Fast Compressed Tries through Path Decompositions

Roberto GrossiGiuseppe Ottaviano*

Università di Pisa

* Part of the work done while at Microsoft Research Cambridge

t

threetrialtriangletrietripletriply

iree

εε l

εε εgle

h r

ep a

ln

Node labelBranching character

Compacted tries

ye

Applications

• String dictionaries– With prefix lookup, predecessor, …– Exploit prefix compression

• Monotone perfect hash functions– “Hollow” or “Blind” tries [ALENEX 09]– Binary tree (no need store branching chars)– No need to store node labels, just lengths (skips)

Height vs. performance

• Tries can be deep – no guarantee on height• Bad with pointer-based trees – ~1 cache miss per child operation

• Worse with succinct tree encodings– Need to access several directories–Many cache misses per child operation– Large constants hidden in the O(1)

Path decomposition

t

iree

εε l

εε εgle

h r

ep a

lnye

triangle

h e p l

Query: triple

Recurse here withsuffix le

Centroid path decomposition• Decompose along the heavy paths– choose the edge that has most descendants

• Height of the decomposed tree: O(log n)– Usually lower

• Average height

Web Queries URLs Synthetic

Compacted trie 11.0 18.1 504.4

Centroid trie 5.2 6.2 2.8

Hollow trie 50.8 67.3 1005.3

Centroid hollow trie 8.0 9.2 2.8

Succinct encoding

• [PODS 08] presents a succinct data structure for centroid path-decomposed tries

• Not practical: need complex operations on succinct trees

• We introduce a simpler and practical encoding• This encoding enables also simple

compression of the labels

Succinct encoding

triangle

h e p l

L : t1ri2a1ngleBP: ( ((( )B : h epl (spaces added for clarity)

• Node label written literally, interleaved with number of other branching characters at that point in array L

• Corresponding branching characters in array B• Tree encoded with DFUDS in bitvector BP– Variant of Range Min-Max tree [ALENEX 10] to support

Find{Close,Open}, more space-efficient (Range Min tree)

Compression of L

...$...index.html$....html$....html$...index.html$

...$...35$...5$...5$...35$

…3 index…5 .html…

Dictionary

• Dictionary codewords shared among labels• Codewords do not cross label boundaries ($)• Use vbyte to compress the codeword ids

Compression of L

• Node labels (t1ri2a1ngle, l1e, …):– each label is suffix of a string in the set– interleaved with few “special characters” 1, 2, 3,…

• Compressible if strings are compressible• Dictionary and parsing computed with

modified Re-Pair– Domain-specific compression can be used instead

• Decompression overhead negligible

Experimental results (time)• Experiments show gains in time comparable to

the gains in height• Confirm that bottleneck is traversal operations

Web Queries URLs Synthetic

Trie 3.5 7.0 119.8

Centroid trie 2.4 4.3 5.1

Hollow trie [ALENEX 09] 16.6 22.4 462.7

Hollow trie 7.2 13.9 137.1

Centroid hollow trie 2.8 4.4 11.1

(microseconds, lower is better)

Code available at https://github.com/ot/path_decomposed_tries

Experimental results (space)

• For strings with many common prefixes, even non-compressed trie is space-efficient

• Labels compression considerably increases space-efficiency

• Decompression time overhead: ~10% Web Queries URLs Synthetic

Hu-Tucker Front Coding 40.9% 24.4% 19.1%

Centroid trie 55.6% 22.4% 17.9%

Centroid trie + compression 31.5% 13.6% 0.4%

(compression ratio, lower is better)

Code available at https://github.com/ot/path_decomposed_tries

Thanks for your attention!

Questions?

References

• [ALENEX 10] D. Arroyuelo, R. Canovas, G. Navarro, and K. Sadakane. Succinct trees in practice. In ALENEX, pages 84–97, 2010.

• [ALENEX 09] D. Belazzougui, P. Boldi, R. Pagh, and S. Vigna. Monotone minimal perfect hashing: searching a sorted table with O(1) accesses. In SODA, pages 785–794, 2009.

• [PODS 08] P. Ferragina, R. Grossi, A. Gupta, R. Shah, and J. S. Vitter. On searching compressed string collections cache-obliviously. In PODS, pages 181–190, 2008.