I want to have some sort of limited indexed Full-text search. With FTS postgres will index all the words in the text, but I want it to track only a given set of words. For example, I have a database of tweets, and I want them to be indexed by special words that I give: awesome, terrible and etc.
If someone will be interested in such a specific thing, I made it by creating a custom dictionary (thanks Mark).
My findings I documented here: https://hackmd.io/#z889TbyuRlm0vFIqFl_AYQ/r1gKJQBZS
Related
I have a word "CenturyLink" which I want to force mongodb to stem to "Centuri" so that when I search for "Century" ,which stems to "Centuri", all the documents with "CenturyLink" are also returned.
So basically I want to add some custom set of key values of "word and it's stem" so that fts indexing is done on the basis of the provided key values.
Is this possible?
Read Query
In Posgres, Full text indexing allows documents to be preprocessed and an index saved for later rapid searching. Preprocessing includes:
Parsing documents into tokens.
Converting tokens into lexemes.
Storing preprocessed documents optimized for searching.
tsvector type is used in Postgres for full text search
tsvector type is different than text type in below aspects:
Eliminates case. Upper/lower case letter are identical
Removes stop words ( and, or, not, she, him, and hundreds of others)-because these words are not relevant for text search
Replaces synonyms and takes word stems (elephant -> eleph). In the full text catalogue, it does not have the word elephant but the word elep.
Can (and should) be indexed with GIST and GIN
Custom ranking with weights & ts_rank
How Elastic search(search engine) has advantage over full text search in Postgres?
fulltext search and elasticsearch are both built on the same basic technology inverted indices so performance is going to be about the same.
FTS is going to be easier to deploy.
ES comes with lucene,
if you want lucene with FTS that will require extra effort.
We're using Postgres and its fulltext feature to search for documents (posts content) in our system, and it works really well.
For autocomplete we want to build index (dictionary?) with all words used in documents and search by most frequent ones.
We will always search for one word. We will never search for phrase.
So if I write:
"th"
I will receive (suppose the most frequent words in our documents):
"this"
"there"
"thoughts"
...
How to do it with Postgres? Or maybe we need some more advanced solution like apache lucene / solr ?
Neither postgres fulltext search (which provides lexems) nor postgres trigrams seems to be suitable for this work. Or maybe I am wrong ?
I don't want to manually parse text and ignore all english stopwords which would be error prone. Postgres does good job with this while building lexems index. But intead of lexems, we need to build and search words dictionary without normalization
Thank you for your assistance
Since it is not possible to find "blueberry" by the word "blue" by using a mongodb full text search, I want to help my users to complete the word "blue" to "blueberry". To do so, is it possible to query all the words in a mongodb full text index -> that I can use the words as suggestions i.e. for typeahead.js?
Language stemming in text search uses an algorithm to try to relate words derived from a common base (eg. "running" should match "run"). This is different from the prefix match (eg. "blue" matching "blueberry") that you want to implement for an autocomplete feature.
To most effectively use typeahead.js with MongoDB text search I would suggest focusing on the prefetch support in typeahead:
Create a keywords collection which has the common words (perhaps with usage frequency count) used in your collection. You could create this collection by running a Map/Reduce across the collection you have the text search index on, and keep the word list up to date using a periodic Incremental Map/Reduce as new documents are added.
Have your application generate a JSON document from the keywords collection with the unique keywords (perhaps limited to "popular" keywords based on word frequency to keep the list manageable/relevant).
You can then use the generated keywords JSON for client-side autocomplete with typeahead's prefetch feature:
$('.mysearch .typeahead').typeahead({
name: 'mysearch',
prefetch: '/data/keywords.json'
});
typeahead.js will cache the prefetch JSON data in localStorage for client-side searches. When the search form is submitted, your application can use the server-side MongoDB text search to return the full results in relevance order.
A simple workaround I am doing right now is to break the text into individual chars stored as a text indexed array.
Then when you do the $search query you simply break up the query into chars again.
Please note that this only works for short strings say length smaller than 32 otherwise the indexing building process will take really long thus performance will be down significantly when inserting new records.
You can not query for all the words in the index, but you can of course query the original document's fields. The words in the search index are also not always the full words, but are stemmed anyway. So you probably wouldn't find "blueberry" in the index, but just "blueberri".
Don't know if this might be useful to some new people facing this problem.
Depending on the size of your collection and how much RAM you have available, you can make a search by $regex, by creating the proper index. E.g:
db.collection.find( {query : {$regex: /querywords/}}).sort({'criteria': -1}).limit(limit)
You would need an index as follows:
db.collection.ensureIndex( { "query": 1, "criteria" : -1 } )
This could be really fast if you have enough memory.
Hope this helps.
For those who have not yet started implementing any database architecture and are here for a solution, go for Elasticsearch. Its a json document driven database similar to mongodb structurally. It has "edge-ngram" analyzer which is really really efficient and quick in giving you did you mean for mis-spelled searches. You can also search partially.
I have a database and want to be able to look up in a table a search that's something like:
select * from table where column like "abc%def%ghi"
or
select * from table where column like "%def%ghi"
Is there a way to index the column so that this isn't too slow?
Edit:
Can I also clarify that the database is read only and won't be updated often.
Options for text search and indexing include:
full-text indexing with dictionary based search, including support for prefix-search, eg to_tsvector(mycol) ## to_tsquery('search:*')
text_pattern_ops indexes to support prefix string matches eg LIKE 'abc%' but not infix searches like %blah%;. A reverse()d index may be used for suffix searching.
pg_tgrm trigram indexes on newer versions as demonstrated in this recent dba.stackexchange.com post.
An external search and indexing tool like Apache Solr.
From the minimal information given above, I'd say that only a trigram index will be able to help you, since you're doing infix searches on a string and not looking for dictionary words. Unfortunately, trigram indexes are huge and rather inefficient; don't expect some kind of magical performance boost, and keep in mind that they take a lot of work for the database engine to build and keep up to date.
If you need just to, for instance, get unique substrings in an entire table, you can create a substring index:
CREATE INDEX i_test_sbstr ON tablename (substring(columname, 5, 3));
-- start at position 5, go for 3 characters
It is important that the substring() parameters in the index definition are
the same as you use in your query.
ref: http://www.postgresql.org/message-id/BANLkTinjUhGMc985QhDHKunHadM0MsGhjg#mail.gmail.com
For the like operator use one of the operator classes varchar_pattern_ops or text_pattern_ops
create index test_index on test_table (col varchar_pattern_ops);
That will only work if the pattern does not start with a % in which case another strategy is required.