Apache Spark Structured Streaming 2.3.0更新模式下Sink识别更新行机制问询

Great question! Let's break this down clearly, since it ties directly to how Spark handles stateful streaming:

1. How does a Sink recognize a new row as an update to an existing row? #

First, a critical prerequisite: you need to use stateful operations in your streaming query. Without state (like simple select or filter transformations), there’s no concept of "existing rows" to update—Spark treats every record as a brand-new entry. Stateful operations include:

• Aggregations paired with groupBy (e.g., groupBy($"user_id").agg(sum($"clicks")))
• Custom state logic via mapGroupsWithState or flatMapGroupsWithState

Once you’re using stateful operations, you must set your query’s output mode to Update (or Complete, though Complete outputs all rows every trigger, not just updates). Here’s the full flow:

• Spark maintains an in-memory (or persistable) state store that tracks the current value for each key defined in your stateful operation.
• When new data arrives, Spark looks up the corresponding state using the key, updates the state value, and checks if the state has changed.
• In Update mode, Spark only sends rows where the state has changed to the Sink.
• Note: Not all built-in Sinks support updates in 2.3.0 (e.g., the basic File Sink is append-only). For custom Sinks like ForeachSink, you’ll need to implement your own logic to match incoming rows to existing data using the state key (like user_id in the aggregation example)—Spark doesn’t explicitly flag rows as "update" vs "insert"; it’s up to you to use the key to identify which entries need updating in your target system.

2. In Update mode, does Spark match all column values or use a hash to identify updates? #

Neither—Spark relies on the key from your stateful operation to track state, and only checks if the state value for that key has changed. Here’s how it works:

• For aggregations like groupBy($"id").agg(sum($"value")), id is the key. Spark uses this key to look up the existing state (the current sum) for that id.
• When new data for an existing id comes in, Spark recalculates the aggregated value. If the new value differs from the stored state, it updates the state and marks this id’s row for sending to the Sink.
• Spark doesn’t compare all column values or compute a hash of the entire row. It only cares about the key (to locate the state) and whether the state value itself has changed.
• For custom state operations (mapGroupsWithState), you have full control over defining what counts as a "changed" state—you can directly compare the old state to the new state you generate, and decide whether to return the row to the Sink.

For example: If you’re aggregating total clicks per user, when a returning user sends a new click, Spark updates their total. Since the total changes, that user’s row is sent to the Sink in Update mode. Spark doesn’t check every column of the incoming click event against existing rows—it uses the user_id key to find the state, and checks if the aggregated total has changed.

内容的提问来源于stack exchange，提问作者user1870400