Memory-Heavy Pods

An easy SQL interview practice problem on DataDriven. Write the query and run it against a real database, with instant feedback.

Domain: SQL
Difficulty: easy
Seniority: L3
Asked in: ESPN

Problem

The SRE team is profiling mid-range memory consumers across the Kubernetes cluster. Pull all unique pod names where memory usage falls between 100 and 500.

Memory-hungry workloads.

Tables

k8s_pods

Columns: pod_id INTEGER, pod_name TEXT, nspace TEXT, status TEXT, restarts INTEGER, cpu_used REAL, mem_used REAL

k8s_pods: 10 of 200 rows
pod_id	pod_name	nspace	status	cpu_used	mem_used
2043	payment-003ef	production	Pending	0.011	37
2086	search-003f6	staging	CrashLoopBackOff	0.022	74
2129	gateway-003fd	monitoring	Completed	0.033	111
2172	worker-00404	kube-system	Error	0.044	148
2215	cron-0040b	default	Terminating	0.055	185
2258	ml-serve-00412	production	Running	NULL	NULL
2301	cache-00419	staging	Pending	0.077	259
2344	auth-00420	monitoring	CrashLoopBackOff	0.088	296
2387	payment-00427	kube-system	Completed	0.099	333
2430	search-0042e	default	Error	0.11	370

Expected output

First 5 of 9 rows
pod_name
gateway-003fd
worker-00404
cron-0040b
cache-00419
auth-00420

Worked solution and explanation

Why this problem exists in real interviews

The interviewer wants to see you apply query construction to k8s_pods.pod_name while accounting for the distribution of nspace. This surfaces as a fundamentals check because small logic errors produce results that look correct at a glance.

Break down the requirements

Step 1: Read from `k8s_pods`

The query targets k8s_pods with 7 columns. Identify which columns are needed for the output.

Step 2: Filter to the target rows

Use BETWEEN in the WHERE clause to select the target range. This is both readable and optimizable by the query planner.

Step 3: Return the result set

Select the required columns with any necessary aliasing or formatting.

The solution

BETWEEN range filter with DISTINCT

SELECT DISTINCT pod_name
FROM k8s_pods
WHERE mem_used BETWEEN 100 AND 500

Common follow-up questions

What happens to your result if `k8s_pods`.pod_name contains NULLs for some rows? (Tests whether the candidate accounts for NULL behavior in aggregates and comparisons on `pod_name`.)
How would you verify that your aggregation on `k8s_pods`.pod_id is not double-counting due to duplicate rows? (Tests data quality awareness and deduplication strategies.)
With millions of distinct values in `k8s_pods`.pod_id, what index strategy would you use to keep this query performant? (Tests indexing knowledge specific to high-cardinality columns like `pod_id`.)