MongoDB Atlas Performance Issues for CapCut Resource Website

[Joe_Root]

Hi everyone,

I’m using MongoDB Atlas to manage the database for my CapCut-related website, which hosts tutorials, templates, and user-generated content. Lately, I’ve been encountering performance issues with certain queries and general database operations:

1. Slow Queries: Queries that involve filtering and sorting based on multiple fields (e.g., video category, popularity, and upload date) are taking significantly longer than expected, even with indexes in place.
2. Aggregation Pipeline Issues: An aggregation pipeline used for generating insights (e.g., most popular tutorials in the last 7 days) occasionally times out when the dataset grows.
3. High Connection Usage: The website’s traffic has grown recently, and I’m noticing that the connection pool is often maxed out, resulting in occasional “too many connections” errors.

Here’s my setup:

• MongoDB Atlas cluster: M10 (shared cluster)
• Data size: ~1GB with ~100,000 documents in the largest collection
• Indexed fields: Primary fields for querying (e.g., category, views, and createdAt) are indexed.
• Application: Using Node.js with Mongoose as the ORM.

I’ve already tried the following:

• Adjusted the connection pool size in my application.
• Reviewed and optimized the query patterns and indexes.
• Enabled database profiling to analyze slow queries.

Despite these efforts, the performance issues persist during peak traffic times. Would upgrading to a higher-tier cluster help significantly, or are there specific optimizations I should explore?

Any insights or suggestions would be greatly appreciated!

Thanks in advance!

[Joe_Root]

@Joe_Root

I’m using MongoDB Atlas to manage the database for my CapCut-related website, which hosts tutorials, templates, and user-generated content. Lately, I’ve been encountering performance issues with certain queries and general database operations:

1. Slow Queries: Queries that involve filtering and sorting based on multiple fields (e.g., video category, popularity, and upload date) are taking significantly longer than expected, even with indexes in place.
2. Aggregation Pipeline Issues: An aggregation pipeline used for generating insights (e.g., most popular tutorials in the last 7 days) occasionally times out when the dataset grows.
3. High Connection Usage: The website’s traffic has grown recently, and I’m noticing that the connection pool is often maxed out, resulting in occasional “too many connections” errors.

Here’s my setup:

• MongoDB Atlas cluster: M10 (shared cluster)
• Data size: ~1GB with ~100,000 documents in the largest collection
• Indexed fields: Primary fields for querying (e.g., category, views, and createdAt) are indexed.
• Application: Using Node.js with Mongoose as the ORM.

I’ve already tried the following:

• Adjusted the connection pool size in my application.
• Reviewed and optimized the query patterns and indexes.
• Enabled database profiling to analyze slow queries.

Despite these efforts, the performance issues persist during peak traffic times. Would upgrading to a higher-tier cluster help significantly, or are there specific optimizations I should explore?

Any insights or suggestions would be greatly appreciated!

Is there anyone who can help me with this? I would love your guidance. Thanks!

[Joe_Root]

@Joe_Root

I’m using MongoDB Atlas to manage the database for my CapCut-related website, which hosts tutorials, templates, and user-generated content. Lately, I’ve been encountering performance issues with certain queries and general database operations:

1. Slow Queries: Queries that involve filtering and sorting based on multiple fields (e.g., video category, popularity, and upload date) are taking significantly longer than expected, even with indexes in place.
2. Aggregation Pipeline Issues: An aggregation pipeline used for generating insights (e.g., most popular tutorials in the last 7 days) occasionally times out when the dataset grows.
3. High Connection Usage: The website traffic has grown recently, and I’m noticing that the connection pool is often maxed out, resulting in occasional “too many connections” errors.

Here’s my setup:

• MongoDB Atlas cluster: M10 (shared cluster)
• Data size: ~1GB with ~100,000 documents in the largest collection
• Indexed fields: Primary fields for querying (e.g., category, views, and createdAt) are indexed.
• Application: Using Node.js with Mongoose as the ORM.

I’ve already tried the following:

• Adjusted the connection pool size in my application.
• Reviewed and optimized the query patterns and indexes.
• Enabled database profiling to analyze slow queries.

Despite these efforts, the performance issues persist during peak traffic times. Would upgrading to a higher-tier cluster help significantly, or are there specific optimizations I should explore?

Any insights or suggestions would be greatly appreciated!

Is there anyone who can help me with this? I would love your guidance. Thanks!