25 April 2018 Engineering

Getting Started with the GCE Discovery Plugin on Google Cloud

By Sherry Ger

Introduction

The discovery module in Elasticsearch is responsible for finding nodes that belong to a cluster and electing a master node. The Google Compute Engine (GCE) discovery plugin uses GCE APIs to perform automatic unicast discovery. In this post, we'll work through setting up a three node Elasticsearch cluster in Google Cloud using the GCE discovery plugin, and create a custom image that can be shared among different projects.

Before we start

To follow along with this blog, there are two prerequisites:

After setting up the project ID and installing the SDK, we have a few more administrative items to do. First, set the default project:

gcloud config set project sherry-test-gce

Then login to Google Cloud:

gcloud auth login

You can also set the default region and zone at this time to omit the --zone and --region flags while using the gcloud tool. This is optional. Here's how:

gcloud config set compute/region us-west1-b

To simplify our tasks ahead, we will add the default project and region and omit them from rest of the commands. Also, all commands here that start with gcloud are executed from the local machine and not on the GCE instances.

A GCE instance with Elasticsearch and GCE discovery plugin

Setup Firewall Rules

We will begin with creating a firewall rule so that the nodes in our Elasticsearch cluster can communicate with each other. Elasticsearch uses the TCP transport module for internal communications; the default port range for the transport module is between 9300 - 9400. In addition, we will open up port 9200 to expose Elasticsearch APIs over HTTP.

gcloud compute firewall-rules create elasticsearch --direction=INGRESS \
  --priority=1000 --network=default --action=ALLOW \
  --rules=tcp:9200,tcp:9300-9400 --source-ranges=0.0.0.0/0

Create an instance using gcloud SDK

Now we are ready to set up a GCE instance:

gcloud compute instances create sherry-instance-gce-discovery \
  --machine-type=n1-standard-2 --subnet=default \
  --min-cpu-platform=Automatic \
  --tags=elasticsearch,http-server,https-server \
  --image=ubuntu-1604-xenial-v20180306 \
  --image-project=ubuntu-os-cloud \
  --boot-disk-size=10GB --boot-disk-type=pd-standard \
  --boot-disk-device-name=sherry-gce-discovery --scopes=compute-ro

The tags argument includes the Elasticsearch firewall rule along with http and https, which are optional. Once this process is complete, you should see the name of the instance, the zone it is created in, its internal and external IP, and the status of the instance.

A few things to note:

  1. The instance we created is called sherry-instance-gce-discovery
  2. The instance is running ubuntu-16.04.
  3. The instance is using --scopes=compute-ro. This enables the GCE discovery module to call required APIs.

Now, that we've created the instance, we need to connect to it:

gcloud compute ssh sherry-instance-gce-discovery

If this is the first time you are using gcloud ssh to access a GCE instance, it will create a ssh key for you.

Besides using the gcloud command line tool, you can connect to the instance using the Google Cloud Web console that is directly accessible from the browser. Another option is to use the external IP address of the instance:

ssh -i ~/.ssh/google_compute_engine 12.34.56.789

Install Java

We will need to install JDK 8 on our new instance:

sudo apt-get update
sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer

# Setting Java 8 as the default (optional)
sudo apt-get install oracle-java8-set-default

# Verify Java version
java -version

Install Elasticsearch and GCE discovery plugin

Installing Elasticsearch is the next task. Please see install Elasticsearch with Debian Package and install Elasticsearch with RPM for installation details. In our case, we will choose an Ubuntu based image:

wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-6.2.3.deb
sudo dpkg -i elasticsearch-6.2.3.deb

Once Elasticsearch is ready, install the GCE discovery plugin. By default, the elasticsearch-plugin command can be found at /usr/share/elasticsearch/bin for both deb and rpm distributions.

sudo /usr/share/elasticsearch/bin/elasticsearch-plugin install discovery-gce

Now, add the following configurations to the /etc/elasticsearch/elasticsearch.yml file:

# By default, the cluster name is elasticsearch
cluster.name: lannister

# Use ${HOSTNAME} to set the node name dynamically
node.name: ${HOSTNAME}

# Using GCE internal hostname to bind the network services
network.host: _gce:hostname_

# Set the minimum master eligible nodes to avoid the split brain issue
discovery.zen.minimum_master_nodes: 2

# Specific GCE discovery plugin settings
cloud.gce.project_id: sherry-test-gce
cloud.gce.zone: us-west1-b
discovery.zen.hosts_provider: gce

All of the GCE discovery plugin settings here are required. Let's take a quick look at them:

cloud.gce.project_id - The Google project ID that we are using

cloud.gce.zone - The GCE zone where the Elasticsearch nodes will live

discovery.zen.hosts_provider - We are using the GCE discovery mechanism

In the near future, we are planning to make the GCE discovery setup even easier by auto-discovering some of these configurations.

Start Elasticsearch

We are ready to start Elasticsearch and verify everything is working.

sudo /etc/init.d/elasticsearch start

Verify the instance is working as expected by tailing the Elasticsearch cluster log to ensure Elasticsearch has started:

tail /var/log/elasticsearch/lannister.log

Then use curl to connect to the node and verify the service is running:

curl ${HOSTNAME}:9200

Note, the above curl command can only run within the Google Cloud Platform. By default, nothing is opened to internet. Log off from the GCE instance as our work here is done.

Create a GCE custom image

We will use the instance that we built to make a custom image. We can share the image with other projects to create additional GCE instances with Elasticsearch and GCE discovery plugin installed. Although you can use the gcloud compute images export command to create a custom image from a virtual disk, we choose to use the manual method so we can clean up the files on the disk before packaging it.

Using our local environment, create a storage bucket to hold the disk image from our instance:

gsutil mb -p sherry-test-gce -c regional -l us-west1 gs://sherry-test-bucket/

We will stop the instance sherry-instance-gce-discovery as well. This is an optional step but ensures the integrity of the disk content in the snapshot.

gcloud compute instances stop sherry-instance-gce-discovery

Take a snapshot of the instance/disk:

gcloud compute disks snapshot sherry-instance-gce-discovery \
  --snapshot-names=sherry-gce-discovery-snapshot

Use the snapshot to build a new GCE disk that we will use to create an image later.

gcloud compute disks create sherry-gce-discovery-disk \
  --source-snapshot sherry-gce-discovery-snapshot

Make a temporary disk that is at least 50% larger than the original image disk size. We will store the raw image of the disk from the instance sherry-instance-gce-discovery and the compressed version that we will be creating on the temporary disk. Since our original disk is 10 GB, our temporary one will be 15 GB.

gcloud compute disks create sherry-gce-discovery-temp-disk --size 15GB

We will create a temporary GCE instance with the storage-rw scope and attach both the disk from the snapshot we made earlier and the temporary disk.

gcloud compute instances create sherry-temp-instance --scopes storage-rw \
  --disk name=sherry-gce-discovery-disk,device-name=sherry-gce-discovery-disk \
  --disk name=sherry-gce-discovery-temp-disk,device-name=sherry-gce-discovery-temp-disk

Connect to the new instance:

gcloud compute ssh sherry-temp-instance

Once on the instance, list the disks and disk partitions available:

ls /dev/disk/by-id/

Now, format and mount the temporary disk:

sudo mkdir /mnt/tmp
sudo mkfs.ext4 -F /dev/disk/by-id/google-sherry-gce-discovery-temp-disk 
sudo mount -o discard,defaults /dev/disk/by-id/google-sherry-gce-discovery-temp-disk /mnt/tmp

Mount the image disk so we can modify and removed files:

sudo mkdir /mnt/image-disk
sudo mount /dev/disk/by-id/google-sherry-gce-discovery-disk-part1 /mnt/image-disk

Delete the Elasticsearch data directory. Be default, it is located at /mnt/image-disk/var/lib/elasticsearch/nodes. Each new instance will create its own Elasticsearch data directory when the Elasticsearch service starts the first time.

Remove SSH key. The authorized_keys file is located in the /mnt/image-disk/home/[USER]/.ssh directory. This step is not required but highly recommended.

Unmount the image disk:

sudo umount /mnt/image-disk/

Create a disk.raw from the image disk on the temporary disk. Please note, it must be called disk.raw.

sudo dd if=/dev/disk/by-id/google-sherry-gce-discovery-disk of=/mnt/tmp/disk.raw bs=4096

Create a tar file of the disk.raw file.

cd /mnt/tmp
sudo tar czvf sherry-gce-discovery-disk.tar.gz disk.raw

Copy the tar file to the bucket we created earlier from the GCE instance directly:

gsutil cp /mnt/tmp/sherry-gce-discovery-disk.tar.gz gs://sherry-test-bucket

Log off the temporary instance after the file transfer is done.

Finally, make a custom image that we can use to create new GCE instances:

gcloud compute images create sherry-gce-discovery-image \
  --source-uri=https://storage.googleapis.com/sherry-test-bucket/sherry-gce-discovery-disk.tar.gz

Create instances based on our custom image

We are ready to create three new instances from the images we just built.

gcloud compute instances create sherry-instance-1 \
  --machine-type=n1-standard-2 --subnet=default \
  --min-cpu-platform=Automatic --tags=elasticsearch,http-server,https-server \
  --image=sherry-gce-discovery-image --image-project=sherry-test-gce \
  --boot-disk-size=20GB --boot-disk-type=pd-standard \
  --boot-disk-device-name=sherry-instance-1 --scopes=compute-ro

gcloud compute instances create sherry-instance-2 \
  --machine-type=n1-standard-2 --subnet=default \
  --min-cpu-platform=Automatic --tags=elasticsearch,http-server,https-server \
  --image=sherry-gce-discovery-image --image-project=sherry-test-gce \
  --boot-disk-size=20GB --boot-disk-type=pd-standard \
  --boot-disk-device-name=sherry-instance-2 --scopes=compute-ro

gcloud compute instances create sherry-instance-3 \
  --machine-type=n1-standard-2 --subnet=default \
  --min-cpu-platform=Automatic --tags=elasticsearch,http-server,https-server \
  --image=sherry-gce-discovery-image --image-project=sherry-test-gce \
  --boot-disk-size=20GB --boot-disk-type=pd-standard \
  --boot-disk-device-name=sherry-instance-3 --scopes=compute-ro

Once completed, logon to each instance and start Elasticsearch. Please see Elasticsearch installation instructions to configure Elasticsearch to start automatically.

From the instance sherry-instance-1, we can run curl sherry-instance-1:9200/_cat/nodes. We should see all three nodes in our cluster listed in the output.

10.138.0.8  14 29 6 0.10 0.11 0.07 mdi * sherry-instance-1
10.138.0.9  17 29 5 0.14 0.17 0.08 mdi - sherry-instance-2
10.138.0.10 13 29 5 0.13 0.22 0.10 mdi - sherry-instance-3

Conclusion

In a future blog, we will install and configure X-Pack to ensure our cluster is secure and production ready and the Google Cloud Storage repository plugin to provide the snapshot and restore capability. If you prefer the convenience of a managed cluster, Elastic Cloud, Elastic's official hosted solution is available on GCP. It comes with all of the X-Pack features and automatic snapshots.