In some blogs and lucene website,I know lucene use data structure "skip list" in inverted index. But I have some puzzle about it.
1:In general,skip list maybe used in memory ,but inverted index is stored in disk. So how lucene use it when search on the index? just scanning it on disk or load it to memory?
2:skip list's insert operator often use random(0,1) to decide whether insert to next level,but in luncene introdution,it seems a fixed interval in every terms,so how lucene create the "skip list" different or not?
Please correct me if I am wrong.
Lucene uses memory in a couple different ways, even though the index is persisted on a disk when the IndexReader is created for searching and for operations like sorting (field cache):
http://blog.mikemccandless.com/2010/07/lucenes-ram-usage-for-searching.html
Basically those binary files get copied into RAM for much faster scanning and reducing I/O. You get a hint in the above link how searching with some parameters can force Lucene to "skip terms in searching" Hence, where that data structure can be used.
Lucene is open source, so you can see the code for yourself what is being used in Java or Lucene.NET for the C# implementation.
see To accelerate posting list skips, Lucene uses skip lists
Related
There are lots of articles on how search engines perform indexing, but couldn't find any information on how they store these indexed records in a way that enables fast querying with scalability. Could someone explain the index storing mechanisms used in search engines or point to any article ?
Solr is able to achieve fast search responses because, instead of searching the text directly, it searches an index instead. This is like retrieving pages in a book related to a keyword by scanning the index at the back of a book, as opposed to searching every word of every page of the book.
This type of index is called an inverted index, because it inverts a page-centric data structure (page->words) to a keyword-centric data structure (word->pages).
Inverted index is a major term in the domain of Information Retrieval and Natural Language Processing. Take a document, note down all the unique words appearing in that document as well as frequency of the words. Here you are ready with your own inverted index. Solr creates similar inverted index of the documents posted to its core using a defined schema. Schema is a blue print which helps Solr in creating invered index of the documents by giving a set of predefined fields in the schema.xml file.
I am pretty new to lucene I am trying to understand the segment merging process. I came across the method optimize(which will merge all the available Lucene index segment at that instance).
My exact question is, Does Optimize merges all the levels of segments & creates one complex segment?
Alternatives in the latest version of Lucene( say Lucene 6.5)?
Is it good to always call the optimize method after the indexing process, so that my index will always have a single segment and searches will be fast?
First of all, it's not needed to always merge segments to just one segment. It could be configured. In principle, idea of merging segments/optimizing index is coming from the implementation of deletes in the Lucene. Lucene do not deleting documents, but rather marking them for deletion, second, new documents are coming into new segments.
Lucene have a lot of per-segment files - like term dictionary and many others, so merging them together will reduce the heap and makes searches faster. However, usually the process of merging isn't that fast.
Overall, you need to have a balance between calling merging/optimizing every time you index new docs and not doing it all. One thing to look at is MergePolicy, which defines different types of merging, with different strategies. If you will not find any suitable for you (which I doubt), you could implement one for your needs.
As in Lucene 6.5 you could use
public void forceMerge(int maxNumSegments) of IndexWriter class
How can we know the index in Lucene is correct?
Detail
I created a simple program that created Lucene indexes and stored it in a folder. Using the diagnostic tool, Luke I could look inside an index and view the content.
I realise Lucene is a standard framework for building a search engine but I wanted to be sure that Lucene indexes every term that existed in a file.
Can I verify that the Lucene index creation is dependable? That not even a single term went missing?
You could always build a small program that will perform the same analysis you use when indexing your content. Then, for all the terms, query your index to make sure that the document is among the results. Repeat for all the content. But personally, I would not waste time on this. If you can open your index in Luke and if you can make a couple of queries, everything is most probably fine.
Often, the real question is whether or not the analysis you did on the content will be appropriate for the queries that will be made against your index. You have to make sure that your index will have a good balance between recall and precision.
I have a problem that in my lucene index files one document can have huge text. now when i search one of these huge text documents lucene/solr does not filter any results even the search term exist in the document text. the reason that i think might be the large number of characters in document text? if yes than how could we tell solr/lucene how much characters to analyze during search, please explain
I am using Solr 1.4.1 can any
Thanks
Ahsan
Lucene can handle huge documents without trouble. It seems unlikely that the document size itself is the problem. Use a tool like Luke to inspect the index and see what terms are associated with some of these large documents.
Also, have you changed the maxFieldLength setting in solrconfig.xml? I am testing out indexing the Bible, at 25 MB of data, and with a maxFieldLength of 10,000, which is the default, only the first 10,000 tokens ever get analysized, which leads to roughly ~2000 unique terms for my document.
If you are using Lucene directly, then there are a couple setting for maxFieldLength, you may have "unlimited" and therefore getting everything. Check the JavaDocs for how to set maxFieldLength.
I'm working on a job portal using asp.net 3.5
I've used Lucene for job and resume search functionality.
Would like to know tips/recommendations if any with respect to Lucene performance optimization, scalability, etc.
Thanks a ton!
I've documented how I used Lucene.NET (in BugTracker.NET) here:
http://www.ifdefined.com/blog/post/2009/02/Full-Text-Search-in-ASPNET-using-LuceneNET.aspx
One thing you should keep in mind is that it is very hard to cluster or replicate lucene indexes in large installations, like fail over scenarios or distributed systems. So you should either have a good way to replicate your index jobs or the whole database.
If you use a sort, watch out for the size of the comparators. When sorts are used, for each document returned by the searcher there will be a comparator object stored for each SortField in the Sort object. Depending on the size of the documents and the number of fields you want to sort on, this can become a big headache.