deploy-cloudrun VS ClickHouse

Examples for products in this category are: Google Cloud Run, AWS App Runner, Azure Container Apps. Each has different scalability, cost, and integration trade-offs.

Cloud Run is a managed platform that enables you to run container based workloads on top of Google infrastructure. Cloud Run automates many of the above steps and allows you to focus on developing and deploying updates to your application.

Serverless computing was also introduced, where the developers focus on their code instead of server configuration.Google offers serverless technologies that include Cloud Functions and Cloud Run.Cloud Functions manages event-driven code and offers a pay-as-you-go service, while Cloud Run allows clients to deploy their containerized microservice applications in a managed environment.

The quickest way is to deploy to Cloud Run. The service will use Dockerfile to build the production image. You can even omit the GOOGLE_APPLICATION_CREDENTIALS env var as these are in GCP’s projects by default.

In our company we use Google Cloud Run to deploy web applications, and every app is built into a docker image. For now we use the default memory limit by Cloud Run which is 256 MB per container. Recently we started to notice that the part of applications go beyond this limit, causing a container to restart and in some cases even resulting to downtime of a service.

It's perhaps a little fiddlier than other options, but you can probably host it on Google Cloud Run and it would fall within the free tier.

You can see more here on how to use the google cloud run github actions.

I wonder what the aversion is to using a plain old server / vps. It's really not that difficult to deploy nowadays [0][1][2][3] and I'd rather get an $8 bill every month as insurance than ever worry about shit like OP just went through. It'll probably be more performant anyway due to cold starts and "edge" still having to hit us-east-1 for data.. cache your static files with Cloud Flare/Front. People are always surprised by how much traffic a single VPS can take[4] and believe it all has to be serverless to be web scale. I believe HN still runs on a single core or something.

There's a ton of places to get cloud credits as well, too many to link, so just Bing™ it

[0] https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_...

[1] https://aws.amazon.com/apprunner/

[2] https://cloud.google.com/run

[3] https://render.com/

[4] https://news.ycombinator.com/item?id=34676186

Google Cloud Run

Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here: https://github.com/ClickHouse/ClickHouse/issues/54864

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...

In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.

When these limits are reached, the CI stops working, and we have to remove the bloat: https://github.com/ClickHouse/ClickHouse/issues/61121

Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.

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

ClickHouse: A fast open-source column-oriented database management system. ClickHouse is designed for real-time analytics on large datasets and excels in high-speed data insertion and querying, making it ideal for real-time monitoring and reporting.

Today we're going to create an UDF (User-defined Function) in Golang that can be run inside Clickhouse query, this function will parse uuid v1 and return timestamp of it since Clickhouse doesn't have this function for now. Inspired from the python version with TabSeparated delimiter (since it's easiest to parse), UDF in Clickhouse will read line by line (each row is each line, and each text separated with tab is each column/cell value):

For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules.

Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.

Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot.

Please check this PR from the proton team: https://github.com/ClickHouse/ClickHouse/pull/54870

curl https://clickhouse.com/ | sh

But I continue to find garbage in some of our CI scripts.

Here is an example: https://github.com/ClickHouse/ClickHouse/pull/58794/files

The right way is to:

- always pin versions of all packages;