Very fast, constant memory-footprint cardinality approximation, including intersection
Project description
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.
# 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(sk1) > 0.0 # Approximately 1.0, due to `Foo`
# 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
sk1.add_entry(e) # Entry-objects can be added to mulitple 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 |
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.
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