poor query structures may result in the query taking a long time to be processed, causing replica lag and maybe even some data loss. In this case, one may think that maybe the storage memory is not enough, and that it probably needs scaling up. This article discusses ...
Monitoring MongoDB databases allows you to improve the performance of your application stack and optimize for costs by enabling you to: Understand the current capacity of your database Observe how utilized resources are Observe the presence of abnormal behavior and performance issues Detect and r...
The chunks themselves and any duplicate parent documents are then dropped, and unique parent documents are passed on to the LLM as context to answer the user query. All of this is achieved using MongoDB’s rich aggregation framework. A visual representation of the retrieval and generation ...
While regular expressions are powerful, they can be resource-intensive, especially when used on large datasets. To optimize performance, consider the following: Indexing: Ensure that the field you are performing the “starts with” query on is indexed. This can significantly speed up the query. ...
To optimize query performance, you should create indexes on frequently queried fields: db.users.createIndex({tenantId:1,email:1},{unique:true})db.books.createIndex({tenantId:1,isbn:1},{unique:true})db.tenants.createIndex({subdomain:1},{unique:true}...
Galera uses optimistic locking, and we had to go very far in the execution of T2 before realizing that the query will not succeed. Multi-node writing is therefore not a good solution at all when the system sees write hotspots. “In-flight” transactions and certification test ...
None of these things describe TokuMX, but it demonstrates that there are many schools of thought on how to optimize databases, and MongoDB in particular. I’d like to elaborate more on what TokuMX really is and how we built it. First, let’s talk about what MongoDB is. MongoDB consis...
The need to bring in separate tooling or write your own to manage growing data volumes reduces agility and adds costs. These customer challenges are top of mind for MongoDB, an AWS Data and Analytics Competency Partner. We are constantly coming up with new ways for customers to optimize ...
Optimize your schema for the most frequent use cases. Do complex aggregation in the schema. An example to explain this is given below: Suppose that a client needs a database design for his blog or website and sees the differences between RDBMS and MongoDB schema design. The website has th...
Amazon’s product search engine is built on top of Lucene, which is a fairly large-scale application (w.r.t. both index size, traffic and use-case complexity). We have found taxonomy-based faceting to work well for us generally, and haven’t needed to do much to optimize bey...