Optimize Memory Allocation for High-Frequency Data Processing

[Fatimasanusi]

Description:
The data ingestion parser consumes excessive RAM when analyzing token contracts with deep, high-frequency transaction histories.

Acceptance Criteria:

• Profile the Rust application using cargo-flamegraph to identify specific memory bottlenecks in the JSON deserialization loop.
• Implement zero-copy deserialization using serde where applicable to map RPC responses directly to memory references.
• Reduce memory consumption by at least 30% when processing contracts with over 100,000 historical transactions.

[Benedict315]

please assign me to this issue

[Benedict315]

please assign me to this issue

[Davichi-1]

Hi, I'd like to work on this issue. I've gone through the description and acceptance criteria and I'm confident I can deliver it. I'll make sure every acceptance criterion is met and submit a clean, tested PR. Please assign it to me.

[grantfox-oss]

🦊 GrantFox — @Menjay7 has been assigned to this issue as part of the Official Campaign | FWC26 campaign!

Next steps:

1. Open a Pull Request referencing this issue (e.g., Closes #11)
2. Your PR will be reviewed by the Trust-Analysis maintainers

Good luck! Track your progress on GrantFox.