#MONGO DB TEXT OR HOW TO#
#MONGO DB TEXT OR FULL#
#MONGO DB TEXT OR CODE#

I imagine that there are a few variables that affect the journey: market size, unit economics, product-market fit, competition, and of course, a good marketing strategy with solid execution.ĭM me if you think the Arbor Growth Partners team can help your Brand achieve escape velocity. In his chart, the inflection point is “brand activation” and you can see that for some brands it takes lots of money/time to achieve escape velocity and for others, the journey is shorter. Its escape velocity is an inflection point, that point where the Brand is known among its core audience, and CACs stabilize (or even fall.)Įric Seufert captured this nicely in a post about the challenges facing DTC start-ups. Similarly, your Brand needs a certain amount of investment to reach its (unique) escape velocity. To start using MongoDB’s full-text search capabilities, you must create a text index on a collection. If you are unfamiliar with JSON, check out our JSON tutorial. A collection can only have one text search index, but that index can cover multiple fields. It stores data in a type of JSON format called BSON. text indexes can include any field whose value is a string or an array of string elements. (To escape earth you need to be going about 25k mph…) MongoDB provides text indexes to support text search queries on string content.

#MONGO DB TEXT OR CODE#

CreateAsync() the above code should be self explanatory, if not please see the documentation here.

A rocket needs a certain amount of effort to escape Earth’s gravity. fuzzy text searching requires the use of a mongodb text index which can be easily created like this: await DB.Index ().

The best analogy that I could think of is: escape velocity. Counterintuitively, customer acquisition costs typically go up as you try and reach a larger audience.

I thought about the ad auction dynamics that take place on platforms like Google and Meta Ads.

#MONGO DB TEXT OR HOW TO#

I thought long and hard about it, considering how to balance the cost/benefit of Brand (upper funnel) and Performance (lower funnel) advertising. You can also specify a default collation per collection when you create them: db.Last month during a QBR, a client asked, “Am I going to have to keep spending this amount to acquire net new clients?” You can create a case-insensitive index like this: db.cities.createIndex( Here's how it works:Ī case-insensitive index is made by specifying a collation with a strength of either 1 or 2. I personally emailed one of the founders to please get this working, and he made it happen! It was an issue on JIRA since 2009, and many have requested the feature.

Starting with MongoDB 3.4, the recommended way to perform fast case-insensitive searches is to use a Case Insensitive Index. However, regex can only work for simple search queries and cannot use indexes efficiently. This is similar to an index in MongoDB and databases in. To search against a string field, we can use the regular expression operator regex directly. There are many words such as the or because that are less useful to search for than hippopotamus. I believe using an extraneous indexer like Apache Lucene is the best option in that case. As MongoDB is a document-oriented NoSQL database, it’s common to store plain text in some fields.

If your field is large, such as a message body, duplicating data is probably not a good option.

This ensures case-sensitive duplication is impossible (having both 'Foo' and 'foo' will not be allowed), and I can search by id = username.toUpperCase() to get a case-insensitive search for username. For instance, I have a User table that has a username which is mixed case, but the id is an uppercase copy of the username. You take a far bigger cpu hit than your query warrants, which could become an issue if you are trying to achieve scale.Īs an alternative, you can store an uppercase copy and search against that. It should be noted that searching with regex's case insensitive /i means that mongodb cannot search by index, so queries against large datasets can take a long time.Įven with small datasets, it's not very efficient. To perform text search, MongoDB uses a text index and the text operator.

#MONGO DB TEXT OR FULL#

Mongodb now supports advanced full text searching, with many features. Text Search on Self-Managed Deployments For self-managed (non-Atlas) deployments, MongoDB's text search capability supports query operations that perform a text search of string content.

Mongo db text or