Troubleshoot service connectivity by aligning selectors with pod labels. Fix api-svc selector mismatch that prevents endpoint population, restore DNS resolution for api-svc.shop.svc.cluster.local, and enable service accessibility. Essential for service discovery, internal cluster networking, DNS resolution, load balancing, and ensuring pods are properly discovered by their services.

Troubleshoot and fix Kubernetes RBAC and ServiceAccount configuration issues preventing pod API access. Diagnose authentication failures, enable token mounting, assign correct ServiceAccount, and fix RoleBinding namespace settings to restore API connectivity. Essential for pod-to-API communication, service discovery, internal monitoring, and applications that need to interact with the Kubernetes control plane.

Parse a list of URLs using Python regex to extract domain names including subdomains, handling various protocols, ports, and URL formats.

def check_inclusion(s1: str, s2: str) -> bool:
if len(s1) > len(s2):
return False

s1_count = [0] * 26
window_count = [0] * 26

for i in range(len(s1)):
    s1_count[ord(s1[i]) - ord('a')] += 1
    window_count[ord(s2[i]) - ord('a')] += 1
    
if s1_count == window_count:
    return True
    
l = 0
for r in range(len(s1), len(s2)):
    window_count[ord(s2[r]) - ord('a')] += 1
    window_count[ord(s2[l]) - ord('a')] -= 1
    l += 1
    
    if s1_count == window_count:
        return True
        
return False

Objective

The task is to write a SQL query to derive insightful metrics for a business analysis, focusing on the performance of different regions. You are required to calculate the total number of orders, the average order value, and total revenue from the customers and orders tables, group...

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Load a sales dataset with missing values, identify column data types, and replace nulls conditionally using pandas - numeric columns with 0 and text columns with "Unknown".

We will select specific columns from Parquet file using Pandas. Pandas is a library that was specifically designed for data analysis and manipulation. Parquet stores data column by column instead of doing that row by row. It is good when we want to read only some specific columns and skip everything else. It will look at the data stored in each group, analyze the minimum and maximum values for each column, and skip the entire row groups that cannot possibly contain matching data. We are required to return only five specific ones: ID, first and last name, email, and total amount of purchases. After selecting all five columns, everything needs to be saved as a new Parquet file called selected data. The first thing that we do is that we import the Pandas library and alias it as pd. Then we need to read the Parquet file into a data frame. In Pandas, when we want to select multiple columns, we pass a list of column names inside of double square brackets. Since Pandas automatically assigns row numbers to every data frame, we set index to false, which means that we do not include the data frame row numbers in the output.

We need to calculate the yearly revenue and the percentage growth year over year for a given set of financial transactions. We have financials table that contains two columns, transaction date, that is date type, and amount, that is numeric. We group all transactions by year and sum them up to get the total revenue per year. A CTE is a temporary result set in SQL that you can reference within a single query. It only exists while that query is running. In our select, we use extract in order to get only year from our transaction date column. We also need to get the total revenue of our amount column using sum. A left join keeps all rows from the left table, even if there is no matching row in the right table. The missing values just become null. We take our CTE yearly revenue and give it an alias current. Using left join, we take the exact same CTE again, but this time we call it prev for previous. We need to subtract current and previous year, divide by previous year, and multiply by 100 to get the percentage. Inside of select, we use round, which is a built-in SQL function that rounds a decimal number to a specified number.

Definition for a binary tree node.

class TreeNode:

def init(self, val=0, left=None, right=None):

self.val = val

self.left = left

self.right = right

def is_same_tree(p: Optional[TreeNode], q: Optional[TreeNode]) -> bool:
if not p and not q:
return True

if not p or not q or p.val != q.val:
    return False
    
return is_same_tree(p.left, q.left) and is_same_tree(p.right, q.right)