Launch Spring-Boot JAR From Different Main Class

I found this very useful stack overflow:

https://stackoverflow.com/a/36552613/857994

It shows you how to start a spring-boot JAR from a different main class.  It’s a quirky solution, but it worked great.  Here’s a slightly more obvious/cleaner copy of the command (mostly for my own future reference):

java -cp presto-ws-3.2.2.jar -Dloader.main=com.company.PrestoQueryRunner org.springframework.boot.loader.PropertiesLauncher

AWS EC2 Varying Generation and Linear Cost Example

Due to increased query sizes on our presto clusters (causing aggregation failures), I’m in the middle of evaluating moving from 16 core 64GB RAM general purpose EC2 machines (m4.4xlarge) to 64 core 256GB RAM general purpose machines  (a 4x increase in power/RAM).

Here is the list of m4 and m5 models for 16-core/64GB and 64-core/256GB specs.  Below, we’ll see how they compare to each other and what the best option is.

Type vCPU ECU Memory (GiB) Instance Storage (GB) Linux/UNIX Usage
m4.4xlarge 16 53.5 64 GiB EBS Only $0.80 per Hour
m4.16xlarge 64 188 256 GiB EBS Only $3.20 per Hour
m5.4xlarge 16 70 64 GiB EBS Only $0.768 per Hour
m5.16xlarge 64 256 256 GiB EBS Only $3.072 per Hour

For reference:

EC2 uses the EC2 Compute Unit (ECU) term to describe CPU resources for each instance size where one ECU provides the equivalent CPU capacity of a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor.

There are a few good things to notice here:

  • For m4.4xlarge to m4.16xlare, we are getting 4x the resources for exactly  4x the cost  ($.80 x 4 = $3.20).  The one exception is we get less than 4x the ECU  units (so technically less than 4x the processing power).  So, compute roughly scales linearly within a model it seems.
  • Pretty much the exact same situation holds true for the m5 models; going from xlarge to 16xlarge is exactly a 4x increase in cost and resources except for ECUs which are a little less than 4x.
  • The m5 models have more ECUs than their m4 counterparts and they  also cost less, so they are a better deal both  performance and cost wise.

So, we’ll go with  m5.16xlarge instances which cost $3.072 an hour.  This comes out to $2,211 a month.

Kubernetes Ingress Service 60 Second Timeout

Kubernetes has multiple levels of timeouts for calls, including at the load balancer, inbound to the ingress itself, and at the individual ingress resources.

Assuming you have the first two configured and are still hitting a timeout on your  app, this is the annotation you need to add to your service’s ingress resource to boost its timeout:

nginx.ingress.kubernetes.io/proxy-read-timeout: "3600"

This will change it up to 1 hour from 1 minute.