4/11/2023 0 Comments Mongo db text or![]() You might be wondering: How does MongoDB know which fields to search since we haven’t specified the fields in the search criteria? Here’s how we did it on our Mongoid document: class Item index() ![]() Since we had specific fields we wanted to include in search, we opted for a compound index, with multiple text fields defined. We had the option of indexing all string fields using a wildcard text index. This is an absolute requirement to perform text search queries on string content. The first step was to create a text index. After exploring our options, one promising solution we found was the use of MongoDB’s Text Search functionality. We decided to move away from using $regex, adopting a more performant implementation until we can do a full search implementation by using something like ElasticSearch. Therefore, it does not scale as the data sets grow in size, and with an increasing number of concurrent users doing searches. This is CPU-bound, and hence, the spikes. While they can be cached in-memory for successive calls, it doesn’t remove the fact that the regular expression needs to be matched one-by-one. In effect, we are using the index in some capacity, but the index alone is not sufficient to perform the query.ĭocuments had to be fetched and scanned from disk, which might not be very problematic for small sets of data. Going back to the sample query log above, it seemed like we were using the index by virtue of performing an IXSCAN.īased on the value of docsExamined, it sure looked like each document was being fetched so that the regular expression can be matched against each of the respective fields relevant to the search. One thing that was problematic with the initial approach is the fact that case-insensitive regular expression queries generally cannot use an index effectively. We have since grown year-over-year, and as we continued to onboard shops with large MongoDB collections, it has become apparent that this implementation can no longer serve the needs of our present stage and scale. This was relatively easy to implement and has served our needs in the early stages. This provided a lot of convenience to the user, with the initial implementation using MongoDB’s $regex operator and code similar to this line in Ruby/Mongoid: (title: regex).or(name: regex) Some years ago, we introduced the ability to let our users perform text searches inside of the Shogun Dashboard and Editor. With this sample query log, it was easy to pinpoint the exact cause. Furthermore, this was not an isolated case it was happening repeatedly in bursts. The field shows 100 as the target completion percentage.That’s 169 seconds for a query to finish, and it had to yield control to other queries more than 7,000 times before it completed. Here's an example that shows the output document format for different stages of index progress:Īn index operation on a "foo" collection and "bar" database that is 60 percent complete will have the following output document. The index progress details show the percentage of progress for the current index operation. If your nested field contains an array (anywhere on the path), that value will be ignored in the index.įor example a compound index containing will work in this case since there's no array on the path: ) If your nested field does not contain an array, the index will work as intended. For queries with multiple filters that don't need to sort, create multiple single field indexes instead of a compound index to save on indexing costs.Ī compound index or single field indexes for each field in the compound index will result in the same performance for filtering in queries.Ĭompounded indexes on nested fields are not supported by default due to limiations with arrays. In the API for MongoDB, compound indexes are required if your query needs the ability to sort on multiple fields at once. Compound indexes (MongoDB server version 3.6+) You can create up to 500 single field indexes per collection. One query uses multiple single field indexes where available. You could create the same single field index on name in the Azure portal: The following command creates an index on the field name: ![]() The sort order of the single field index does not matter. You can create indexes on any single field. You can't create compound indexes using the indexing policy editor in the Data Explorer. ![]()
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