Apache Fluss — PK tables, lake tiering, streaming vs batch consistency

5 h
requires STH-02

From your world. One copy that serves both a sub-second lookup and a historical batch scan

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1Apache Fluss is designed to fill a gap between Kafka and a lakehouse. What is this gap? AIt replaces object storage with a relational database BIt is a columnar, low-latency streaming store. Its recent data is hot (sub-second) and its history tiers to the lake, so it serves both latencies from one copy CIt is a faster batch scheduler DIt is a drop-in replacement for dbt

5What is the read-consistency invariant for a tiered Fluss table? AThe streaming read always returns MORE rows than the batch read BAfter tiering quiesces, a streaming read and a batch read of the lake tier return the same set of rows CThe batch read is always stale and never matches DOnly the hot tier is ever consistent

6Why must a batch read of the $lake tier be bounded with a wall-clock deadline? ATo rate-limit the output BTiering is asynchronous and the source can be unbounded. Without a deadline the read could hang. A bounded read returns when the tiered data has landed CBecause Fluss forbids batch reads DTo force exactly-once semantics

7Which conditions must be true for a Fluss table to tier to Paimon and to be read as $lake? (choose all) (select all that apply) A'table.datalake.enabled'='true' is set on the table BThe server knows the lake format and warehouse (datalake.format=paimon, datalake.paimon.warehouse) Cpaimon-flink is on the classpath for the $lake read path DThe table must be a log table with no primary key

8When is Fluss + lake tiering NOT worth its operational cost? AOne copy must serve both a sub-second lookup and a historical scan at scale BYou only ever need batch/OLAP and nothing reads a hot tier, so you write straight to Paimon/Iceberg instead CFreshness SLAs are strict DThe fast store and the warehouse must stay consistent automatically