Confluent Cloud Flink Sql

Confluent Cloud Flink SQL

Enforce the CC-Flink-vs-OSS-Flink dialect boundary and the CLI-driven verification loop for any Confluent Cloud Flink SQL work. Apache Flink OSS training data is a trap — CC rejects or silently mishandles a long list of otherwise-valid Flink SQL constructs.

Scope note: this skill's reference material is built around the OSS-vs-CC dialect boundary — traps in constructs that exist in both dialects but behave differently. It does not yet catalog CC-only DDL that has no OSS counterpart (e.g. CREATE MATERIALIZED TABLE, CREATE MODEL/AI_COMPLETE, CREATE AGENT, USE CATALOG). For those, verify directly against the CC Flink SQL reference rather than expecting a trap entry here.

Non-negotiables

1. CC Flink ≠ Apache Flink. Verify every API, SQL construct, and runtime behavior against:
   • Confluent Cloud Flink docs
   • CC Flink SQL reference
   • Confluent Terraform provider
   • Live confluent flink shell against the user's compute pool
2. No mocks in verification. Integration claims require real confluent CLI runs. Unit tests may mock; anything calling itself "end-to-end verification" may not.
3. Record decisions somewhere durable. Ask the user where they want dialect traps and verification notes tracked (e.g. docs/flink-dialect-traps.md in their project) before writing any new file — don't assume a docs/ layout.
4. Secrets never in repo. CREATE CONNECTION parameters are Terraform-injected, never hardcoded. Gitignore .tfvars, .tfstate*, *.secret* from day one.
5. EXPLAIN before CREATE. Always EXPLAIN a query before statement create — catches parse/type errors without consuming CFUs.
6. Don't invent identifiers. Use <placeholder> for any topic, table, statement, or resource name you haven't verified.

Reference files

Load these on demand when the topic matches — do not read them all upfront:

Red flags

Stop and consult references/dialect-traps.md if you catch yourself writing any of these:

• DataStream API (Java/Scala) — not supported on CC. Table API + PTF are GA in Java; Python Table API is Open Preview with no PTF yet
• CREATE CATALOG ... — catalog = CC environment, not creatable
• SET 'execution.checkpointing.*' — CC-managed, not settable
• CREATE TABLE ... WITH ('connector' = 'kafka', ...) — tables auto-map from topics
• 'value.format' = 'json' — must be 'json-registry' (or another SR-backed format)
• WITH cte AS (...) INSERT INTO ... — CC requires the CTE AFTER INSERT INTO
• GROUP BY TUMBLE(ts, INTERVAL ...) — must use the TVF form: TUMBLE(TABLE t, DESCRIPTOR(ts), ...)
• LATERAL TABLE(UNNEST(...)) — parse error; use CROSS JOIN UNNEST(...)
• PROCTIME() — not supported; use External Tables/KEY_SEARCH_AGG or an event-time temporal join (avoid a regular join against an upsert-kafka topic — it retains the whole table in state)
• CREATE FUNCTION f AS '...' without USING JAR — CC UDFs require an uploaded artifact
• Savepoints / STOP WITH SAVEPOINT — not exposed on CC
• --sql-file flag — doesn't exist; use --sql "$(cat file.sql)"
• DROP TABLE — deletes the physical Kafka topic and its data on CC, not just metadata; confirm before running

Verification loop

Canonical validation loop for any CC Flink SQL claim:

0. EXPLAIN the query in flink shell — catches syntax and type errors for free.
1. Write a minimal reproducer.
2. Present the plan and wait for explicit user confirmation before running anything that creates or modifies a real resource. State: the statement name and SQL, the compute pool/database/environment it targets, and any side effects (DDL creates a Kafka topic and Schema Registry subject; every run consumes CFUs). Do not proceed to step 3 without a go-ahead.
3. Run: confluent flink statement create <name> --sql "$(cat repro.sql)" --compute-pool <id> --database <cluster> --environment <env> --wait
4. Observe. Consume downstream: confluent kafka topic consume <topic> --cluster <id> --from-beginning --value-format <matching-format> 2>/dev/null | grep -v '^%' — match <matching-format> to the sink's value.format (see references/formats-and-serialization.md [blocked]; jsonschema for json-registry, avro for avro-registry, protobuf for proto-registry, string for raw)
5. Record the command + output for later reference.

Escalation-required states (no silent workarounds):

• Statement PENDING > 60s → confluent flink statement exception list <name> --cloud <provider> --region <region>
• UDF deploy "jar not found" → confluent flink artifact list --cloud <provider> --region <region>
• Schema mismatch → DESCRIBE <table>, diff against the producer schema
• Egress denied → check CREATE CONNECTION + USING CONNECTIONS clause

See references/cli-reference.md [blocked] for full flag schemas and timing expectations.

Anti-patterns

• Apache Flink docs or Stack Overflow answers tagged apache-flink cited as CC authority
• LLM memory of "Flink SQL syntax" used without CC verification
• Mocking the confluent CLI in anything claiming end-to-end verification
• terraform apply -auto-approve on the first run of a root module
• Committing .tfvars, .tfstate*, .terraform/, *.secret*
• Swallowing Flink statement exceptions — fail loud; read statement exception list
• Hardcoded secrets in CREATE CONNECTION or UDF source

Tutorials

developer.confluent.io/tutorials/#flink mixes OSS and CC tutorials.

Filter rule: Only use tutorials that list "Confluent Cloud" in prerequisites or use confluent flink shell. Apply the dialect trap table to any SQL copied from a tutorial — many target OSS Flink or Kafka Streams and are not CC-compatible as-is.

Source-of-truth hierarchy

1. references/dialect-traps.md (this skill) — canonical, consolidated
2. Per-project CLAUDE.md — references this skill, adds project-specific context
3. A per-project trap log, if the user wants one kept — ask where before creating it

When a new trap is discovered during a session, tell the user so they can decide whether to record it in their project's own notes. Do not edit this skill's own installed files (references/dialect-traps.md or elsewhere) — propose the change and let the user (or a separate PR to this skill's repo) apply it.

References

• Confluent Cloud Flink docs
• Confluent Cloud Flink SQL reference
• Confluent Terraform provider
• Confluent CLI reference — Flink
• Tutorials (filter for CC only)