npids 0.0.5

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npids

This package provides time- and space-efficient bi-directional lookups for identifiers. Contents are mmap'd, eliminating most load times and allowing for efficient caching through the file system.

Motivation

It's often helpful to map an external string identifier to an integer index and vice versa. Existing techniques for doing this in Python are either slow or require a lot of memory.

Getting Started

Install via pip:

pip install npids

Build a lookup:

from npids import Lookup
Lookup.build(['id1', 'id2', 'id3'], 'path/to/lookup.npids')

Perform forward lookups (index to ID)

lookup = Lookup('path/to/lookup.npids')
# individual indices
lookup.fwd[0] # -> 'id1'
lookup.fwd[2] # -> 'id3'
# multiple indices
lookup.fwd[0,1] # -> ['id1', 'id2']
# works with numpy too
lookup.fwd[np.array([0,1])] # -> array(['id1', 'id2'], dtype='<U3')

Perform inverse lookups (ID to index)

lookup = Lookup('path/to/lookup.npids')
# individual IDs
lookup.inv['id1'] # -> 0
lookup.inv['id3'] # -> 2
# multiple IDs
lookup.inv['id1', 'id3'] # -> [0, 2]
# works with numpy too
lookup.inv[np.array(['id1', 'id3'])] # -> array([0, 2])

That's about it!

Codecs

The following codecs are currently supported for forward and inverted lookups. The file format is flexible, allowing new codecs to be added in the future.

Forward:

• fixedbytes: Every item is stored as a fixed number of bytes (with optional prefix). This serves as a fallback if other forward codecs do not work.
• intsequence: A sequence of integers (e.g., 49, 50, 51) is identifed (with optional prefix); only metadata about the sequence is stored.
• intstored: Integers are identified (with optional prefix), but they are not in a periodic sequence (e.g., 49, 55, 21). The integer values are encoded and stored.
• uuid: UUIDs are identified (with optional prefix). The byte values of the UUIDs are stored.

Inverse:

• hash: Hashes of every item are stored on disk, enabling O(1) lookups (but with extra storage). This serves as a fallback if other inverse codecs do not work.
• intsequence: The values only consist of a single forward intsequence block; these values can be used to compute the indices.
• intstored: The values consist of only intstored blocks with values in sorted order. These values can be deconstructed and looked up in the foward codec using a binary search.

Benchmarks

The following benchmarks test the speed of building, forward/inverse lookups (10k random lookups, both "cold" and "hot"), and the size of the structure. Rows marked with * indicate that the values include additional overheads that are not directly related to operation -- namely, full engines include content indexing.

• npids: This software
• inmem: A simple Python lookup structure in memory (a list and a dict), backed by a plain text file that is read into memory
• Terrier: Terrier engine acccessed via the pyterrier package
• Lucene: Apache Lucene accessed via the pyserini package

The benchmarks show that npids is a reasonable choice for performing ID lookups. Although it is a bit slower than loading them all into memory, it avoids the considerable upfront cost of doing so. Compared to other approaches for loading them from disk (Lucene, Terrier), it consumes far less storage, is built faster, and (usually) performs the lookups considerably faster.

msmarco-passage (8.8M docnos: 0, 1, 2, ...)

System	Build Time	Cold Fwd	Hot Fwd	Cold Inv	Hot Inv	File Size
inmem	5.95s	4ms	1ms	6ms	2ms	1.3GB
npids	13.88s	6ms	6ms	4ms	2ms	206B
Lucene	* 55.39s	119ms	53ms	194ms	60ms	* 130.3MB
Terrier	* 3m53s	121ms	107ms	1.60s	218ms	* 502.9MB

msmarco-document (3.2M docnos: D1555982, D301595, D1359209, ...)

System	Build Time	Cold Fwd	Hot Fwd	Cold Inv	Hot Inv	File Size
inmem	1.44s	3ms	1ms	5ms	2ms	27.9MB
npids	13.02s	6ms	5ms	8ms	8ms	42.5MB
Lucene	* 25.57s	142ms	61ms	162ms	62ms	* 67.6MB
Terrier	* 1m26s	111ms	103ms	866ms	197ms	* 195.0MB

hc4/fa (486k docnos: 9064520f-bc4d-4118-a30e-7d99f5adc612, e34ce085-cc13-4a1f-90e4-81a7fbfd7f0d, fa2fc4eb-4f97-4bee-bf92-a7330a80c66f, ...)

System	Build Time	Cold Fwd	Hot Fwd	Cold Inv	Hot Inv	File Size
inmem	0.14s	2ms	1ms	5ms	1ms	18.0MB
npids	2.81s	21ms	20ms	32ms	31ms	11.8MB
Lucene	* 4.26s	145ms	79ms	163ms	75ms	* 49.4MB
Terrier	* 14.76s	125ms	107ms	564ms	187ms	* 85.1MB