hosts VS ClickBench

Not by default but a blocklist can be found here https://github.com/StevenBlack/hosts

While it is now unblocked, the Steven Black list has been blocking a lot of innocent CDNs.

jQuery: https://github.com/StevenBlack/hosts/issues/2520

How does this compare to using a hosts file with known ad servers?

like: https://github.com/StevenBlack/hosts

I use the Hosts file to block a ton of ads and that works really well. https://github.com/StevenBlack/hosts Something worth considering if your ad blocker isn't working well.

The no-tracking project used by RaspAP is shutting down, so we took the opportunity to search for open source blocklist alternatives. Among the best is Steven Black's hosts list: https://github.com/StevenBlack/hosts

No idea, api.radar.io is on the block list since January 2020.

The commit's comment is "major update from adaway.org"

https://github.com/stevenblack/hosts/commit/4fa0470

You can also use a declarative adblocker like uBlock Origin Lite [1], which only provides the browser with a list of elements to filter, but doesn't have any permissions to read content or perform requests. Or simply use your hosts file to apply OS-wide filtering with no browser add-ons needed: https://github.com/StevenBlack/hosts

Be aware that if you use these "passive" blocking methods, there are some sites like YouTube where you will see ads, because in these cases it's necessary to actually manipulate page content to hide them. What you can do is use a traditional adblocker but enable it only for these few sites where the declarative approach is not enough, take a look at [2] for more details.

[1] https://github.com/uBlockOrigin/uBOL-home

[2] https://seirdy.one/posts/2022/06/04/layered-content-blocking...

https://github.com/StevenBlack/hosts if you want to do it on your PC.

When I select my custom hosts file, it basically breaks internet. However, if I choose a custom hosts file that is a copy of the dev's default, or if I just add a few lines to it, it will work. If I add too many lines, or use a different hosts file altogether (like the ones recommended by the dev), all connectivity breaks. Of course the latest official LetMeBlock is installed and mDNSResponder killed/restarted. I'm using Dopamine on A12+.

TimescaleDB primarily serves operational use cases: Developers building products on top of live data, where you are regularly streaming in fresh data, and you often know what many queries look like a priori, because those are powering your live APIs, dashboards, and product experience.

That's different from a data warehouse or many traditional "OLAP" use cases, where you might dump a big dataset statically, and then people will occasionally do ad-hoc queries against it. This is the big weather dataset file sitting on your desktop that you occasionally query while on holidays.

So it's less about "can you store weather data", but what does that use case look like? How are the queries shaped? Are you saving a single dataset for ad-hoc queries across the entire dataset, or continuously streaming in new data, and aging out or de-prioritizing old data?

In most of the products we serve, customers are often interested in recent data in a very granular format ("shallow and wide"), or longer historical queries along a well defined axis ("deep and narrow").

For example, this is where the benefits of TimescaleDB's segmented columnar compression emerges. It optimizes for those queries which are very common in your application, e.g., an IoT application that groups by or selected by deviceID, crypto/fintech analysis based on the ticker symbol, product analytics based on tenantID, etc.

If you look at Clickbench, what most of the queries say are: Scan ALL the data in your database, and GROUP BY one of the 100 columns in the web analytics logs.

- https://github.com/ClickHouse/ClickBench/blob/main/clickhous...

There are almost no time-predicates in the benchmark that Clickhouse created, but perhaps that is not surprising given it was designed for ad-hoc weblog analytics at Yandex.

So yes, Timescale serves many products today that use weather data, but has made different choices than Clickhouse (or things like DuckDB, pg_analytics, etc) to serve those more operational use cases.

We tested with 43 Clickbench SQL queries. Queries on the Variant columns are about 10% slower than those on pre-defined static columns, and 8 times faster than those on JSON columns. (For I/O reasons, most cold runs on JSONB data failed with OOM.)

I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench

It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.

I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398

Indeed, ClickHouse results were run on an older instance type of the same family and size (c5.4xlarge for ClickHouse and c6a.4xlarge for Timescale), so if anything ClickHouse results are at a slight disadvantage.

This is an open source benchmark - we'd love contributions from Timescale enthusiasts if we missed something: https://github.com/ClickHouse/ClickBench/

Interesting project! Thank you for open sourcing and sharing. Agree that local and embedded analytics are an increasing trend, I see it too.

A couple of questions:

* I’m curious what the difficulties were in the implementation. I suspect it is quite a challenge to implement this support in the current SQLite architecture, and would curious to know which parts were tricky and any design trade-off you were faced with.

* Aside from ease-of-use (install extension, no need for a separate analytical database system), I wonder if there are additional benefits users can anticipate resulting from a single system architecture vs running an embedded OLAP store like DuckDB or clickhouse-local / chdb side-by-side with SQLite? Do you anticipate performance or resource efficiency gains, for instance?

* I am also curious, what the main difficulty with bringing in a separate analytical database is, assuming it natively integrates with SQLite. I may be biased, but I doubt anything can approach the performance of native column-oriented systems, so I'm curious what the tipping point might be for using this extension vs using an embedded OLAP store in practice.

Btw, would love for you or someone in the community to benchmark Stanchion in ClickBench and submit results! (https://github.com/ClickHouse/ClickBench/)

Disclaimer: I work on ClickHouse.

interesting. curious if anyone has benchmarked it relative to other dbs. like: https://benchmark.clickhouse.com/