Large-scale Search

Practical Relevance Ranking for 11 Million Books, Part 3: Document Length Normalization.

In Part 2 we argued that most relevance ranking algorithms used for ranking text documents are based on three fundamental features:

Practical Relevance Ranking for 11 Million Books, Part 2: Document Length and Relevance Ranking

Document Length and Relevance Ranking

Practical Relevance Ranking for 11 Million Books, Part 1

Practical Relevance Ranking for 11 Million Books, Part 1

This is the first in a series of posts about our work towards practical relevance ranking for the 11 million books in the HathiTrust full-text search application.

A Tale of Two Solrs

When we first started working on large scale search we confronted the issue of whether to index pages or complete books as our fundamental unit of indexing.[i]   We had some concerns about indexing on the page level.  We knew we would need to scale to 10-20 million books and at an average of 300 pages per book that comes out to about 6 billion pages.  At that time we did not think that Solr would scale to 6 billion pages.[ii]  If we indexed by page, we also wanted to be able

Multilingual Issues Part 1: Word Segmentation

At the core of the Solr/Lucene search engine is an inverted index.  The inverted index has a list of tokens and a list of the documents that contain those tokens. In order to index text, Solr needs to break strings of text into “tokens.”  In English and Western European languages spaces are used to separate words, so Solr uses whitespace to determine what is a token for indexing.   In a number of languages the words are not separated by spaces.

Forty Days and Forty Nights: Re-indexing 7+ million books (part 1)

Forty days forty nights: Re-indexing 7+ million books (part 1)

Forty days and forty nights; That’s how long we estimated it would take to re-index all 7+ million volumes in HathiTrust. Because of this forty day turnaround time, when we found a problem with our current indexing, we were reluctant to do a complete re-index. Whenever feasible we would just re-index the affected materials.

Too Many Words Again!

After Mike McCandless increased the limit of unique words in a Lucene/Solr index segment from 2.4 billion words to around 274 billion words, we thought we didn't need to worry about having too many words (See We recently discovered that we were wrong!

Making personal collections from Large Scale Search Results

We just released a new feature in our full-text Large Scale Search. When you do a search,you will see check boxes next to each search result. You can select items you want from the search results and create a personal collection. This should make it much easier to do repeated searches and explore a targeted subset of the HathiTrust volumes. If you are not logged in, the collection will be temporary. If you log in you can save the collection permanently.

Too Many Words!

When we read that the Lucene index format used by Solr has a limit of 2.1 billion unique words per index segment,  we didn't think we had to worry.  However, a couple of weeks ago, after we optimized our indexes on each shard to one segment, we started seeing java "ArrayIndexOutOfBounds" exceptions in our logs.  After a bit of investigation we determined that indeed, most of our index shards contained over 2.1 billion unique words and some queries were triggering these exeptions.  Currently ea

Performance at 5 million volumes

On November 19, 2009, we put new hardware into production to provide full-text searching against about 4.6 million volumes.  Currently we have about 5.3 million volumes.  The average response time is about  3 seconds,  90% of queries take under 4 seconds, 9% of queries take between 4 seconds and 24 seconds, and 1% of queries take longer than 24 seconds.