This chapter is about upgrading the Kubernetes cluster with kubeadm.
So let's get started.
1. Upgrade kubeadm:
[user@k8s-control]$ sudo apt-get update && \ sudo apt-get install -y --allow-change-held-packages kubeadm=1.21.1-00
Check for the upgraded version:
[user@k8s-control]$ kubeadm version
2. Drain the control plane node:
[user@k8s-control]$ kubectl drain k8s-control --ignore-daemonsets
3. Run the plan for upgrade:
[user@k8s-control]$ sudo kubeadm upgrade plan v1.21.1
4. Upgrade the control plane components:
[user@k8s-control]$ sudo kubeadm upgrade apply v1.21.1
5. Upgrade kubelet and kubectl on the control plane node:
[user@k8s-control]$ sudo apt-get update && \ sudo apt-get install -y --allow-change-held-packages kubelet=1.21.1-00 kubectl=1.21.1-00
6. Restart kubelet:
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When your presto cluster deals with heavy data processing queries and presto workers are running on spot instances using spot.io, the graceful shutdown will surely gonna help you out to avoid query failures due to abrupt spot termination.
Abrupt spot termination can unstable the presto cluster with errors like -” Internal Server Error ” or “Service Unavailable”. We can avoid this with two simple steps:
Step 1 . Install Spotinst agent which can monitor for spot termination every 10 seconds:
curl -fsSL https://s3.amazonaws.com/spotinst-public/services/spotinst-agent-2/elastigroup-agent-init.sh | \
SPOTINST_ACCOUNT_ID=”<spotinst account id>” \
SPOTINST_TOKEN=”spotinst auth token" \
To make your prestodb access secure , its always a good idea to enable password based login. Here we will be discussing two password based login methods :
File based authentication and LDAP authentication.
So lets get started.
For both the methods , append below in your config.properties file
1.Password Based Login using a file:
Create a file in the same directory where your config.properties file is located.
These changes are to be done in coordinator node.
Below is the default home_path comes with starburst presto ami
$ touch /var/lib/presto/etc/password-authenticator.properties
now paste the below content and save the…
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This lab is focused on deploying Prestodb cluster using aws marketplace starburst image . For first 15 days you can subscribe to the free trial where you just have to pay for the server you want to use.
Subscribe for starburst presto , and we are good to go for deploying our first presto cluster on aws.
create an IAM role for creating presto cluster and attach below as a policy
Steps to follow:
2. Now download the below file :
This lab is all about executing queries in prestodb with amazon s3 in the back end .We will be executing DDL(data definition language) and DML(data manipulation Language) statements . For configuring Presto Cluster , follow Chapter 2
In this , we will be creating a table which will act as a logical separation for the data , and then we will insert some records , define the format in which we want to insert the data , and finally will query the data.
Create a table :
presto-cli --server localhost:8080 --catalog hive
presto> use default
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In this lab , we will be setting up presto cluster with one coordinator node and one worker node. You can follow same settings to sync up “n” number of worker nodes along with the coordinator.
Note: For understanding presto architecture and how a cluster works in presto , you can visit chapter 1
a. The above tar will contain a single directory,
presto-server-0.250, which will be called in the installation directory ,
b. Java version: OpenJDK 64-Bit Server VM (build 126.96.36.199+1-Ubuntu-0ubuntu1.18.04) ,
and Ubuntu 18.04.5 LTS
What is Prestodb?
Prestodb(Presto) is an opensource SQL query engine which is used for speeding up the execution of analytics queries against data of any size.
It can be used with relational databases like MySQL, PostgreSQL, Amazon Redshift, Microsoft SQL Server, and Teradata ; and non-relational databases like Hadoop Distributed File System (HDFS), Amazon S3, Cassandra, MongoDB, and HBase.
Presto queries data where it is stored , and there is no need for moving data to any analytics system separately. Query execution takes place in parallel over a memory based architecture which allows to return response in seconds.