Software engineerwho writes it down.
…projects, notes and a résumé in one place.
1
ClarifyWhat the requirement actually needs2
DesignData model, flow, cloud service tradeoffs3
BuildFront to back, through tests4
ShipDeploy, watch it run, watch the bill5
Write it downNotes, case studies and decisions worth keepingI'm Johnny, a software engineer in Taiwan. I build systems and design the solutions behind them, and I keep notes on what I'm working on, what I'm studying, and what I learn along the way. This site is my résumé and my notebook — and the place I show my work.
APPLIED IN — E-COMMERCE
Membership e-commerce platform · 3,000+ members
APPLIED IN — PAYMENTS
Cross-border e-commerce payments · 5 providers · €100K–1M+/mo
STUDYING
UIUC MCS, Cloud Computing · CS425 Distributed Systems
(02)
Selected work
Two production systems — the problem, the decisions, the data flow and the results behind each one.
All projects →(03)
Writing
Notes on what I'm building, what I'm studying and what I learned the hard way — e-commerce, payments, and technical course notes.
Browse the archive →Filter by section
Latest
- SAA-C03 Study Map: Learn Decisions, Not a List of ServicesTurn the four exam domains into a repeatable way to read requirements, eliminate distractors and select an architecture. · 1 min
- AWS Identity and Security: Follow the Request, Then Find the DenyConnect IAM evaluation, Organizations, KMS, secrets, network protection and detection services into one security model. · 2 min
- AWS Networking and Edge: Trace the Packet Before Choosing the ServiceConnect subnets, routing, security, private access, hybrid links, DNS and global delivery through packet paths. · 2 min
- AWS Compute and Elasticity: Match Runtime, Scaling Unit, and Failure ModelChoose among EC2, Auto Scaling, load balancers, containers and Lambda by control, workload shape and state. · 1 min
- AWS Storage: Choose by Access Pattern, Sharing, and RecoveryCompare object, block and file storage before choosing classes, replication, lifecycle and hybrid transfer tools. · 1 min
- AWS Databases and Caches: Start From the Data ContractChoose RDS, Aurora, DynamoDB, ElastiCache or a purpose-built database from consistency, query and scale requirements. · 1 min
- AWS Messaging and Serverless: Design Delivery Semantics FirstSeparate queues, pub/sub, streams and orchestration before connecting Lambda, API Gateway and event-driven workloads. · 1 min
- AWS Observability and Governance: Metrics, Events, Audit, and ConfigurationStop mixing CloudWatch, EventBridge, CloudTrail and Config by asking what each service records and acts on. · 1 min
- AWS Resilience and Migration: Let RTO and RPO Drive the ArchitectureSeparate high availability from disaster recovery, then map business recovery targets to replication and migration tools. · 1 min
- AWS Data and Analytics: Choose the Engine From the QuestionMap ad hoc SQL, warehouses, ETL, streams, search, dashboards and clusters to the service that owns each job. · 1 min
- SAA-C03 Final Review: Cost Decisions and the Traps Worth MemorizingFinish the series with cost levers, frequently confused pairs and a compact method for reviewing practice questions. · 2 min
- From Relations to SQL: The Model Behind a QueryRelations, schemas, keys, bags, NULL, and SQL's logical processing order—one mental model for reasoning about query results. · 3 min
- Joins, NULL, and Set Operations: Keeping Multi-Table Queries CorrectJoin cardinality, outer-join filters, NULL-safe anti-joins, and when UNION ALL is the honest operation. · 2 min
- Aggregation, Views, and Updates: Choosing the Right BoundaryGROUP BY semantics, ordinary and materialized views, atomic updates, and when a stored routine is worth the coupling. · 2 min
- Constraints and Triggers: Put Invariants Where Every Writer Must ObeyA practical hierarchy for NOT NULL, CHECK, UNIQUE, foreign keys, and triggers—with failure behavior you can test. · 2 min
- From Conceptual Model to Tables: Preserving Meaning During DesignA repeatable path from requirements to entities, relationships, keys, cardinalities, and relational tables. · 2 min
- Functional Dependencies and Normalization: From Closure to 3NF and BCNFA worked reasoning chain for attribute closure, candidate keys, minimal covers, lossless decomposition, 3NF, and BCNF. · 2 min
- MongoDB Data Modeling: Flexible Schema Still Requires DesignChoose embedding or references from access patterns, growth bounds, update ownership, and measurable query plans. · 2 min
- Graph Data Modeling and Cypher: Make Relationships First-ClassModel nodes, relationships, direction, and traversal cost in Neo4j without turning every dataset into a graph. · 2 min
- B+ Trees and Hash Indexes: Choosing Through Page I/OTree structure, fanout math, update cost, runnable PostgreSQL, EXPLAIN interpretation, and a practical index decision table. · 3 min
- Transactions, Serializability, and Isolation: Reasoning About Concurrent WritesFrom ACID and schedules to conflict graphs, anomalies, isolation levels, locks, and evidence from two database sessions. · 2 min
- From Relational Algebra to Physical Operators: Where Query Cost Comes FromSeparate logical meaning from scan, sort, hash, and join algorithms, then estimate their page-I/O cost. · 2 min
- Query Optimization: Cardinality Before CostRule-based rewrites, selectivity estimates, join order, physical-plan cost, and a disciplined way to read estimation errors. · 2 min
- Relational, Document, Graph, or Raw Data: Choose From the WorkloadCompare data models by invariants, access paths, update ownership, relationship depth, latency, and operational cost. · 2 min
- Database Recovery: WAL, Checkpoints, Undo, and RedoUnderstand what survives a crash, why write-ahead logging works, and how buffer policies determine undo and redo work. · 3 min
- Parallel and Distributed Databases: Optimize Data Movement FirstConnect partitioning, replication, parallel joins, skew, distributed commit, and consensus without collapsing them into one topic. · 3 min
- Information Integration: From Messy Sources to Trustworthy DataA practical guide to schema matching, entity resolution, provenance, merge policy, ETL, and federated queries. · 3 min
- Research Nexus: What a Real Dataset Changed About the Database DesignA design review of an 11-table research-discovery database built from DBLP-style citation data and Scholar enrichment. · 3 min
- NoDB Revisited: Querying Raw Data Without Paying the Load Cost FirstA practical critique of NoDB's data-to-query argument, selective parsing, positional maps, caching, and break-even point. · 3 min
- Cost-Aware Learned Caching: From LBSC to a Testable System ProposalWhy miss count is the wrong objective for heterogeneous storage, how LBSC learns eviction, and how to scope a credible follow-up experiment. · 4 min
- Clouds Are Distributed Systems With an Operating ModelStart from partial failure, concurrency, elasticity, virtualization, and the economic reason cloud systems exist. · 2 min
- From Napster to Chord: Why Peer-to-Peer Systems Became StructuredCompare centralized indexes, flooding, supernodes, BitTorrent swarms, and DHT routing through state, messages, and failure behavior. · 2 min
- Gossip, Membership, and Failure Detection Under UncertaintyConnect epidemic dissemination, membership views, heartbeats, suspicion, false positives, and scalable failure detection. · 2 min
- Time Without a Global Clock: Causality and Multicast OrderMove from clock synchronization to Lamport timestamps, vector clocks, FIFO, causal, and total-order multicast. · 1 min
- Global Snapshots: Capture Distributed State Without Stopping the WorldUnderstand consistent cuts, in-transit messages, and the Chandy-Lamport marker algorithm. · 1 min
- Consensus and Paxos: Majorities Preserve One DecisionSeparate consensus safety from progress, then follow proposals, acceptors, ballots, and quorum intersection in Paxos. · 1 min
- MapReduce: Move Computation to Data and Recompute on FailureUnderstand map, shuffle, reduce, partitioning, stragglers, speculative execution, and lineage-based fault tolerance. · 1 min
- From CAP to Cassandra and HBase: Consistency Is an API ContractConnect partition behavior, consistency models, quorums, LSM-style writes, Cassandra, and HBase without reducing design to CAP slogans. · 2 min
- Data Quality Starts With the Question, Not the DatasetUse fitness for use, quality dimensions, and cost to decide what must be cleaned and what is already good enough. · 3 min
- Profile Before You Clean: Turn Suspicion Into Measurable DefectsBuild a data profile that separates syntax, schema, semantic, duplicate, missingness, and distribution problems before changing values. · 2 min
- Regular Expressions for Data Cleaning: Match Structure, Not MeaningBuild testable regexes for validation, extraction, and transformation while keeping parsing and semantic checks separate. · 2 min
- OpenRefine: Explore First, Transform Second, Export the RecipeUse facets, clustering, GREL, reconciliation, and operation history without turning interactive cleaning into an unrepeatable black box. · 2 min
- Integrity Constraints as Executable Data-Quality RulesTranslate keys, references, domains, and cross-column business rules into violation queries and enforceable constraints. · 2 min
- Datalog and Recursive Queries: A Small Language for Data RelationshipsUnderstand facts, rules, joins, recursion, fixed points, and how the same reasoning appears in recursive SQL and data-quality checks. · 2 min
- Database Repair: When Several Clean Versions Are PossibleReason about minimal repairs, repair policies, stable conclusions, and consistent query answers without hiding ambiguity. · 2 min
- Data-Cleaning Workflows: Automation Is Not ReproducibilityDesign cleaning pipelines around explicit inputs, outputs, parameters, dependencies, validation gates, and rerunnable evidence. · 2 min
- Data Provenance: Explain Where a Result Came FromSeparate prospective and retrospective provenance, model lineage at useful granularity, and annotate scripts with YesWorkflow-style dependencies. · 2 min
- Taxonomy Alignment: Same Labels Do Not Guarantee the Same MeaningAlign hierarchical vocabularies with explicit semantic relations, logical constraints, possible worlds, and human review. · 2 min
- Cleaning 17,545 Historical Menus Without Erasing the EvidenceA project retrospective on profiling, canonical cities and meals, conservative inference, constraints, provenance, and honest validation. · 3 min
- MinHash and LSH: Find Near-Duplicates Without Comparing Every PairTurn documents into shingles, compress Jaccard similarity into signatures, then use LSH to generate a small candidate set. · 2 min
- Association Rules: From Apriori Pruning to FP-GrowthUnderstand support, confidence and lift, then see why Apriori and FP-Growth spend work differently. · 1 min
- Clustering and Classification: Similar Tools, Different QuestionsConnect k-means, decision trees and SVMs through objectives, assumptions, boundaries and evaluation. · 1 min
- Spark RDDs: Lazy Execution, Lineage, Partitions, and ShufflesRead Spark performance from the execution model instead of treating transformations as ordinary collection methods. · 1 min
- Spark DataFrames and SQL: Give the Optimizer More InformationUnderstand why schema, logical plans, Catalyst and columnar execution often beat opaque RDD code. · 1 min
- Beyond Batch Spark: Streaming, Graphs, and Machine-Learning PipelinesUse one execution foundation for incremental streams, property graphs and repeatable feature-to-model workflows. · 1 min
(04) — Work with me
Got a problem to solve?
01Requirement shaping
Turn goals, user needs and constraints into clear scope, priorities and an executable technical plan.
02Architecture & delivery
Design domain models, data flows, APIs, cloud and integrations, then carry the plan through tested production code.
03Scale, reliability & cost
Find bottlenecks, failure paths and cost drivers, then plan sensible growth without premature complexity.
04Technical ownership
Own trade-offs, milestones and technical quality from discovery to launch, or strengthen an existing team where it needs help.