3. Data Modeling and Sync Logic

this section focuses on designing your app’s data model to support ditto’s peer to peer sync across devices properly structuring your data is crucial to ensure efficient and reliable sync behavior, especially in decentralized environments we’ll explore how to model data for local persistence and eventual consistency in a distributed network docid\ mqkbvx4swaplzw3a8fhbv learn how to use write transactions to combine multiple database operations into a single atomic action, ensuring consistency this guide covers cross collection operations and offers tips for implementing asynchronous global transactions to prevent main thread blocking and improve performance docid\ ocfwwfyemuczy3zwufk2v when managing complex data structures, using flat models instead of deeply embedded objects improves sync performance and reduces replication time this guide covers best practices for choosing between embedded and flat data models, optimizing document size, and handling large datasets to enhance efficiency in distributed environments docid\ kjnmnnyvuuhbv9nxcfdq2 in distributed environments like ditto mesh, time discrepancies across devices can cause inconsistent timestamps this guide covers best practices for handling timestamp differences, using iso 8601 formatting, and leveraging protocols like ntp to ensure accurate timing and reliable event tracking across peers docid\ kytoaeyktidoyr3hnrlm3 implement a time to live (ttl) eviction strategy to optimize performance and memory management in time sensitive environments set document retention logic to ensure only recent data persists locally, with options for big peer or small peer management of ttl based evictions docid 58qa5zetnn1ccm0idigkv in a distributed environment, maintaining data consistency during schema changes is crucial this guide offers strategies for implementing schema versioning patterns, ensuring backward and forward compatibility, and handling asynchronous updates to prevent data conflicts across peers