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We were building an FAQ solution. When we had too many documents, we created chunks of those documents and stored them in the Milvus database. When someone asked any questions present in the document, we would create an embedding of that document, and we found them in our chunks stored in Milvus. This way, we got the top three results or chunks from Milvus.
The best feature of Milvus was finding the closest chunk from a huge amount of data.
I've heard that when we store too much data in Milvus, it becomes slow and does not work properly.
I have been using Milvus for around one year.
Around eight to ten teams were working with the solution in our organization.
Milvus is an open-source solution.
We were deploying Milvus using Docker on our AWS. Recommending Milvus to other users depends on their data size and chunk amounts. Based on those parameters, users can decide whether to go with Milvus or find other options, like OpenSearch from AWS. New users can easily learn to use Milvus for the first time by following the documentation page. It was easy to integrate Milvus with our existing services like Python.
Overall, I rate the solution an eight out of ten.
We primarily use MongoDB as a database to handle large volumes of data. It's useful when we need to manage millions of records quickly.
The cloud solution offered by MongoDB, known as Atlas, has been invaluable to us. It provides a good, user-friendly interface. The ease of use and scalability make it stand out compared to relational databases.
Improvements could be made in query optimization, particularly when performing lookups or joining tables. Input functions to restrict the amount of data passed in pipelines would be beneficial for this purpose. Additionally, enhancing the documentation to make it more beginner-friendly is crucial. As someone with seven years of experience with MongoDB, I find the ecosystem intuitive, but newcomers often need help with the documentation.
We have been using MongoDB for seven years now, and we are currently using the latest version.
The platform has good stability.
We contacted the MongoDB team for assistance whenever we encountered issues, particularly when optimizing queries that took too long. They helped us understand the root cause of the problem and provided us with discounts on certain occasions. For instance, when we accidentally used a larger instance, which resulted in higher costs, they understood the situation and offered us a discount to mitigate the expense.
Positive
We chose MongoDB over other solutions because it can handle large volumes of data and is flexible in data manipulation.
The initial setup process is easy.
The product is affordable, but I don't have direct comparisons with other products as I haven't been involved in the billing process.
The NoSQL structure, particularly the document-based data management, has made data management easier for me. MongoDB's approach to handling data in documents rather than traditional tables has been particularly beneficial.
MongoDB's document-oriented model improves development speed by providing each document with its built-in key or ID, similar to a primary key in relational databases. This inherent ID facilitates faster execution of operations, such as searching for specific documents. Additionally, it allows for creating additional indexes, further enhancing performance. The familiarity of JSON-like structure makes it easy for developers, both front-end and back-end, to work with, leading to quicker development and visualization of data.
I advise others to understand the fundamentals of databases and how they store data. They should start with online videos to grasp the ecosystem, focusing on concepts like data storage. The document-oriented structure with binary objects is crucial for individuals with a programming background.
Several limitations related to querying certain documents can be challenging. They express concerns about the lookup process, where we sometimes need to fetch a large amount of data simultaneously. Additionally, there are limitations regarding the size of documents, which may require restructuring or storing data.
I rate it an eight out of ten.
