Proposal for dbfs file access api with mocked test suite #3236
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danzafar
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nfx
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don't directly depend on py4j, see techniques. Otherwise the approach is good.
pyproject.toml
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| "sqlglot>=25.5.0,<25.30", | ||
| "astroid>=3.3.1"] | ||
| "astroid>=3.3.1", | ||
| "py4j==0.10.9.7"] |
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we cannot add py4j as a dependency to UCX, as it'll have to be packaged with transitive dependencies to run CLI.
| def _jvm(self): | ||
| try: | ||
| _jvm = self._spark._jvm | ||
| self._java_import(_jvm, "org.apache.hadoop.fs.FileSystem") |
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| self._java_import(_jvm, "org.apache.hadoop.fs.FileSystem") | |
| FileSystem = jvm.org.apache.hadoop.fs.FileSystem |
use the same technique as here, where you don't have to depend directly on py4j:
https://github.com/databrickslabs/ucx/blame/main/src/databricks/labs/ucx/hive_metastore/locations.py#L397-L401
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Changes
scan-tables-in-mounts.Why this PR?
Refactoring
scan-tables-in-mountsto enhance performance will need to leverage parallelized crawling ofdbfs:/mnt/locations. @nfx suggested using backend Hadoop libraries throughpy4jfor this through theSparkSession. Since this is a fairly big change, I wanted to make sure we are aligned on the backend file lister before I proceed to modify any existing files.Enable
dbfsfile listingThis PR proposes the
DbfsFilesclass which uses theSparkSession's java backend withpy4jto leverage the following Hadoop libraries for efficientdbfs:/(andmount) file access:org.apache.hadoop.fs.FileSystemorg.apache.hadoop.fs.PathAs of now, the only useful method is
list_dir, but if the approach is confirmed I plan to add crawling capabilities leveragingdatabricks.labs.blueprint.parallel.Test strategy
A testing strategy was painstakingly created to:
scan-tables-in-mounts. I created a simple trie-based mock file system which can be leveraged forscan-tables-in-mountsfunctionality after the refactor. This mock file system also has it's own unit tests.Functionality
databricks labs ucx .........Tests