Difference between Presto vs. Spark underlying architecture

Section 4 — The Trade-offs (The 'Senior' part)
CAP Theorem: Presto: CP for query correctness—coordinator ensures consistent result. Spark: AP during execution—executor failure triggers retry; driver coordinates. Both assume partition tolerance in distributed clusters.

Cost vs. Performance: Athena (Presto) $5/TB scanned vs EMR Spark $0.10/hr + EC2. Athena best for ad-hoc BI (100ms–1s). EMR Spark best for ETL (minutes). Use Athena for BI; Spark for pipelines.

Blast Radius: Presto coordinator fail: in-flight queries fail; workers idle until restart. Spark driver fail: job lost; use cluster mode for driver HA. Executor fail: stage retries.

Section 5 — Pro-Tip
Pro-Move: Presto for BI (sub-second); Spark for ETL (minutes)—different engines, different jobs; never treat them as interchangeable.
Red Flag: Claiming Presto and Spark are interchangeable—shows lack of architectural depth. From a Principal Engineer perspective, the key differentiators are operational rigor—defined SLAs, runbooks, and chaos testing—and cost consciousness—right-sizing, reserved capacity, and incremental processing to minimize compute. The failure modes we guard against include partition events (Kafka ISR, consumer rebalance), poison messages (DLQ with alerting), and offset loss (S3 checkpoint). Interview red flags include missing idempotency (duplicates on retry), no DLQ (one bad record blocks the pipeline), and checkpointing to ephemeral storage (state lost on preemption). Production systems require monitoring of consumer lag, data freshness SLOs, and cost per record processed. Schema evolution should be additive-only with Schema Registry; partitioning strategies must align with query filters (date, region); blast radius is contained through replication, circuit breakers, and graceful degradation. When choosing between CP and AP: ledger and warehouse layers favor consistency; streams and caches favor availability. Cost optimization: Glue for bursty jobs under 2 hours; EMR for sustained 8+ hour workloads. Always quantify improvements—latency reduction, cost savings, volume handled. Data skew mitigation via salting and AQE prevents hotspot tasks; exactly-once semantics require idempotent sinks; fan-out patterns enable multiple consumers without duplication. TTL policies on Bronze reduce storage cost; incremental processing cuts compute by 90% versus full scans. Replication factor of three with min.insync.replicas=2 ensures durability; consumer count should match or exceed partition count; event-time over processing-time handles late arrivals correctly. Medallion architecture separates raw from curated; quality gates at Silver prevent bad data propagation; conformed dimensions enable cross-mart consistency. In interviews, demonstrate production experience by citing specific metrics: P95 latency, cost per million events, recovery time objective. Avoid generic answers; tie each design choice to a measurable outcome. The trade-off between consistency and availability is per-component: choose CP for financial transactions, AP for analytics. Scale testing should cover 10x peak load; runbooks should document failure recovery steps. Blue-green deployments enable zero-downtime schema evolution; view abstraction with COALESCE supports additive column migration. For real-time systems, define SLOs before building—lag under five minutes and freshness under one hour are common targets. Correlation IDs in log records enable end-to-end tracing when debugging production incidents. Reserve capacity for traffic spikes; implement circuit breakers to prevent cascading failures across dependent services. Document design decisions and their trade-offs for future maintainability. This demonstrates production-grade system design thinking.