Kafka System Design Interview Questions
Kafka System Design Interview Questions
Kafka design problems for data engineer interview prep.
Kafka system design interview questions for data engineer roles. Partition strategy for high-throughput ingest. Consumer groups and rebalance protocol. Exactly-once with transactional writes and read_committed. Replication factor and ISR. Log retention and compaction. Kafka Streams for in-Kafka processing.
Kafka system design questions in data engineer interviews test seven recurring concerns. Partition strategy: how many partitions, which key. Throughput targeting drives partition count (typically 10-20 MB/sec per partition for safe headroom; 580 MB/sec peak ingest needs 29-58 partitions). Key choice determines ordering guarantees (all events for one user_id land on one partition if keyed by user_id; ordering preserved within partition). Partition count is hard to change post-creation; size for 2-3x growth.
Consumer groups and rebalance protocol. A consumer group is the unit of parallelism for downstream processing; each partition is consumed by exactly one consumer in the group. Rebalance happens when consumers join or leave, redistributing partitions. Cooperative rebalancing (Kafka 2.4+) reduces rebalance pain by not stopping all consumers during the transition. For data engineer interview rounds: the rebalance story matters because it determines processing latency during scale events. Sticky partition assignment minimizes data movement during rebalance.
Exactly-once semantics. Kafka transactional API plus consumer isolation_level = read_committed gives end-to-end exactly-once within a Kafka cluster. Producer writes to multiple topics in a single transaction; consumer reads only committed messages. Combined with idempotent producer (enable.idempotence = true), this provides at-least-once delivery promoted to exactly-once effect. Limitation: works within the Kafka cluster boundary; sinks outside Kafka need their own idempotency (MERGE INTO, dedup on composite key).
Replication and ISR. Replication factor 3 is the default for production data engineer pipelines. ISR (in-sync replicas) is the set of replicas currently caught up to the leader. Producer acks: acks = 0 (fire and forget), acks = 1 (leader confirms), acks = all (all ISR confirm). For data engineer pipelines, acks = all with min.insync.replicas = 2 provides durability against single-broker failure. Unclean leader election (allowing out-of-sync replicas to become leader) trades durability for availability; default is false in modern Kafka.
Log retention and compaction. Two retention modes. Time-based (retention.ms): events older than N hours/days are deleted. Default 7 days for most data engineer use cases (allows replay window). Compaction (cleanup.policy = compact): only the latest value per key is retained; older values are garbage-collected. Useful for change-log topics (the latest state per primary key is what matters). Both modes can compose: compacted topics with time-based retention for the compacted state.
Kafka Streams for in-Kafka processing. Kafka Streams is the embedded Java library for transformations and aggregations within Kafka topics without an external compute cluster. Useful for lightweight processing (enrichment, filtering, simple aggregation). State is stored in RocksDB locally on each instance and changelog topics in Kafka for fault tolerance. Trade-off: Kafka Streams is Kafka-only and Java-only (or Scala). Larger workloads use Flink or Spark Structured Streaming.
Companies that emphasize Kafka heavily in data engineer interviews: Stripe (financial-data pipelines with transactional Kafka), Netflix (Kafka for streaming ingest, Mantis on top), Uber (Kafka for ride-dispatch analytics, Kafka Streams for some flows), LinkedIn (Kafka was created at LinkedIn; deep Kafka expertise expected). Confluent Cloud and Amazon MSK are common managed offerings.
- How many Kafka partitions does a data engineer pipeline need?
- Roughly throughput-in-MBps divided by 10-20 MB/sec per partition for safe headroom. 580 MB/sec peak ingest = 29-58 partitions. Partition count is hard to change post-creation (existing consumers do not see new partitions without reconfiguration). Size for 2-3x expected growth. More partitions also mean more producer-side memory and more controller load; do not over-provision.
- How does Kafka achieve exactly-once semantics?
- Kafka transactional API plus consumer isolation_level = read_committed. Producer writes to multiple topics in a single transaction; consumer reads only committed messages. Combined with idempotent producer (enable.idempotence = true), provides exactly-once within the Kafka cluster. Sinks outside Kafka (warehouses, databases) need their own idempotency: MERGE INTO with run_id, dedup on composite key.
- What is the difference between Kafka acks 0, 1, and all?
- acks=0: producer does not wait for any acknowledgment. Fire and forget. Risk of data loss on broker failure. acks=1: producer waits for the leader broker to confirm. Loss possible if leader fails before replication. acks=all (also acks=-1): producer waits for all in-sync replicas to confirm. Combined with min.insync.replicas=2 and replication factor 3, survives single-broker failure with zero data loss. Default for data engineer production pipelines.
- What is a Kafka consumer group and how does rebalance work?
- Consumer group is the unit of parallelism. Each partition is consumed by exactly one consumer in the group. When consumers join or leave, Kafka rebalances by reassigning partitions. Cooperative rebalancing (Kafka 2.4+) does not stop all consumers; sticky assignment minimizes data movement. Rebalance latency matters for SLA during scale events; faster autoscale means more frequent rebalances.
- When does a data engineer use Kafka log compaction?
- When the topic represents the latest state per key rather than a sequence of events. Change-log topics (CDC, materialized views). Compaction garbage-collects older values for the same key, retaining only the latest. Combined with time-based retention for the compacted state. Useful for building state-rebuild logic in Kafka Streams or Flink.
- What is the difference between Kafka and Kinesis from a data engineer interview perspective?
- Functionally similar: distributed, partitioned, durable, append-only log. Kafka is open-source with the largest ecosystem (Kafka Connect, Schema Registry, ksqlDB, Confluent). Kinesis is AWS-managed with simpler ops and tighter AWS integration. Kafka has lower latency, higher throughput per partition, more flexibility. Kinesis has lower operational overhead. Choose based on company stack.
- What is Kafka Streams and when does a data engineer use it?
- Kafka Streams is the embedded Java library for transformations within Kafka topics without an external compute cluster. Useful for lightweight processing: enrichment, filtering, simple aggregation. State in RocksDB locally with changelog topics for fault tolerance. Trade-off: Kafka-only and Java-only. Larger workloads use Flink or Spark Structured Streaming. For pipelines that need cross-system joins or write to non-Kafka sinks, pick Flink or Spark.
124 practice problems matching this filter. Difficulty: medium (57), hard (67).
Pipeline Architecture (124)
- 45 Minutes Turned Into 3.5 Hours - medium - Spark jobs are running. Just not fast enough.
- 600 Million Events a Day - hard - 600 million events a day. Two years of retention.
- A Clean Number for Every Merchant - hard - Raw payment logs in. Clean merchant summaries out.
- A Million Cars Phoning Home - hard - Every vehicle is a sensor. Deploy the pipeline to catch it all.
- Analysts Are Slowing the Store Down - medium - Orders placed. Data warehouse hungry.
- A New Column on a Billion Rows - hard - Add and backfill a new column to a billion-row production table with zero downtime.
- A Shared Drive Full of Contracts - medium - Buried in PDFs. The data is in there somewhere.
- A Stream All Day and a File at Midnight - hard - Real-time and batch. Same pipeline. No compromises.
- Badging Items That Already Sold Out - hard - Same-day delivery. The features have to be faster.
- Basel, CCAR, and Monday Morning - medium - The regulator does not accept 'eventually consistent.'
- Bikes Before Rush Hour - hard - Bikes in, bikes out. The city needs to predict demand.
- Credit for Every Touch - medium - They saw the ad, clicked the email, then bought. Who gets credit?
- Doubling Every Six Months - hard - Tuesdays are quiet. Black Friday is not.
- Eight-Hour-Old Positions - hard - Positions shift by the second. The math cannot lag.
- Eight Teams, Eight Latencies - medium - Millions of gamers. The architecture decision changes everything.
- End of Day Is Too Late - medium - Every swipe tells a story.
- Equities, ETFs, and the SEC - hard - Fractional shares, multi-currency, point-in-time. All of it.
- Event System for Multiple Consumers - hard - One event, many hungry consumers.
- Every Dataset Needs a Paper Trail - hard - The FDA has opinions about your data pipeline.
- Every Deal Is a Financial Transaction - hard - Real money on the table. Reconstruct every hand.
- Every Device, Every Impression - hard - Every ad seen. Every second watched. Real-time.
- Every Device Has Its Own Dialect - medium - Three sources. Three formats. Same workout.
- Every Firm Formats It Differently - medium - The regulator changed the format. Again. Handle it.
- Every Format Imaginable - hard - PDFs, HL7, JSON. All of it lands in the same lake.
- Everyone Wants the Same Data, Differently - hard - How you store it decides how fast you can read it.
- Every Region Exports Its Own Way - medium - Sales data, BigQuery, Dataflow. Make it all sing.
- Every Scan, Every Parcel, Every Pin Code - medium - Out for delivery. Delivered. Except the events arrived backwards.
- Fifty Thousand Retailers - medium - Retail data at CPG scale. Every SKU, every store.
- The Box That Won't Fit the Data - hard
- Five Times the Traffic, Five Times the Bill - hard - Scale up when needed. Do not bankrupt the team.
- Five Years of Cron Jobs - hard - Half the jobs run on cron. Half run on events. All of it has to move.
- Flying Blind Until Midnight - hard - Intraday risk, full lineage. The regulator is watching.
- Four Teams, One Topic, No Agreement - hard - Everybody is writing to it. Nobody documented it. Now production is fragile.
- Greenfield Build for Six Sources - hard - Infrastructure as code. Meaning as a service.
- Half a Million Rental Cars - medium - Every vehicle is reporting. Every rental matters.
- The Identity Problem - hard - Old systems. New demands. The same customer appears under three different names.
- Listens From Everywhere, Counted Once - hard - Phones, tablets, laptops. And some of them report late.
- Live Viewers, Live Billing - hard - The stream is live. The data cannot wait.
- Near-Real-Time Trending Dishes Dashboard - hard - The dish rankings update faster than the kitchen.
- Nested Docs, Flat Reports - medium - Two databases. One direction. No data left behind.
- Nightly Exports Are Too Slow - medium - Healthcare claims change constantly. The warehouse cannot fall behind.
- 4,500 Stores Before Sunrise - medium - The shelves open at 7. The data better be there.
- Not Every Team Can See Every Row - hard - Everyone can see the bucket. Not everyone should.
- One Bill Across Three Clouds - medium - AWS, Azure, GCP. Three bills. One truth.
- One Earthquake, Ten Thousand Tweets - hard - The firehose is on. Separate signal from noise.
- Out of the Data Center - medium - The on-prem servers are not getting any younger.
- The Speed Layer - medium - Dashboards can't wait for raw logs. Something has to happen upstream.
- Prove the Number Is Right - hard - Bad data in fintech is not just messy. It is expensive.
- Real Data, Fake Patients - hard - Dev needs production data. HIPAA says absolutely not.
- The Register Never Sleeps - medium - Every swipe lands in the warehouse. The table has to stay current without breaking.
- Recommendations Now, Royalties Later - medium - The catalog updated. Did anyone notice?
- Replicate It Without Breaking It - hard - The source changed. The lake needs to know immediately.
- Risk Models on Week-Old Data - medium - Loan approved. Loan denied. Every decision is an event.
- SaaS API Connector with Incremental Sync - medium - The API has rate limits. You have deadlines.
- Same-Day Sales, Every Store - medium - The cash register data needs to be queryable by morning.
- The Living Table - medium - Data lands continuously. History must survive every update.
- Score It Before It Clears - hard - The fraudsters move fast. Your pipeline has to move faster.
- Ship Before Fraud Finishes Checking - hard - The claim looks clean. The fraud model disagrees.
- Six Hours to Miss a Deadline - medium - The rebuild works. It just doesn't finish in time.
- Six Hours to Refresh Every Number - medium - Ratings change. The incremental model has to keep pace.
- Six Million Rows Before the Market Opens - medium - One massive CSV. Millions of timestamps.
- Six Sources, One Platform - medium - ADF orchestrates. Unity Catalog governs. Nothing leaks.
- Sixty Minutes, Every Hour - medium - Every hour, on the hour. No excuses.
- Stores and the Site, Together - hard - The registers never stop ringing.
- Store, Site, and Distributor - medium - Sales data is piling up. Someone has to make sense of it.
- The Acquisition Still Taking Bookings - hard - Two systems, two schemas. One truth.
- The Agency That Changes the Columns - medium - The schema changed overnight. Again.
- The Analysts Cannot Touch Production - medium - Production is the source. Analytics needs its own copy.
- The Analyst Who Saw the Salary Data - hard - Two incidents. One shared lake. The access model was never designed, just assumed.
- The API Drip Feed - medium - The API gives you 100 records at a time. You need millions.
- The Bad Row That Broke the Dashboard - medium - Bad records cannot reach the warehouse.
- The Binding and the Claim - medium - Policies are instant. Claims take their time.
- The Booking That Came Three Ways - hard - PMS, OTA, and website all think they took the reservation first.
- The Boutique That Sold in Six Currencies - hard - Every sale is real. The rate it was converted at depends on who is asking.
- The Bucket Full of Resumes - medium - A thousand resumes. Structured data inside each one.
- The Carrier Moving to Azure - medium - Claims arrive messy. The medallion cleans them up.
- The Claim That Picks Its Own Lane - medium - Three entry points. Different workflows. All must route correctly.
- The Clicks We Throw Away - hard - Every tap, swipe, and scroll. At scale.
- The Clock That Runs Two Ways - hard - Nightly batch and live events. One dashboard.
- The Consent Stitcher - medium - Consent was given. Or was it? Stitch the records together.
- The Dashboard and the Attribution Model - hard - Streaming and batch. One pipeline to rule them.
- The Decision Before the Door Closes - hard - The window to stop it is smaller than you think.
- The Distributor Filing Problem - medium - Hundreds of suppliers. One warehouse. One deadline.
- The Event Pile - hard - 600 million clicks a day. The budget is not infinite.
- The Fare Aggregator - medium - Airfares shift every minute. Catch the best ones.
- The Fleet That Never Stops - hard - Every truck is talking. Not everyone can hear them yet.
- The Leaderboard That Costs $25K a Month - hard - Product wants it live. Engineering has a price tag.
- The Meal Kit That Knows You - medium - What they ordered says a lot about what they want next.
- The Migration That Cannot Break Morning - hard - It all works today. Moving it without losing a single report is the hard part.
- The Models Going Stale - hard - The model is only as good as what you feed it.
- The Panel and the Set-Top Boxes - hard - Set-top boxes tell you who watched. Projection tells you how many.
- The Patients We Cannot Move - hard - Patient data stays local. Insights have to be global.
- The Points Arrive Two Days Late - medium - The bank data shows up late. The rewards were already sent.
- The Provider That Sometimes Sleeps - medium - The models run at dawn. The data has to be there first.
- The Query That Used to Be Fast - medium - Queries used to be fast. Something changed.
- The Queue That Wouldn't Stop Growing - medium - 500,000 messages behind and the number keeps climbing.
- The Revenue That Was Wrong for Two Weeks - medium - Nobody caught it until the CFO asked a question. Design the system that catches it first.
- The Sale That Needs to Land Now - medium - Three channels feeding one view. Not all of them speak the same language.
- The Signals That Power Recommendations - medium - Fresh signals, many teams, one pipeline.
- The User Who Asked to Be Forgotten - hard - Users want their data erased. Completely.
- The Vendor Who Never Warns You - medium - Every month, something is different. The dashboards have no idea.
- The What-If Machine - hard - A million slots. A thousand campaigns. Every combination matters.
- The Whiteboard Exercise - medium - Marker in hand. Draw the whole thing.
- Thirty Cities, One Forecast - hard - Five cities. Five data formats. One prediction.
- Thirty Countries, One Solvency Number - hard - Premiums collected globally. Losses happen locally.
- Thirty Million Unique Jobs a Year - hard - One press run, many orders. Group them right.
- Thousands of Practices, One Dataset - hard - Patient records in, operational insights out.
- Three Providers, One Workout - hard - The same ride, reported three times.
- Three Regions, One Finance Team - hard - Payments from everywhere. One consistent report.
- Three Regions, One Report - hard - Three regions, billions of payments, one merchant summary by 6 AM.
- Towers and Phones, Same Story - hard - Tower signals meet app events. Somewhere in between is the truth.
- Traders, Risk, and the Regulators - medium - Markets move in milliseconds. The pipeline has to keep up.
- Two Hundred Million Redirects - medium - Billions of clicks. One tiny code. Two very different clocks.
- Two Million Boxes by Monday Morning - hard - Shipped, maybe. Delivered, debatable.
- Two Systems, One Room Count - hard - Two booking systems. Rooms do not duplicate themselves.
- Two Ways to Catch a Change - medium - Two ways to watch the database. Each has a cost.
- Two Years of Every Click - hard - Every click, every aisle, every day for two years.
- Two Years of Clicks, Cheap - hard - Two years of clicks. Every query has to be affordable.
- What Everyone Is Watching - hard - Someone is watching. Capture everything.
- What Should We Recommend Tonight - hard - They ordered pad thai twice. That means something.
- Where Is Every Truck, Right Now - medium - Trucks are moving. Every ping counts.
- Which Promotion Is Actually Working - hard - Was the promotion worth it? The data knows.
- Who Is Churning and Why - medium - Subscribers churn. The pipeline cannot.
- Who Saw the Ad Twice - hard - TV and digital. Same viewer, two measurement worlds.