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Attempted regex of string that contains multiple numbers as different base counts

Currently building an encryption module in python 3.8 and have run into a snag with a desired feature/upgrade. Looking for some assistance in finding a solution that would be more helpful than writing a 'string crawler' to parse out an encrypted string of data.
In my first 'official' release everything works fine, but this is due to how much easier it is to split a string based of off easily identifiable prefixes in a string. For example, '0x' in a hexadecimal or '0o' in an octal.
The current definitions for what a number can inhabit use the aforementioned base types along with support for counts of 2-10 as '{n}bXX'.
What I currently have implemented works just fine for the present design, but having trouble with trying to come up with something that can handle higher bases (at least up to 64) that isn't going to be bulky or slow; also, the redesign is having trouble parsing out a string which contains multiple base counts, post assignment to their corresponding characters.
TL;DR - If I have an encoded string like so: "0x9a0o25179b83629b86740xc01d64b9HM-70o5521"
I would like it to be split as: [0x9a, 0o2517, 9b8362, 9b8674, 0xc0ld, 64b9HM-7, 0o5521]
and need help finding a better solution than: r'(?:0x)|(?:9b)|...'

marisa trie suffix compression?

I'm using a custom Cython wrapper of this marisa trie library as a key-value multimap.
My trie entries look like key 0xff data1 0xff data2 to map key to the tuple (data1, data2). data1 is a string of variable length but data2 is always a 4-byte unsigned int. The 0xff is a delimiter byte.
I know a trie is not the most optimal data structure for this from a theoretical point of a view, but various practical considerations make it the best available choice.
In this use case, I have about 10-20 million keys, each one has on average 10 data points. data2 is redundant for many entries (in some cases, data2 is always the same for all data points for a given key), so I had the idea of taking the most frequent data2 entry and adding a ("", base_data2) data point to each key.
Since a MARISA trie, to my knowledge, does not have suffix compression and for a given key each data1 is unique, I assumed that this would save 4 bytes per data tuple that uses a redundant key (plus adding in a single 4-byte "value" for each key). Having rebuilt the trie, I checked that the redundant data was no longer being stored. I expected a sizable decrease in both serialized and in-memory size, but in fact the on-disk trie went from 566MB to 557MB (and a similar reduction in RAM usage for a loaded trie).
From this I concluded that I must be wrong about there being no suffix compression. I was now storing the entries with a redundant data2 number as key 0xff data1 0xff, so to test this theory I removed the trailing 0xff and adjusted the code that uses the trie to cope. The new trie went down from 557MB to 535MB.
So removing a single redundant trailing byte made a 2x larger improvement than removing the same number of 4-byte sequences, so either the suffix compression theory is dead wrong, or it's implemented in some very convoluted way.
My remaining theory is that adding in the ("", base_data2) entry at a higher point in the trie somehow throws off the compression in some terrible way, but it should just be adding in 4 more bytes when I've removed many more than that from lower down in the trie.
I'm not optimistic for a fix, but I'd dearly like to know why I'm seeing this behavior! Thank you for your attention.

As I suspected, it's caused by padding.
in lib/marisa/grimoire/vector/vector.h, there is the following function:
void write_(Writer &writer) const {
writer.write((UInt64)total_size());
writer.write(const_objs_, size_);
writer.seek((8 - (total_size() % 8)) % 8);
}
The key point is: writer.seek((8 - (total_size() % 8)) % 8);. After writing each chunk, the writer pads to the next 8 bytes boundary.
This explains the behavior you are seeing, as part of the data removed by the initial shortening of the key was replaced with padding.
When you removed the extra byte, it brought the key size below the next boundary limit, resulting in a major size change.
Practically, what this means is that, since the padding code is in the serialization part of the library, you are probably getting the in-memory savings you were expecting, but that did not translate into on-disk savings. Monitoring program RAM usage should confirm that.
If disk size is your concern, then you might as well simply deflate the serialized data, as it seems MARISA does not apply any compression whatsoever.

Fast hash for strings

I have a set of ASCII strings, let's say they are file paths. They could be both short and quite long.
I'm looking for an algorithm that could calculate hash of such a strings and this hash will be also a string, but will have a fixed length, like youtube video ids:
https://www.youtube.com/watch?v=-F-3E8pyjFo
^^^^^^^^^^^
MD5 seems to be what I need, but it is critical for me to have a short hash strings.
Is there a shell command or python library which can do that?

As of Python 3 this method does not work:
Python has a built-in hash() function that's very fast and perfect for most uses:
>>> hash("dfds")
3591916071403198536
You can then make it unsigned:
>>> hashu=lambda word: ctypes.c_uint64(hash(word)).value
You can then turn it into a 16 byte hex string:
>>> hashu("dfds").to_bytes(8,"big").hex()
Or an N*2 byte string, where N is <= 8:
>>> hashn=lambda word, N : (hashu(word)%(2**(N*8))).to_bytes(N,"big").hex()
..etc. And if you want N to be larger than 8 bytes, you can just hash twice. Python's built-in is so vastly faster, it's never worth using hashlib for anything unless you need security... not just collision resistance.
>>> hashnbig=lambda word, N : ((hashu(word)+2**64*hashu(word+"2"))%(2**(N*8))).to_bytes(N,"big").hex()
And finally, use the urlsafe base64 encoding to make a much better string than "hex" gives you
>>> hashnbigu=lambda word, N : urlsafe_b64encode(((hashu(word)+2**64*hash(word+"2"))%(2**(N*8))).to_bytes(N,"big")).decode("utf8").rstrip("=")
>>> hashnbigu("foo",16)
'ZblnvrRqHwAy2lnvrR4HrA'
Caveats:
Be warned that in Python 3.3 and up, this function is
randomized and won't work for some use cases. You can disable this with PYTHONHASHSEED=0
See https://github.com/flier/pyfasthash for fast, stable hashes that
that similarly won't overload your CPU for non-cryptographic applications.
Don't use this lambda style in real code... write it out! And
stuffing things like 2**32 in your code, instead of making them
constants is bad form.
In the end 8 bytes of collision resistance is OK for a smaller
applications.... with less than a million entries, you've got
collision odds of < 0.0000001%. That's a 12 byte b64 encoded
string. But it might not be enough for larger apps.
16 bytes is enough for a UUID/OID in a cache, etc.
Speed comparison for producing 300k 16 byte hashes from a bytes-input.
builtin: 0.188
md5: 0.359
fnvhash_c: 0.113
For a complex input (tuple of 3 integers, for example), you have to convert to bytes to use the non-builtin hashes, this adds a lot of conversion overhead, making the builtin shine.
builtin: 0.197
md5: 0.603
fnvhash_c: 0.284

I guess this question is off-topic, because opinion based, but at least one hint for you, I know the FNV hash because it is used by The Sims 3 to find resources based on their names between the different content packages. They use the 64 bits version, so I guess it is enough to avoid collisions in a relatively large set of reference strings. The hash is easy to implement, if no module satisfies you (pyfasthash has an implementation of it for example).
To get a short string out of it, I would suggest you use base64 encoding. For example, this is the size of a base64-encoded 64 bits hash: nsTYVQUag88= (and you can get rid or the padding =).
Edit: I had finally the same problem as you, so I implemented the above idea: https://gist.github.com/Cilyan/9424144

Another option: hashids is designed to solve exactly this problem and has been ported to many languages, including Python. It's not really a hash in the sense of MD5 or SHA1, which are one-way; hashids "hashes" are reversable.
You are responsible for seeding the library with a secret value and selecting a minimum hash length.
Once that is done, the library can do two-way mapping between integers (single integers, like a simple primary key, or lists of integers, to support things like composite keys and sharding) and strings of the configured length (or slightly more). The alphabet used for generating "hashes" is fully configurable.
I have provided more details in this other answer.

You could use the sum program (assuming you're on linux) but keep in mind that the shorter the hash the more collisions you might have. You can always truncate MD5/SHA hashes as well.
EDIT: Here's a list of hash functions: List of hash functions

Something to keep in mind is that hash codes are one way functions - you cannot use them for "video ids" as you cannot go back from the hash to the original path. Quite apart from anything else hash collisions are quite likely and you end up with two hashes both pointing to the same video instead of different ones.
To create an Id like the youtube one the easiest way is to create a unique id however you normally do that (for example an auto key column in a database) and then map that to a unique string in a reversible way.
For example you could take an integer id and map it to 0-9a-z in base 36...or even 0-9a-zA-Z in base 62, padding the generated string out to the desired length if the id on its own does not give enough characters.

Efficient data interchange format using only C-style fprintf() statements?

I need to transfer the very large dataset (between 1-10 mil records, possibly much more) from a domain-specific language (whose sole output mechanism is a C-style fprintf statement) to Python.
Currently, I'm using the DSL's fprintf to write records to a flat file. The flat file looks like this:
x['a',1,2]=1.23456789012345e-01
x['a',1,3]=1.23456789012345e-01
x['a',1,4]=1.23456789012345e-01
y1=1.23456789012345e-01
y2=1.23456789012345e-01
z['a',1,2]=1.23456789012345e-01
z['a',1,3]=1.23456789012345e-01
z['a',1,4]=1.23456789012345e-01
As you can see the structure of each record is very simple (but the representation of the double-precision float as a 20-char string is grossly inefficient!):
<variable-length string> + "=" + <double-precision float>
I'm currently using Python to read each line and split it on the "=".
Is there anything I can do to make the representation more compact, so as to make it faster for Python to read? Is some sort of binary-encoding possible with fprintf?

Err....
How many times per minute are you reading this data from Python?
Because in my system I could read such a file with 20 million records (~400MB) in well under a second.
Unless you are performing this in a limited hardware, I'd say you are worrying too much about nothing.
>>> timeit("all(b.read(20) for x in xrange(0, 20000000,20) ) ", "b=open('data.dat')", number=1)
0.2856929302215576
>>> c = open("data.dat").read()
>>> len(c)
380000172

A compact binary format for serializing float values is defined in the basic encoding rules (BER). There they are called "reals". There are implementations of BER for Python available, but also not too hard to write. There are libraries for C as well. You could use this format (that's what it was designed for), or a variant (CER, DER). One such Python implementation is pyasn1.

Shortest hash in python to name cache files

What is the shortest hash (in filename-usable form, like a hexdigest) available in python? My application wants to save cache files for some objects. The objects must have unique repr() so they are used to 'seed' the filename. I want to produce a possibly unique filename for each object (not that many). They should not collide, but if they do my app will simply lack cache for that object (and will have to reindex that object's data, a minor cost for the application).
So, if there is one collision we lose one cache file, but it is the collected savings of caching all objects makes the application startup much faster, so it does not matter much.
Right now I'm actually using abs(hash(repr(obj))); that's right, the string hash! Haven't found any collisions yet, but I would like to have a better hash function. hashlib.md5 is available in the python library, but the hexdigest is really long if put in a filename. Alternatives, with reasonable collision resistance?
Edit:
Use case is like this:
The data loader gets a new instance of a data-carrying object. Unique types have unique repr. so if a cache file for hash(repr(obj)) exists, I unpickle that cache file and replace obj with the unpickled object. If there was a collision and the cache was a false match I notice. So if we don't have cache or have a false match, I instead init obj (reloading its data).
Conclusions (?)
The str hash in python may be good enough, I was only worried about its collision resistance. But if I can hash 2**16 objects with it, it's going to be more than good enough.
I found out how to take a hex hash (from any hash source) and store it compactly with base64:
# 'h' is a string of hex digits
bytes = "".join(chr(int(h[i:i+2], 16)) for i in xrange(0, len(h), 2))
hashstr = base64.urlsafe_b64encode(bytes).rstrip("=")

The birthday paradox applies: given a good hash function, the expected number of hashes before a collision occurs is about sqrt(N), where N is the number of different values that the hash function can take. (The wikipedia entry I've pointed to gives the exact formula). So, for example, if you want to use no more than 32 bits, your collision worries are serious for around 64K objects (i.e., 2**16 objects -- the square root of the 2**32 different values your hash function can take). How many objects do you expect to have, as an order of magnitude?
Since you mention that a collision is a minor annoyance, I recommend you aim for a hash length that's roughly the square of the number of objects you'll have, or a bit less but not MUCH less than that.
You want to make a filename - is that on a case-sensitive filesystem, as typical on Unix, or do you have to cater for case-insensitive systems too? This matters because you aim for short filenames, but the number of bits per character you can use to represent your hash as a filename changes dramatically on case-sensive vs insensitive systems.
On a case-sensitive system, you can use the standard library's base64 module (I recommend the "urlsafe" version of the encoding, i.e. this function, as avoiding '/' characters that could be present in plain base64 is important in Unix filenames). This gives you 6 usable bits per character, much better than the 4 bits/char in hex.
Even on a case-insensitive system, you can still do better than hex -- use base64.b32encode and get 5 bits per character.
These functions take and return strings; use the struct module to turn numbers into strings if your chosen hash function generates numbers.
If you do have a few tens of thousands of objects I think you'll be fine with builtin hash (32 bits, so 6-7 characters depending on your chosen encoding). For a million objects you'd want 40 bits or so (7 or 8 characters) -- you can fold (xor, don't truncate;-) a sha256 down to a long with a reasonable number of bits, say 128 or so, and use the % operator to cut it further to your desired length before encoding.

The builtin hash function of strings is fairly collision free, and also fairly short. It has 2**32 values, so it is fairly unlikely that you encounter collisions (if you use its abs value, it will have only 2**31 values).
You have been asking for the shortest hash function. That would certainly be
def hash(s):
return 0
but I guess you didn't really mean it that way...

You can make any hash you like shorter by simply truncating it. md5 is always 32 hex digits, but an arbitrary substring of it (or any other hash) has the proper qualities of a hash: equal values produce equal hashes, and the values are spread around a bunch.

I'm sure that there's a CRC32 implementation in Python, but that may be too short (8 hex digits). On the upside, it's very quick.
Found it, binascii.crc32

If you do have a collision, how are you going to tell that it actually happened?
If I were you, I would use hashlib to sha1() the repr(), and then just get a limited substring of it (first 16 characters, for example).
Unless you are talking about huge numbers of these objects, I would suggest that you just use the full hash. Then the opportunity for collision is so, so, so, so small, that you will never live to see it happen (likely).
Also, if you are dealing with that many files, I'm guessing that your caching technique should be adjusted to accommodate it.

We use hashlib.sha1.hexdigest(), which produces even longer strings, for cache objects with good success. Nobody is actually looking at cache files anyway.

Condsidering your use case, if you don't have your heart set on using separate cache files and you are not too far down that development path, you might consider using the shelve module.
This will give you a persistent dictionary (stored in a single dbm file) in which you store your objects. Pickling/unpickling is performed transparently, and you don't have to concern yourself with hashing, collisions, file I/O, etc.
For the shelve dictionary keys, you would just use repr(obj) and let shelve deal with stashing your objects for you. A simple example:
import shelve
cache = shelve.open('cache')
t = (1,2,3)
i = 10
cache[repr(t)] = t
cache[repr(i)] = i
print cache
# {'(1, 2, 3)': (1, 2, 3), '10': 10}
cache.close()
cache = shelve.open('cache')
print cache
#>>> {'(1, 2, 3)': (1, 2, 3), '10': 10}
print cache[repr(10)]
#>>> 10

Short hashes mean you may have same hash for two different files. Same may happen for big hashes too, but its way more rare.
Maybe these file names should vary based on other references, like microtime (unless these files may be created too quickly).