---
title: "The Cost of Not Knowing MongoDB, Part 3: appV6R0 to appV6R4"
description: "To alleviate a disk throughput bottleneck on a MongoDB server, this optimization pass focuses on shrinking document sizes across application revisions appV6R0 through appV6R4. The implementation replaces an array of items with a dynamic schema document, using date segments such as days or combined months and days as field names to store status totals. Redundant date components already encoded in the binary _id field are omitted from internal document keys. Bulk upsert operations rely on updateOne with the $inc operator to update matching date keys or create documents when missing. Aggregation pipelines process reports using the $objectToArray and $reduce operations to calculate totals across date intervals while preserving a single index on _id."
---

# The Cost of Not Knowing MongoDB, Part 3: appV6R0 to appV6R4

[MongoDB](https://yomu.fyi/company/mongodb) · Artur Costa · Oct 9, 2025

**Type:** Benchmark

## Summary

To alleviate a disk throughput bottleneck on a MongoDB server, this optimization pass focuses on shrinking document sizes across application revisions appV6R0 through appV6R4. The implementation replaces an array of items with a dynamic schema document, using date segments such as days or combined months and days as field names to store status totals. Redundant date components already encoded in the binary \_id field are omitted from internal document keys. Bulk upsert operations rely on updateOne with the $inc operator to update matching date keys or create documents when missing. Aggregation pipelines process reports using the $objectToArray and $reduce operations to calculate totals across date intervals while preserving a single index on \_id.

## Context

The MongoDB server faced a disk throughput bottleneck, creating the need to reduce overall document size in the application.

## Approach / What changed

Developers replaced array storage with a dynamic schema document where date values serve as dynamic field names storing status totals, omitted date parts already present in the binary \_id, and used updateOne with $inc alongside $objectToArray aggregation pipelines.

## Takeaways

- Changing the items structure from an array to a dynamic schema document allows date values to act as field keys, reducing repetitive date storage.
- Omitting year and month components from dynamic keys is viable when those components are already encoded in the binary \_id field.
- When benchmarked with 500 million events, appV6R1 lowered total data size to 8.19GB and total size per event to 20.2B compared to appV5R3.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Performance](https://yomu.fyi/topic/performance), [Refactoring](https://yomu.fyi/topic/refactoring), [Scalability](https://yomu.fyi/topic/scalability)

- Source: [MongoDB](https://www.mongodb.com/company/blog/technical/cost-of-not-knowing-mongodb-part-3-appv6r0-appv6r4)
- Source URL: https://www.mongodb.com/company/blog/technical/cost-of-not-knowing-mongodb-part-3-appv6r0-appv6r4
- Ingested by Yomu: 2026-08-27T15:04:07.580Z

[Read original post](https://www.mongodb.com/company/blog/technical/cost-of-not-knowing-mongodb-part-3-appv6r0-appv6r4)