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How to Recurse Data Lake Folders with Synapse Spark Pools

By default, mssparkutils file listing capabilities don't come with a recursion flag - probably by design.

One of the gotchas I ran into when going through a recent OpenHack was manually clicking through a Data Lake with a hefty number of file sources and partitions. I was in my Synapse notebook crunched for time, wishing there was a way to recursively list all files from a root directory, to figure out what DataFrames I needed to import, what the schema looks like, what the transformation pipeline is going to look like etc.

The out-of-the-box mssparkutils.fs.ls('path') command returns something like this:

Default list
Default list

We're also left with the default formatting above, as the display() command is geared towards DataFrames. Calling display(mssparkutils.fs.ls(root)) returns:

AssertionError: dataType <py4j.java_gateway.JavaMember object at 0x7fa481050358> should be an instance of <class 'pyspark.sql.types.DataType'>
Traceback (most recent call last):

  File "/home/trusted-service-user/cluster-env/env/lib/python3.6/site-packages/notebookutils/visualization/display.py", line 96, in display
    df = _convert(data)

Since mssparkutils.fs.ls(root) returns a list object instead.

deep_ls & convertfiles2df for Synapse Spark Pools

The following functions functionally achieve recursion and pretty printing in Synapse Spark Pool notebooks:

def deep_ls(path: str, max_depth=1):
    List all files and folders in specified path and
    subfolders within maximum recursion depth.

    # List all files in path
    li = mssparkutils.fs.ls(path)

    # Return all files
    for x in li:
        if x.size != 0:
            yield x

    # If the max_depth has not been reached, start
    # listing files and folders in subdirectories
    if max_depth > 1:
        for x in li:
            if x.size != 0:
            for y in deep_ls(x.path, max_depth - 1):
                yield y

    # If max_depth has been reached,
    # return the folders
        for x in li:
            if x.size == 0:
                yield x

def convertfiles2df(files):
    Converts FileInfo object into Pandas DataFrame to enable display
    # Disable Arrow-based transfers since the Pandas DataFrame is tiny
    spark.conf.set("spark.sql.execution.arrow.enabled", "false")

    schema = ['path','name','size']
    df = pd.DataFrame([[getattr(i,j) for j in schema] for i in files], columns = schema).sort_values('path')

For convertfiles2df, we're basically taking the list returned by mssparkutils.fs.ls, and converting it into DataFrame, so it works with the notebook display command.

# Get files
files = list(deep_ls(root, max_depth=20))

# Display with Pretty Printing

The example call above returns:

Recursive list
Recursive list

Much easier on the eye, and saves going through all the folders manually!

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If you have any questions or suggestions, feel free to open an issue on GitHub!

© 2021 Raki Rahman.