helm-charts VS Druid

The example https://github.com/DataDog/helm-charts/blob/main/charts/datadog/values.yaml they provide list of all variables supported by the agent so should be pretty straight forward to copy paste the stuff that you need into your own values.yaml

Not to mention that, by default, they bill by the whole calendar month for each infrastructure and APM host. Scale your Kubernetes cluster up and then back down? That'll be an extra $18 + $36 per additional node (not $15 + $31 – that's the contract pricing, not the on-demand pricing), even if they were only online for a few days – even if they were only online for thirty seconds. Swap out a node? By default they bill by unique instances, not by number of instances, so they'll bill you for that, too.

If you ask them about it, they'll “happily” put you onto hourly on-demand billing (which seems to fix the unique vs. count thing, too), which is more expensive if you let something run on-demand for a whole month... but isn't the point of an on-demand service that you're not running it for a whole billing period?

Not to mention that their agent logs fairly noisily, and of course its logs count toward your quota! I upgraded a cluster without also upgrading the agent, and didn't notice for about a week that each agent was happily spamming away about some long-deprecated Kubernetes API no longer being available[0]. At $2.55/million log lines and fewer than a million lines logged, this was not a costly mistake, but it's the principle of the thing. Why should an incompatibility in their agent (which their dashboard could specifically alert about, but doesn't!) cost me money?

[0] https://github.com/DataDog/helm-charts/issues/620#issuecomme...

After that, we have to edit values.yaml file, which will be used as a configuration file used by datadog agent running in the cluster. The fields I overrided(edited) are as below.

These are the commands to install the Datadog agent in your cluster using Helm v3 with the default values. Make sure to copy your API key from Datadog dashboard in the install command.

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.