Let's say I have a collection with this structure,
student_id,score,score_type
I have an index on score, and I want to query score of student with id=10000 and order the results by score.
I ran the query on my dataset and this is what the query plan is,
1: First the db uses the index on score to sort the documents.
2: Then it does the filter on the document with id:1000
Even though we use an index here, all the docs are examined here for the match(since there is no index on student_id). My question is that if all the documents are to be examined,why doesn't the db consider this alternate plan
1: Do a collection wide search and do the filtering.
2: Then use the index on score to do the sorting.
Here sorting will be done on a smaller dataset, so it should be faster.
What is wrong with the second plan?
Only one index can be used per query.
So if you want to query for a key and sort for another, you need a multi key index:
db.collection.ensureIndex({student_id:1,score:1})
db.collection.find({student_id: 1000}).sort({score:1})
Related
When a certain query is done on a mongodb collection, if there are multiple indexes that can be used to perform the query, how does mongodb choose the index for the query?
for an example, in a 'order' collection, if there are two indexes for columns 'customer' and 'vendor', and a query is issued with both customer and vendor specified, how does mongodb decide whether to use the customer index or the vendor index?
Is there a way to instruct mongodb to prefer a certain index over another, for a given query?
When a certain query is done on a mongodb collection, if there are
multiple indexes that can be used to perform the query, how does
mongodb choose the index for the query?
You can generate a query plan for a query you are trying to analyze - see what indexes are used and how they are used. Use the explain method for this; e.g. db.collection.explain().find(). The explain takes a parameter with values "queryPlanner" (the default), "executionStats" and "allPlansExecution". Each of these have different plan output.
The query optimizer generates plans for all the indexes that could be used for a given query. In your example order collection, the two single field indexes (one each for the fields customer and vendor) are possible candidates (for a query filter with both the fields). The optimizer uses each of the plans and executes them for a certain period of time and chooses the best performing candidate (this is determined based upon factors like - which returned most documents in least time, and other factors). Based upon this it will output the winning and rejected plans and these can be viewed in the plan output. You will see one of the indexes in the winning plan and the other in the rejected plan in the output.
MongoDB caches the plans for a given query shape. Query plans are cached so that plans need not be generated and compared against each other every time a query is executed.
Is there a way to instruct mongodb to prefer a certain index over
another, for a given query?
There are couple of ways you can use:
Force MongoDB to use a specific index using the hint() method.
Set Index Filters to specify which indexes the optimizer will evaluate for a query shape. Note that this setting is not persisted after a server shutdown.
Their official website states:
MongoDB uses multikey indexes to index the content stored in arrays. If you index a field that holds an array value, MongoDB creates separate index entries for every element of the array. These multikey indexes allow queries to select documents that contain arrays by matching on element or elements of the arrays.
You can checkout This article for more information
For your second query, you can try creating custom indexes for documents. Checkout their Documentation for the same
I have two fields scheduledStamp and email in a mongodb collection called inventory.
Having the following jpa query:
fun findAllByScheduledStampAfterAndEmailEquals(scheduledStamp:Long,email:String):List<Inventory>
What is the best way to index this collection?
I want to have less indexes as possible, avoiding unnecessary indexes.
Knowing that:
This collection can have more than million entries (index is needed)
Querying by:
db.inventory.find({ scheduledStamp: {$gt:1594048295294}})
for sure results few entries
Querying by:
db.inventory.find({ email: "abc#gmail.com"})
for sure results few entries
If you need to support query only on email : Indexing email is must
If you need to support query only on scheduledStamp: Indexing scheduledStamp is must
If you want of query on both, a third index is required. But you can create a compound index to cover this query and one of the above queries.
Since Mongo follows prefix match for selecting index:
You may have index on {"email":1} and {"scheduledStamp:1","email":1}
OR
You may have index on {"scheduledStamp":1} and {"email:1","scheduledStamp":1}
But since you said these fields return few documents:
Just having 2 indexes on {"email":1} and {"scheduledStamp":1} may perform good if not optimum.
So, I read the following definition of indexes from [MongoDB Docs][1].
Indexes support the efficient execution of queries in MongoDB. Without indexes, MongoDB must perform a collection scan, i.e. scan every document in a collection, to select those documents that match the query statement. If an appropriate index exists for a query, MongoDB can use the index to limit the number of documents it must inspect.
Indexes are special data structures that store a small portion of the
collection’s data set in an easy to traverse form. The index stores
the value of a specific field or set of fields, ordered by the value
of the field. The ordering of the index entries supports efficient
equality matches and range-based query operations. In addition,
MongoDB can return sorted results by using the ordering in the index.
I have a sample database with a collection called pets. Pets have the following structure.
{
"_id": ObjectId(123abc123abc)
"name": "My pet's name"
}
I created an index on the name field using the following code.
db.pets.createIndex({"name":1})
What I expect is that the documents in the collection, pets, will be indexed in ascending order based on the name field during queries. The result of this index can potentially reduce the overall query time, especially if a query is strategically structured with available indices in mind. Under that assumption, the following query should return all pets sorted by name in ascending order, but it doesn't.
db.pets.find({},{"_id":0})
Instead, it returns the pets in the order that they were inserted. My conclusion is that I lack a fundamental understanding of how indices work. Can someone please help me to understand?
Yes, it is misunderstanding about how indexes work.
Indexes don't change the output of a query but the way query is processed by the database engine. So db.pets.find({},{"_id":0}) will always return the documents in natural order irrespective of whether there is an index or not.
Indexes will be used only when you make use of them in your query. Thus,
db.pets.find({name : "My pet's name"},{"_id":0}) and db.pets.find({}, {_id : 0}).sort({name : 1}) will use the {name : 1} index.
You should run explain on your queries to check if indexes are being used or not.
You may want to refer the documentation on how indexes work.
https://docs.mongodb.com/manual/indexes/
https://docs.mongodb.com/manual/tutorial/sort-results-with-indexes/
How costly is it to index some fields in MongoDB,
I have a table where i want uniqueness combining two fields, Every where i search they suggested compound index with unique set to true. But what i was doing is " Appending both field1_field2 and making it a key, so that field2 will be always unique for field1.(and add Application logic) As i thought indexing is costly.
And also as MongoDB documentation advices us not to use Custom Object ID like auto incrementing number, I end up giving big numbers to Models like Classes, Students etc, (where i could have used easily used 1,2,3 in sql lite), I didn't think to add a new field for numbering and index that field for querying.
What are the best practices advice for production
The advantage of using compound indexes vs your own indexed field system is that compound indexes allows sorting quicker than regular indexed fields. It also lowers the size of every documents.
In your case, if you want to get the documents sorted with values in field1 ascending and in field2 descending, it is better to use a compound index. If you only want to get the documents that have some specific value contained in field1_field2, it does not really matter if you use compound indexes or a regular indexed field.
However, if you already have field1 and field2 in seperate fields in the documents, and you also have a field containing field1_field2, it could be better to use a compound index on field1 and field2, and simply delete the field containing field1_field2. This could lower the size of every document and ultimately reduce the size of your database.
Regarding the cost of the indexing, you almost have to index field1_field2 if you want to go down that route anyways. Queries based on unindexed fields in MongoDB are really slow. And it does not take much more time adding a document to a database when the document has an indexed field (we're talking 1 millisecond or so). Note that adding an index on many existing documents can take a few minutes. This is why you usually plan the indexing strategy before adding any documents.
TL;DR:
If you have limited disk space or need to sort the results, go with a compound index and delete field1_field2. Otherwise, use field1_field2, but it has to be indexed!
The relevant fields of the documents in my collection are the following:
{
point: {
type: Point,
coordinates: [15.6446464, 45.231323]
}
score: 24
}
I have a 2dsphere index on point and a "normal", descending index on score.
I want to run the following query:
db.properties.find({point: {$geoWithin: <some polygon> }}).sort({score: -1}).limit(2000)
Is there any way to make mongo use the index on point for the find part, and then the index on score for sorting?
The collection has about 700k documents, the find part can return tens of thousands of documents, each of which has up to a MB.
The current problem is that, when using the point index, the size of the returned collection is too big for sorting in memory. When using the score index, the query is too slow because of a sequential scan on coordinates.
When executing your current query, MongoDB will only use the index on point. After running the find you will have a subset of the data and therefore Mongo can no longer use the index on score. You could instead create a composite index on point and score with score indexed in descending order. Even though the first values are unique, it helps to speed up the sorting as MongoDB can use the index to sort on score rather than having to scan through the entire document (which can be up to a MB in size).
The composite index follows the general rule of thumb when indexing. In general the order of an index should be:
Fields on which you will query for exact values.
Fields on which you will sort.
Fields on which you will query for a range of values.
However, as per your comment this composite index is not very fast and this suggest that MongoDB can't load the entire index into memory. How much RAM have you allocated to MongoDB ? Is there any chance you can increase this ?