fix(execution-service): surface init-time fatal errors to the websocket (#5781) ### What changes were proposed in this PR? When workflow execution initialization fails, the error was recorded into the execution metadata store but never pushed to the websocket, so connected frontend clients saw nothing — particularly for failures during `WorkflowExecutionService` construction, which happens *before* the execution is published to subscribers. `WorkflowService.initExecutionService`'s catch arm now, after `errorHandler(e)` records the fatal error, pushes a `WorkflowErrorEvent` (carrying the recorded fatal errors) to `errorSubject` — the workflow-level channel that `connect()` subscribers listen on — so init-time failures surface in the UI. | init failure | before | after | |---|---|---| | during `WorkflowExecutionService` construction (pre-publish) | logged + stored, invisible to the UI | `WorkflowErrorEvent` delivered to the frontend | | during `executeWorkflow()` | recorded; UI delivery depended on subscription timing | `WorkflowErrorEvent` delivered to the frontend | The push is extracted into a small `reportFatalErrorsToSubscribers` method so it can be unit-tested without a database (the init path itself is DB-bound). ### Any related issues, documentation, discussions? Resolves #5782. Discovered while splitting #5700 (loop operators) into smaller PRs; this fix is independent of that feature and applies to `main` on its own. ### How was this PR tested? New `WorkflowServiceSpec` (TDD, red → green): pins that `reportFatalErrorsToSubscribers` delivers a `WorkflowErrorEvent` to a `connect()` subscriber carrying exactly the fatal errors recorded in the execution state store (single error, and all errors when several are present). `sbt "WorkflowExecutionService/testOnly *WorkflowServiceSpec"` passes (2/2); scalafmt + scalafix clean. ### Was this PR authored or co-authored using generative AI tooling? Co-authored with Claude Opus 4.8 in compliance with ASF.
Apache Texera (Incubating) is an open-source platform for human-AI collaborative data science using visual workflows. It enables human analysts to construct, execute, and refine data analysis tasks through an intuitive GUI, assisted by AI agents that understand natural-language instructions. Texera is well suited for a wide range of applications, including “AI for Science,” by making advanced AI and data science capabilities accessible to a broader community. It can run on a laptop for local use or be deployed in the cloud to support scalable processing of large datasets.
The platform has the following key features:
Please cite Texera as
@article{DBLP:journals/pvldb/WangHNKALLDL24, author = {Zuozhi Wang and Yicong Huang and Shengquan Ni and Avinash Kumar and Sadeem Alsudais and Xiaozhen Liu and Xinyuan Lin and Yunyan Ding and Chen Li}, title = {Texera: {A} System for Collaborative and Interactive Data Analytics Using Workflows}, journal = {Proc. {VLDB} Endow.}, volume = {17}, number = {11}, pages = {3580--3588}, year = {2024}, url = {https://www.vldb.org/pvldb/vol17/p3580-wang.pdf}, timestamp = {Thu, 19 Sep 2024 13:09:37 +0200}, biburl = {https://dblp.org/rec/journals/pvldb/WangHNKALLDL24.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }