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
Related
Currently, I have a MongoDB instance, which contains a collection with a lot of entities. Each entity contains a string attribute, which represents some text. My goal is to provide a strict text search in the collection. It should work as a MySQL query:
SELECT *
FROM texts
WHERE text LIKE '%test%';
MongoDB text index would be great, but it doesn't provide a strict search. How I could organize a strict search for such data? Could I do some optimization?
I already checked other software (such as ElasticSearch, Lucene, MongoDB, ClickHouse), but I haven't found options to do it. Searching as now took too much time.
In mongoDB you can do it as follow:
db.texts.find({ text:/test/ })
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
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.
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.
Some Background: I'm planning to use MongoDB as the publishing frontend db for a few of my websites. The actual data will be kept in a SQL Server db and there will be background jobs that will populate the MongoDB at predefined time intervals for readonly purposes to boost website performance.
The Situation: I have a table 'x' that i translated into a mongo collection, everything worked fine.
'x' has a column 'c' that was originally a NVARCHAR(MAX) in the source db and has multilingual text in it.
When I was searching by column 'c', mongo was doing fullscan on the collection.
So I tried doing an ensureIndex({c : 1 }) which worked but when I checked the mongodb logs it showed me that 90% of the data could not be indexed as [Key Too Large To Index] !!
And thus is has indexed 10% of the data and now only returns results from that 10% !!
What are my alternatives ??
Note: I was using this column to do full text searching in SQL Server, now im not sure if I should go ahead with Mongo or not :(
Try to run your mongod process with this parameter:
sudo mongod --setParameter failIndexKeyTooLong=false
And than try again.
if you need to search text inside a large string you can use one of those:
keyword splitting
regular expression
the former has the downside that you need some "logic" to combine the keyword to make a search, the latter heavily impacts on performance.
probably if you really need full text search the best option is to use an external indexer like solr or lucene.
Since you can do some elaboration, you could extract some key words and put them in a field:
_keywords : [ "mongodb" , "full search" , "nosql" ]
and make an index on that.
Don't use mongo for full text searching
its not designed for that. Yes obviously you will get an error key too large on indexing for long string values.
Better approach would be using full text search servers (solr/lucene or sphinx) if your main concern is search.
Recent (2.4 and above) MongoDB builds provide a couple other options:
As the OP's stated desire is for full text search, the right approach would be to use a text index which directly supports that use case.
For an exact match index on long string values you can use a hashed index.