Conclusion: Real-time is a promise you keep
Real-time processing isn’t a tool choice—it’s a commitment to freshness, predictable tail latency, and correctness. Define the freshness you actually need, measure it end to end (event_time → first_queryable_time), and keep the pipeline only as complex as required to hit that promise. Use streaming where logic must happen before storage, serve most questions from a real-time OLAP engine with fast joins and targeted materialized views, and isolate workloads so spikes don’t blow your P95/P99. Do that, and you’ll ship experiences that feel current, make better decisions faster, and control cost as you scale.
Detailed Q&A (engineer-friendly, copy-paste ready)
What exactly does “real-time” mean here?
Real-time is a freshness SLO: the maximum allowed delay from event to when a normal query can see the correct record. It’s verified continuously, not assumed.
How is “queryable” defined in practice?
Durable write, schema checks and dedup passed, atomically visible to readers, and—only if the endpoint relies on them—indexes/materialized views have refreshed within their freshness budgets.
Do I need sub-second everywhere?
No. Use sub-second for key-based gates/lookups (fraud, rate limiting, feature flags). For most analytics and ops, seconds to tens of seconds is the sweet spot.
How do I measure freshness correctly?
Record event_time at the source and compute first_queryable_time at your serving tier. Track distributions (P50/P95/P99) per endpoint; alert on SLO budget burn, not single spikes.
Is “real-time” the same as “streaming”?
No. Streaming is a technique. Real-time is the outcome. You can meet a 10–30s SLO using streams or micro-batches—as long as ingest, visibility, and serving meet the SLO.
When do I actually need Flink?
When logic must happen in the stream: CEP, event-time windowing with strict lateness rules, or heavy volume reduction. Keep it lean; push optional enrichment and most joins to the serving layer.
How long should Kafka retention be?
Long enough to rebuild state after a logic or schema change—typically days to weeks for critical topics. Use compaction for change-log topics where “latest by key” is enough.
What’s the simplest viable ingest path?
CDC for mutable OLTP facts and Kafka for events, landing into your serving store with atomic visibility. Make sinks idempotent so replays don’t double-apply.
How do I handle late or out-of-order events?
Use event time with watermarks and allowed lateness. Keep aggregates idempotent and maintain a replay path so you can correct windows if needed.
What if my facts change often (upserts/deletes)?
Pick a serving layer that supports primary-key upserts and partial updates, so only changed columns are written. Example: StarRocks PK tables apply CDC upserts/deletes directly with partial updates, keeping read paths fast.
Do joins kill latency?
Not if you model for them and your engine is built for it. Partition by time, bucket by hot keys, broadcast small dimensions, colocate heavy joins, and rely on runtime filters and a cost-based optimizer. Add MVs only for the heaviest, most popular patterns.
When should I create a materialized view?
After you’ve measured pain. Promote top SPJG patterns (select–project–join–group) and let auto-rewrite route eligible queries. Give each MV a freshness budget (e.g., 5–30s) and retire low-hit MVs.
How do I keep P95/P99 predictable under load?
Isolate workloads. Put interactive dashboards/APIs in their own pool or resource group with reserved capacity; run exploration and heavy jobs elsewhere. Enforce timeouts and row limits at user-facing endpoints.
Which metrics should I watch daily?
Freshness SLO (event_time → first_queryable_time), query P95/P99 per route, Kafka consumer lag, watermark lag, compaction queue depth and throughput, MV lag and hit rate, scanned vs returned rows, CPU/memory pressure, and any reject/oom counters.
Do indexes/MVs need to refresh before data is “real”?
Only if that endpoint depends on them. Many paths can read base tables immediately; maintain small freshness budgets for secondary structures when acceptable.
How do I avoid hot partitions and skew?
Choose partitioning that matches filters (usually time) and bucket by hot keys (user_id, merchant_id). If skew appears, rebucket or add salt; monitor top-key distributions.
What’s the right way to write data—tiny trickles or batches?
Micro-batch. Row-at-a-time writes create too many small files/segments and starve compaction. Aim for tens of thousands of rows per chunk where possible.
How should the lake fit into real-time?
Keep hot slices in the serving store and query deep history in the lake. Example: StarRocks external tables over Iceberg let you join hot facts with older partitions without copying them.
Do I really need sub-second dashboards?
Only if the user experience or decision demands it. Seconds-level freshness is usually enough and far cheaper. Save sub-second for where it moves revenue, risk, or UX.
What are common anti-patterns to avoid?
Over-denormalizing in streams, putting all business logic in Flink, short Kafka retention, chasing averages instead of P95/P99, uncontrolled MVs, no workload isolation, and ignoring schema contracts.
How do I plan for reprocessing and backfills?
Keep enough Kafka retention, document replay runbooks, make sinks idempotent, and ensure batch replays respect the same schema contracts as real-time.
How do I budget for cost without losing speed?
Tie performance targets to business value. Favor seconds-level freshness by default, add MVs only where measured pain exists, and protect tail latency with isolation instead of brute-force overprovisioning.
What about multi-tenancy?
Use per-tenant quotas and resource groups; pin premium or mission-critical tenants to reserved capacity. Track P95/P99 by tenant and alert on degradation.
How do I roll schema changes safely?
Version schemas, treat breaking changes as releases, canary new producers, and quarantine poison records at ingest. Keep clear evolution paths in your contracts.
Where does a real-time OLAP engine like StarRocks add the most value?
Serving seconds-fresh, join-heavy analytics at high concurrency; handling mutability with primary-key upserts and partial updates; accelerating heavy patterns with incremental MVs and auto-rewrite; isolating workloads with resource groups; and querying lake history via external tables when needed.
What’s a good first project to prove this out?
Pick one high-value dashboard or API with clear SLOs (e.g., promo pacing or fraud gating). Instrument freshness and P95/P99, ingest via CDC/Kafka into the serving tier, add one or two targeted MVs, and isolate the workload. Expand once the SLO stays green.
If I remember only one thing, what should it be?
Write down the freshness and tail-latency you promise, measure them continuously, and keep the architecture as small as it can be—and no smaller.
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