Team Specific Technical Stack and Backend Systems Questions
Discuss the team's specific technologies mentioned in the job description (Node.js, Python, Java, PostgreSQL, MongoDB, AWS, Azure, etc.). Ask about their backend architecture, how they handle scalability and reliability, deployment practices, and monitoring/alerting. Inquire about recent technical decisions or challenges they've faced. Show interest in learning their specific tech stack and systems. Ask realistic questions about the ramp-up period and learning curve.
MediumTechnical
48 practiced
Your team currently uses Excel spreadsheets to track capacity forecasts and on-call rotations. Propose a migration plan to an automated system using tools like Jira for rotations, Grafana for capacity dashboards, and a small service or Google Sheets + APIs to automate roster generation. Include data model changes, migration steps, and rollout strategy to minimize disruption.
Sample Answer
Overview: Migrate from ad-hoc Excel tracking to an automated, reliable system where Jira manages rotations, Grafana surfaces capacity dashboards, and a small roster service (or Google Sheets with APIs) generates and publishes schedules. Goal: better auditability, fewer manual errors, automated on-call handoffs, and data-driven capacity planning.Data model (from Excel → normalized services)- Person: id, name, email, team, timezone, skills, max_weekly_hours- Shift/OncallSlot: id, person_id, start, end, rotation_id, role (primary/secondary), timezone- Rotation: id, name, rules (length, follow-the-sun, handover_delay), escalation_policy (links to Jira)- CapacityMetric: service_id, timestamp, load, headcount_required, utilization_pct- Service/Team: id, name, SLOs, priority, contactsMapping: Excel columns → these entities; preserve history as AuditLog entries.Architecture & integrations- Jira: create a "Rotations" project or use custom issue type + automation rules for assignment/handovers; use webhooks to trigger roster updates and on-call notifications.- Roster service: small REST service (Python/Node) or Google Apps Script that reads rotation rules + availability and writes Shift records; exposes API consumed by Jira and Grafana.- Grafana: source capacity metrics from Prometheus/Influx; join CapacityMetric with Team data for dashboards and capacity forecasting panels.- Notification: integrate with PagerDuty/Slack via Jira webhooks or direct from roster service.Migration steps1. Discovery (1-2 weeks): inventory spreadsheets, identify owners, common columns, edge rules (exceptions, overrides), and required SLAs.2. Design (1 week): finalize data model, Jira scheme (issue types/fields/workflows), roster service API, and Grafana dashboards.3. Prototype (2 weeks): build roster service + Jira automation for one team; create Grafana dashboard template.4. ETL & Import (1 week): clean Excel, map fields, import People and historical Shifts into the roster DB; create Jira users/groups.5. Pilot (2-4 weeks): run one or two teams in parallel (shadow mode) — automated roster generated and Jira assignments created, but notifications go to a test channel; compare outputs against Excel.6. Validation & Feedback (1 week): reconcile differences, fix edge cases (overlaps, timezones, leaves).7. Gradual Rollout (2-6 weeks): onboard additional teams in cohorts; enable live notifications; update runbooks.8. Cutover & Decommission (1 week): freeze Excel as source of truth, finalize sync, archive spreadsheets, remove manual processes.Rollout strategy to minimize disruption- Phased pilots: start with low-risk team; use shadow runs for 2+ cycles before switching to live.- Backwards compatibility: keep a one-way sync from roster DB → Google Sheets for teams reluctant to switch.- Training & docs: run 60–90 minute workshops, create quick reference guides, and publish runbooks for common tasks (swap, PTO, emergency override).- Monitoring & KPIs: track schedule generation success rate, on-call overlaps, missed handoffs, Jira assignment accuracy, and SLO incident correlation for 30 days post-rollout.- Rollback plan: retain Excel as read-only fallback for 2 weeks; ability to disable Jira automations and revert notifications to manual process.- Governance: designate rotation owners, enforce update cadence, and set periodic audits.Risks & mitigations- Data quality issues → pre-migration cleaning and validation scripts.- Timezone/overlap bugs → extensive unit tests, integration tests, and simulated schedules.- User resistance → shadowing, incremental opt-in, and visible benefits (less manual effort, fewer paging errors).This plan delivers automated, auditable on-call rotations and capacity forecasts while minimizing disruption through pilots, parallel runs, and clear rollback/runbooks.
MediumTechnical
46 practiced
Implement an idempotent AWS Lambda function in Python (pseudocode acceptable) that applies a SQL migration to a PostgreSQL database. Include how the function acquires a migration lock to prevent concurrent runs, records applied migrations to avoid reapplying, and how it handles retries and partial failures safely.
Sample Answer
Approach: use a Postgres advisory lock to prevent concurrent Lambda runs, maintain a migrations table to record applied migration IDs (so re-invocations skip already-run migrations), run each migration inside a single DB transaction, and use retries with exponential backoff for transient DB/network errors. Use pg_try_advisory_lock so the function returns quickly if another runner holds the lock (or can wait with timeout).Key points / reasoning:- Advisory lock prevents concurrent runs across Lambdas or CI.- Recording migration IDs ensures idempotency: re-invocations skip applied migrations.- Each migration runs in a transaction so partial changes are rolled back if it fails.- For transient failures, rely on Lambda retry + implement client-side exponential backoff wrapper around connecting/executing if desired.- Use schema_migrations table (PRIMARY KEY) to make INSERT idempotent; alternatively use INSERT ... ON CONFLICT DO NOTHING.- Ensure migrations are deterministic and reversible; test in staging.
python
import os, time, psycopg2, json
MIGRATIONS = [
{"id":"20251201_add_users", "sql":"ALTER TABLE users ADD COLUMN last_seen timestamptz;"},
# ...
]
def lambda_handler(event, context):
conn = psycopg2.connect(os.environ['DATABASE_URL'], connect_timeout=5)
try:
with conn:
with conn.cursor() as cur:
# try advisory lock (64-bit key split into two ints)
cur.execute("SELECT pg_try_advisory_lock(hashtext(%s)::bigint);", ('migrations_lock',))
locked = cur.fetchone()[0]
if not locked:
# another process is running migrations — exit idempotently
return {"status":"skipped","reason":"lock_held"}
# ensure migrations table exists
cur.execute("""
CREATE TABLE IF NOT EXISTS schema_migrations (
id text PRIMARY KEY,
applied_at timestamptz default now()
);""")
for mig in MIGRATIONS:
cur.execute("SELECT 1 FROM schema_migrations WHERE id=%s;", (mig['id'],))
if cur.fetchone():
continue # already applied
try:
# run migration in its own transaction to avoid partial apply
cur.execute("BEGIN;")
cur.execute(mig['sql'])
cur.execute("INSERT INTO schema_migrations(id) VALUES(%s);", (mig['id'],))
cur.execute("COMMIT;")
except Exception:
cur.execute("ROLLBACK;")
raise
# release lock
cur.execute("SELECT pg_advisory_unlock(hashtext(%s)::bigint);", ('migrations_lock',))
return {"status":"ok"}
finally:
conn.close()EasyTechnical
37 practiced
Describe Dockerfile best practices for building secure, minimal Node.js container images. Provide a concise multi-stage Dockerfile example and explain choices such as base image selection, npm ci vs npm install, non-root user, minimizing layers, and caching strategies.
Sample Answer
To build secure, minimal Node.js images: pick a small vetted base, build in a separate stage, install deterministic deps, drop root, minimize layers and surface area, and use caching-friendly steps.Approach:- Use official node images only when building; ship a minimal runtime (distroless or node:slim).- Use npm ci for reproducible install from package-lock.json.- Multi-stage build to compile/transpile in builder, copy only artifacts.- Create a non-root user, remove package manager and build tools from final image.- Order Dockerfile to leverage layer caching (copy package*.json first).- Minimize layers by combining RUN commands and cleaning caches.Example multi-stage Dockerfile:Key choices explained:- Base image: build uses full Node image; runtime uses distroless/slim to reduce CVE surface and image size.- npm ci vs npm install: npm ci is faster, deterministic and fails if lockfile mismatches—prefer for CI.- Non-root user: reduces blast radius if container is compromised.- Minimize layers: combine RUN steps and remove caches during build to reduce size.- Caching: copy package*.json and run dependency install before copying source so dependency layer is reused when code changes.- Security extras: scan images for vulnerabilities, pin node versions, set read-only filesystem where possible, and limit container capabilities.
dockerfile
# builder
FROM node:18-bullseye AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --production=false
COPY . .
RUN npm run build
# runtime (minimal)
FROM gcr.io/distroless/nodejs:18
WORKDIR /app
# create non-root user in final image if base supports; distroless expects numeric uid
# copy only built artifacts and production deps
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/package*.json ./
COPY --from=builder /app/node_modules ./node_modules
USER 65532
CMD ["dist/index.js"]HardTechnical
62 practiced
You observe increased tail latency for a distributed call chain Node.js -> Java -> Python. Describe a systematic triage to find the root cause using distributed tracing, logs, and metrics. After identifying likely causes, list infra-level and code-level optimizations you might apply to reduce tail latency.
Sample Answer
Start by clarifying scope & impact: confirm which services, percent of requests affected, SLA/SLO breaches, and when it began.Triage (systematic steps)1. Metrics first: check service-level metrics (p50/p95/p99 latency, error rate, throughput) for Node, Java, Python, and infra (CPU, memory, GC, network, load balancer, disk I/O). Identify where p99 spikes align in time.2. Distributed tracing: pick representative p99 traces. Trace waterfall to find which span(s) dominate tail latency (is it Node client, Java processing, Python backend, or network/queueing?). Look for red flags: long wait times, retries, time spent in queue or GC, or blocking syscalls.3. Logs & context: correlate trace IDs to logs at each service. Search for exceptions, timeouts, retries, DB or downstream calls, backpressure signals, thread pool saturation, or lock contention.4. Host-level/infra checks: check container throttling, CPU steal, cgroups limits, autoscaling events, network error rates, database slow queries, and load-balancer health checks.5. Reproduce & isolate: create synthetic load to reproduce tails; run single-span microbenchmarks for suspect components; enable higher-sample tracing for problem windows.6. Hypothesis + validate: form hypotheses (e.g., Java GC pauses, Python worker starvation, Node event-loop blocking, network packet loss) and validate via metrics/traces/logs.Common likely root causes- GC pauses or thread-pool saturation in Java- Blocking synchronous code or CPU work on Node event loop- Heavy startup/serialization in Python workers or limited concurrency (e.g., single-threaded w/ long tasks)- Retries amplifying latency- Network packet loss, overloaded load balancer, or connection pool exhaustion- Resource contention on hosts (CPU/memory/IO)Optimizations — infra-level- Right-size instances, add capacity or autoscale more aggressively for p99 traffic- Use node affinity / anti-affinity to spread load; avoid noisy-neighbor VMs- Tune cgroups/CPU shares and remove throttling; ensure network-level QoS where available- Increase connection pool sizes and tuning (DB and HTTP client)- Use LBs with consistent hashing to reduce cache misses; enable keep-alives- Improve caching (edge, app-level, or memcache/Redis) to cut downstream calls- Upgrade network (MTU, NIC drivers) and reduce hop count; enable TCP tuning (keepalive, window scaling)- Adjust JVM/Garbage Collector settings (G1/ ZGC) and provision headroomOptimizations — code-level- Add timeouts and bounded retries with jitter; fail fast and circuit-breakers- Profile and fix blocking operations: move CPU-heavy work to worker threads or async tasks (Node: libuv offload; Python: multiprocessing or async frameworks)- Avoid synchronous I/O in hot paths; stream large payloads; use backpressure and bounded queues- Optimize serialization (use binary formats, avoid unnecessary copies), reduce payload sizes- Tune thread pools and executor sizes (Java: increase executor queue, tune max threads, use non-blocking libraries)- Reduce tail amplification: limit global locks, reduce head-of-line blocking, shard work to avoid contention- Add hedging for long remote calls (duplicate to another replica with care) or prioritize p99-sensitive requests- Improve observability: add high-cardinality tags, measure queue times, instrument histograms for spansFinish by proposing short-term mitigations (rate-limit, increase capacity, temporary caching) and a postmortem plan: root cause, permanent fixes, SLO adjustments, tests (load/chaos), and monitoring alerts for the specific root metrics (e.g., JVM pause time, event-loop lag, connection pool exhaustion).
MediumTechnical
40 practiced
Your Azure DevOps CI pipeline often breaks due to flaky integration tests for Java services. As the SRE responsible for CI reliability, describe the steps you would take to triage flakiness, strategies to reduce it (test isolation, mocking, stable infra), and CI changes that preserve developer feedback velocity while improving pipeline stability.
Sample Answer
Situation: Our Azure DevOps CI is failing frequently because Java integration tests are flaky, slowing developers and causing alerts.Triage (first 48–72 hours)- Collect data: pipeline history, test failure patterns, failure stack traces, agent logs, timestamps, and test duration. Use Azure DevOps test run artifacts and build logs.- Classify failures: deterministic (logic), environmental (time/date, network), resource (OOM, CPU), ordering/timing (race conditions), and dependency (external service).- Reproduce locally: run failing tests repeatedly in developer container or same VM image to determine non-determinism.- Measure impact: compute flaky rate by test, by suite, and by pipeline stage; prioritize top 10% tests causing ~80% failures.Strategies to reduce flakiness- Test isolation: ensure tests reset state (DB, caches) between runs; use ephemeral test databases (Testcontainers) and unique test namespaces; avoid shared writable resources.- Mocking/fake dependencies: replace external HTTP/GRPC services with WireMock or in-process fakes for CI; reserve a small set of true integration tests that run against stable staging dependencies.- Stabilize infra: use immutable, versioned build agent images; pin JVM, library, and OS versions; allocate consistent CPU/memory for agents; use containerized agents to reduce host variance.- Improve tests: add timeouts, deterministic seeds, remove sleeps in favor of explicit waits, assert on observable outcomes, detect and fix race conditions, and use thread dump analysis for concurrency issues.CI changes (preserve developer feedback velocity)- Split pipeline into fast unit-test stage and separate integration-test stage. Run unit tests and static checks on PRs (fast feedback). Run full integration suite on merge or nightly.- Test triage automation: auto-flake detection (rerun flaky tests N times) and quarantine: if a test passes intermittently, mark it flaky and run it in a quarantine job while notifying owners.- Parallelize deterministic tests and shard long suites across agents to maintain runtime.- Conditional gating: allow PRs to pass with flaky-test exemptions if unit tests and critical integration smoke tests succeed, while preventing merges if quarantined critical tests fail.- Canary/merge queue: use a gated merge pipeline or queue to run full integration tests before merging to main, preventing broken main builds without slowing author feedback.- Visibility: add dashboards (flaky tests, failure trends) and alert when flakiness rises above SLO. Create automation to create Azure DevOps work items for high-impact flaky tests.Result & ongoing process- Prioritize fixes for top-flaky tests, stabilize infra images, and adopt the split-pipeline + quarantine approach. Expect immediate reduction in CI interruptions while maintaining fast PR feedback. Continuously measure flaky-rate and iterate: aim for <1% flaky failures and short MTTR by tracking owners, dashboards, and automated reruns.
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