inverted-index

Is there anyway in which I can create indexes in Solr to perform full-text search from HBase for Near Real Time. I didn't wanted to store the whole text in my solr indexes. Made "stored=false" Note: - Keeping in mind, I am working on large datasets and want to do Near Real Time search. WE are talking TB/PB of data. UPDATED Cloudera Distribution : 5.4.x is used with Cloudera Search components. Solr : 4.10.x HBase : 1.0.x Indexer Service : Lily HBase Indexer with cloudera morphlines Is there any other NRT Indexer services or frameworks which can be used instead of Lily on Cloudera . Just a

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,

I am making a inverted index using hadoop and python. I want to know how can I include the byte offset of a line/word in python. I need something like this hello hello.txt@1124 I need the locations for making a full inverted index. Please help. Like this? file.tell() Return the file’s current position, like stdio's ftell(). http://docs.python.org/library/stdtypes.html#file-objects Unfortunately tell() does not function since OP is using stdin instead of a file. But it is not hard to build a wrapper around it to give what you need. class file_with_pos(object): def __init__(self, fp): self.fp =

I have a full inverted index in form of nested python dictionary. Its structure is : {word : { doc_name : [location_list] } } For example let the dictionary be called index, then for a word " spam ", entry would look like : { spam : { doc1.txt : [102,300,399], doc5.txt : [200,587] } } I used this structure as python dict are pretty optimised and it makes programming easier. for any word 'spam', the documents containig it can be given by : index['spam'].keys() and posting list for a document doc1 by: index['spam']['doc1'] At present I am using cPickle to store and load this dictionary. But the

So, I have this index as a dict. index = {'Testfil2.txt': ['nisse', 'hue', 'abe', 'pind'], 'Testfil1.txt': ['hue', 'abe', 'tosse', 'svend']} I need to invert the index so it will be a dict with duplicates of values merged into one key with the 2 original keys as values, like this: inverse = {'nisse' : ['Testfil2.txt'], 'hue' : ['Testfil2.txt', 'Testfil1.txt'], 'abe' : ['Testfil2.txt', 'Testfil1.txt'], 'pind' : ['Testfil2.txt'], 'tosse' : ['Testfil1.txt'], 'svend' : ['Testfil1.txt'] Yes, I typed the above by hand. My textbook has this function for inverting dictionaries: def invert_dict(d):