标签：#cli

I was testing out CH-Ops - an admin GUI for self-hosted ClickHouse - on a simple setup: 1 shard, 2 replicas. Stumbled onto the replication queue almost by accident. Here's what I did: I stopped one of the nodes (let's call it Node B), then inserted some data through the other one (Node A). Just wanted to see what would happen. Then, while Node B was still down, I checked it in CH-Ops. It had stuff sitting in its replication queue. My first assumption was: okay, this must be showing what's left to replicate across the cluster - the total pending replication work. So I switched over and checked Node A, the one that was actually up and had just received the insert. Its queue was empty. That didn't match what I expected at all. If the queue was a cluster-wide "here's what still needs to replicate" view, Node A should've shown something too - it was the one that had the fresh data now waiting to reach Node B. Instead it was Node B, the down one, sitting there with pending tasks. That mismatch is what sent me digging. Turns out the queue isn't cluster-wide at all - it's specific to each ClickHouse instance. Once I brought Node B back up, its queue drained in seconds and the data showed up. That whole experiment is basically the entire post in miniature. Here's the mental model I ended up with. A Queue Belongs to a Replica, Not to the Table This is the first thing to get straight. With a ReplicatedMergeTree table, you can have multiple replicas holding copies of the same data. It's tempting to think of replication as one shared pipe between them. It isn't. Each replica keeps its own local replication queue . So if you see: Replica 1 → queue_size = 0 Replica 2 → queue_size = 25 that doesn't mean 25 operations are waiting somewhere in the middle for both replicas to pick up. It means Replica 2, specifically, has 25 tasks it hasn't finished yet. Once that clicked for me, the rest of the system made a lot more sense. So Where Do These Tasks Come From? Replication in ClickHouse

The shift in the fish’s diet is having significant downstream impacts on the environment and climate.

A database client supporting 90+ engines sounds like a dependency-management problem disguised as a UI. My late-night question was simpler: how much of that complexity does t8y2/dbx carry before the first connection? The interesting claim is its small footprint—around 20 MB—combined with desktop, CLI, Docker, AI, and MCP Server modes. That is a much different architecture from shipping one heavy client per database vendor. The real test is not today’s +420 stars; it is startup latency, resident memory, and whether an unused adapter stays out of the hot path. Under the Hood The likely execution model is a shared core with database-specific drivers around it. The desktop interface, CLI, Docker image, and MCP endpoint become different front doors to the same connection and query layers. That design has two useful consequences: Connection handling and query behavior can stay consistent across interfaces. New database support does not require duplicating authentication, result formatting, or export logic. The edge case is driver loading. If all 90+ integrations initialize eagerly, startup and memory usage will grow quickly. Lazy loading is therefore more important than the headline database count. A Minimal Measurement Pass After downloading a release binary, I used this deliberately boring check: chmod +x ./dbx /usr/bin/time -v ./dbx --help 2>&1 \ | grep -E 'Elapsed|Maximum resident' For a source checkout, the first useful inspection is: git clone https://github.com/t8y2/dbx.git cd dbx find . -maxdepth 2 \( -name 'go.mod' -o -name 'Cargo.toml' -o -name 'Dockerfile' \) -print This avoids guessing the build system and immediately exposes whether the advertised modes are separate binaries, containers, or wrappers. Trade-offs I Would Watch A compact binary does not guarantee a compact running process. TLS libraries, database drivers, schema introspection, query history, and result grids can dominate memory after startup. MongoDB and Redis also do not fit neatly into a relat

I built a checker for a configuration directory. The time went not into adding rules, but into deciding what not to add . Things you could detect are easy to think of. That was never the constraint. One false positive is enough to get the tool thrown out A checker is asymmetric. A miss goes unnoticed. The cost is only that you did not learn something you could have. A false positive stops the reader and demands a decision: is this actually wrong? And once someone has been burned, they read every finding with suspicion . Twice, and the tool comes out of CI. So a checker that calls a valid configuration broken is worse than no checker. Better ten rules with no false positives than thirty with one. That is obvious in the abstract and hard in practice, because while you are writing the code, every "oh, I could check that too" pulls in the other direction. No citation, no rule So I fixed one condition for adding a rule: Only check what the official documentation states outright — as an error, as skipped, or as ignored. If the documentation does not say it, the rule does not go in, however wrong the pattern looks. What this buys is that the judgement stops living in my memory. "I'm fairly sure that form was invalid" is not a citation, and my memory goes stale the moment the tool it describes releases a new version. In the implementation, every finding carries its reason: export interface Finding { severity : " error " | " warn " ; file : string ; line ?: number ; /** what is wrong, in one sentence */ message : string ; /** why that can be claimed — includes the source URL */ because : string ; } Making because required is the point. A rule you cannot justify cannot be written , because the type will not let you leave the field out. If no source comes to mind, the rule never gets implemented. The tests enforce it too: for ( const f of findings ) { if ( ! f . because . includes ( " https:// " )) fail ( `no source: ${ f . message } ` ); } One finding without a source URL fai

The grid has been straining under the weight of new AI data centers, and that has utilities courting fusion startups.

If you’ve had to fill up your vehicle’s gas tank or buy a plane ticket lately, you’ve probably felt the effects of rising fossil-fuel prices. But farmers buying fertilizer for their crops are especially aware of just how far the ripple effects of the conflict in Iran have spread. Fertilizer prices have been on a…

The World Meteorological Organization is warning that the climate phenomenon that has the Pacific running a fever will last until at least February, with dire consequences for weather worldwide.

The Builders Stage is returning to TechCrunch Disrupt, bringing together founders, startup operators, and investors for practical conversations on what it takes to build and scale.

Fervo's deal with Google could expand to 1 gigawatt of geothermal power, enough to supply a very large AI data center in Utah.

September is full of opportunities to expand your knowledge of the night sky. Mark your calendars.

As he steps down from his post as Apple CEO today, Tim Cook leaves behind a mostly positive environmental record. As far as billionaire tech moguls and the environment go, Cook will likely be remembered as one of the better ones - certainly far from the worst. But it will become harder for the company […]

Newlight has raised a $9 million seed round and completed an 8,500-nautical-mile test run from Singapore to Ghana.

Fertilizer is crucial for the global food supply, but making it uses a lot of energy and produces a lot of emissions. Some companies hope microbes can help. A growing body of research shows that seeding the soil around a crop’s roots with beneficial microbes can help feed the plant, providing crucial nitrogen to help…

“One more warning sign here of what we're facing in a warmer world.”

(Translation of my Japanese article on Zenn.) This is part 4 of a series where I keep delegating implementation to AI without being able to read the code, building a vulnerability triage CLI called triage-lens. This installment is about distribution rather than the tool's internals: the tool had been sitting on GitHub, and I published it to PyPI so a single pip install triage-lens brings it in. A confession first. Shipping took more nerve than any of the feature work did. And three "leaks" actually turned up right before release. From the installing side to the installed side I can't read code, but I have typed pip install before. Years ago I dabbled in Python out of curiosity, and the one thing that stuck was the experience of a useful tool arriving in one line. Now that I'm the one publishing, the other side of that one line finally became concrete. Someone builds a thing, shapes it into a package, and puts it on the public shelf called PyPI. That's why it installs in one line anywhere in the world. My turn to put something on the shelf. I delegated the release work to AI too: package metadata, the release workflow, and one thing I insisted on. Instead of an API token, authentication to PyPI uses Trusted Publishing (OIDC). Nothing like a long-lived password gets stored anywhere; you declare "trust publishes from this workflow in this GitHub repository" and that's it. A secret you never hold is a secret that can't leak. The pre-release check caught three real ones In this project, nothing goes out to a public repository without passing a mechanical check. Procedures and tests, not eyeballs, verify that no personal or development-only information is mixed in. For three releases it came up empty. That's what insurance looks like. On the fourth run it caught something real. Three somethings. First, test code had slipped into the distribution. The packaging tool's default behavior had a path where the whole development test suite gets bundled along. I was about to scat

If you run ClickHouse in production, you're probably on 26.3 LTS. And now 26.8 LTS has been announced, which means the LTS-to-LTS upgrade conversation starts again. Here's the thing most release posts skip: this is not a one-release hop. Going from 26.3 LTS to 26.8 LTS means crossing 26.4, 26.5, 26.6 and 26.7 as well. Every breaking change in those four releases applies to you, and some of the ones most likely to ruin your day aren't in 26.8 at all. So instead of writing another "here are the 26.8 features" post, I wanted to write the thing I'd actually want before scheduling this upgrade: what breaks, what silently changes, what order to do things in, and what you get for the trouble. A note on release timing As of writing (27 August 2026), 26.8 has been announced but is not fully released yet. The release branch is cut and versioned (v26.8.1.1-lts), but the tag and Docker images have not been published yet, and the upstream changelog still marks the 26.8 section as in progress. By the time you read this, the tag has probably landed. Check for yourself: curl -s https://raw.githubusercontent.com/ClickHouse/ClickHouse/master/utils/list-versions/version_date.tsv \ | awk -F '\t' '$1 ~ /^v26\.8\./ {print "26.8 is released - newest: " $1 " (" $2 ")"; f=1; exit} END {if (!f) print "26.8 not released yet"}' version_date.tsv is the list ClickHouse maintains of every released version and its date, so this is the most direct answer available - no auth, no rate limit, nothing to download. As of writing it prints 26.8 not released yet . Worth knowing: the Docker image will lag whatever that command tells you. The Docker Official Images repo trails the GitHub tags by a few patch versions - clickhouse:lts currently resolves to 26.3.20.7 even though 26.3.24.4 has already shipped. So don't treat a missing image as evidence the release hasn't happened. Either way, the timing works in your favour. Historically ClickHouse LTS releases pick up several patch releases quickly - 26.7 had

MIT Technology Review’s How To series helps you get things done. Your thermostat may not look like a power plant. Neither does your electric vehicle, home battery, or HVAC system. But utility and energy companies increasingly want to treat them like one. A virtual power plant, or VPP, is a collection of household devices (such…

EVs account for under 10% of total new-vehicle sales in the US, and the numbers are declining. From a climate perspective, that’s pretty dismal, especially because the transportation sector is the single biggest source of greenhouse-gas emissions in the country. One thing that could help turn that around? Slate Auto’s new truck—a vehicle that seems…

The eclipse will obscure about 93 percent of the moon’s surface. Here are the peak times and tips for the best viewing experience.

It's 8:52 AM and 86 degrees Fahrenheit (30 Celsius) where I live in Southern California. My husband just came back from a morning outing with our four-month-old. "How was the botanic garden?" I ask him. "It was okay. It was just too hot," he tells me. I didn't expect us to spend so much of […]