pyspark.sql.DataFrameReader.orc¶
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DataFrameReader.orc(path, mergeSchema=None, pathGlobFilter=None, recursiveFileLookup=None, modifiedBefore=None, modifiedAfter=None)[source]¶ Loads ORC files, returning the result as a
DataFrame.New in version 1.5.0.
- Parameters:
- pathstr or list
- mergeSchemastr or bool, optional
sets whether we should merge schemas collected from all ORC part-files. This will override
spark.sql.orc.mergeSchema. The default value is specified inspark.sql.orc.mergeSchema.- pathGlobFilterstr or bool
an optional glob pattern to only include files with paths matching the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter. It does not change the behavior of partition discovery. # noqa
- recursiveFileLookupstr or bool
recursively scan a directory for files. Using this option disables partition discovery. # noqa
modification times occurring before the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
- modifiedBeforean optional timestamp to only include files with
modification times occurring before the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
- modifiedAfteran optional timestamp to only include files with
modification times occurring after the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
Examples
>>> df = spark.read.orc('python/test_support/sql/orc_partitioned') >>> df.dtypes [('a', 'bigint'), ('b', 'int'), ('c', 'int')]