@Rocky looking for some general guidance around replaying failed events into StarRocks. If a service writes events into StarRocks via stream loads, and some events later need to be replayed after a processing failure (potentially arriving out of their original order), what are the recommended patterns to avoid problems like stale overwrites, duplicate rows, or inconsistent state/enrichment? Is it generally safer to replay events back through the original processing pipeline, or to write replayed events directly into StarRocks? Also wondering whether there are specific StarRocks table models or features that are commonly used to handle late-arriving or replayed data safely

To handle replaying failed events and late-arriving data safely in StarRocks, the recommended pattern is centered around the Primary Key (PK) table model and the Conditional Update feature. 1. Recommended Table Model: Primary Key Table The Primary Key table is specifically designed for real-time updates and is the most suitable model for event replay scenarios. Unlike other models, it handles updates using a "Delete+Insert" strategy, which provides significantly better query performance for mutable data. 2. Preventing Stale Overwrites: Conditional Updates When replaying data, events may arrive out of their original order. To prevent a "stale" older record from overwriting a newer one already in the table, use the Conditional Update feature (available since v2.5). * How it works: You specify a column (typically a version number or a timestamp like

updated_at

) as the

merge_condition

. * Behavior: StarRocks will only apply the update if the value in the incoming record's condition column is greater than or equal to the value currently stored in the table. * Usage: * Stream Load: Add the header

-H "merge_condition: version_col"

. * Routine Load / Broker Load: Set the property

"merge_condition" = "version_col"

. * SQL:

INSERT INTO table PROPERTIES("merge_condition" = "version_col") VALUES (...)

. 3. Avoiding Duplicate Rows: Load Labels To ensure idempotency (exactly-once semantics) during retries: * Labels: Every load job (Stream Load, Broker Load, etc.) should be assigned a unique Label. * If a load fails or times out, you can retry it using the same label. StarRocks checks the label; if it was already successfully committed, the retry is ignored, preventing duplicate data. * Note: Labels are kept for a limited time (default 3 days, configurable via

label_keep_max_second

). 4. Handling Inconsistent State & Enrichment The choice between replaying through the pipeline vs. writing directly to StarRocks depends on where the "enrichment" happens: * Replay through original pipeline (Recommended): If your service performs complex enrichments (e.g., joining with external caches, calculating state) before writing to StarRocks, you should replay events through the pipeline. This ensures the enrichment logic is applied consistently to the replayed data. * Direct write to StarRocks: Only use this for "fixes" where the data is already fully formed. If you use Partial Updates (updating only specific columns), you can fix individual fields without needing the full event payload, which is very efficient for targeted corrections. 5. Summary of Key Features Feature | Benefit for Replay/Late-Data

merge_condition

| Ensures only the latest data "wins" regardless of arrival order.

__op

column | Allows you to specify if an event is an Upsert (0) or Delete (1) during replay. Partial Updates | Updates only specific columns, preserving existing state in other columns. Labels | Prevents duplicate ingestion when retrying failed load jobs. Expert Tip: If you are using Flink, the StarRocks Flink Connector handles labels and checkpoints automatically to provide end-to-end exactly-once semantics, which is the most robust way to manage these failures. References • loading/Load_to_Primary_Key_tables.md • table_design/table_types/primary_key_table.md • loading/Flink-connector-starrocks.md