I have a big collection of many million records consisting of:
{
"id1":string,
"id2":string,
"correlation":number
}
Which represents the relationships between pairs of records.
I would like to be able to efficiently run such queries as
db.collection.find({id1: 1}).sort({correlation: -1})
So, getting records by field id1 and sorting them by correlation field (in the descending order).
What kind of index(es) would be the most appropriate for such scenario?
I think the solution is to create the compound index like the following:
db.collection.createIndex({"id1": 1, "correlation": -1})
In my case all the queries of the form
db.collection.find({id1: id}).sort({correlation: -1})
run almost instantaneously.
Related
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: {$gt:1594048295294}})
for sure results few entries
Querying by:
db.inventory.find({ email: "abc#gmail.com"})
for sure results few entries
If you need to support query only on email : Indexing email is must
If you need to support query only on scheduledStamp: Indexing scheduledStamp is must
If you want of query on both, a third index is required. But you can create a compound index to cover this query and one of the above queries.
Since Mongo follows prefix match for selecting index:
You may have index on {"email":1} and {"scheduledStamp:1","email":1}
OR
You may have index on {"scheduledStamp":1} and {"email:1","scheduledStamp":1}
But since you said these fields return few documents:
Just having 2 indexes on {"email":1} and {"scheduledStamp":1} may perform good if not optimum.
Considering I have search pannel that inculude multiple options like in the picture below:
I'm working with mongo and create compound index on 3-4 properties with specific order.
But when i run a different combinations of searches i see every time different order in execution plan (explain()). Sometime i see it on Collection scan (bad) , and sometime it fit right to the index (IXSCAN).
The selective fields that should handle by mongo indexes are:(brand,Types,Status,Warehouse,Carries ,Search - only by id)
My question is:
Do I have to create all combination with all fields with different order , it can be 10-20 compound indexes. Or 1-3 big Compound Index , but again it will not solve the order.
What is the best strategy to deal with big various of fields combinations.
I use same structure queries with different combinations of pairs
// Example Query.
// fields could be different every time according to user select (and order) !!
db.getCollection("orders").find({
'$and': [
{
'status': {
'$in': [
'XXX',
'YYY'
]
}
},
{
'searchId': {
'$in': [
'3859447'
]
}
},
{
'origin.brand': {
'$in': [
'aaaa',
'bbbb',
'cccc',
'ddd',
'eee',
'bundle'
]
}
},
{
'$or': [
{
'origin.carries': 'YYY'
},
{
'origin.carries': 'ZZZ'
},
{
'origin.carries': 'WWWW'
}
]
}
]
}).sort({"timestamp":1})
// My compound index is:
{status:1 ,searchId:-1,origin.brand:1, origin.carries:1 , timestamp:1}
but it only 1 combination ...it could be plenty like
a. {status:1} {b.status:1 ,searchId:-1} {c. status:1 ,searchId:-1,origin.brand:1} {d.status:1 ,searchId:-1,origin.brand:1, origin.carries:1} ........
Additionally , What will happened with Performance write/read ? , I think write will decreased over reads ...
The queries pattern are :
1.find(...) with '$and'/'$or' + sort
2.Aggregation with Match/sort
thanks
Generally, indexes are only useful if they are over a selective field. This means the number of documents that have a particular value is small relative to the overall number of documents.
What "small" means varies on the data set and the query. A 1% selectivity is pretty safe when deciding whether an index makes sense. If an particular value exists in, say, 10% of documents, performing a table scan may be more efficient than using an index over the respective field.
With that in mind, some of your fields will be selective and some will not be. For example, I suspect filtering by "OK" will not be very selective. You can eliminate non-selective fields from indexing considerations - if someone wants all orders which are "OK" with no other conditions they'll end up doing a table scan. If someone wants orders which are "OK" and have other conditions, whatever index is applicable to other conditions will be used.
Now that you are left with selective (or at least somewhat selective) fields, consider what queries are both popular and selective. For example, perhaps brand+type would be such a combination. You could add compound indexes that match popular queries which you expect to be selective.
Now, what happens if someone filters by brand only? This could be selective or not depending on the data. If you already have a compound index on brand+type, you'd leave it up to the database to determine whether a brand only query is more efficient to fulfill via the brand+type index or via a collection scan.
Continue in this manner with other popular queries and fields.
So you have subdocuments, ranged queries, and sorting by 1 field only.
It can eliminate most of the possible permutations. Assuming there are no other surprises.
D. SM already covered selectivity - you should really listen what the man says and at least upvote.
The other things to consider is the order of the fields in the compound index:
fields that have direct match like $eq
fields you sort on
fields with ranged queries: $in, $lt, $or etc
These are common rules for all b-trees. Now things that are specific to mongo:
A compound index can have no more than 1 multikey index - the index by a field in subdocuments like "origin.brand". Again I assume origins are embedded docs, so the document's shape is like this:
{
_id: ...,
status: ...,
timestamp: ....,
origin: [
{brand: ..., carries: ...},
{brand: ..., carries: ...},
{brand: ..., carries: ...}
]
}
For your query the best index would be
{
searchId: 1,
timestamp: 1,
status: 1, /** only if it is selective enough **/
"origin.carries" : 1 /** or brand, depending on data **/
}
Regarding the number of indexes - it depends on data size. Ensure all indexes fit into RAM otherwise it will be really slow.
Last but not least - indexing is not a one off job but a lifestyle. Data change over time, so do queries. If you care about performance and have finite resources you should keep an eye on the database. Check slow queries to add new indexes, collect stats from user's queries to remove unused indexes and free up some room. Basically apply common sense.
I noticed this one-year-old topic, because I am more or less struggling with a similar issue: users can request queries with an unpredictable set of the fields, which makes it near to impossible to decide (or change) how indexes should be defined.
Even worse: the user should indicate some value (or range) for the fields that make up the sharding-key, otherwise we cannot help MongoDB to limit its search in only a few shards (or chunks, for that matter).
When the user needs the liberty to search on other fields that are not necessariy the ones which make up the sharding-key, then we're stuck with a full-database search. Our dbase is some 10's of TB size...
Indexes should fit in RAM ? This can only be achieved with small databases, meaning some 100's GB max. How about my 37 TB database ? Indexes won't fit in RAM.
So I am trying out a POC inspired by the UNIX filesystem structures where we have inodes pointing to data blocks:
we have a cluster with 108 shards, each contains 100 chunks
at insert time, we take some fields of which we know they yield a good cardinality of the data, and we compute the sharding-key with those fields; the document goes into the main collection (call it "Main_col") on that computed shard, so with a certain chunk-number (equals our computed sharding-key value)
from the original document, we take a few 'crucial' fields (the list of such fields can evolve as your needs change) and store a small extra document in another collection (call these "Crucial_col_A", Crucial_col_B", etc, one for each such field): that document contains the value of this crucial field, plus an array with the chunk-number where the original full document has been stored in the 'big' collection "Main_col"; consider this as a 'pointer' to the chunk in collecton "Main_col" where this full document exists. These "Crucial_col_X" collections are sharded based on the value of the 'crucial' field.
when we insert another document that has the same value for some 'crucial' field "A", then that array in "Crucial_col_A" with chunk-numbers with be updated (with 'merge') to contain the different or same chunk number of this next full document from "Main_col"
a user can now define queries with criteria for at least one of those 'crucial' fields, plus (optional) any other criteria on other fields in the documents; the first criterium for the crucial field (say field "B") will run very quickly (because sharded on the value of "B") and return the small document from "Crucial_col_B", in which we have the array of chunk-numbers in "Main_col" where any document exists that has field "B" equal to the given criterium. Then we run a second set of parallel queries, one for each shardkey-value=chunk-number (or one per shard, to be decided) that we find in the array from before. We combine the results of those parallel subqueries, and then apply further filtering if the user gave additional criteria.
Thus this involves 2 query-steps: first in the "Crucial_col_X" collection to obtain the array with chunk-numbers where the full documents exist, and then the second query on those specific chunks in "Main_col".
The first query is done with a precise value for the 'crucial' field, so the exact shard/chunk is known, thus this query goes very fast.
The second (set of) queries are done with precise values for the sharding-keys (= the chunk numbers), so these are expected to go also very fast.
This way of working would eliminate the burden of defining many index combinations.
I have MongoDB collection with ~100,000,000 records.
On the website, users search for these records with "Refinement search" functionality, where they can filter by multiple criteria:
by country, state, region;
by price range;
by industry;
Also, they can review search results sorted:
by title (asc/desc),
by price (asc/desc),
by bestMatch field.
I need to create indexes to avoid full scan for any of combination above (because users use most of the combinations). Following Equality-Sort-Range rule for creating indexes, I have to create a lot of indexes:
All filter combination × All sortings × All range filters, like the following:
country_title
state_title
region_title
title_price
industry_title
country_title_price
country_industry_title
state_industry_title
...
country_price
state_price
region_price
...
country_bestMatch
state_bestMatch
region_bestMatch
...
In reality, I have more criteria (including equality & range), and more sortings. For example, I have multiple price fields and users can sort by any of that prices, so I have to create all filtering indexes for each price field in case if the user will sort by that price.
We use MongoDB 4.0.9, only one server yet.
Until I had sorting, it was easier, at least I could have one compound index like country_state_region and always include country & state in the query when one searches for a region. But with sorting field at the end, I cannot do it anymore - I have to create all different indexes even for location (country/state/region) with all sorting combinations.
Also, not all products have a price, so I cannot just sort by price field. Instead, I have to create two indexes: {hasPrice: -1, price: 1}, and {hasPrice: -1, price: -1} (here, hasPrice is -1, to have records with hasPrice=true always first, no matter price sort direction).
Currently, I use the NodeJS code to generate indexes similar to the following (that's simplified example):
for (const filterFields of getAllCombinationsOf(['country', 'state', 'region', 'industry', 'price'])) {
for (const sortingField of ['name', 'price', 'bestMatch']) {
const index = {
...(_.fromPairs(filterFields.map(x => [x, 1]))),
[sortingField]: 1
};
await collection.ensureIndex(index);
}
}
So, the code above generates more than 90 indexes. And in my real task, this number is even more.
Is it possible somehow to decrease the number of indexes without reducing the query performance?
Thanks!
Firstly, in MongoDB (Refer: https://docs.mongodb.com/manual/reference/limits/), a single collection can have no more than 64 indexes. Also, you should never create 64 indexes unless there will be no writes or very minimal.
Is it possible somehow to decrease the number of indexes without reducing the query performance?
Without sacrificing either of functionality and query performance, you can't.
Few things you can do: (assuming you are using pagination to show results)
Create a separate (not compound) index on each column and let MongoDB execution planner choose index based on meta-information (cardinality, number, etc) it has. Of course, there will be a performance hit.
Based on your judgment and some analytics create compound indexes only for combinations which will be used most frequently.
Most important - While creating compound indexes you can let off sort column. Say you are filtering based on industry and sorting based on price. If you have a compound index (industry, price) then everything will work fine. But if you have index only on the industry (assuming paginated results), then for first few pages query will be quite fast, but will keep degrading as you move on to next pages. Generally, users don't navigate after 5-6 pages. Also, you have to keep in mind for larger skip values, the query will start to fail because of the 32mb memory limit for sorting. This can be overcome with aggregation (instead of the query) with allowDiskUse enable.
Check for keyset pagination (also called seek method) if that can be used in your use-case.
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!