Performance Tuning

• Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.
• Accelerated geographical data pipeline performance by 50x by improving SQL programming and data modeling across PostgreSQL, MS SQL, and Google Cloud BigQuery.
• Improved geographical map application performance by 10x through strategic database transition from MSSQL to PostgreSQL, optimizing processing and data security.
• Resolved 1,000 issues in geographical data and time‑series data, using GDAL, ArcGIS, PostGIS, Mapbox, QGIS, SQL (PL/pgSQL, Transact‑SQL), Bash, ensuring high‑quality big data processing.
• Architected, created, and managed 100 PostgreSQL, MS SQL, and Google BigQuery data warehouse databases with primarily GIS and time‑series data, optimizing performance and scalability.
• Designed, deployed, and maintained 10 PostgreSQL and MS SQL servers on Ubuntu Linux VPS, ensuring optimal server performance and reliability.
• Accelerated PostgreSQL performance by 10x via strategic indexing, partitioning, and query optimization, enhancing database efficiency for user, tenant, geospatial, and time‑series electrical data.
• Directed full‑stack GIS map development, overseeing PostgreSQL, Mapbox, ReactJS, and NodeJS to deliver an integrated solution.
• Overhauled internal processes, saving 8,000 hours by improving software architecture, systems, and scheduling efficiency.
• Enhanced project efficiency, saving 150 hours per month across 30 projects by optimizing workflows and resource management.
• Administered 40 websites on Ubuntu Linux hosting servers with Apache and Nginx, ensuring high availability and performance.
• Engineered 600 PL/pgSQL‑based ETL/ELT pipelines to streamline complex data processing workflows across multiple PostgreSQL development and production environments.
• Designed a JSON passthrough architecture where PostgreSQL functions return complete JSON forwarded verbatim by the Go API, eliminating intermediate unmarshalling and decoupling the frontend from schema changes.
• Adopted UUID v7 time‑ordered identifiers (PostgreSQL 18) as entity keys to reduce B‑tree index fragmentation and speed up queries.
• Modeled the maritime domain into 348 normalized tables across 34 PostgreSQL schemas — professionals, companies, ships, jobs, reviews and the rest — with SMALLINT lookups and UUID v7 keys.
• Built the Next.js 16 frontend with deliberate SSR, SSG and CSR strategies, and a reusable prefetched‑server‑page factory that removes the N+1 fetch cascade from each authenticated page moved onto it.
• Delivered full Progressive Web App support — installable and offline‑capable — with Workbox runtime caching via next‑pwa.
• Moved slow work off the request path onto a River job queue — 15 worker modules, 8 scheduled tasks and 20 pg_cron jobs — so a request returns while the work behind it carries on.
• Built fail‑closed abuse controls — 22 Redis‑backed rate limiters, Cloudflare Turnstile, request idempotency and an origin lock — so the platform sheds bots and floods instead of trusting its callers.
• Built first‑party product analytics in PostgreSQL — 47 functions over partitioned event tables that prune themselves — pseudonymised behind a rotating salt and gated on the visitor's consent.
• Built the loyalty and reputation system — 67 functions over a 31‑table ledger, with leagues, badges and a redemption shop — taking a row lock on the balance to close the double‑spend window.
• Ran the whole company on one 512 MB single‑core host — a git forge, a web server serving seven domains, Tor, two alternate‑protocol servers, backups and intrusion banning — by treating 464 MB of usable memory as the binding architectural constraint.
• Built dead‑man's‑switch monitoring that pings only while memory and disk are healthy, so a degraded host raises an alert by going silent — and caught six variable names saying "free" where the check correctly measured "available", an order of magnitude apart on a 464 MB box.
• Built a multilingual static site in Hugo across 104 template files including 49 partials, publishing every page in four representations from one content tree in three languages.
• Cut the site's browser‑driven quality gate from 1,636 seconds to 615 by scheduling its checks longest‑first through a worker pool bounded to four lanes, after measuring that alphabetical order cost 320 seconds against 224.
• Cut the stylesheet bundle from 87 KB to 31 KB by taking a base64 font out of it, and dropped 756 KB across 22 files that were published on every deploy and referenced by nothing.
• Vendored a QR encoder — Reed‑Solomon over GF(256), fixed module layout, eight mask patterns — in 522 lines rather than take a third runtime dependency, and verified it by reading the finished matrix back with an independently written decoder.
• Stopped an application filling memory at 41 MB a second — a recorded 111 GB of compressed pages on a 36 GB machine — by bounding every event stream, subscribing by event type and putting a rate budget on logging, taking 610,996 log lines down to 1,411.
• Adopted Swift 6 complete strict concurrency with no actors, bridging a blocking C event loop to the main actor through one producer, one consumer and one ordering — after establishing that a task per event loses the ordering the interface depends on.
• Took the test suite from six tests over a sixty‑second limit to 135 passing in 8.9 seconds by profiling the main thread and removing the two calls it sat inside for 3,989 samples out of 4,017.

This work is part of what we offer as Backend & API Development, Database Design & Modeling, Frontend Development, Platform & Solution Architecture, Database Performance Tuning, Site Reliability & Monitoring, Data Pipeline Development (ETL/ELT) and GIS & Geospatial Solutions.

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