Unable to read files from google cloud storage bucket from jupyter notebook running on data proc cluster
I am working on a Data Proc Spark cluster with an initialization action to install Jupyter notebook. I am unable to read the csv files stored on the google cloud storage bucket, however I am able to read the same files when I work on Spark Shell
Below is the error code I am getting
import pandas as pd
import numpy as np
data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
FileNotFoundError Traceback (most recent call last)
<ipython-input-20-2457012764fa> in <module>
----> 1 data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)
676 skip_blank_lines=skip_blank_lines)
677
--> 678 return _read(filepath_or_buffer, kwds)
679
680 parser_f.__name__ = name
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
438
439 # Create the parser.
--> 440 parser = TextFileReader(filepath_or_buffer, **kwds)
441
442 if chunksize or iterator:
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
785 self.options['has_index_names'] = kwds['has_index_names']
786
--> 787 self._make_engine(self.engine)
788
789 def close(self):
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
1012 def _make_engine(self, engine='c'):
1013 if engine == 'c':
-> 1014 self._engine = CParserWrapper(self.f, **self.options)
1015 else:
1016 if engine == 'python':
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1706 kwds['usecols'] = self.usecols
1707
-> 1708 self._reader = parsers.TextReader(src, **kwds)
1709
1710 passed_names = self.names is None
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
FileNotFoundError: File b'gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv' does not exist
Location path for the CSV file
gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv
I have also made sure that the csv file is stored in the same storage bucket as attached to the data proc and have made sure that the file is in UTF-8-Encoded csv format
Can anyone please help me how to read the files stored in google bucket from jupyter notebook running on a dataproc cluster in google cloud.
Kindly let me know if more information is required
Thanks in Advance!!
python jupyter-notebook google-cloud-storage google-cloud-dataproc
add a comment |
I am working on a Data Proc Spark cluster with an initialization action to install Jupyter notebook. I am unable to read the csv files stored on the google cloud storage bucket, however I am able to read the same files when I work on Spark Shell
Below is the error code I am getting
import pandas as pd
import numpy as np
data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
FileNotFoundError Traceback (most recent call last)
<ipython-input-20-2457012764fa> in <module>
----> 1 data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)
676 skip_blank_lines=skip_blank_lines)
677
--> 678 return _read(filepath_or_buffer, kwds)
679
680 parser_f.__name__ = name
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
438
439 # Create the parser.
--> 440 parser = TextFileReader(filepath_or_buffer, **kwds)
441
442 if chunksize or iterator:
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
785 self.options['has_index_names'] = kwds['has_index_names']
786
--> 787 self._make_engine(self.engine)
788
789 def close(self):
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
1012 def _make_engine(self, engine='c'):
1013 if engine == 'c':
-> 1014 self._engine = CParserWrapper(self.f, **self.options)
1015 else:
1016 if engine == 'python':
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1706 kwds['usecols'] = self.usecols
1707
-> 1708 self._reader = parsers.TextReader(src, **kwds)
1709
1710 passed_names = self.names is None
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
FileNotFoundError: File b'gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv' does not exist
Location path for the CSV file
gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv
I have also made sure that the csv file is stored in the same storage bucket as attached to the data proc and have made sure that the file is in UTF-8-Encoded csv format
Can anyone please help me how to read the files stored in google bucket from jupyter notebook running on a dataproc cluster in google cloud.
Kindly let me know if more information is required
Thanks in Advance!!
python jupyter-notebook google-cloud-storage google-cloud-dataproc
What kernel are you using in Jupyter? Python or Spark?
– tix
Nov 14 '18 at 2:29
I have actually tried using both the Kernels, Python and Py-Spark
– Tushar Mehta
Nov 14 '18 at 2:59
add a comment |
I am working on a Data Proc Spark cluster with an initialization action to install Jupyter notebook. I am unable to read the csv files stored on the google cloud storage bucket, however I am able to read the same files when I work on Spark Shell
Below is the error code I am getting
import pandas as pd
import numpy as np
data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
FileNotFoundError Traceback (most recent call last)
<ipython-input-20-2457012764fa> in <module>
----> 1 data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)
676 skip_blank_lines=skip_blank_lines)
677
--> 678 return _read(filepath_or_buffer, kwds)
679
680 parser_f.__name__ = name
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
438
439 # Create the parser.
--> 440 parser = TextFileReader(filepath_or_buffer, **kwds)
441
442 if chunksize or iterator:
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
785 self.options['has_index_names'] = kwds['has_index_names']
786
--> 787 self._make_engine(self.engine)
788
789 def close(self):
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
1012 def _make_engine(self, engine='c'):
1013 if engine == 'c':
-> 1014 self._engine = CParserWrapper(self.f, **self.options)
1015 else:
1016 if engine == 'python':
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1706 kwds['usecols'] = self.usecols
1707
-> 1708 self._reader = parsers.TextReader(src, **kwds)
1709
1710 passed_names = self.names is None
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
FileNotFoundError: File b'gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv' does not exist
Location path for the CSV file
gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv
I have also made sure that the csv file is stored in the same storage bucket as attached to the data proc and have made sure that the file is in UTF-8-Encoded csv format
Can anyone please help me how to read the files stored in google bucket from jupyter notebook running on a dataproc cluster in google cloud.
Kindly let me know if more information is required
Thanks in Advance!!
python jupyter-notebook google-cloud-storage google-cloud-dataproc
I am working on a Data Proc Spark cluster with an initialization action to install Jupyter notebook. I am unable to read the csv files stored on the google cloud storage bucket, however I am able to read the same files when I work on Spark Shell
Below is the error code I am getting
import pandas as pd
import numpy as np
data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
FileNotFoundError Traceback (most recent call last)
<ipython-input-20-2457012764fa> in <module>
----> 1 data = pd.read_csv("gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv")
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)
676 skip_blank_lines=skip_blank_lines)
677
--> 678 return _read(filepath_or_buffer, kwds)
679
680 parser_f.__name__ = name
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
438
439 # Create the parser.
--> 440 parser = TextFileReader(filepath_or_buffer, **kwds)
441
442 if chunksize or iterator:
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
785 self.options['has_index_names'] = kwds['has_index_names']
786
--> 787 self._make_engine(self.engine)
788
789 def close(self):
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
1012 def _make_engine(self, engine='c'):
1013 if engine == 'c':
-> 1014 self._engine = CParserWrapper(self.f, **self.options)
1015 else:
1016 if engine == 'python':
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1706 kwds['usecols'] = self.usecols
1707
-> 1708 self._reader = parsers.TextReader(src, **kwds)
1709
1710 passed_names = self.names is None
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
FileNotFoundError: File b'gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv' does not exist
Location path for the CSV file
gs://dataproc-78r5fe64b-a56d-4f5f4-bcf9-e1b7t6fb9d8f-au-southeast1/notebooks/datafile.csv
I have also made sure that the csv file is stored in the same storage bucket as attached to the data proc and have made sure that the file is in UTF-8-Encoded csv format
Can anyone please help me how to read the files stored in google bucket from jupyter notebook running on a dataproc cluster in google cloud.
Kindly let me know if more information is required
Thanks in Advance!!
python jupyter-notebook google-cloud-storage google-cloud-dataproc
python jupyter-notebook google-cloud-storage google-cloud-dataproc
edited Nov 14 '18 at 1:20
asked Nov 14 '18 at 1:14
Tushar Mehta
407
407
What kernel are you using in Jupyter? Python or Spark?
– tix
Nov 14 '18 at 2:29
I have actually tried using both the Kernels, Python and Py-Spark
– Tushar Mehta
Nov 14 '18 at 2:59
add a comment |
What kernel are you using in Jupyter? Python or Spark?
– tix
Nov 14 '18 at 2:29
I have actually tried using both the Kernels, Python and Py-Spark
– Tushar Mehta
Nov 14 '18 at 2:59
What kernel are you using in Jupyter? Python or Spark?
– tix
Nov 14 '18 at 2:29
What kernel are you using in Jupyter? Python or Spark?
– tix
Nov 14 '18 at 2:29
I have actually tried using both the Kernels, Python and Py-Spark
– Tushar Mehta
Nov 14 '18 at 2:59
I have actually tried using both the Kernels, Python and Py-Spark
– Tushar Mehta
Nov 14 '18 at 2:59
add a comment |
1 Answer
1
active
oldest
votes
The reason Spark can read from GCS is that we configure it to use the GCS connector for paths that start with gs://
. You probably want to use spark.read.csv(gs://path/to/files/)
to read CSV file(s) into a Spark dataframe.
You can read and write to GCS using pandas, but it's a bit more complicated. This stackoverflow post lists some options.
Side note: If you're using Pandas, you should use a single node cluster since pandas code will not be distributed across a cluster.
Thanks Karthik, Have been trying to find a solution for quite some time and I understand that pandas code would be distributed across cluster.
– Tushar Mehta
Nov 18 '18 at 21:22
Pandas is not distributed across the cluster, unless you're using something like Dask.
– Karthik Palaniappan
Nov 20 '18 at 17:02
Sorry There was a typo, I meant that I understand that pandas would not be distributed accross for cluster. And a lot of thanks for helping out with the issue.
– Tushar Mehta
Nov 21 '18 at 20:37
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The reason Spark can read from GCS is that we configure it to use the GCS connector for paths that start with gs://
. You probably want to use spark.read.csv(gs://path/to/files/)
to read CSV file(s) into a Spark dataframe.
You can read and write to GCS using pandas, but it's a bit more complicated. This stackoverflow post lists some options.
Side note: If you're using Pandas, you should use a single node cluster since pandas code will not be distributed across a cluster.
Thanks Karthik, Have been trying to find a solution for quite some time and I understand that pandas code would be distributed across cluster.
– Tushar Mehta
Nov 18 '18 at 21:22
Pandas is not distributed across the cluster, unless you're using something like Dask.
– Karthik Palaniappan
Nov 20 '18 at 17:02
Sorry There was a typo, I meant that I understand that pandas would not be distributed accross for cluster. And a lot of thanks for helping out with the issue.
– Tushar Mehta
Nov 21 '18 at 20:37
add a comment |
The reason Spark can read from GCS is that we configure it to use the GCS connector for paths that start with gs://
. You probably want to use spark.read.csv(gs://path/to/files/)
to read CSV file(s) into a Spark dataframe.
You can read and write to GCS using pandas, but it's a bit more complicated. This stackoverflow post lists some options.
Side note: If you're using Pandas, you should use a single node cluster since pandas code will not be distributed across a cluster.
Thanks Karthik, Have been trying to find a solution for quite some time and I understand that pandas code would be distributed across cluster.
– Tushar Mehta
Nov 18 '18 at 21:22
Pandas is not distributed across the cluster, unless you're using something like Dask.
– Karthik Palaniappan
Nov 20 '18 at 17:02
Sorry There was a typo, I meant that I understand that pandas would not be distributed accross for cluster. And a lot of thanks for helping out with the issue.
– Tushar Mehta
Nov 21 '18 at 20:37
add a comment |
The reason Spark can read from GCS is that we configure it to use the GCS connector for paths that start with gs://
. You probably want to use spark.read.csv(gs://path/to/files/)
to read CSV file(s) into a Spark dataframe.
You can read and write to GCS using pandas, but it's a bit more complicated. This stackoverflow post lists some options.
Side note: If you're using Pandas, you should use a single node cluster since pandas code will not be distributed across a cluster.
The reason Spark can read from GCS is that we configure it to use the GCS connector for paths that start with gs://
. You probably want to use spark.read.csv(gs://path/to/files/)
to read CSV file(s) into a Spark dataframe.
You can read and write to GCS using pandas, but it's a bit more complicated. This stackoverflow post lists some options.
Side note: If you're using Pandas, you should use a single node cluster since pandas code will not be distributed across a cluster.
answered Nov 16 '18 at 22:35
Karthik Palaniappan
69547
69547
Thanks Karthik, Have been trying to find a solution for quite some time and I understand that pandas code would be distributed across cluster.
– Tushar Mehta
Nov 18 '18 at 21:22
Pandas is not distributed across the cluster, unless you're using something like Dask.
– Karthik Palaniappan
Nov 20 '18 at 17:02
Sorry There was a typo, I meant that I understand that pandas would not be distributed accross for cluster. And a lot of thanks for helping out with the issue.
– Tushar Mehta
Nov 21 '18 at 20:37
add a comment |
Thanks Karthik, Have been trying to find a solution for quite some time and I understand that pandas code would be distributed across cluster.
– Tushar Mehta
Nov 18 '18 at 21:22
Pandas is not distributed across the cluster, unless you're using something like Dask.
– Karthik Palaniappan
Nov 20 '18 at 17:02
Sorry There was a typo, I meant that I understand that pandas would not be distributed accross for cluster. And a lot of thanks for helping out with the issue.
– Tushar Mehta
Nov 21 '18 at 20:37
Thanks Karthik, Have been trying to find a solution for quite some time and I understand that pandas code would be distributed across cluster.
– Tushar Mehta
Nov 18 '18 at 21:22
Thanks Karthik, Have been trying to find a solution for quite some time and I understand that pandas code would be distributed across cluster.
– Tushar Mehta
Nov 18 '18 at 21:22
Pandas is not distributed across the cluster, unless you're using something like Dask.
– Karthik Palaniappan
Nov 20 '18 at 17:02
Pandas is not distributed across the cluster, unless you're using something like Dask.
– Karthik Palaniappan
Nov 20 '18 at 17:02
Sorry There was a typo, I meant that I understand that pandas would not be distributed accross for cluster. And a lot of thanks for helping out with the issue.
– Tushar Mehta
Nov 21 '18 at 20:37
Sorry There was a typo, I meant that I understand that pandas would not be distributed accross for cluster. And a lot of thanks for helping out with the issue.
– Tushar Mehta
Nov 21 '18 at 20:37
add a comment |
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What kernel are you using in Jupyter? Python or Spark?
– tix
Nov 14 '18 at 2:29
I have actually tried using both the Kernels, Python and Py-Spark
– Tushar Mehta
Nov 14 '18 at 2:59