Blog - kimserey lam

Sep 26, 2026

AWS Credential Chain in DuckDB

When DuckDB reads or writes Parquet files in S3, it needs AWS credentials. Rather than hardcoding access keys, you can tell DuckDB to discover credentials at runtime using the same chain the AWS SDK uses — environment variables, IRSA tokens, SSO profiles, ECS task roles, or EC2 instance metadata. This post covers how the credential chain works, what each step looks for, and how it connects to DuckLake in both local development and production.

Sep 23, 2026

Deploying DuckLake on AWS with CloudFormation

DuckLake needs two pieces of infrastructure: an S3 bucket for Parquet data files and a PostgreSQL database for the metadata catalog. This post walks through standing up that infrastructure on AWS using CloudFormation — a single YAML template that creates all the resources as a unit. Along the way, we cover KMS encryption, bucket policies, Aurora PostgreSQL configuration, IAM roles for workload access, and the deployment workflow.

Sep 19, 2026

OpenTelemetry Delta Temporality and Datadog

When you ship OpenTelemetry metrics to Datadog, the choice between cumulative and delta temporality changes how your dashboards behave. This post covers the practical differences, what Datadog actually stores, and how to query each instrument type correctly.

Sep 16, 2026

OpenTelemetry in Go — Traces, Metrics, Logs and the Collector

OpenTelemetry gives you traces, metrics, and logs from a single instrumentation library with a vendor-neutral export path. This post walks through adding OpenTelemetry to a Go HTTP server from scratch — setting up providers, auto-instrumenting HTTP, creating manual spans, recording custom metrics, emitting correlated logs, configuring the Collector, and running the full stack with Docker Compose. It finishes with a side-by-side comparison of the OTel metrics API against the Prometheus client library.

Sep 12, 2026

Go Building Services — HTTP, Signals, Migrations and Reflection

The previous tutorials covered Go’s type system, concurrency primitives, and error handling. This one shifts to production patterns you need when building real services: serving HTTP requests, managing process lifecycle with signal handling, running database migrations with embedded files, and using reflection for metaprogramming. Each topic stands on its own, but together they represent the kind of infrastructure code that appears in nearly every Go service.

Sep 09, 2026

Go Standard Library — Packages, IO, JSON and Testing

This post covers four day-to-day essentials for writing Go programs: organizing code into packages and modules, streaming data through the io.Reader and io.Writer interfaces, serializing and deserializing JSON, and writing tests. These are the building blocks you will reach for in nearly every Go project — packages keep code organized, the I/O interfaces let you compose data pipelines, JSON handles serialization, and the testing package gives you a lightweight framework for verifying everything works.

Sep 05, 2026

Go Concurrency — Goroutines, Channels and Sync

Go was designed with concurrency as a first-class feature. Rather than relying on OS threads and shared-memory locking as the primary model, Go provides goroutines (lightweight threads managed by the Go runtime), channels (typed conduits for communication between goroutines), and a select statement for multiplexing channel operations. The standard library rounds this out with a context package for cancellation and deadlines, and a sync package for traditional mutual exclusion when channels are not the right fit. This tutorial walks through all five building blocks, starting from basic goroutines and working up to a concurrent cache protected by read-write locks and atomics.

Sep 02, 2026

Go Error Handling and Resource Management

Go treats errors as ordinary values — there are no exceptions, no try/catch, and no hidden control flow. Every function that can fail returns an error alongside its result, and the caller decides what to do with it. For cleanup, Go provides defer, which guarantees a function call runs when the enclosing function returns, regardless of how it returns. Together, explicit error returns and defer give you predictable error propagation and deterministic resource management without the complexity of exception hierarchies or finalizers. In this post we cover Go’s error handling patterns — from basic checks through wrapping, sentinel errors, and context-aware error classification — then move to defer patterns including LIFO ordering, loop pitfalls, closure capture semantics, named return manipulation, and panic recovery.

Aug 29, 2026

Go Type System — Interfaces, Embedding and Generics

Go takes a distinctive approach to polymorphism and code reuse. There are no classes, no inheritance hierarchies, and no explicit implements declarations. Instead, Go provides three orthogonal mechanisms: interfaces for abstraction, embedding for composition, and generics for type-safe parameterization. Each one is simple on its own, and together they cover the same ground that class hierarchies cover in other languages — with less coupling. In this post, we’ll work through all three, starting with how Go interfaces are satisfied implicitly, then moving to struct and interface embedding, and finishing with generic functions and types.

Aug 26, 2026

Go Basics — Types, Memory and Data Structures

This is the first tutorial in a series on Go. It covers three foundational topics: pointers and references (how Go passes data around and when mutations are visible), structs with methods and receivers (how Go models data and behavior without classes), and slices and maps (the two built-in data structures you will use constantly). Each section builds on the previous one — pointers explain why receiver types matter, and receiver types explain how slice and map patterns work in practice.

Aug 22, 2026

DuckLake — Open Lakehouse with DuckDB

DuckLake is a lakehouse format built as a DuckDB extension. It separates metadata from data: a catalog (a local .ducklake file or a PostgreSQL database) tracks table schemas, snapshots, and file pointers, while Parquet files hold the actual data on a local filesystem or in S3-compatible object storage. This separation is the central design decision — the catalog knows what files exist, what columns they contain, and what value ranges they hold, but it never stores the data itself. In this post, we’ll go from running DuckLake locally to running it with PostgreSQL and MinIO, look at how partitioning organizes data in S3, and see how DuckLake’s query engine prunes files and pushes predicates down to avoid reading data it doesn’t need.

Aug 19, 2026

AWS Networking for Aurora RDS PostgreSQL

Running PostgreSQL in AWS isn’t just about creating the database — you first build the network it sits in. With Aurora RDS, you are the network engineer: you decide the IP range, which parts are reachable from the internet, what traffic is allowed between resources, and which data centers your database runs in. This post walks through the core networking concepts — VPC, subnets, gateways, route tables, security groups, and DB subnet groups — that you need to understand before provisioning Aurora RDS PostgreSQL.

Aug 15, 2026

Debezium PostgreSQL Connector — Failure Modes and Slot Management

Debezium’s reliability comes from PostgreSQL’s replication slots — they guarantee that no WAL is discarded before the connector has consumed it, so a crashed connector can catch up without data loss. But that same guarantee creates the biggest operational risk: an abandoned slot tells PostgreSQL to hold WAL indefinitely, and on a busy database the WAL can fill the disk. This post covers connector failure and recovery, leaked slots, and how Debezium handles schema evolution.

Aug 12, 2026

Debezium PostgreSQL Connector — Configuration That Matters

The default Debezium connector config produces usable events, but the defaults leave important gaps — before values are missing on UPDATEs and DELETEs, all tables are captured including system tables, and each table gets its own Kafka topic. This post covers the configuration knobs that close those gaps: replica identity, table filtering, publication modes, and single-topic routing with the ByLogicalTableRouter transform.

Aug 08, 2026

Debezium PostgreSQL Connector — CDC in Action

Debezium is a distributed CDC platform that tails PostgreSQL’s Write-Ahead Log and produces a Kafka event for every row change. It runs as a connector inside Kafka Connect, which means you deploy it by POSTing a JSON config to a REST API — no custom code needed. This post walks through setting up a local Debezium stack, registering a connector, and reading the events it produces.