We have a container, my app, built from this file and the issue is that when we try to stop it, it gets killed only after ten second timeout. It can not handle a sigterm signal, which should terminate this container. Our task is that we need to fix the application script and docker file to properly handle sigterm and ensure that container exits gracefully within 20 seconds after we run docker stop. We can use docker inspect to see what happened with our container. We've got exit code 137 and what exit code 137 indicates is that it's signal kill. In the bash script there's a loop, but what we do not have is trap command, which is used for handling gracefully and exits. We've added trap, which is built in shell command to intercept Unix signals. The signal that we'll trap is term, which stands for sigterm. The cleanup is the name of the function that we'll use to call when we get sigterm. We need to change the docker file and exactly the line where we have CMD. The problem is that when we receive sigterm signal, the sh gets the signal but not server.sh. This means that server.sh will receive this sigterm signal and can gracefully exit.

Kubernetes Pod Restart Troubleshooting: Fix web-app CrashLoopBackOff health Namespace. Diagnose and resolve web-app pod restart loops in health namespace where nginx serves port 80 / correctly but Kubernetes kills healthy containers. Master livenessProbe misconfiguration, CrashLoopBackOff diagnosis, pod events analysis, and probe timing optimization for stable web application deployments. Perfect for production troubleshooting, observability debugging, health check mastery, nginx stability, and Kubernetes reliability engineering.

Fix Kubernetes readiness probes: create health check scripts for Redis and MySQL dependencies, resolve container startup issues, and ensure pod readiness.

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 invert_tree(root: Optional[TreeNode]) -> Optional[TreeNode]:
if not root:
return None

# Swap
root.left, root.right = root.right, root.left

# Recurse
invert_tree(root.left)
invert_tree(root.right)

return root

How to Retrieve and Order Product Data in SQL Based on Category and Price for an Interview

Objective

You are provided with a products table that includes columns id, name, category, and price. The task is to write an SQL query to fetch the name, category, and price of all pr...

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We will compare database and CSV file records using SQLite. We have two data sources. The first one is a CSV file. The second source is a SQLite database. SQLite is a lightweight database that stores everything in a single file. We can run it directly inside of our Python program. We are required to find the customer IDs that are out of sync and save the comparison report as a JSON file. We import three things, SQLite 3 library, that will help us to connect and make queries with our database. Then we import CSV to read those files row by row, and JSON to save the comparison report. A set in Python is basically a data structure that stores unique values. Using connect function, we will open the database file. We create a cursor, which is a tool that executes SQL statements. Fetchall function will retrieve all the results as a list of tuples. We will use the concept of set subtraction, and in order to find the extra IDs in the SQL file, we will have to subtract the CSV IDs from the database. Dump function will convert the dictionary to a JSON format.

Extract a proportionally representative sample from a dataset using pandas stratified sampling to ensure each region maintains its distribution in the sample.

Spark is a big data framework that is designed to process massive amounts of data across multiple computers at the same time. Instead of tables like in SQL, Spark uses data frames. We have only one file that is called orders.csv with 5,000 records. Our main goal here is to repartition the data frame to eight partitions and print the task count in the format that it equals to eight. When Spark reads this file, it doesn't process it as one giant block. Instead, it splits it into smaller chunks called partitions. Each partition gets sent to a different executor. An executor is a process that runs on its chunk of data independently. Repartition is a built-in Spark method that lets us manually control how many partitions our data frame has. We pass the number that we want, and Spark will redistribute all the data across exactly that many partitions. repartition.rdd will convert our data frame to an RDD format, which is a data structure of Spark that holds the partitioned data. getNumPartitions is a built-in function that counts and returns the number of partitions.

WITH employee_agg AS (
SELECT
project_id,
COUNT(employee_id) AS total_employees,
COUNT(DISTINCT role) AS unique_roles
FROM {{ ref("employees") }}
GROUP BY project_id
),
equipment_agg AS (
SELECT
project_id,
SUM(cost) AS total_equipment_cost
FROM {{ ref("equipment") }}
GROUP BY project_id
)
SELECT
p.project_id,
p.project_name,
p.start_date,
p.end_date,
DATEDIFF('day', p.start_date, p.end_date) AS duration_days,
COALESCE(e.total_employees, 0) AS total_employees,
COALESCE(e.unique_roles, 0) AS unique_roles,
COALESCE(eq.total_equipment_cost, 0) AS total_equipment_cost
FROM {{ ref("projects") }} p
LEFT JOIN employee_agg e
ON p.project_id = e.project_id
LEFT JOIN equipment_agg eq
ON p.project_id = eq.project_id

WITH combined AS (
SELECT Country, Year, GDP FROM {{ ref("gdp_domestic") }}
UNION ALL
SELECT Country, Year, GDP FROM {{ ref("gdp_international") }}
),
with_lag AS (
SELECT
Country,
Year,
GDP,
LAG(GDP) OVER (PARTITION BY Country ORDER BY Year) AS prev_GDP
FROM combined
)
SELECT
Country,
Year,
ROUND(((GDP - prev_GDP) / prev_GDP) * 100, 2) AS GDP_growth_rate
FROM with_lag
ORDER BY Country ASC, Year ASC

SELECT
u.city,
COUNT(t.order_id) AS total_orders
FROM
trades t
INNER JOIN users u ON t.user_id = u.user_id
WHERE
t.status = 'Completed'
GROUP BY
u.city
ORDER BY
total_orders DESC
LIMIT
3;

WITH
ranked AS (
SELECT
ticker,
TO_CHAR(date, 'Mon-YYYY') AS month_year,
open,
ROW_NUMBER() OVER (
PARTITION BY
ticker
ORDER BY
open DESC
) AS high_rank,
ROW_NUMBER() OVER (
PARTITION BY
ticker
ORDER BY
open ASC
) AS low_rank
FROM
stock_prices
)
SELECT
h.ticker,
h.month_year AS highest_month,
h.open AS highest_open,
l.month_year AS lowest_month,
l.open AS lowest_open
FROM
ranked h
JOIN ranked l ON h.ticker = l.ticker
AND l.low_rank = 1
WHERE
h.high_rank = 1
ORDER BY
h.ticker;

Master JavaScript alert handling with Selenium. Learn prompt interaction, dynamic input processing, and comprehensive alert verification....

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