468da03e23
## What changes were proposed in this pull request? Spark currently incorrectly continues to use cached data even if the underlying data is overwritten. Current behavior: ```scala val dir = "/tmp/test" sqlContext.range(1000).write.mode("overwrite").parquet(dir) val df = sqlContext.read.parquet(dir).cache() df.count() // outputs 1000 sqlContext.range(10).write.mode("overwrite").parquet(dir) sqlContext.read.parquet(dir).count() // outputs 1000 <---- We are still using the cached dataset ``` This patch fixes this bug by adding support for `REFRESH path` that invalidates and refreshes all the cached data (and the associated metadata) for any dataframe that contains the given data source path. Expected behavior: ```scala val dir = "/tmp/test" sqlContext.range(1000).write.mode("overwrite").parquet(dir) val df = sqlContext.read.parquet(dir).cache() df.count() // outputs 1000 sqlContext.range(10).write.mode("overwrite").parquet(dir) spark.catalog.refreshResource(dir) sqlContext.read.parquet(dir).count() // outputs 10 <---- We are not using the cached dataset ``` ## How was this patch tested? Unit tests for overwrites and appends in `ParquetQuerySuite` and `CachedTableSuite`. Author: Sameer Agarwal <sameer@databricks.com> Closes #13566 from sameeragarwal/refresh-path-2. |
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