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config.py
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config.py
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import os
class DataSource:
def __init__(self, name, path, file_type = 'csv'):
# a human-friendly name for the data source to distinguish it from the others. E.g. 'kaggle'
self.name = name
# path to the directory containing collection of datasets in this data source
self.path = path
# extension of the tables in this data source (usually 'csv')
self.file_type = file_type
class ProfilerConfig:
# list of data sources to process
# data_sources = [DataSource(name='smaller_real',
# path=os.path.expanduser('~/projects/kglids/storage/data_sources/dataset_storage/experiments/smallerReal/benchmark'),
# file_type='csv')]
data_sources = []
# directory to save the generated column profiles.
output_path = os.path.expanduser('~/projects/kglids/storage/profiles/tus_profiles_fine_grained')
# whether to run Spark in local or cluster mode.
is_spark_local_mode = True
# number of workers (processes) to use when profiling columns. Defaults to the number of threads.
n_workers = os.cpu_count()
# maximum memory in GB to be used by Spark
max_memory = 25
profiler_config = ProfilerConfig()