing.

[20]:

!pip install pyparsing

Requirement already satisfied: pyparsing in /home/johannes/.local/share/workon/noodles/lib/python3.6/site-packages


The following code will parse the stellar types we used before:

[21]:

from pyparsing import Literal, replaceWith, OneOrMore, Word, nums, oneOf

def roman_numeral_literal(string, value):
return Literal(string).setParseAction(replaceWith(value))

one = roman_numeral_literal("I", 1)
four = roman_numeral_literal("IV", 4)
five = roman_numeral_literal("V", 5)

roman_numeral = OneOrMore(
(five | four | one).leaveWhitespace()) \
.setName("roman") \
.setParseAction(lambda s, l, t: sum(t))

integer = Word(nums) \
.setName("integer") \
.setParseAction(lambda t:int(t[0]))

mkStar = oneOf(list("OBAFGKM")) + integer + roman_numeral

[22]:

list(mkStar.parseString('B2IV'))

[22]:

['B', 2, 4]

[23]:

roman_class = {
'I': 'supergiant',
'II': 'bright giant',
'III': 'regular giant',
'IV': 'sub-giants',
'V': 'main-sequence',
'VI': 'sub-dwarfs',
'VII': 'white dwarfs'
}


## Appendix B: measuring SHA-256 performance¶

[24]:

import timeit
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = "serif"
from scipy import stats

def benchmark(size, number=10):
"""Measure performance of SHA-256 hashing large arrays."""
data = numpy.random.uniform(size=size)
return timeit.timeit(
stmt=lambda: array_sha256(data),
number=number) / number

sizes = numpy.logspace(10, 25, 16, base=2, dtype=int)
timings = numpy.array([[benchmark(size, 1) for size in sizes]
for i in range(10)])

sizes_MB = sizes * 8 / 1e6
timings_ms = timings.mean(axis=0) * 1000
timings_err = timings.std(axis=0) * 1000

slope, intercept, _, _, _ = stats.linregress(
numpy.log(sizes_MB[5:]),
numpy.log(timings_ms[5:]))

print("scaling:", slope, "(should be ~1)")
print("speed:", numpy.exp(-intercept), "GB/s")

ax = plt.subplot(111)
ax.set_xscale('log', nonposx='clip')
ax.set_yscale('log', nonposy='clip')
ax.plot(sizes_MB, numpy.exp(intercept) * sizes_MB,
label='{:.03} GB/s'.format(numpy.exp(-intercept)))
ax.errorbar(sizes_MB, timings_ms, yerr=timings_err,
marker='.', ls=':', c='k', label='data')
ax.set_xlabel('size ($MB$)')
ax.set_ylabel('time ($ms$)')
ax.set_title('SHA-256 performance', fontsize=10)
ax.legend()
plt.savefig('sha256-performance.svg')
plt.show()

scaling: 1.0064819484 (should be ~1)
speed: 0.305958896153 GB/s


## Implementation¶

A Registry object roughly consists of three parts. It works like a dictionary searching for Serialisers based on the class or baseclass of an object. If an object cannot be identified through its class or baseclasses the Registry has a function hook that may use any test to determine the proper Serialiser. When neither the hook nor the dictionary give a result, there is a default fall-back option.