ArangoDB VS Druid

ArangoDB

... or working in an environment or on a problem for which functional patterns apply.

Suppose you are writing a "CRUD" app that writes to a relational database, how do you apply functional programming to that? The whole point of an application like that is that it makes side effects.

In some cases you can break those problems down into functional pieces. Consider Python drivers for a product like

https://www.arangodb.com/

One major problem is that you want drivers that work synchronously and asynchronously, the structure of the average api call is something like

   def query(parameters):

First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.

Some databases like ArangoDB (https://www.arangodb.com/) allow you to use Javascript instead of SQL.

However, using a type-unsafe, turing-complete language introduces type unsafety and turing-complete problems to the query layer; the usual problems we know and love, such as infinite loops, runtime type errors, exceptions, and the like.

Personally, I'm looking forward to a WASM runtime for databases -- so we can run webassembly on the database. This COULD be carefully designed to be statically checked and, possibly, make it really hard to write runaway loops.

When it comes to graphdb's, my favorite is still ArangoDB, definitely worth checking out if you are looking for alternatives.

https://www.arangodb.com

Personally my favorite db for pet projects is

https://www.arangodb.com/

I think you hear very little about it because ADB users see it as a "secret weapon" to crush their competitors with. I've done large ontology work (MESH and other health ontologies) and IoT work (keep several years of sensor readings for sensors in my house) and workflow systems (select interesting HN articles or jobs I want to apply to) and it has never let me down. I haven't run a real instance serving customers in the cloud though.

For the last few years every eng manager I have worked with has been a fan of

https://www.postgresql.org/

In the early 2000s I thought it overpromised and underdelivered and called it CrashGreSlow but after MySQL got bought by Oracle the pgsql team has worked hard to improve it I think it is great today. It supports all kinds of advanced features such as stored procs, full-text search, JSON equivalent fields, etc.

Hi! I recently came across ArangoDb and used in some POCs, but I really want to know if someone here already used it in a Real World environment or even if chose to not use in a production environment. So... have you ever used ArangoDb? Why? Why not?

For mutual friends, we can build a social graph for every user. Each node in the graph will represent a user and a directional edge will represent followers and followees. After that, we can traverse the followers of a user to find and suggest a mutual friend. This would require a graph database such as Neo4j and ArangoDB.

ArangoDB

This a NOSQL database built for high availability and high scalability, a perfect fit for implementing persistence in microservices. ArangoDB is an open source native multi-model database that supports graph, document and key-value data models allowing users to freely combine all data models in a single query. Dive deeper into this database and its features here.

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.