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Fields that contain data that is not natively supported by Parqyet format, such as numpy arrays, are serialized into byte arrays. Images maybe compressed using png or jpeg compression. Serializing fields on a thread pool speeds up this process in some cases (e.g. a row contains multiple images). This PR adds a pool executor argument to `dict_to_spark_row` enabling user to pass a pool executor that would be used for parallelizing this serialization. If no pool executor is specified, the encoding/serialization is performed on the caller thread.
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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When writing data into a petastorm dataset. Before a pyspark sql.Row
object is created, fields containing data that is not natively supported
by Parqyet format, such as numpy arrays, are serialized into byte
arrays. Images maybe compressed using png or jpeg compression.
Serializing fields on a thread pool speeds up this process in some
cases (e.g. a row contains multiple images).