High-Volume Multi-Site Quoting
“Compressing the payload. Moving computation closer to the experience.”
Compressing the payload. Moving computation closer to the experience.
What Made the Problem Difficult
High-volume multi-site quoting in telecommunications and connectivity infrastructure involves configuring hundreds or thousands of physical site locations, complex bandwidth tiers, and dynamic product catalogs within a single commercial negotiation.
Core Platform Constraint
The quoting experience had to remain a native Salesforce Lightning Web Component (LWC) interface. However, transferring and processing massive quoting datasets overwhelmed browser memory, caused UI thread freezing, and collided with platform payload and heap limits.
- !Quoting experience must remain strictly native within Salesforce Lightning Web Components (LWC)
- !Prevent browser tab crashes and UI thread freezes during massive multi-site payload ingestion
- !Operate reliably within Salesforce platform transport limits and LWC security boundaries
- !Do not force sales reps or partners out of Salesforce into external standalone web portals
- !Enable lightning-fast search and location filtering across large commercial catalog datasets
What System Was Designed
Architected an end-to-end data compression and distributed search retrieval pipeline. The native LWC client leverages the pako library via platformResourceLoader to compress payloads in browser memory. Compressed streams travel across an AWS supporting transport layer that orchestrates high-volume processing and interfaces with dual search engines: a high-throughput Rust search engine for deep indexing, and an agile TypeScript search engine for dynamic in-memory filtering. Targeted results hydrate incrementally back into the native Salesforce UX.
Pivotal Architectural Choices
Key Architectural Decisions
Client-Side Binary Compression (pako) vs. Off-Platform Redirection
Industry convention redirects high-volume users to off-platform external portals when payloads exceed CRM tolerances.
- Offload quoting entirely to standalone external web app
- Heavy server-side Apex pagination with dozens of queries
- Uncompressed REST batch polling
Preserves native Salesforce workflows and CRM context for account executives while shrinking data payloads over the wire by up to 80-90%.
Dual-Engine Retrieval Topology (Rust + TypeScript)
Location filtering requires heavy index scanning across vast geographic data, while interactive quoting requires dynamic UI-level adjustments.
- Single monolithic database query
- Pure client-side array filtering in LWC
Assigns each workload to its ideal runtime: Rust provides zero-overhead index retrieval, while TypeScript delivers rapid in-memory rule adjustments.
What Was Actually Built
Integrated the pako gzip compression library inside Salesforce LWC static resources using lightning/platformResourceLoader.
Engineered binary array-buffer streaming across network boundaries to bypass JSON serialization bloat.
Built an AWS supporting transport layer handling stream ingestion, multi-site chunking, and worker orchestration.
Developed a dedicated Rust search engine service delivering high-throughput spatial and attribute indexing.
Implemented a complementary TypeScript search engine for agile client/edge filtering and ranking.
Constructed an incremental hydration virtual DOM renderer in LWC to ensure buttery 60fps scrolling across thousands of quote lines.
Deliberate Architectural Compromises
Trade-offs & Mitigations
⚖Browser Decompression CPU Overhead vs Network Transfer Time
Immunity to network payload caps and dramatic reduction in transit latency.
Client browser CPU expends cycles decompressing binary payloads.
Chunked decompression scheduled via micro-task yielding to prevent blocking user interaction frames.
⚖Specialized Dual-Language Stack (Rust/TS) vs Monolithic Framework
Extreme indexing throughput from Rust combined with rapid UI agility from TypeScript.
Engineering governance across multiple language ecosystems.
Strict contract-first protocol specifications governing data serialization between services.
Verified Outcomes
Preserved 100% native Salesforce LWC quoting experience without external portal redirects
Eliminated browser memory exhaustion and tab crashes during high-volume multi-site quoting
Engineered robust end-to-end data transport handling massive multi-site quoting datasets
Specific throughput, site volume, and compression metrics: [VERIFY WITH KULDEEP]
System Schematic & Data Flow
Native LWC uses pako gzip compression, AWS transport, and dual Rust/TS search engines to handle massive multi-site quoting datasets.
Text alternative for screen readers: Architecture flow: Native LWC UI to pako Gzip Engine via Client In-Memory Compression; pako Gzip Engine to AWS Transport Layer via Compressed Binary Stream; AWS Transport Layer to Rust & TS Engines via Dual Search Execution; Rust & TS Engines to Native Salesforce UX via Targeted Result Hydration
Architectural Conclusion
“A Salesforce-native experience can remain native without forcing Salesforce or the browser to carry every byte of a high-volume quoting problem.”
- •Architecting around platform constraints does not mean abandoning the platform; it means being intentional about where data is compressed, where it travels, and where it is computed.
- •Native LWC can be supercharged with battle-tested libraries through static resources to conquer massive data volumes.