Ganeshan Venkataraman ganeshan

Performance Analysis Report

NeuralForecast NHITS Integration Performance Validation

Date: June 2025
Analysis Period: Complete Integration Lifecycle
Report Type: Comprehensive Performance Validation

🎯 Key Features Documented

V8

V8 is Google’s open source high-performance JavaScript and WebAssembly engine, written in C++. It is used in Chrome and in Node.js, among others. It implements ECMAScript and WebAssembly, and runs on Windows 7 or later, macOS 10.12+, and Linux systems that use x64, IA-32, ARM, or MIPS processors. V8 can run standalone, or can be embedded into any C++ application.

SpiderMonkey

SpiderMonkey is Mozilla’s JavaScript and WebAssembly Engine, used in Firefox, Servo and various other projects. It is written in C++, Rust and JavaScript. You can embed it into C++ and Rust projects, and it can be run as a stand-alone shell. It can also be [compiled](https://bytecodealliance.org/articles/making-javascript-run-fast-on

PostgreSQL is Enough

Background and Cron Jobs

Keycloak Cluster Configuration (How to)

This is a short and simple example on how to build a proper Keycloak cluster, using DNS_PING as discovery protocol and an NGINX server as reverse proxy.

If you prefer to use JDBC_PING, see @xgp's example gist here: https://gist.github.com/xgp/768eea11f92806b9c83f95902f7f8f80

Please see also my video about Keycloak Clustering: http://www.youtube.com/watch?v=P96VQkBBNxU
NOTE: The video covers JDBC_PING protocol and uses the legacy Keycloak Wildfly distribution!

	{
	"id":"https://schema.management.azure.com/schemas/2018-05-01/policyDefinition.json#",
	"$schema":"http://json-schema.org/draft-04/schema#",
	"title":"Policy Definition",
	"description":"This schema defines Azure resource policy definition, please see https://azure.microsoft.com/en-us/documentation/articles/resource-manager-policy/ for more details.",
	"type":"object",
	"properties":{
	"if":{
	"oneOf":[
	{

	-- Don't forget to first sign-in through sso using aws sso login
	CREATE OR REPLACE SECRET secret(
	TYPE s3,
	PROVIDER credential_chain
	);

	-- first let's create a Ducklake catalog
	CREATE SECRET(
	TYPE postgres,
	HOST '',

	# The NYC Taxis Dataset - https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

	pip install parquet-cli

	parq data/yellow_tripdata_2022-01.parquet

	parq data/yellow_tripdata_2022-01.parquet --schema

	parq data/yellow_tripdata_2022-01.parquet --head 10

	IAM Policy
	---
	{
	"Version": "2012-10-17",
	"Statement": [
	{
	"Effect": "Allow",
	"Action": [
	"rds-db:connect"
	],

	import pandas as pd
	import numpy as np

	def get_dataset(size):
	# Create Fake Dataset
	df = pd.DataFrame()
	df['size'] = np.random.choice(['big','medium','small'], size)
	df['age'] = np.random.randint(1, 50, size)
	df['team'] = np.random.choice(['red','blue','yellow','green'], size)
	df['win'] = np.random.choice(['yes','no'], size)