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12-byte (4+4+4) records (term, doc, freq). These are generated as we parse docs. Must now sort 100M such 12-byte records by term. Define a Block ~ 10M such records Can easily fit a couple into memory. Will have 10 such blocks to start with. Basic idea of algorithm: Accumulate postings for each block, sort, write to disk. Then merge the blocks into one long sorted order. BSBI: Blocked sort-based Indexing (Sorting with fewer disk seeks) 精品 BSBI: Blocked sort-based Indexing 精品 Remaining problem with sort-based algorithm Our assumption was: we can keep the dictionary in memory. We need the dictionary (which grows dynamically) in order to implement a term to termID mapping. Actually, we could work with term,docID postings instead of termID,docID postings . . . . . . but then intermediate files become very large. (We would end up with a scalable, but very slow index construction method.) 精品 SPIMI: Single-pass in-memory indexing Key idea 1: Generate separate dictionaries for each block – no need to maintain term-termID mapping across blocks. Key idea 2: Don’t sort. Accumulate postings in postings lists as they occur. With these two ideas we can generate a complete inverted index for each block. These separate indexes can then be merged into one big index. 精品 SPIMI-Invert Merging of blocks is analogous to BSBI. 精品 Distributed indexing For web-scale indexing (don’t try this at home!): must use a distributed computing cluster Individual machines are fault-prone Can unpredictably slow down or fail How do we exploit such a pool of machines? 精品 Google data centers Google data centers mainly contain commodity machines. Data centers are distributed around the world. Estimate: a total of 1 million servers, 3 million processors/cores (Gartner 2007) Estimate: Google installs 100,000 servers each quarter. Based on expenditures of 200–250 million dollars per year This would be 10% of the computing capacity of the world!?! 精品 Google data centers If in a non-fault-tolerant system with