+1
Test environment: - OS: macOS (Darwin 25.2.0, ARM64/Apple Silicon) - Java: OpenJDK 21.0.1 LTS (TencentKonaJDK) - Fluss: 0.9.0-incubating (binary distribution) - Flink: 2.2.0 - Connector: `fluss-flink-2.2-0.9.0-incubating.jar` (built from 0.9 source) - Fluss Cluster: local-cluster mode (1 CoordinatorServer + 1 TabletServer) - Flink Cluster: Standalone (1 JobManager + 1 TaskManager, process=4096m) Tested Flink + Fluss complex types (ARRAY/MAP/ROW) with 71 test cases, all passed:*OK* - ARRAY type PK table (create/insert/point-query/upsert/delete/streaming-query): *OK* (10 cases) - MAP type PK table (create/insert/point-query/upsert/delete/streaming-query): *OK* (9 cases) - ROW type PK table (create/insert/point-query/upsert/delete/streaming-query): *OK* (9 cases) - Nested complex types — ARRAY\<ROW\>, MAP\<STRING,ARRAY\>, ROW\<MAP+ARRAY\> (create/insert/point-query): *OK* (11 cases) - Log table (no PK) with ARRAY/MAP/ROW (create/insert/streaming-query): *OK* (9 cases) - Edge cases — empty ARRAY/MAP, NULL complex types, large ARRAY(50 elements), deep nesting ARRAY\<MAP\<STRING,ROW\>\>: *OK* (16 cases) - Lookup Join with complex type dimension table (ARRAY+MAP+ROW): *OK* (7 cases) Detailed test cases: | # | Category | Test Case | Mode | Result | |---|----------|-----------|------|--------| | 1 | ARRAY PK Table | Create table with ARRAY\<INT\>/ARRAY\<STRING\>/ARRAY\<DOUBLE\>/ARRAY\<BOOLEAN\> | batch | *OK* | | 2 | ARRAY PK Table | Insert 3 rows with ARRAY data | batch | *OK* | | 3 | ARRAY PK Table | Point query (id=1) | batch | *OK* | | 4 | ARRAY PK Table | Point query (id=2) | batch | *OK* | | 5 | ARRAY PK Table | Point query (id=3) | batch | *OK* | | 6 | ARRAY PK Table | Upsert update ARRAY (id=1) | batch | *OK* | | 7 | ARRAY PK Table | Verify update (id=1 int_arr=[99,88,77]) | batch | *OK* | | 8 | ARRAY PK Table | Delete record (id=3) | batch | *OK* | | 9 | ARRAY PK Table | Verify delete (id=3 not exists) | batch | *OK* | | 10 | ARRAY PK Table | Streaming full scan ARRAY table | streaming | *OK* | | 11 | MAP PK Table | Create table with MAP\<STRING,INT\>/MAP\<STRING,STRING\>/MAP\<INT,DOUBLE\> | batch | *OK* | | 12 | MAP PK Table | Insert 3 rows with MAP data | batch | *OK* | | 13 | MAP PK Table | Point query (id=1) | batch | *OK* | | 14 | MAP PK Table | Point query (id=2) | batch | *OK* | | 15 | MAP PK Table | Upsert update MAP (id=2) | batch | *OK* | | 16 | MAP PK Table | Verify update (id=2 updated=999) | batch | *OK* | | 17 | MAP PK Table | Delete record (id=3) | batch | *OK* | | 18 | MAP PK Table | Verify delete (id=3 not exists) | batch | *OK* | | 19 | MAP PK Table | Streaming full scan MAP table | streaming | *OK* | | 20 | ROW PK Table | Create table with ROW\<city,zipcode,street\>/ROW\<phone,email\> | batch | *OK* | | 21 | ROW PK Table | Insert 3 rows with ROW data | batch | *OK* | | 22 | ROW PK Table | Point query (id=1) | batch | *OK* | | 23 | ROW PK Table | Point query (id=2) | batch | *OK* | | 24 | ROW PK Table | Upsert update ROW (id=2 city→Hangzhou) | batch | *OK* | | 25 | ROW PK Table | Verify update (id=2) | batch | *OK* | | 26 | ROW PK Table | Delete record (id=3) | batch | *OK* | | 27 | ROW PK Table | Verify delete (id=3 not exists) | batch | *OK* | | 28 | ROW PK Table | Streaming full scan ROW table | streaming | *OK* | | 29 | Nested Types | Create table ARRAY\<ROW\<name,price,qty\>\> | batch | *OK* | | 30 | Nested Types | Insert ARRAY\<ROW\> data | batch | *OK* | | 31 | Nested Types | Point query ARRAY\<ROW\> (id=1001) | batch | *OK* | | 32 | Nested Types | Point query ARRAY\<ROW\> (id=1002) | batch | *OK* | | 33 | Nested Types | Create table MAP\<STRING,ARRAY\<INT\>\> | batch | *OK* | | 34 | Nested Types | Insert MAP\<STRING,ARRAY\> data | batch | *OK* | | 35 | Nested Types | Point query MAP\<STRING,ARRAY\> (id=1) | batch | *OK* | | 36 | Nested Types | Create table ROW\<name,MAP,ARRAY\> | batch | *OK* | | 37 | Nested Types | Insert ROW nested MAP+ARRAY data | batch | *OK* | | 38 | Nested Types | Point query ROW nested (id=1) | batch | *OK* | | 39 | Nested Types | Point query ROW nested (id=2) | batch | *OK* | | 40 | Log Table (no PK) | Create ARRAY log table | batch | *OK* | | 41 | Log Table (no PK) | Insert ARRAY log data | batch | *OK* | | 42 | Log Table (no PK) | Streaming query ARRAY log | streaming | *OK* | | 43 | Log Table (no PK) | Create MAP log table | batch | *OK* | | 44 | Log Table (no PK) | Insert MAP log data | batch | *OK* | | 45 | Log Table (no PK) | Streaming query MAP log | streaming | *OK* | | 46 | Log Table (no PK) | Create ROW log table | batch | *OK* | | 47 | Log Table (no PK) | Insert ROW log data | batch | *OK* | | 48 | Log Table (no PK) | Streaming query ROW log | streaming | *OK* | | 49 | Edge Cases | Create empty ARRAY table | batch | *OK* | | 50 | Edge Cases | Insert empty ARRAY[] | batch | *OK* | | 51 | Edge Cases | Point query empty ARRAY | batch | *OK* | | 52 | Edge Cases | Create empty MAP table | batch | *OK* | | 53 | Edge Cases | Insert empty MAP[] | batch | *OK* | | 54 | Edge Cases | Point query empty MAP | batch | *OK* | | 55 | Edge Cases | Create NULL complex type table | batch | *OK* | | 56 | Edge Cases | Insert NULL ARRAY/MAP/ROW | batch | *OK* | | 57 | Edge Cases | Point query all NULL (id=1) | batch | *OK* | | 58 | Edge Cases | Point query non-NULL (id=2) | batch | *OK* | | 59 | Edge Cases | Create large ARRAY table | batch | *OK* | | 60 | Edge Cases | Insert ARRAY with 50 elements | batch | *OK* | | 61 | Edge Cases | Point query large ARRAY | batch | *OK* | | 62 | Edge Cases | Create deep nesting ARRAY\<MAP\<STRING,ROW\>\> | batch | *OK* | | 63 | Edge Cases | Insert deep nesting data | batch | *OK* | | 64 | Edge Cases | Point query deep nesting | batch | *OK* | | 65 | Lookup Join | Create dimension table (ARRAY+MAP+ROW) | batch | *OK* | | 66 | Lookup Join | Insert dimension data | batch | *OK* | | 67 | Lookup Join | Point query dimension (id=1) | batch | *OK* | | 68 | Lookup Join | Point query dimension (id=2) | batch | *OK* | | 69 | Lookup Join | Create fact stream table | batch | *OK* | | 70 | Lookup Join | Insert fact data | batch | *OK* | | 71 | Lookup Join | Lookup Join query (fact ⋈ dim with complex types) | streaming | *OK* | Best, ForwardXu ForwardXu [email protected] 原始邮件 发件人：Jark Wu <[email protected]> 发件时间：2026年2月13日 18:41 收件人：dev <[email protected]> 主题：Re: [VOTE] Release Fluss 0.9.0-incubating (RC1) +1 (binding) - Checked sums and signatures: *OK* - Check the LICENSE and NOTICE files are correct: *OK* - Checked the jars in the staging repo: *OK* - Checked source distribution doesn't include binaries: *OK* - Build from source with Java11 and Maven 3.8.6: *OK* - Checked version consistency in pom files: *OK* - Went through the quick start: *OK* - Checked release notes and release blog: *OK* - Run Flink Engine quickstart with Flink 2.2 and Fluss binary distribution: *OK* - Run "Real-Time Analytics With Flink" quickstart: *OK* - Tested Flink + Fluss for auto-increment column and agg merge engine: *OK* (There is a bug on the doc which I have pushed a hotfix [3]) Btw, it seems the Download page update PR is missing. (not blocker) Best, Jark [1] https://fluss.apache.org/docs/0.9/table-design/table-types/pk-table/#auto-increment-column [2] https://fluss.apache.org/docs/0.9/table-design/merge-engines/aggregation/ [3] https://github.com/apache/fluss/commit/074d7563f3f414e574de55fd5c1361135b337496 On Thu, 12 Feb 2026 at 20:03, yuxia <[email protected]> wrote: > > Hi everyone, > > Please review and vote on the release candidate #1 for the Apache Fluss > version 0.9.0-incubating, as follows: > [ ] +1, Approve the release > [ ] -1, Do not approve the release (please provide specific comments) > > > The complete staging area is available for your review, includes: > > The official source release and binary convenience releases to be deployed > to: > * https://dist.apache.org/repos/dist/dev/incubator/fluss/fluss-0.9.0-incubating-rc1 > > Helm charts are available on: > * https://dist.apache.org/repos/dist/dev/incubator/fluss/helm-chart > (NB: you have to build the Docker images locally with the version > 0.9.0-incubating in order to test Helm charts) > > All the files are signed with the key with fingerprint 56A9F259A4C18F9C, > you can find the KEYS file here: > * https://dist.apache.org/repos/dist/release/incubator/fluss/KEYS > > All artifacts to be deployed to the Maven Central Repository: > * https://repository.apache.org/content/repositories/orgapachefluss-1004/ > > Git tag for the release: > * https://github.com/apache/fluss/releases/tag/v0.9.0-incubating-rc1 > > Git commit for the release: > * > https://github.com/apache/fluss/commit/d6fd1f1f607a2672bff5d18d5ca811bfa920bbd7 > > Website pull request for the release announcement blog post > * https://github.com/apache/fluss/pull/2590 > > Upgrade note for the new release: > * > https://github.com/apache/fluss/blob/release-0.9/website/docs/maintenance/operations/upgrade-notes-0.9.md > > Docker images for the release candidate: > * fluss: apache/fluss:0.9.0-incubating-rc1 > > Please download, verify and test. To learn more about how to verify: > https://fluss.apache.org/community/how-to-release/verifying-a-fluss-release/ > > > The vote will be open for at least 72 hours. It is adopted by majority > approval, with at least 3 PPMC affirmative votes. > > > Best regards, > Yuxia
