MariaDB VS ClickHouse

MariaDB [1] is a community developed fork of MySQL, so you can feel free to use that in place of MySQL.

Another alternative would be Percona [2] which is a drop-in replacement for Oracle MySQL.

About PostgreSQL I haven't had that much of personal experience with it, but for sure I plan to learn it, because it has been battle-tested for years with lots of well-known projects, such as Django framework, for instance.

[1] https://github.com/MariaDB/server

[2] https://github.com/percona/percona-server

aka mariadb-dumpslow (aka mysqldumpslow) or pt-query-digest

My recollection is that it was created when Oracle took over MySQL in order to continue to have a community-driven GPLv2 "mysql compatible" server; I was expecting them to be more different than they are, but for comparison one is a "clean" GPLv2" and the other has more words and a link to oracle.com: https://github.com/mysql/mysql-server/blob/mysql-8.0.31/LICE... https://github.com/MariaDB/server/blob/mariadb-10.11.1/COPYI...

git clone --recursive --depth 1 --branch 10.5 https://github.com/MariaDB/server.git cmake . # needs cmake3 make make install

RUN git clone \ --branch mariadb-10.5.15 \ --depth 1 \ --recurse-submodules \ https://github.com/mariadb/server.git \ ~/server

Hi everyone! Our team just released EdgelessDB, an open-source database built on MariaDB that runs completely inside Intel SGX enclaves. As storage engine, it uses RocksDB with a custom encryption engine. The engine uses AES-GCM and is optimized for RocksDB’s specific SST file layout and the enclave environment. It has some nice properties like global confidentiality and verifiability and it considers strong attackers like malicious admins or rootkits. It also delivers rather low overheads (<10% for the TPC-C benchmark on Azure). In short: all data is only ever decrypted inside the enclave. This is different from other databases, where data and corresponding keys are processed in the clear in memory. We believe this is useful because (1) it’s very secure and (2) it enables some interesting use cases, like secure data pooling between parties. If you’re interested in trying it out: here’s a quickstart guide. In essence, you can run the Docker image with a single command on any recent Intel Xeon with SGX. Code and more info can be found on GitHub. Would be great to get your feedback on this :-)

MariaDB has 268 contributors vs 83 contributors to MySQL *

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;