Lecture 4: Index Construction Related to Chapter 4:

Introduction to Information RetrievalIntroduction to Information Retrieval

Introduction to

Information Retrieval

Lecture 4: Index Construction

Related to Chapter 4: http://nlp.stanford.edu/IR-book/pdf/04const.pdf


This lecture Scaling index construction

Distributed indexing

Dynamic indexing

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Documents are parsed to extract words and these are saved with the Document ID.

I did enact JuliusCaesar I was killed i' the Capitol; Brutus killed me.

Doc 1

So let it be withCaesar. The nobleBrutus hath told youCaesar was ambitious

Doc 2

Recall inverted index constructionTerm Doc #I 1did 1enact 1julius 1caesar 1I 1was 1killed 1i' 1the 1capitol 1brutus 1killed 1me 1so 2let 2it 2be 2with 2caesar 2the 2noble 2brutus 2hath 2told 2you 2caesar 2was 2ambitious 2

Sec. 4.2


Term Doc #I 1did 1enact 1julius 1caesar 1I 1was 1killed 1i' 1the 1capitol 1brutus 1killed 1me 1so 2let 2it 2be 2with 2caesar 2the 2noble 2brutus 2hath 2told 2you 2caesar 2was 2ambitious 2

Term Doc #ambitious 2be 2brutus 1brutus 2capitol 1caesar 1caesar 2caesar 2did 1enact 1hath 1I 1I 1i' 1it 2julius 1killed 1killed 1let 2me 1noble 2so 2the 1the 2told 2you 2was 1was 2with 2

Key step After all documents have been

parsed, the inverted file is sorted by terms and then by Doc#

We focus on this sort step.We have 100M items to sort.

Sec. 4.2


Scaling index construction In-memory index construction does not scale

Can’t stuff entire collection into memory, sort, then write back

How can we construct an index for very large collections?

Sec. 4.2


Hardware basics Many design decisions in information retrieval are

based on the characteristics of hardware

We begin by reviewing hardware basics

Sec. 4.1


Hardware basics Access to data in memory is much faster than access

to data on disk.

Servers used in IR systems now typically have several GB of main memory, sometimes tens of GB.

Available disk space is several (2–3) orders of magnitude larger.

Sec. 4.1


Hardware basics It takes a while for the disk head to move to the part

of the disk where the data are located: seek time.

Therefore chunks of data that will be read together should therefore be stored contiguously.

In fact transferring one large chunk of data from disk to memory is faster than transferring many small chunks.

Sec. 4.1


Hardware basics Disk I/O is block-based: Reading and writing of entire

blocks (as opposed to smaller chunks).

Block sizes: 8KB to 256 KB.

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RCV1: Our collection for this lecture Shakespeare’s collected works definitely aren’t large

enough for demonstrating many of the points in this course.

As an example for applying scalable index construction algorithms, we will use the Reuters RCV1 collection.

This is one year of Reuters newswire. It isn’t really large enough either, but it’s publicly

available and is at least a more plausible example.

Sec. 4.2


A Reuters RCV1 document

Sec. 4.2


Reuters RCV1 statistics symbol statistic value N documents 800,000 M terms 400,000 tokens 100,000,000

Sec. 4.2


Sort-based index construction As we build the index, we parse docs one at a time. At 12 bytes (4+4+4) per token entry (term, doc, freq),

demands a lot of space for large collections. Why 4 bits per term?

“Term to term-id” dictionary and “term-id to doc-id” dictionary.

T = 100,000,000 in the case of RCV1 We can do this in memory in 2009, but typical

collections are much larger. E.g., the New York Times provides an index of >150 years of newswire

Thus: We need to store intermediate results on disk.

Sec. 4.2


Sort using disk as “memory”? Can we use the same index construction algorithm

for larger collections, but by using disk instead of memory?

Sorting T = 100,000,000 records on disk is too slow Too many (random) disk seeks.

Sec. 4.2


The solution? We need to use an external sorting algorithm, that is,

one that uses disk.

For acceptable speed, the central requirement of such an algorithm is that it minimize the number of random disk seeks during sorting.

Sequential disk reads are far faster than seeks.

Two methods: BSBI SPIMI (not present in our course)

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BSBI: Blocked Sort-Based Indexing (i) segments the collection into parts of equal size, (ii) sort and invert the termID–docID pairs of each

part in memory, First, we sort the termID–docID pairs. Next, we collect all termID–docID pairs with the same

termID into a postings list (iii) stores intermediate sorted results on disk, (iv) merges all intermediate results into the final

index.

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Introduction to Information RetrievalIntroduction to Information Retrieval Sec. 4.2


Example Must now sort 100M records.

Define a Block ~ 10M such records Can easily fit a couple into memory. Will have 10 such blocks to start with.

Sec. 4.2


How to merge the sorted runs?

Sec. 4.2


How to merge the sorted runs? Open all block files simultaneously. Maintain small read buffers for the ten blocks and a

write buffer for the final merged index we are writing.

In each iteration, we select the lowest termID that has not been processed yet.

All postings lists for this termID are read and merged, and the merged list is written back to disk.

Each read buffer is refilled from its file when necessary.

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How to merge the sorted runs? Providing you read decent-sized chunks of each block into

memory and then write out a decent-sized output chunk, then you’re not killed by disk seeks

Sec. 4.2


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)

The solution: SPMI method.

Sec. 4.3




Dynamic indexing

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Distributed indexing For web-scale indexing (don’t try this at home!):

must use a distributed computing cluster a group of tightly coupled computers that work together closely

Individual machines are fault-prone Can unpredictably slow down or fail

How do we exploit such a pool of machines? Estimate: Google ~1 million servers, 3 million

processors/cores (Gartner 2007)

Sec. 4.4


Distributed indexing Maintain a master machine directing the indexing job

considered “safe”. Break up indexing into sets of (parallel) tasks. Master machine assigns each task to an idle machine

from a pool.

Sec. 4.4


Parallel tasks We will use two sets of parallel tasks

Parsers Inverters

Break the input document collection into splits Each split is a subset of documents (corresponding to

blocks in BSBI/SPIMI)

Sec. 4.4


Parsers Master assigns a split to an idle parser machine Parser reads a document at a time and emits (term,

doc) pairs Parser writes pairs into j partitions Each partition is for a range of terms’ first letters

(e.g., a-f, g-p, q-z) – here j = 3.

Sec. 4.4


Inverters An inverter collects all (term,doc) pairs (= postings)

for one term-partition. Sorts and writes to postings lists

Sec. 4.4


Data flow

splits

Parser

Parser

Parser

Master

a-f g-p q-z

a-f g-p q-z

a-f g-p q-z

Inverter

Inverter

Inverter

Postings

a-f

g-p

q-z

assign assign

Mapphase

Segment files Reducephase

Sec. 4.4


MapReduce The index construction algorithm we just described is

an instance of MapReduce. MapReduce (Dean and Ghemawat 2004) is a robust

and conceptually simple framework for distributed computing without having to write code for the distribution part.

Sec. 4.4




Dynamic indexing

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Dynamic indexing Up to now, we have assumed that collections are

static. They rarely are:

Documents come in over time and need to be inserted. Documents are deleted and modified.

This means that the dictionary and postings lists have to be modified: Postings updates for terms already in dictionary New terms added to dictionary

Sec. 4.5


The simplest approach Periodically reconstruct the index from scratch.

This is a good solution if The number of changes over time is small Delay in making new documents searchable is acceptable Enough resources are available to construct a new index

while the old one is still available for querying.

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Another approach Maintain “big” main index New docs go into` “small” auxiliary index Search across both, merge results Deletions

Invalidation bit-vector for deleted docs Filter docs output on a search result by this invalidation

bit-vector Documents are updated by deleting and reinserting

them. Periodically, re-index into one main index.

Sec. 4.5


Merging

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Introduction to Information RetrievalIntroduction to Information Retrieval Sec. 4.5


Logarithmic merge

Sec. 4.5


Other sorts of indexes Positional indexes

Same sort of sorting problem … just larger Building character n-gram indexes:

As text is parsed, enumerate n-grams. For each n-gram, need pointers to all dictionary terms

containing it – the “postings”. Note that the same “postings entry” will arise repeatedly

in parsing the docs – need efficient hashing to keep track of this. E.g., that the trigram uou occurs in the term deciduous will be

discovered on each text occurrence of deciduous Only need to process each term once

Sec. 4.5


Final considerations So far, postings lists are ordered with respect to docID. As we see in Chapter 5, this is advantageous for

compression. However, this structure for the index is not optimal when

we build ranked retrieval systems (chapter 6 and 7). In ranked retrieval, postings are often ordered according

to weight or impact. In a docID-sorted index, new documents are always

inserted at the end of postings lists. In an impact-sorted index, the insertion can occur

anywhere, thus complicating the update of the inverted index.

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