MariaDB VS Druid

MariaDB

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 *

Apache Druid

Apache Druid: Focused on real-time analytics and interactive queries on large datasets. Druid is well-suited for high-performance applications in user-facing analytics, network monitoring, and business intelligence.

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.

https://github.com/apache/druid

It's always a question of tradeoffs.

The awesome-selfhosted project has a nice list of open-source analytics projects. It's really good inspiration to dig into these projects and find out about the technology choices that other open-source tools in the space have made.

Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Here’s my analytical pipeline for Github stars data using Nifi, Kafka and Druid.

Apache Druid is part of the modern data architecture. It uses a special data format designed for analytical workloads, using extreme parallelisation to get data in and get data out. A shared-nothing, microservices architecture helps you to build highly-available, extreme scale analytics features into your applications.

Apache Druid

But then you realize there are other databases out there focused specifically on analytical use cases with lots of data and complex queries. Newcomers like ClickHouse, Pinot, and Druid (all open source) respond to a new class of problem: The need to develop applications using endpoints published on analytical queries that were previously confined only to the data warehouse and BI tools.

Datadog's product is a bit too close to Apache Druid to have named their design system so similarly.

From https://druid.apache.org/ :

> Druid unlocks new types of queries and workflows for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data.

Maybe rare for someone to be seeking their first coding job at that age. But plenty of us are in our 50s or older and still coding up a storm. And not necessarily ancient tech or anything. My current project exposes analytics data from Apache Druid and Cassandra via Go microservices hosted in K8s.