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.
Related
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/
I 'm familiar with mongodb.
you know, there are many index types in mongodb, such as:
multikey index : http://docs.mongodb.org/manual/core/index-multikey/
, which is very useful for keyword search, I ever used it to build a simple search engine.
compound index is also very useful in mongodb : http://docs.mongodb.org/manual/tutorial/create-a-compound-index/ which is used for multi fields' query.
but I need to migrate my database from mongodb to hbase, do you know some similar index in hbase which can realize the same function with multikey and compound index in mongodb?
HBase doesn't support secondary indexes, that's one of the trade-offs in order to be able to scale to massive data sets. These are the options you have:
http://hbase.apache.org/book/secondary.indexes.html
It all depends on the amount of data you're going to handle and your access patterns. For me, both dual writing to "index" tables & summary tables are the best approaches, just keep in mind that this has to be done manually.
There is no concept of indexing in HBase as of now. I know there is some demand within the community for Indexing. But there are other projects which provide indexing on top of Hbase, One particular one i looked at was Huawei Hindex
It is not recommended to use ElasticSearch as the only storage from some obvious reasons like security, transactions etc. So how it is usually used together with other database?
Say, I want to store some documents in MongoDB and be able to effectively search by some of their properties. What I'd do would be to store full document in Mongo as usual and then trigger insertion to ElasticSearch but I'd insert only searchable properties plus MongoDB ObjectID there. Then I can search using ElasticSearch and having ObjectID found, go to Mongo and fetch whole documents.
Is this correct usage of ElasticSearch? I don't want to duplicate whole data as I have them already in Mongo.
The best practice is for now to duplicate documents in ES.
The cool thing here is that when you search, you don't have to return to your database to fetch content as ES provide it in only one single call.
You have everything with ES Search Response to display results to your user.
My 2 cents.
You may like to use mongodb river take a look at this post
There are more issue then the size of the data you store or index, you might like to have MongoDB as a backup with "near real time" query for inserted data. and as a queue for the data to indexed (you may like to use mongodb as cluster with the relevant write concern suited for you application
To be true, After typing the Question title only, i had a look about DB indexing in Wiki.
Now i know something about Indexing in general. But, still i have some questions on MongoDB indexing.
What is indexing in MongoDB? What it will exactly do, If i index a collection?
What i can do with indexing in MongoDB?
Will i able to use it for searching specific data?
Can anyone explain it with the below set of documents in a Collection in some MongoDB?
{ "_id":"das23j..", "x": "1", "y":[ {"RAM":"2 GB"}, {"Processor":"Intel i7"}, {"Graphics Card": "NVIDIA.."}]}
Thanks!!!
An index speeds up searching, at the expense of storage space. Think of the index as an additional copy of an attribute's (or column's) data, but in order. If you have an ordered collection you can perform something like a binary search, which is much faster than a sequential search (which you'd need if the data wasn't ordered). Once you find the data you need using the index, you can refer to the corresponding record.
The tradeoff is that you need the additional space to store the "ordered" copy of that column's data, and there's a slight speed tradeoff because new records have to be inserted in the correct order, a requisite for the quick search algorithms to work.
For details on mongodb indexing see http://www.mongodb.org/display/DOCS/Indexes.
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.