since 1.9 mongo supports multi location documents http://www.mongodb.org/display/DOCS/Geospatial+Indexing#GeospatialIndexing-MultilocationDocuments
we are using doctrine mongo odm and this functionality is vital for our project.
does anybody know how to create a multi location document in mongo odm?
Cross-referencing this with the same question you asked in mongodb-odm issue.
I don't believe ODM needs any special support for this, just as it doesn't for multi-key indexing of array fields. Try defining a geo index on the field containing your array of points. Also, I'd note that ODM's geo documenation suggests using an embedded object to hold coordinates. MongoDB will support that, but an array (i.e. [x, y]) is preferred.
Related
In the MongoDB docs it is stated that
Indexes are special data structures [1] that store a small portion of
the collection’s data set in an easy to traverse form.
How can I see these data structures? Is it possible?
I was going through this question and I saw that in this answer they gave an example of a schema for an index. Is there such a thing in MongoDB that is what I am trying to see. I am trying to understand indexes in MongoDB better.
When you create an index in Mongo (using createIndex) you specify which fields the index will use, or what you call the index "schema".
As mentioned in the docs these indexes are built as b-trees (don't read too much into this as indexes are a "black box" for us users), viewing the exact tree structure is not possible, but you can use indexStats to get some more information on an index you created.
MongoDB 3.2 is now providing a filter expression to partially index the collection.
Based on that feature, I wonder how MongoDB could help me for the following case.
I do have many pre defined queries very near the filter expression. The principle will be to create many filter expression index.
The index will in fact keep the ids of the maching document and will be updated on each document changes.
For performance reason, I prefer to use the index engine from MongoDB that trying to use an external tools with Trigger solution.
How could I accomplish such feature by extending MongoDB. Any others noSQL could help ?
Thanks
I want to know as we have index creation feature in mognodb to speed up the query process https://docs.mongodb.org/v3.0/indexes/ what do we have for elasticsearch for this purpose? I googled it but I was unable to find any suitable information, I used indexing in mongodb on most frequently used fields to speed up the query process and now I want to do same in elasticsearch i want to know is there anything that elasticsearch provides .Thanks
Elasticsearch also has indices: https://www.elastic.co/blog/what-is-an-elasticsearch-index
They are also used as part of the database's key features to provide swift search capabilities.
It is annoying that "index" is used in a different context with ES and many other databases. I'm not as familiar with MongoDB so I'll resort to their documentation at v3.0/core/index-types.
Basically Elasticsearch was designed to serve efficient "filtering" (yes/no queries) and "scoring" (relevance ranking via tf-idf etc.), and it uses Lucene as the underlying inverted index.
MongoDB concepts and their ES counter-parts:
Single Field Index: trivially supported, perhaps as not_analyzed fields for exact matching
Compound Index: Lucene applies AND filter condition via efficient bitmaps, can ad-hoc merge any "single field" indexes
Multikey Index: Transparent support, no difference values and an array of values
Geospatial Index: directly supported via geo-shapes
Text Index: In some way ES was optimized for this use-case as analyzed field type
In my view at search applications relevance is more important that plain filtering the results, as some words occur at almost every document and thus are less relevant when searching.
Elasticsearch has other very useful concepts as well such as aggregations, nested documents and child/parent relationships.
I have a sparse database. Some fields are of Boolean type (these fields should be indexed), some other fields are of Nominal type (again, these fields should also be indexed) whereas some other fields are of Text type (but those ones should not be indexed). I would like to save my data in a database so that I can search based on any combination of the indexed fields and get back the results. Should I consider using Elasticsearch, MongoDB or another databases?
Any help is appreciated.
According to above mentioned description I suggest MongoDB is best suitable for your requirement as MongoDB has powerful index management and it supports multiple types of indexes.
Indexes allow MongoDB to process and fulfill queries quickly by
creating small and efficient representations of the documents in a
collection.
For more detailed description regarding index types in mongodb please refer the documentation mentioned in following URL
https://docs.mongodb.org/manual/core/index-types/
If you have RDBMS you probably have to use Solr to index your relational tables to fully nested documents.
Im new to non-sql databases like Mongodb, CouchDB and Cassandra, but it seems to me that the data you save is already in that document structure like the documents saved in Solr/Lucene.
Does this mean that you don't have to use Solr/Lucene when using these databases?
Is it already indexed so that you can do full-text search?
It depends on your needs. They have a full text search. In CouchDB the search is Lucene (same as solr). Unfortunately, this is just a full text index, if you need complex scoring or DisMax type searching, you'll likely want the added capabilities of an independent Solr Index.
Solr (Lucene) uses an algorithm to returns relevant documents from a query. It will returns a score to indicate how relevant each document is related to the query.
It is different than what a database (relational or not) does, which is returning results that matches or not a query.