# Revamping Myntra App Analytics persistence with KMP and SQLite

DevFeed: [Revamping Myntra App Analytics persistence with KMP and SQLite](<https://devfeed.tech/articles/revamping-myntra-app-analytics-persistence-with-kmp-and-sqlite-20139.md>)

Original publisher: [Read original article](<https://medium.com/myntra-engineering/revamping-myntra-app-analytics-persistence-with-kmp-and-sqlite-516751e24d63?source=rss----7484818e9f88---4>)

Author: Kartik Sharma

Published: 2026-03-31T13:01:09Z

Content type: article

Language: en

Sources: [Myntra](<https://devfeed.tech/sources/myntra.md>)

Topics: [App](<https://devfeed.tech/topics/app.md>), [SQLite](<https://devfeed.tech/topics/sqlite.md>), [Persistence](<https://devfeed.tech/topics/persistence.md>), [SDK](<https://devfeed.tech/topics/sdk.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [legacy](<https://devfeed.tech/topics/legacy.md>), [Error Handling](<https://devfeed.tech/topics/error-handling.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [app](<https://devfeed.tech/tags/app.md>), [databases](<https://devfeed.tech/tags/databases.md>), [error-handling](<https://devfeed.tech/tags/error-handling.md>), [ios](<https://devfeed.tech/tags/ios.md>), [kotlin-multiplatform](<https://devfeed.tech/tags/kotlin-multiplatform.md>), [legacy](<https://devfeed.tech/tags/legacy.md>), [mobile-app-development](<https://devfeed.tech/tags/mobile-app-development.md>), [persistence](<https://devfeed.tech/tags/persistence.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [sqlite](<https://devfeed.tech/tags/sqlite.md>)

## AI overview

This engineering post describes Myntra's effort to re-architect the persistence layer of its app analytics SDK. It explains how a legacy Android pipeline using in-memory queues and SharedPreferences created performance and data-reliability bottlenecks as event volume grew, and introduces a SQLite-backed persistence engine intended to improve app stability and data integrity.

## Source excerpt

Introduction In the dynamic landscape of e-commerce, data is the bedrock of decision-making, and app stability is the foundation of user experience. At Myntra, where millions of users engage with our platform daily, ensuring the reliability of both is paramount. This necessity drove us to re-architect a critical piece of our infrastructure: the persistence layer of the Myntra app's analytics SDK. This post details our journey of augmenting our legacy analytics SDK with a robust new persistence engine backed by SQLite[1], achieving significant gains in app stability and data integrity. Background: The Limits of the Legacy Pipeline For years, our legacy in-house analytics SDK was the workhorse for collecting user interaction data. However, as our app's event volume grew, the SDK's aging persistence and processing logic created significant bottlenecks that led to degraded app performance and compromised the reliability of analytics data our product teams depended on. On Android: The Legacy Pipeline and its Limits On Android, the legacy analytics pipeline followed a straightforward event processing model: Capture, Persist & Batch: Events generated by used actions were held in an in-memory queue and then serialized into a persistent queue backed by SharedPreferences[2]. Once a batch threshold was met (typically 10 events), the batch was dispatched to the server. Background Flushing: Periodic background jobs flushed unsent events and retried failed batches. Error Handling: Failed batches were pushed to a separate retry queue, with older events silently purged if the queue hit its cap. High level overview of legacy android pipeline At the time of the initial implementation, SharedPreferences was a pragmatic choice to prioritize velocity. The ecosystem for local databases on Android was still maturing, and raw SQLite often required significant boilerplate compared to the simplicity of a key-value store. However, SharedPreferences is architecturally limited for high-throughp