crun VS ClickHouse

Yep pretty much.

The executables bundle crun (a container runtime)[0], and a fuse implementation of squashfs and overlayfs. Appended to that is a squashfs of the image.

At runtime the squashfs and overlayfs are mounted and the container is started.

[0]: https://github.com/containers/crun

cpu: 4 disk: 60 memory: 12 arch: host hostname: colima autoActivate: true forwardAgent: false # I only tested this with 'docker', not 'containerd': runtime: docker kubernetes: enabled: false version: v1.24.3+k3s1 k3sArgs: [] network: address: true dns: [] dnsHosts: host.docker.internal: host.lima.internal # Added: # - containerd-snapshotter: true (meaning containerd will be used for pulling images) # - default-runtime / runtimes: crun (instead of the default 'runc') docker: default-runtime: crun features: buildkit: true containerd-snapshotter: true runtimes: crun: path: /usr/local/bin/crun vmType: vz rosetta: true mountType: virtiofs mountInotify: false cpuType: host # This provisioning script installs WasmEdge and builds crun with wasmedge support: provision: - mode: system script: | [ -f /etc/docker/daemon.json ] && echo "Already provisioned!" && exit 0 echo "Install system updates:" apt-get update -y apt-get upgrade -y echo "Install WasmEdge and crun dependencies:" # NOTE: packages curl git python3 already installed: apt-get install -y make gcc build-essential pkgconf libtool libsystemd-dev libprotobuf-c-dev libcap-dev libseccomp-dev libyajl-dev libgcrypt20-dev go-md2man autoconf automake criu apt-get clean -y - mode: user script: | [ -f /etc/docker/daemon.json ] && echo "Already provisioned!" && exit 0 echo "Installing WasmEdge:" curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | sudo bash -s -- -p /usr/local echo echo "`wasmedge -v` installed!" # NOTE: I failed to Configure Wasmtime properly - turned off for now: #echo "Installing Wasmtime:" #curl -sSf https://wasmtime.dev/install.sh | bash #sudo cp .wasmtime/bin/* /usr/local/bin/ #rm -rf .wasmtime #echo "`wasmtime -V` installed!" echo "Install crun:" git clone https://github.com/containers/crun cd crun ./autogen.sh #./configure --with-wasmedge --with-wasmtime ./configure --with-wasmedge make sudo make install crun -v echo "crun installed! Replacing runc with crun:" # NOTE: replacing runc with runc is to simplify containerd config TRC=`which runc` sudo rm -rf $TRC sudo cp `which crun` $TRC echo "Configuring containerd:" sudo mkdir -p /etc/containerd/ containerd config default | sudo tee /etc/containerd/config.toml >/dev/null echo "Restarting/reloading docker/containerd services:" sudo systemctl daemon-reload sudo systemctl restart containerd # As soon as Colima writes its /etc/docker/daemon.json file (right after this provisioning script), # it will also start the Docker daemon. If we stop Docker here, the changes will actually take effect: sudo systemctl stop docker sshConfig: true mounts: [] env: {}

On this note, I was really surprised to find Red Hat's OCI runtime is written in C: https://github.com/containers/crun

Is anyone working on a Rust version?

It's interesting that, in light of things like this, you still see large software companies adding support for new components written in non-memory safe languages (e.g. C)

As an example Red Hat OpenShift added support for crun(https://github.com/containers/crun) this year(https://cloud.redhat.com/blog/whats-new-in-red-hat-openshift...), which is written in C as an alternative to runc, which is written in Go(https://github.com/opencontainers/runc)...

Kubernetes needs an OCI runtime to run containers with. Crun is one implementation it can use.

Docker also appears to be able to use crun for it's engine as well. https://github.com/containers/crun/issues/37

crun

Looks like https://github.com/containers/crun/issues/255 - start there.

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;