Blog - kimserey lam

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.

Aug 05, 2026

PostgreSQL Logical Replication and CDC

PostgreSQL logical replication lets an external consumer receive a stream of row-level changes (INSERT, UPDATE, DELETE, TRUNCATE) from the database in real time. It piggybacks on the Write-Ahead Log — the same mechanism Postgres already uses for crash recovery — and exposes decoded, row-level events over a TCP connection using Postgres’s own wire protocol. This is the foundation for Change-Data-Capture (CDC): a consumer connects, creates a replication slot, and starts receiving a stream of changes it can forward to Kafka, another database, a search index, or anywhere else.

Aug 01, 2026

Why Parquet, and Why It Works So Well on S3

If you’ve been following the OLAP series, Parquet will look familiar — it’s essentially the production-grade, standardized version of what we built in Phase 1 and Phase 2. Phase 1’s columnar vectors map to Parquet’s column-by-column layout within row groups. Phase 2’s row groups with column segments and zone maps map directly to Parquet’s row groups with column chunks and min/max statistics. The main structural difference is that our format stores segment headers inline before each column’s data, so you walk through the file sequentially. Parquet moved all that metadata into a footer with precomputed absolute byte offsets — and that single design decision is what makes the S3 range-request pattern possible. This post is about why that matters.

Parquet is a columnar binary file format with a metadata footer at the end of the file. The footer contains the schema, the location (byte offset and length) of every column chunk in every row group, and statistics like min/max and null counts for each. Because all of this is laid out with precise byte offsets, a reader that can fetch arbitrary byte ranges can navigate a Parquet file surgically — read the footer, decide what it actually needs, and fetch only those bytes.

Jul 29, 2026

Transaction Isolation and Exported Snapshots in PostgreSQL

Transaction isolation levels control what data a transaction can see when other transactions are concurrently modifying the database. Understanding them is essential for CDC (Change Data Capture), where an initial snapshot of the database must be taken at the exact point a replication slot is created — ensuring no gaps or overlaps between the snapshot and the WAL stream.

This post explains the four isolation levels, the read anomalies they prevent, how MVCC snapshots work under the hood, and how PostgreSQL’s exported snapshot mechanism ties it all together for CDC.

Jul 25, 2026

OLAP – Phase 10 Sorting, Parallel Execution, REPL, and Server

The database can parse SQL, plan queries, join tables, and aggregate results. But it’s missing four things that make it usable: ORDER BY (sorting results), parallel execution (using multiple CPU cores), an interactive REPL for exploring data, and a TCP server for remote connections.

This phase ties everything together into a complete, runnable OLAP engine.

Jul 22, 2026

OLAP – Phase 9 Query Planner and Optimizer

The parser produces an AST — a syntactic representation of the SQL query. But the AST doesn’t know whether table names exist, what types columns have, or how to execute the query efficiently. This phase bridges the gap with three components: a binder that resolves names and types, an optimizer that rewrites the logical plan for efficiency, and a physical planner that maps logical operators to executable physical operators.

The pipeline is: AST → Binder → Logical Plan → Optimizer → Physical Planner → Physical Plan.

Jul 18, 2026

OLAP – Phase 8 SQL Parser

The execution engine from Phases 5-7 works, but it requires manually constructing operator trees and expression objects in Go code. A real database accepts SQL text from users. This phase builds a SQL parser that converts a SQL string into an Abstract Syntax Tree (AST) — a structured representation that the planner (Phase 9) can transform into a physical execution plan.

The parser has two stages: a lexer that splits the SQL string into tokens, and a recursive descent parser that builds the AST from the token stream.

Jul 15, 2026

OLAP – Phase 7 Hash Join

Analytical queries rarely touch a single table. “Revenue by region” needs to join orders with regions. “Sales by product category” needs to join sales with products with categories. The hash join is the standard OLAP join algorithm — it builds a hash table from one table (the smaller one), then probes it with the other (the larger one), achieving O(n + m) complexity instead of nested-loop’s O(n x m).

This phase implements the HashJoinOperator with support for INNER and LEFT joins, including NULL key handling.

Jul 11, 2026

OLAP – Phase 6 Hash Aggregation

The scan/filter/project pipeline from Phase 5 processes rows one batch at a time — it’s a streaming operation where each batch flows through independently. Aggregation (GROUP BY with SUM, COUNT, AVG) is fundamentally different: it needs to see all the data before producing any output. This makes it a pipeline breaker — it consumes all input, builds an internal state, then emits results.

This phase implements GROUP BY aggregation using a hash table: the HashAggregateOperator, the GroupByHashTable, and five aggregate functions (COUNT, SUM, AVG, MIN, MAX).

Jul 08, 2026

OLAP – Phase 5 Vectorized Expressions and Scan/Filter/Project

The storage layer can persist and read columnar data. Now we need to query it. This phase builds the execution engine’s foundation: expressions that evaluate on entire vectors at once (2048 rows per call), and three physical operators — SeqScan, Filter, and Projection — that form the basic query pipeline.

The key insight is vectorized execution: instead of evaluating amount > 100 one row at a time, we evaluate it on 2048 rows at once in a tight loop. This eliminates per-row function call overhead and lets the CPU pipeline and cache work efficiently.

Jul 04, 2026

OLAP – Phase 4 Table, Catalog, and Bulk Loading

Phases 1-3 built the storage primitives — vectors, row groups, column segments, and compression. But there’s no concept of a “table” yet. There’s no way to define a schema (CREATE TABLE), no way to add rows one at a time, no way to bulk-load from a CSV, and no way to persist and reload the database across restarts.

This phase adds the Table (schema + row groups + append state), the Catalog (table registry with JSON manifest), and a CSV loader for bulk ingestion.

Jul 01, 2026

OLAP – Phase 3 Compression

Columnar storage has a property that row-based storage doesn’t: values in a column tend to be similar. A status column might have only 5 distinct values across millions of rows. A timestamp column is monotonically increasing. A year column spans a narrow range. Compression codecs exploit these patterns to shrink data 5-10x, and because columns are stored independently, each column gets the codec that fits its data best.

This phase implements four compression codecs — RLE, Dictionary, Bitpacking, and Delta — with automatic selection that tries all applicable codecs and picks the smallest.

Jun 27, 2026

OLAP – Phase 2 Columnar Storage

Phase 1 gave us in-memory vectors and data chunks. But a database needs to persist data to disk and read it back. The challenge is designing a disk format that preserves the benefits of columnar layout: read only the columns you need, skip entire sections when the data can’t match your query.

This phase builds two structures: RowGroups (horizontal partitions of a table, each holding up to 122,880 rows) and ColumnSegments (one column’s serialized data within a row group, annotated with min/max zone maps).

Jun 24, 2026

OLAP – Phase 1 Vectors and DataChunks

The fundamental difference between OLTP and OLAP databases is how they store and process data. An OLTP database like PostgreSQL stores rows — when you insert a customer record, all columns (name, email, balance) sit together on one page. An OLAP database like DuckDB stores columns — all names sit together, all emails sit together, all balances sit together. This layout is what makes analytical queries fast: SELECT SUM(balance) FROM customers only touches the balance column, ignoring everything else.

This phase builds the two foundational data structures of a columnar engine: Vectors (typed columnar arrays) and DataChunks (batches of column vectors).