Velox: Inside Meta's C++ Vectorized Execution Engine Behind Presto and Spark
Most engineers meet Velox indirectly. If you have run a query on Presto’s C++ worker (Prestissimo) or accelerated Spark through Apache Gluten, Velox is the library that actually executed your plan. Created at Meta and now developed with partners including...
Inside Apache Spark: How One Line of SQL Becomes a Cluster Job
The best description of this project is the one its authors wrote: the README's first line calls Apache Spark "a unified analytics engine for large-scale data processing." That phrasing undersells what the repository actually holds. Behind that sentence sits a...
Presto Under the Hood: A Source Tour of the Distributed SQL Engine That Queri...
Presto describes itself in one line in its own README: “Presto is a distributed SQL query engine for big data.” That modest sentence hides one of the most influential architectures in data infrastructure. Born at Facebook to let engineers query...
Apache Paimon: Inside the Streaming Lakehouse's LSM-Tree Table Format
Modern ML systems are hungry for fresh tabular data. Feature tables churn all day: a user’s click counts change, an item’s embedding ages, a fraud score drifts. Classically you had to choose between a streaming log like Kafka, which is...
Apache Iceberg Source Tour: The Table Format That Puts ACID on Object Storage
On a data lake, a table is just a folder of files — until you need transactions. The moment two jobs write the same directory, or a query starts while a compaction job finishes, plain folders stop answering “what exactly...
Ibis: One Python DataFrame API That Compiles to Every Query Engine You Ship
Every serious ML and LLM workflow hits the same wall eventually. You prototype a feature workflow on pandas or a local engine, it works beautifully on ten gigabytes, and then someone says “great — now run it over the full...
Apache Gluten: Pushing Spark SQL Down to Native Engines With Substrait
Every serious machine learning project eventually hits the same wall: the model is only as fast as the data preparation that feeds it. Feature engineering at petabyte scale is overwhelmingly SQL-shaped work — filters, joins, aggregations, window functions — and...
Apache Flink Under the Hood: A Source Tour of True Streaming, Event Time, and...
Most machine-learning systems treat streaming data as plumbing: events arrive in Kafka, a nightly batch job in Spark rebuilds aggregates, and the model serving tier reads whatever is freshest in Redis. Apache Flink attacks that gap differently. It is a...
DataFusion: The Rust and Arrow Query Engine Powering Embeddable Analytics
Modern AI and ML work is, more often than we admit, query-shaped. Before a model sees a tensor, somebody filters events, joins features, aggregates sessions, and dedupes rows out of a lake of Parquet files. Enter Apache DataFusion: an extensible...
cuDF: Inside NVIDIA's GPU DataFrame Engine That Speaks Fluent pandas
Every serious ML workflow eventually hits the same wall: the model trains on the GPU, but the data prep before it runs on the CPU. Feature engineering, joins, group-by aggregations, CSV and Parquet parsing — all of it grinds through...
Zipkin Under the Hood: A Source Tour of the Tracing System That Started It All
Every AI stack you operate today — an API gateway routing to a model server, an agent orchestrating tool calls, a RAG chain fanning out to an embedding service and a vector database — creates the same debugging problem Zipkin...
Vector: The Rust Router That Moves Your LLM's Logs, Metrics, and Traces
Ask anyone running a large language model service what their telemetry situation looks like and watch them wince. A single serving cluster can emit millions of structured log lines per day — prompts, completions, tool calls, guardrail verdicts — plus...
Grafana Tempo Under the Hood: Kafka, Block Builders and a TraceQL Engine That...
An AI application in 2026 is not one program. It is a chain: a router picks a model, a retriever fetches context from a vector store, an inference server runs the prompt, tool calls fan out to external APIs, and...
Telegraf Under the Hood: The Plugin-Driven Go Agent That Moves Your Metrics
Training and serving AI models is a telemetry problem as much as a compute problem. A training run is only as trustworthy as the numbers you can see while it happens: GPU utilization and memory pressure from every node, host...
Inside Apache SkyWalking: A Source Tour of the OAP Server That Watches AI Inf...
Modern AI infrastructure is a distributed-systems problem wearing a lab coat. One “ask the model” request can cross a gateway, a queue, a vector database, and a GPU inference service — and when latency spikes, someone must find which hop...
Pyroscope: A Source Tour of Grafana's Continuous Profiling Backend
Anyone who has served a large language model in production knows the feeling: the GPU sits at 97% utilization on your dashboard, yet tokens per second quietly sag during the evening peak. Dashboards built on metrics from Prometheus tell you...
Grafana Mimir Under the Hood: HA Dedup, Tenant TSDBs and Sharded Block Reads ...
Every serious ML platform eventually hits the same wall: metrics. A modern inference fleet — hundreds of replicas across a dozen models, each exporting request latency, GPU utilization, KV-cache pressure, token throughput and queue depth — outgrows a single Prometheus...
Logstash Under the Hood: The JRuby + JVM Engine That Turns Raw Logs into Sear...
Logstash is Elastic’s server-side data processing engine. It collects events from a multitude of sources at once, parses and transforms them with a small DSL that reads like configuration, and then ships each event to a stash of your choice....
Fluent Bit Under the Hood: The C Engine That Ships Logs From Your GPU Fleet
Training and serving large models is now a telemetry problem as much as a compute problem. Every GPU node writes journald and container logs, every inference server emits latency and throughput metrics, every fine-tuning run streams progress lines that someone...
cAdvisor Source Tour: How Google's Container Advisor Turns cgroups into Metrics
Every serious GPU cluster conversation eventually hits the same wall: somebody asks what a specific container was actually doing at 3 a.m. — how much CPU it burned, whether it was network-starved during a checkpoint upload, or why memory pressure...
Trino Under the Hood: A Source Tour of the Distributed SQL Engine Behind Fede...
Modern AI systems are data-starved in a very specific way: the features a model needs rarely live in one place. Click events stream into a log broker, sensor telemetry lands in a time-series store, historical training rows sit on object...
Inside RisingWave: A Source Tour of the Rust Streaming Database Powering Alwa...
If you build AI systems, you eventually hit the same wall: your model was trained on a snapshot, but the world kept moving. Feature vectors go stale, dashboards lag, retrieval indexes drift. RisingWave attacks that problem from the database side....
Inside Redpanda: A C++ Source Tour of the Kafka-Compatible Streaming Engine
Modern ML systems live and die on event streams. Training features arrive as sequences of user actions, model predictions come back as telemetry, and drift detection watches both. The infrastructure that carries those events has traditionally been the JVM: Apache...
Inside QuestDB: A Java Source Tour of the Column Store Built for Real-Time Te...
Every serious ML deployment is a telemetry factory. Training clusters emit GPU utilization, temperature, and job metrics; inference servers log request latencies and prediction distributions; drift monitors compare live feature windows against training snapshots. All of that data is time-stamped,...