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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. Sec. 4.2. Inverted index construction. - PowerPoint PPT Presentation
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Introduction to Information Retrieval
Introduction to
Information Retrieval
Lecture 4: Index Construction
Related to Chapter 4: http://nlp.stanford.edu/IR-book/pdf/04const.pdf
Introduction to Information Retrieval
This lecture Scaling index construction
Distributed indexing
Dynamic indexing
2
Introduction to Information Retrieval
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
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
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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
Introduction to Information Retrieval
Hardware basics Many design decisions in information retrieval are
based on the characteristics of hardware
We begin by reviewing hardware basics
Sec. 4.1
Introduction to Information Retrieval
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 orders of magnitude larger.
Sec. 4.1
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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.
9
Introduction to Information Retrieval
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
Introduction to Information Retrieval
A Reuters RCV1 document
Sec. 4.2
Introduction to Information Retrieval
Reuters RCV1 statistics symbol statistic
value N documents
800,000 M terms
400,000 tokens
100,000,000
Sec. 4.2
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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)
15
Introduction to Information Retrieval
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.
16
Introduction to Information Retrieval Sec. 4.2
Introduction to Information Retrieval
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
Introduction to Information Retrieval
How to merge the sorted runs?
Sec. 4.2
Introduction to Information Retrieval
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.
20
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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
Introduction to Information Retrieval
This lecture Scaling index construction
Distributed indexing
Dynamic indexing
23
Introduction to Information Retrieval
An example of distributed processing 10000$ prize for recognizing the content of a file that
are encrypted with DES algorithm.
96 days later, Rocke Verser won this prize with distributed processing. 70000 computers were used for this purpose. Each computer tested a small part of the key search space.
24
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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
Introduction to Information Retrieval
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
Introduction to Information Retrieval
Inverters An inverter collects all (term,doc) pairs (= postings)
for one term-partition.
Sorts and writes to postings lists
Sec. 4.4
Introduction to Information Retrieval
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 Reduce
phase
Sec. 4.4
Introduction to Information Retrieval
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
Introduction to Information Retrieval
This lecture Scaling index construction
Distributed indexing
Dynamic indexing
32
Introduction to Information Retrieval
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
Introduction to Information Retrieval
The simplest approach Periodically reconstruct the index.
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.
34
Introduction to Information Retrieval
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
Introduction to Information Retrieval
Merging Number of merges: times.
is the size of auxiliary index. is the total number of postings.
Thus, the overall time complexity is
We can do better by logarithmic merge: Introducing indexes of size
36
Introduction to Information Retrieval
Logarithmic Merging
37
Main index
1
2
3
4
5
6
7
8
K: the number of times, auxiliary is filled.
Introduction to Information Retrieval Sec. 4.5
Introduction to Information Retrieval
Logarithmic merge
Sec. 4.5
Introduction to Information Retrieval
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.
40