Druid VS Scylla

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.

Fortunately, it is super easy to build your own leveraging the binary distribution and existing docker.sh.

Then I decided to talk to my boss and ask him if I could work with the YARG guys and the condition was to create something cool enough to implement ScyllaDB (NoSQL Wide-column Database) since I'm working as a Developer Advocate there. You won't believe how the simplicity and scalability brought by ScyllaDB perfectly fit the needs of YARG.in!

ScyllaDB now has initial support for a new replication algorithm: tablets...

Scylla which is a C++ implementation of the Cassandra distributed K:V store https://github.com/scylladb/scylladb

What you say is impossible, we pretty successfully apply at ScyllaDB (see https://github.com/scylladb/scylladb/commits/master).

I'm not sure 100% of the commits compile & pass all tests - there may be some mistakes - but generally we're in a pretty good state, and the clean git log is being successfully used for bisecting.

If you want even larger scale - if I understand correctly, the Linux kernel practices a similar thing, which is where we got this practice from (ScyllaDB founders came from kernel development). And since Git was originally created to help developing Linux - that's where you want to look for good practices.

Examples: - Vitess - MySQL cluster - YugabyteDB - ScyllaDB - Couchbase - ArangoDB