tbragin
ClickHouse gets lazier (and faster): Introducing lazy materialization
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Make Before Break: Faster Scaling Mechanics for ClickHouse Cloud
Open Sourcing Kubenetmon
How We Rebuilt the Cloud Console (While Running It)
Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work: https://github.com/ClickHouse/ClickHouse/issues/54864#issuec...
Databricks now has fully hosted too, but Snowflake still did not go the other way to introduce BYOC. Newer entrants, however - RedPanda, AnyScale, ClickHouse... - are going the way of providing both BYOC and pure SaaS options in their cloud. There are many other reasons to prefer BYOC, legal, security, data privacy, but network egress costs is one of them.
Thank you! Glad it helped you.
ClickHouse is in particular well-optimized to take on real-time analytical workloads from BigQuery - both functionally and from a cost perspective.
For anyone wanting to try it yourself, there guide to follow when syncing or migrating workloads from BigQuery to ClickHouse, with some benchmarks and back-of-the-envelope cost comparisons: https://clickhouse.com/blog/clickhouse-bigquery-migrating-da...
This is an open source benchmark - we'd love contributions from Timescale enthusiasts if we missed something: https://github.com/ClickHouse/ClickBench/
See results from Gitlab benchmarking ClickHouse vs TimescaleDB: https://gitlab.com/gitlab-org/incubation-engineering/apm/apm...
Key findings:
* ClickHouse has a much smaller data volume footprint in all cases by almost a factor of 10.
* There are very few ClickHouse queries that have >1s latency at q95. TimescaleDB has multiple >1s latencies, including a few in the range of 15-25s.
Disclaimer: I work at ClickHouse
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.
Disclaimer: I work at ClickHouse.
Gemini: Google's Quantum Leap in AI
ANN Vector Search with SQL-Powered LSH and Random Projections
I also saw astute comments from folks that recognize that if the product manager role does not exist as a distinct role, someone else in the organization has to take on the work of determining what to build and when, based on market and customers needs - usually engineering or product marketing. There is nothing wrong with that, btw, just different way to slice the same pie of responsibilities. Finally, several folks rightfully pointed out that a product manager (person accountable for building the right product for the market) is very different from a project or program manager (a person coordinating internal activities).
The only thing I'd add here is that product management roles can also be very different, depending on the stage of the company (early stage vs late stage), the type of product it builds (e.g. B2B vs B2C), and the industry vertical. Looking at the author's LI profile, he seems to work for larger companies, in the financial sector, in the UK. Perhaps in that world (which I perceive to be a fairly rigid, regulated industry), the role of the product manager is quite different from what I have personally experienced working as a PM in the Bay Area for early and mid stage startups.
+1 to that! Welcome to the ClickHouse community!
Thank you! Looks really interesting!
I personally agree that real-time OLAP databases have potential to better serve workloads currently in Postgres or cloud data warehouses that need real-time ingest and analytical queries. And simplifying developer experience on top of that, so you don't have to learn about all the details of a powerful database, really speeds up developer velocity.
I'm curious, how you see your project differs from GraphJSON (https://www.graphjson.com/) and Tinybird (https://www.tinybird.co/)?
Congratulations again on the launch!
I joined Mastodon myself in the past year, and at least so far I have not been able to see much engagement there, though I am still trying to figure it out. The biggest issue I see is lack of cross-server search, aside from by tag, but also just not enough post activity. Maybe I am still not following the right people.
The Slack redesign is dividing opinion
For me, apparently I like to stick out my thumb and then index finger, when I say "first (second, third)", which triggers thumbs up first and baloons second. Really? Totally unhelpful and awkward.
As far as specific task delegation, if you are looking at your task list, I'd recommend a modification of "Eisenhower Matrix", that looks like this:
- Important / One-time Tasks - Keep for yourself.
- Important / Frequently Repeating Tasks - Delegate with high priority.
- Not important / Frequent tasks - Delegate with low priority.
- Not important / One time tasks - Delete.