逐日AI

面试题库

共 328 题,当前筛选 1 题。

标签
还有 126 个标签
#agent-loop2#api-design2#chunking2#cost-tradeoff2#debugging2#distributed-systems2#error-handling2#hybrid-search2#llm-as-judge2#multi-agent2#multi-hop2#oauth2#operations2#pipeline-design2#prompt-caching2#rag2#recall2#retrospective2#retry2#scheduling2#sse2#tool-permissions2#trade-offs2#access-control1#agentic-rag1#agents-sdk1#analytics1#architecture-review1#behavioral1#budget-control1#caching1#cancellation1#checkpointing1#circuit-breaker1#citation-verification1#client1#client-integration1#coding-agent1#compaction1#compliance1#concurrency1#confused-deputy1#context-engineering1#contextual-retrieval1#copyright1#correctness1#cost-control1#customer-support1#data-quality1#deployment1#distribution1#embedding-migration1#error-propagation1#escalation1#evidence1#faithfulness1#fallback1#feedback-loop1#fencing-token1#filter-pushdown1#filtering1#framework-design1#graph-rag1#guardrails1#handoff1#image-generation1#integration1#iterative-scan1#json-parsing1#labeling1#latency1#latency-budget1#least-privilege1#long-context1#long-session1#long-term-memory1#mcp1#message-bus1#methodology1#model-migration1#multi-tenancy1#notifications1#ocr1#offline-testing1#project-storytelling1#protocol-versions1#quality1#query-rewriting1#rate-limiting1#reconnect1#refusal1#replay1#reproducibility1#rerank1#resume1#retrieval1#risk-assessment1#rollout1#routing1#runtime1#safety1#scaling1#self-introduction1#split-brain1#state-management1#state-persistence1#statelessness1#stdio-transport1#storytelling1#subagent1#subagents1#subscriptions1#test-strategy1#thresholds1#timezone1#token-accounting1#tool-design1#tool-schema1#tools1#trust-boundary1#ux1#verification1#versioning1#workflow-design1#workflow-engine1#zero-downtime1

30 天从前端工程师到 Agent 工程师

D8 为什么 Gateway/Worker 分离;Postgres 表设计(sessions/runs/messages)+ Drizzle

  • 消息总线是至少一次投递,同一条消息被重复投递时,怎么保证不会产生两条 run?With at-least-once delivery, how do you guarantee a redelivered message does not create two runs?
    国内高频海外高频深入#idempotency#database#reliability

    分析过程 · 先想清楚再作答

    1. 这题在考幂等的落点在哪一层。凡是答「在代码里先查一下有没有,没有再插入」的,基本当场结束——因为那正是这题想筛掉的答案。
    2. 先把前提摊开:重复不是意外。总线是至少一次语义、客户端会超时重发、用户会手抖双击,同一句话到达两次是必然事件。所以设计目标不是「避免重复到达」,而是「重复到达时结果相同」。
    3. 然后给推导:幂等需要一个由请求内容决定的键。随机 UUID 每次都不同,等于没有幂等;正确取法是把会话 id、客户端消息 id、消息内容拼起来做哈希,客户端没有消息 id 时退用内容加一个粗粒度时间窗。
    4. 结论落在存储层:在 runs 表的这一列上加唯一约束,插入写成「冲突就什么都不做」,返回零行时回查那条已有的 run,把同一个 runId 返回给用户。两次请求、一条 run、一个 runId。
    5. 解释为什么「先查后插」不行,这是本题的分水岭:两个 Gateway 实例可以同时查、同时发现没有、同时插入,这两步之间有一个应用层拦不住的时间窗;它窄到压测复现不出来,上线后每天漏几条。**幂等的最终裁判必须是数据库的唯一约束**,应用层的判断只是为了少一次插入尝试。
    6. 可以预期的追问:那消费侧的重复执行呢?答:唯一约束保证了只有一条 run,但 Worker 可能重复拿到同一条 run,所以状态迁移也要带条件更新(只有当前状态是 pending 时才能改成 running),并且用一个显式的迁移白名单挡住「已完成的 run 被推回运行中」这种会覆盖用户已收到回复的情况。

    How to reason about it · think before answering

    1. This question is about which layer idempotency lives in. Anyone who answers 'check whether it exists, then insert' has usually just failed it — that is exactly the answer being screened out.
    2. State the premise: duplicates are not accidents. The bus is at-least-once, clients retry on timeout, users double-click. The same message arriving twice is certain, so the goal is not to prevent duplicates but to make duplicates produce the same result.
    3. Then derive the key: idempotency needs a key derived from request content. A random UUID differs every time and buys nothing; hash the session id, the client message id and the message body together, falling back to content plus a coarse time bucket when the client has no id.
    4. Land it in storage: put a unique constraint on that column in the runs table, write the insert as on-conflict-do-nothing, and when it returns zero rows read back the existing run and return the same run id. Two requests, one run, one id.
    5. Explain why check-then-insert fails, which is the whole point: two gateway instances can query, both see nothing, and both insert. The window between the two statements cannot be closed in application code, it is too narrow to reproduce under load tests, and it leaks a few bad rows every day in production. The database's unique constraint has to be the final arbiter; the application-level check only saves a wasted insert.
    6. Expect the follow-up: what about duplicate execution on the consumer side? The unique constraint gives you one run, but a worker can still receive it twice, so status changes need conditional updates (move to running only if the current status is pending) plus an explicit transition whitelist that blocks a finished run from being pushed back to running and overwriting a reply the user already saw.

    答题要点

    • 重复投递是必然事件,设计目标是「重复到达时结果相同」,不是「避免重复」
    • 幂等键必须由请求内容决定:会话 id 加客户端消息 id 加内容做哈希,随机 UUID 等于没有幂等
    • 在 runs 的幂等键列上建唯一约束,插入用「冲突就什么都不做」,零行时回查已有 run 返回同一个 runId
    • 先查后插在并发下必然出双份,两条语句之间的时间窗应用层拦不住,幂等的最终裁判是数据库唯一约束
    • 消费侧还要用条件更新加状态迁移白名单,避免同一条 run 被重复执行或把已完成的回复覆盖掉

    Key points

    • Redelivery is certain, so the goal is identical outcomes on duplicates, not preventing duplicates
    • The idempotency key must be derived from request content — session id plus client message id plus body, hashed; a random UUID buys nothing
    • Put a unique constraint on that column, insert with on-conflict-do-nothing, and read back the existing run when zero rows return
    • Check-then-insert races under concurrency; the window between the statements cannot be closed in application code, so the unique constraint must be the final arbiter
    • On the consumer side add conditional status updates and a transition whitelist so a finished run is never re-run or overwritten