How can I ensure idempotent table writes when using foreachBatch for writing to multiple Delta Lake tables in Databricks?
To ensure idempotent table writes when using `foreachBatch` for writing to multiple Delta Lake tables in Databricks, you can use the `txnAppId` and `txnVersion` options provided by Delta tables. These options help Delta Lake identify duplicate writes and ignore them, making your writes idempotent.

Here's a summary of the steps to follow:

1. Set the `txnAppId` option to a unique string that you can pass on each DataFrame write. You can use the StreamingQuery ID as `txnAppId`.
2. Set the `txnVersion` option to a monotonically increasing number that acts as the transaction version.
3. Use the same `txnAppId` and `txnVersion` for all writes within a batch.

Here's an example code snippet demonstrating this pattern:

```python
app_id =... # A unique string that is used as an application ID.

def writeToDeltaLakeTableIdempotent(batch_df, batch_id):
  batch_df.write.format(...).option("txnVersion", batch_id).option("txnAppId", app_id).save(...) # location 1
  batch_df.write.format(...).option("txnVersion", batch_id).option("txnAppId", app_id).save(...) # location 2

streamingDF.writeStream.foreachBatch(writeToDeltaLakeTableIdempotent).start()
```

By following these steps, you can ensure idempotent table writes when using `foreachBatch` for writing to multiple Delta Lake tables in Databricks.