Alex explains distributed column stores, how they work, why they're so fast, and why that's a fundamental requirement for observability.
Columnar Storage is Normalization
Something I didn't understand for a while is that the process of turning row-oriented data into column-oriented data isn't a totally bespoke, foreign concept...
Column Storage for the AI Era
In the past few years, we’ve seen a cambrian explosion of new columnar formats, challenging the hegemony of Parquet: Lance, Fastlanes, Nimble, Vortex, AnyBlox, F3 (File Format for the Future). The thinking is that the context has changed so much that the design of yore (the previous decade) is not going to cut it moving forward. This seemed a bit intriguing to me, especially since the main contribution of Parquet has been to provide a standard for columnar storage. Parquet is not simply a file format. As an open source project hosted by the ASF, it acts as a consensus building machine for the industry. Creating six new formats is not going to help with interroperability. I spent some time to understand a bit better how things actually changed and how Parquet needs to adapt to meet the demands of this new era. In this post I’ll discuss my findings.
Cassandra: a decentralized structured storage system: ACM SIGOPS Operating Systems Review: Vol 44, No 2
Cassandra is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure. Cassandra aims to run on top of an ...
Observability 2.0 — Observability Is About Asking Any Question | alok87.in
For years, the observability industry has been telling developers two contradictory things: "Build reliable systems" and "Log less, it's too expensive." This post is about a different shape of data — wide events — that gives you more answers for less money.
Designing Data-Intensive Applications (DDIA) — an O’Reilly book by Martin Kleppmann (The Wild Boar Book)
NoSQL… Big Data… Scalability… CAP Theorem… Eventual Consistency… Sharding…
Comment on: Permissioned Data Diary 2: Buckets - Daniel&#...
"a bucket doesn’t necessarily imply a physical container sitting on one PDS. Consider how threads ..." The distributed-vs-centralized storage question is where this gets really interesting. If buc...
Why does the Matrix ecosystem seem like such a mess right now?
48 votes, 20 comments. I’ve spent the past two days trying to figure out what I should actually deploy if I want a future-proof self-hosted Matrix…
A Critique of the CAP Theorem
The CAP Theorem is a frequently cited impossibility result in distributed systems, especially among NoSQL distributed databases. In this paper we survey some of the confusion about the meaning of CAP, including inconsistencies and ambiguities in its definitions, and we highlight some problems in its formalization. CAP is often interpreted as proof that eventually consistent databases have better availability properties than strongly consistent databases; although there is some truth in this, we show that more careful reasoning is required. These problems cast doubt on the utility of CAP as a tool for reasoning about trade-offs in practical systems. As alternative to CAP, we propose a "delay-sensitivity" framework, which analyzes the sensitivity of operation latency to network delay, and which may help practitioners reason about the trade-offs between consistency guarantees and tolerance of network faults.
Distributed Systems lecture series
R2 | Scalable solution for distributed object storage
Cloudflare R2 is a distributed object storage solution with zero egress fees. Get started with our scalable object storage service today.
What is Observability (o11y)? And Why Ops Teams Need It Now
Observability for modern ops: correlation across metrics, logs, traces, and events to cut MTTR, curb alert fatigue, and connect service health to business.
Ryan Dahl on Twitter / X
Introducing celld: a self-hosted, distributed Durable Objects and Workers implementation- celld = V8 + S3 + SQLite + LTX+ Tokio- programmed with exactly the Cloudflare Workers/DO JavaScript APIs and config- order of magnitude cheaper at scale- writes are durable before… pic.twitter.com/KCBf6U5DQw— Ryan Dahl (@rough__sea) August 5, 2026
AWS re:Invent 2018: Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321)
