gitlab-runner VS ClickHouse

#!/bin/bash # ### Script to initialize a GitLab runner on an existing AWS EC2 instance with NVME disk(s) # # - script is not interactive (can be run as user_data) # - will reboot at the end to perform NVME mounting # - first NVME disk will be used for GitLab custom cache # - last NVME disk will be used for Docker data (if only one NVME, the same will be used without problem) # - robust: on each reboot and stop/start, disks are mounted again (but data may be lost if stop and then start after a few minutes) # - runner is tagged with multiple instance data (public dns, IP, instance type...) # - works with a single spot instance # - should work even with multiple ones in a fleet, with same user_data (not tested for now) # # /!\ There is no prerequisite, except these needed variables : MAINTAINER=zenika RUNNER_NAME="majestic-runner" GITLAB_URL=https://gitlab.com/ GITLAB_TOKEN=XXXX # prepare docker (re)install sudo apt-get -y install apt-transport-https ca-certificates curl gnupg lsb-release sysstat curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list >/dev/null sudo apt-get update # needed to use the docker.list # install gitlab runner curl -L "https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh" | sudo bash sudo apt-get -y install gitlab-runner # create NVME initializer script cat </home/ubuntu/nvme-initializer.sh #!/bin/bash # # To be run on each fresh start, since NVME disks are ephemeral # so first start, start after stop, but not on reboot # inspired by https://stackoverflow.com/questions/45167717/mounting-a-nvme-disk-on-aws-ec2 # date | tee -a /home/ubuntu/nvme-initializer.log ### Handle NVME disks # get NVME disks bigger than 100Go (some small size disk may be there for root, depending on server type) NVME_DISK_LIST=\$(lsblk -b --output=NAME,SIZE | grep "^nvme" | awk '{if(\$2>100000000000)print\$1}' | sort) echo "NVME disks are: \$NVME_DISK_LIST" | tee -a /home/ubuntu/nvme-initializer.log # there may be 1 or 2 NVME disks, then we split (or not) the mounts between GitLab custom cache and Docker data export NVME_GITLAB=\$(echo "\$NVME_DISK_LIST" | head -n 1) export NVME_DOCKER=\$(echo "\$NVME_DISK_LIST" | tail -n 1) echo "NVME_GITLAB=\$NVME_GITLAB and NVME_DOCKER=\$NVME_DOCKER" | tee -a /home/ubuntu/nvme-initializer.log # format disks if not sudo mkfs -t xfs /dev/\$NVME_GITLAB | tee -a /home/ubuntu/nvme-initializer.log || echo "\$NVME_GITLAB already formatted" # this may already be done sudo mkfs -t xfs /dev/\$NVME_DOCKER | tee -a /home/ubuntu/nvme-initializer.log || echo "\$NVME_DOCKER already formatted" # disk may be the same, then already formated by previous command # mount on /gitlab-host/ and /var/lib/docker/ sudo mkdir -p /gitlab sudo mount /dev/\$NVME_GITLAB /gitlab | tee -a /home/ubuntu/nvme-initializer.log sudo mkdir -p /gitlab/custom-cache sudo mkdir -p /var/lib/docker sudo mount /dev/\$NVME_DOCKER /var/lib/docker | tee -a /home/ubuntu/nvme-initializer.log ### reinstall Docker (which data may have been wiped out) # docker (re)install sudo apt-get -y reinstall docker-ce docker-ce-cli containerd.io docker-compose-plugin | tee -a /home/ubuntu/nvme-initializer.log echo "NVME initialization succesful" | tee -a /home/ubuntu/nvme-initializer.log EOF # set NVME initializer script as startup script sudo tee /etc/systemd/system/nvme-initializer.service >/dev/null <

GitLab team member here, thanks for sharing.

> Still not a big fan of how stiff Yaml pipelines feel in Gitlab CI

Maybe the pipeline editor in "Build > Pipeline editor" can help with live linting, or more advanced features such as parent-child pipelines or merge trains.

If you need tips for optimizing the CI/CD pipeline, suggest following these tips in the docs https://docs.gitlab.com/ee/ci/pipelines/pipeline_efficiency.... or a few more tips in my recent talk "Efficient DevSecOps pipelines in cloud-native world", slides from Chemnitz Linux Days 2023 in https://docs.google.com/presentation/d/1_kyGo_cWi5dKyxi3BfYj...

> and that tickets for what seems like a simple feature [1] hang around for years, but it is nice.

Thanks for sharing. (FYI for everyone) The linked issue suggests a Docker cache cleanup script, which might be helpful. https://gitlab.com/gitlab-org/gitlab-runner/-/issues/27332#n... -> https://docs.gitlab.com/runner/executors/docker.html#clear-t...

If only competitors could do better...

https://gitlab.com/gitlab-org/gitlab-runner/-/issues/2797

GitLab MR for the CACHE_COMPRESSION_LEVEL implementation

And in general just making caching stuff easier. I feel like it is unnecessarily complicated for example to cache apt-get in Gitlab which I assume makes most people not do it.

https://gitlab.com/gitlab-org/gitlab-runner/-/issues/991#not...

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;