pyhyperminhash 0.2.0

Very fast, constant memory-footprint cardinality approximation, including intersection

Hyperminhash for Python

Very fast, constant memory-footprint cardinality approximation, including intersection and union operation.

The class Sketch can be used to count unique elements that were encountered during the instance's lifetime. Unlike e.g. when using a set, the memory consumed by the Sketch-instance does not grow as elements are added; each Sketch-instance consumes approximately 32kb of memory, independent of the number of elements.

Installation

pip install pyhyperminhash

Basic usage

# Construct an empty Sketch
sk = pyhyperminhash.Sketch()
assert not sk  # The Sketch is empty
sk.add('Foo')  # Add elements
sk.add('Bar')
sk.add(42)
assert sk  # The Sketch is not empty
assert float(sk) == sk.cardinality()  # Approximately 3.0
assert int(sk) == len(sk)  # Approximately 3

# Sketches can be combined...

sk2 = pyhyperminhash.Sketch()
sk2.add('Foobar')
sk2 |= sk  # sk2 now holds all elements that were in `sk` or `sk2`

sk3 = pyhyperminhash.Sketch()
sk3.add(42)
sk3.add('Foo')
assert sk3.intersection(sk2) > 0.0  # Approximately 1.0, due to `Foo`

# `fast=True` uses the faster upstream estimate with a slightly looser
# correction model; for most inputs the difference is tiny.
assert sk3.intersection(sk2, fast=True) > 0.0
assert sk3.similarity(sk2, fast=True) > 0.0

# Compare one Sketch against many others
others = [sk, sk2, sk3]
assert sk3.intersection_many(others) == [sk3.intersection(other) for other in others]
assert sk3.similarity_many(others) == [sk3.similarity(other) for other in others]

# Sketches can be stored for later use

buf = sk.save()  # Serialize the Sketch into a bytes-buffer
new_sk = pyhyperminhash.Sketch.load(buf)  # Deserialize a Sketch
assert len(new_sk) == len(sk)

# Sketches can be constructed from iterators directly, which avoids
# materializing all objects simultaneously.

# This is also faster than adding elements one by one.
sk = pyhyperminhash.Sketch.from_iter(iter(range(100)))

# Read all lines; uses binary mode to avoid Unicode encoding+decoding
with open('complaints.txt', 'rb') as f:
    sk = pyhyperminhash.Sketch.from_iter(f)

# Add an entire file-like object
with open('complaints.txt', 'rb') as f:
    sk.add_reader(f)

# Entry-objects can be used to construct what represents a single
# object from multiple parts. They consume very little memory.

# Add sub-objects that don't fit into memory
e = pyhyperminhash.Entry()
with open('complaints.txt', 'rb') as f:
    e.add(f.read(4096))  # Add two parts to this Entry
    e.add(f.read(4096))
    sk.add_entry(e)  # This counts as one object

sk2 = pyhyperminhash.Sketch()
sk2.add_entry(e)  # Entry-objects can be added to multiple Sketches

# Entry-objects can be "forked" at the state they are currently at
e2 = e.fork()
e2.add("Foo")
sk.add_entry(e2)  # This is a new object in `Sketch`...

e3 = e.fork()
e3.add("Bar")
sk.add_entry(e3)  # So is this, without re-computing `e`

It is very much preferred to provide bytes to Sketch.add() and Entry.add(), in order to avoid double-hashing the content. Also, notice that the Python interpreter probably randomizes hash-values at interpreter-startup (in order to prevent certain kinds of DOS-attacks). The randomness introduced here means that the count-approximation provided by this package may not be stable from run to run.

See the documentation for the underlying implementation for additional information.

Usage as a sqlite3 aggregate function

class PyHyperminhashCounter:
    def __init__(self):
        self.sk = pyhyperminhash.Sketch()

    def step(self, *args) -> None:
        self.sk += args

    def finalize(self) -> int:
        return int(self.sk)


con = sqlite3.connect(":memory:")
# Create the function for the current connection
con.create_aggregate("pyhmh", -1, PyHyperminhashCounter)

# Some dummy data
cur = con.execute("CREATE TABLE test(i, j)")
cur.executemany("INSERT INTO test(i, j) VALUES(?, ?)",
                ((f'foo{i % 4291}bar', i % 819)
                 for i in range(1000000)))

# Returns exactly 502047
cur.execute("SELECT COUNT(*) FROM (SELECT DISTINCT i, j FROM test)")
print(cur.fetchone()[0])

# Returns approximately 500000, but faster
cur.execute("SELECT pyhmh(i, j) FROM test")
print(cur.fetchone()[0])

Performance examples

Reading a file of 50 million lines, ~1,000,000 unique elements, 20 characters per line.

Method	Wall-clock time	Memory consumed	Count
Count lines	2.3 seconds	nil	50,000,000
set()	14.99 seconds	~144 megabytes	1,048,576
Sketch.from_iter()	1.8 seconds	~32 kilobytes	1,041,936

For repeatable local benchmarks of the Python-facing API, use the pyperf suite:

python3 -m venv .venv
source .venv/bin/activate
maturin develop -r
pip install pyperf
python3 examples/bench.py

FAQ

• Can I extract an element that was previously added from a Sketch?
  • No.
• Can I check if an element has previously been added to a Sketch?
  • No.
• Wow, why use this, then?
  • It's very fast at counting unique things. And it does not use much memory.