I need a collection to store rendered formulas. I've done this:
class FormulaImage(Document):
formula = me.StringField(primary_key=True)
image = me.ImageField()
I am in doubt about using possibly large strings as index. Should I add a formula hash field and use it as primary index? Or MongoDB does this by itself?
There are some restrictions on creating MongoDB indexes. The one that will affect you is this one:
The total size of an index entry, which can include structural
overhead depending on the BSON type, must be less than 1024 bytes.
So if you're not sure what the size of your strings will be, you're best option is to create a separate hash over that string and use index on that column.
More details on index restrictions here
Related
Is there a difference between a wildcard search index like $** and text indexes that I create for each of the fields in the collection ?
I do see a small difference in response time when I individually create text indexes. Using individual indexes, returns a better response. I am not able to post an example now, but will try to.
A wildcard text search will index every field that contains string data for each document in the collection (https://docs.mongodb.com/manual/core/index-text/#wildcard-text-indexes).
Because you are essentially increasing the number of fields indexed with a wild card text index, it would take longer to run compared to targeting specific fields for a text index.
Since you can only have one text index per collection (https://docs.mongodb.com/manual/core/index-text/#create-text-index), its worth considering which fields you plan on querying against beforehand.
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!
I'd like to use a MongoDB unique compound index (with two fields) as a covering index by adding two more fields. Can I specify the uniqueness of the four field index is defined by the first two fields only?
Reading the documentation it sounds like I may have to have one compound four field index for the covering, and another two field index purely for asserting the uniqueness constraint.
You are right that you need to indices for achieving what you want. And there is nothing wrong with it. While uniqueness is checked during writes (and the according index will be used for it), the other index will either be used automatically or you can hint MongoDB to use it.
I'm planning to add a Collection to a mongodb database that will have a text field that should be unique for each Document. Lookups from this Collection will almost always be based on this field. This field can contain as many as 100+ chars.
My question is, should this field be the _id field, or should I just add an index for it? What would the performance impact for either approach be?
I suggest you to use your unique text as _id.
It will reduce data size and eliminate an index. Here is the reference. 9th page will guide you.
We have two types of high-volume queries. One looks for docs involving 5 attributes: a date (lte), a value stored in an array, a value stored in a second array, one integer (gte), and one float (gte).
The second includes these five attributes plus two more.
Should we create two compound indices, one for each query? Assume each attribute has a high cardinality.
If we do, because each query involves multiple arrays, it doesn't seem like we can create an index because of Mongo's restriction. How do people structure their Mongo databases in this case?
We're using MongoMapper.
Thanks!
Indexes for queries after the first ranges in the query the value of the additional index fields drops significantly.
Conceptually, I find it best to think of the addition fields in the index pruning ever smaller sub-trees from the query. The first range chops off a large branch, the second a smaller, the third smaller, etc. My general rule of thumb is only the first range from the query in the index is of value.
The caveat to that rule is that additional fields in the index can be useful to aid sorting returned results.
For the first query I would create a index on the two array values and then which ever of the ranges will exclude the most documents. The date field is unlikely to provide high exclusion unless you can close the range (lte and gte). The integer and float is hard to tell without knowing the domain.
If the second query's two additional attributes also use ranges in the query and do not have a significantly higher exclusion value then I would just work with the one index.
Rob.