May 19, 2021, Brazil. In this illustration the homepage of the Ubuntu website is displayed on the computer screen.
Image: Rafael Henrique/Adobe Stock

Apache Druid is a real-time analytics database that was designed for lighting quick slice-and-dice analytics on massive sets of data. You can easily run Apache Druid from a desktop version of Linux – or a Linux server with a GUI – and then load data to begin to parse.

Apache Druid includes features such as:

  • Column-oriented storage
  • Native search indexes
  • Streaming and batch ingest
  • Flexible schemas
  • Time-optimized partitioning
  • SQL support
  • Horizontal scalability
  • Easy operation

Apache Druid is a great option for use cases that require real-time ingestion, fast queries and high uptime.

I’m going to walk you through the process of getting Apache Druid running on Pop!_OS Linux (though it can be run on any Linux distribution) and then show you how to load sample data.

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What you’ll need

The only things you’ll need to make this work are a running instance of Linux complete with a desktop environment and a user with sudo privileges.

That’s it. Let’s make some database magic.

How to install Java 8

At the moment, Apache Druid only supports Java 8, so we have to make sure it’s installed and set as the default. To install Java 8 on a Ubuntu-based desktop distribution, log into the machine, open a terminal window, and issue the command:

sudo apt install openjdk-8-jdk -y

After the installation completes, you then need to set Java 8 as the default. Do this with the command:

sudo update-alternatives --config java

You should see a list of all Java versions that are currently installed on the machine. Make sure to select the number that corresponds to Java 8.

A word on Apache Druid services

What we’re going to launch is a micro instance of Apache Druid, which requires 4 CPUs and 16GB of RAM. There are 6 different service configurations for Apache Druid, which are:

  • Nano-Quickstart: 1 CPU, 4GB RAM
  • Micro-Quickstart: 4 CPU, 16GB RAM
  • Small: 8 CPU, 64GB RAM
  • Medium: 16 CPU, 128GB RAM
  • Large: 32 CPU, 256GB RAM
  • X-Large: 64 CPU, 512GB RAM

Depending on the size of your data and needs. When you get into massive troves of data, it’s recommended that Apache Druid be deployed as a cluster. However, since we’re just getting introduced to Apache Druid, the micro instance will be just fine.

How to download and unpack Apache Druid

With Java installed, it’s time to download and unpack Apache Druid. Back at the terminal window, download the latest version (make sure to check the Apache Druid download page to verify this is the latest release) with the command:

wget https://dlcdn.apache.org/druid/0.22.1/apache-druid-0.22.1-bin.tar.gz

Unpack the downloaded file with:

tar xvfz apache-druid-0.22.1-bin.tar.gz

Change into the newly-created directory with:

cd apache-druid-0.22.1

Start the service with:

./bin/start-micro-quickstart

The Apache Druid service should launch without a problem. Do note, that you will not get your terminal back as the service runs until you cancel it with CTRL + C.

How to access the Apache Druid console

On the same machine that’s running Apache Druid, open a web browser and point it to http://localhost:8888. Unfortunately, Apache Druid is set up such that you cannot reach it from a remote machine, which is why we install it on a desktop machine.

The Apache Druid console will greet you (Figure A).

Figure A

Image: Jack Wallen/TechRepublic. The Apache Druid console is very clean and user-friendly.

How to load data

We’re going to load up a predefined sample of data, found in the quickstart/tutorial/directory. The sample is called wikiticker-2015-09-12-sampled.json.gz.

From the console, click Load Data on the top row. In the resulting window (Figure B), click Local Disk.

Figure B

Image: Jack Wallen/TechRepublic. There are several sources you can pull data from.
Image: Jack Wallen/TechRepublic. There are several sources you can pull data from.

Click Connect Data (on the right side of the window) and then, in the resulting sidebar (Figure C), type quickstart/tutorial as the base directory and wikiticker-2015-09-12-sampled.json.gz in the File Filter section.

Figure C

Image: Jack Wallen/TechRepublic. There are several sources you can pull data from.
Image: Jack Wallen/TechRepublic. Adding the tutorial data into the console.

Click Apply and you should see a fairly large amount of data appear in the main window (Figure D).

Figure D

Image: Jack Wallen/TechRepublic. Our data has been loaded.
Image: Jack Wallen/TechRepublic. Our data has been loaded.

Click Next: Parse Data at the bottom right and you’ll be presented with a listing of the data in a more readable format (Figure E).

Figure E

Image: Jack Wallen/TechRepublic. Our data is much more readable now.
Image: Jack Wallen/TechRepublic. Our data is much more readable now.

Click Next: Parse Time and you can view the data against particular timestamps (Figure F).

Figure F

Image: Jack Wallen/TechRepublic. Sorting the data according to timestamp.
Image: Jack Wallen/TechRepublic. Sorting the data according to timestamp.

Click Next: Transform and you can then perform per-row transforms of the column values to either create new columns or alter those that already exist.

Keep clicking through the data and, at any point, you can run queries and filter data as needed. In the Configure Schema section (Figure G), you can even specify the granularity of your queries and add dimensions and metrics.

Figure G

Image: Jack Wallen/TechRepublic. Configuring the schema for the imported data.
Image: Jack Wallen/TechRepublic. Configuring the schema for the imported data.

And that’s pretty much the basics of Apache Druid. Although we’ve only skimmed the surface of what this powerful data analytics platform can do, you should be able to get a pretty good feel of how it works by playing around with the sample data.

When you’re finished working, make sure to go back to the terminal window and stop the Apache Druid service with CTRL + C.

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