Hưng Vũ Hungsiro506

Cursor's Active Memory

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Active Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD

Fundamental Principles:

Always write clean, simple, and modular code with clear, consistent naming.
Prioritize simplicity in implementation and
aim for files under 200 lines
focusing on core functionality before any optimization.
Test thoroughly after every meaningful change.
Think through the problem and write 2–3 reasoning paragraphs before coding.
Use clear, easy-to-understand language and short sentences in both code and comments.

AI Coding Workflow Generator

Project Concept

[ENTER A BRIEF CONCEPT - Just 1-3 sentences about what you want to build]

Technical Context (Optional)

[ANY TECHNICAL CONSTRAINTS OR PREFERENCES - Languages, frameworks, deployment targets, etc.]

Instructions

Given a field named eventType with mapping.

"itemType" : {
  "type" : "text",
  "fields" : {
      "keyword" : {
      "type" : "keyword",
 "ignore_above" : 256

	next_xid = 1
	active_xids = set()
	records = []

	def new_transaction():
	global next_xid
	next_xid += 1
	active_xids.add(next_xid)
	return Transaction(next_xid)

	#
	# Adding new kubectl config to the existing ~/.kube/config
	#

	export KUBECONFIG=~/.kube/config:new_config_file
	kubectl config view --flatten >> /tmp/kube_config
	rm ~/.kube/config
	cp /tmp/kube_config ~/.kube/config

	// Purpose:
	// - Ensures that a resource is deterministically disposed of once it goes out of scope
	// - Use this pattern when working with resources that should be closed or managed after use
	//
	// The benefit of this pattern is that it frees the developer from the responsibility of
	// explicitly managing resources

	import scala.io.Source
	import java.io._

	scala> val df = spark.sqlContext.read.csv("/data/dns/cached_ip/*")
	df: org.apache.spark.sql.DataFrame = [_c0: string]

	scala> val cached = df
	cached: org.apache.spark.sql.DataFrame = [_c0: string]

	scala> val npic = spark.sqlContext.read.csv("/data/dns/npic_dns/*")
	npic: org.apache.spark.sql.DataFrame = [_c0: string]

	scala> val allo = spark.sqlContext.read.csv("/user/hungvd8/internet_user_profile_duration/Allocated-IPs2017-11-21.csv/*")

	scala> val dns = spark.sqlContext.read.parquet("/data/dns/dns-extracted-two-hours/2017-11-22-02/out/")
	dns: org.apache.spark.sql.DataFrame = [value: string]

	scala> val splited = dns.withColumn("temp",split(col("value"),"\\t"))
	splited: org.apache.spark.sql.DataFrame = [value: string, temp: array<string>]

	scala> val df = splited.select((0 until 25).map(i => col("temp").getItem(i).as(s"col$i")): _*)
	df: org.apache.spark.sql.DataFrame = [col0: string, col1: string ... 23 more fields]

	scala> val npic = df.where("col24 = '-1'").select("col2")

	date -d '1 hour ago' '+%Y-%m-%d'
	`Result` : 2017-11-13
	date -d '1 hour ago' '+%Y-%m-%d %H'
	Result : 2017-11-13 22

	date -d '1 hour ago' '+%H'
	22
	date '+%H'
	23