Major Data Structures used to store data by Search engines

By | May 10, 2017

Related Read – Introduction to search engines


Big Files are virtual files spanning multiple file systems and are addressable by 64-bit integers. The allocation among multiple file systems is handled automatically. The BigFiles package also handles allocation and deallocation of file descriptors, since the operating systems do not provide enough for our needs.


The repository contains the full HTML of every web page. Each page is compressed using zlib. The choice of compression technique is a tradeoff between speed and compression ratio. We chose zlib’s speed over a significant improvement in compression offered bzip. In the repository, the documents are stored one after the other and are prefixed by docID, length, and URL.

Document Index:

The document index keeps information about each document. It is a fixed width ISAM (Index sequential access mode) index, ordered by docID. The information stored in each entry includes the current document status, a pointer into the repository, a document checksum, and various statistics. If the document has been crawled, it also contains a pointer to a variable width file called docinfo which contains its URL and title. Otherwise, the pointer points into the URLlist whichcontains just the URL.


The lexicon has several different forms. The current lexicon contains 14 million words. It is implemented in two parts — a list of the words and a hash table of pointers.

Hit Lists:

A hit list corresponds to a list of occurrences of a particular word in a particular document including position, font, and capitalization information. Hit lists account for most of the space used in both the forward and the inverted indices. Because of this, it is important to represent them as efficiently as possible. Google considers several alternatives for encoding position, font, and capitalization simple encoding, a compact encoding, and Huffman coding.

Forward Index:

The forward index is actually already partially sorted. It is stored in a number of barrels. Each barrel holds a range of wordID’s. If a document contains words that fall into a particular barrel, the docID is recorded into the barrel, followed by a list of wordID’s with hit lists which correspond to those words. Instead of storing actual wordID’s, it stores each worded as a relative difference from the minimum worded that falls into the barrel the worded is in.

Inverted Index:

The inverted index consists of the same barrels as the forward index, except that they have been processed by the sorter. For every valid wordID, the lexicon contains a pointer into the barrel that wordID falls into. It points to a doclist of docID’s together with their corresponding hit lists. This doclist represents all the occurrences of that word in all documents.

Anatomy & Working of Search Engines >>

Author: Ravi Bandakkanavar

A Techie, Blogger, Web Designer, Programmer by passion who aspires to learn new Technologies every day. It has been 6 years since I have been publishing articles and enjoying every bit of it. I want to share knowledge and build a great community with people like you.

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