def _pipeline_cell(args, cell_body):
"""Implements the BigQuery cell magic used to validate, execute or deploy BQ pipelines.
The supported syntax is:
%%bigquery pipeline [-q|--sql <query identifier>] <other args> <action>
[<YAML or JSON cell_body or inline SQL>]
Args:
args: the arguments following '%bigquery pipeline'.
cell_body: optional contents of the cell interpreted as YAML or JSON.
Returns:
The QueryResultsTable
"""
if args['action'] == 'deploy':
raise Exception('Deploying a pipeline is not yet supported')
env = {}
for key, value in _utils.notebook_environment().iteritems():
if isinstance(value, gcp.bigquery._udf.UDF):
env[key] = value
query = _get_query_argument(args, cell_body, env)
if args['verbose']:
print query.sql
if args['action'] == 'dryrun':
print(query.sql)
result = query.execute_dry_run()
return gcp.bigquery._query_stats.QueryStats(total_bytes=result['totalBytesProcessed'],
is_cached=result['cacheHit'])
if args['action'] == 'run':
return query.execute(args['target'], table_mode=args['mode'], use_cache=not args['nocache'],
allow_large_results=args['large']).results
def _pipeline_cell(args, cell_body):
"""Implements the BigQuery cell magic used to validate, execute or deploy BQ pipelines.
The supported syntax is:
%%bigquery pipeline [-q|--sql <query identifier>] <other args> <action>
[<YAML or JSON cell_body or inline SQL>]
Args:
args: the arguments following '%bigquery pipeline'.
cell_body: optional contents of the cell interpreted as YAML or JSON.
Returns:
The QueryResultsTable
"""
if args['action'] == 'deploy':
raise Exception('Deploying a pipeline is not yet supported')
env = {}
for key, value in _utils.notebook_environment().iteritems():
if isinstance(value, gcp.bigquery._udf.FunctionCall):
env[key] = value
query = _get_query_argument(args, cell_body, env)
if args['verbose']:
print query.sql
if args['action'] == 'dryrun':
print(query.sql)
result = query.execute_dry_run()
return gcp.bigquery._query_stats.QueryStats(total_bytes=result['totalBytesProcessed'],
is_cached=result['cacheHit'])
if args['action'] == 'run':
return query.execute(args['target'], table_mode=args['mode'], use_cache=not args['nocache'],
allow_large_results=args['large']).results
def bigquery(line, cell=None):
"""Implements the bigquery cell magic for ipython notebooks.
The supported syntax is:
%%bigquery <command> [<args>]
<cell>
or:
%bigquery <command> [<args>]
Use %bigquery --help for a list of commands, or %bigquery <command> --help for help
on a specific command.
Args:
line: the magic line.
cell: the body of the notebook cell.
Returns:
The result of processing the magic.
"""
namespace = {}
if line.find('$') >= 0:
# We likely have variables to expand; get the appropriate context.
namespace = _utils.notebook_environment()
return _utils.handle_magic_line(line, cell, _bigquery_parser, namespace=namespace)
def _create_cell(args, cell_body):
"""Implements the BigQuery cell magic used to create datasets and tables.
The supported syntax is:
%%bigquery create dataset -n|--name <name> [-f|--friendly <friendlyname>]
[<description>]
or:
%%bigquery create table -n|--name <tablename> [--overwrite]
[<YAML or JSON cell_body defining schema to use for tables>]
Args:
args: the argument following '%bigquery create <command>'.
"""
if args['command'] == 'dataset':
try:
gcp.bigquery.DataSet(args['name']).create(friendly_name=args['friendly'],
description=cell_body)
except Exception as e:
print 'Failed to create dataset %s: %s' % (args['name'], e)
else:
if cell_body is None:
print 'Failed to create %s: no schema specified' % args['name']
else:
try:
record = _utils.parse_config(cell_body, _utils.notebook_environment(), as_dict=False)
schema = gcp.bigquery.Schema(record)
gcp.bigquery.Table(args['name']).create(schema=schema, overwrite=args['overwrite'])
except Exception as e:
print 'Failed to create table %s: %s' % (args['name'], e)
def _sample_cell(args, cell_body):
"""Implements the bigquery sample cell magic for ipython notebooks.
Args:
args: the optional arguments following '%%bigquery sample'.
cell_body: optional contents of the cell interpreted as SQL, YAML or JSON.
Returns:
The results of executing the sampling query, or a profile of the sample data.
"""
env = _utils.notebook_environment()
query = None
table = None
view = None
if args['query']:
query = _get_query_argument(args, cell_body, env)
elif args['table']:
table = _get_table(args['table'])
elif args['view']:
view = _utils.get_notebook_item(args['view'])
if not isinstance(view, gcp.bigquery.View):
raise Exception('%s is not a view' % args['view'])
else:
query = gcp.bigquery.Query(cell_body, values=env)
count = args['count']
method = args['method']
if method == 'random':
sampling = gcp.bigquery.Sampling.random(percent=args['percent'], count=count)
elif method == 'hashed':
sampling = gcp.bigquery.Sampling.hashed(field_name=args['field'],
percent=args['percent'],
count=count)
elif method == 'sorted':
ascending = args['order'] == 'ascending'
sampling = gcp.bigquery.Sampling.sorted(args['field'],
ascending=ascending,
count=count)
elif method == 'limit':
sampling = gcp.bigquery.Sampling.default(count=count)
else:
sampling = gcp.bigquery.Sampling.default(count=count)
if query:
results = query.sample(sampling=sampling)
elif view:
results = view.sample(sampling=sampling)
else:
results = table.sample(sampling=sampling)
if args['verbose']:
print results.sql
if args['profile']:
return _utils.profile_df(results.to_dataframe())
else:
return results
def _execute_cell(args, cell_body):
"""Implements the BigQuery cell magic used to execute BQ queries.
The supported syntax is:
%%bigquery execute [-q|--sql <query identifier>] <other args>
[<YAML or JSON cell_body or inline SQL>]
Args:
args: the arguments following '%bigquery execute'.
cell_body: optional contents of the cell interpreted as YAML or JSON.
Returns:
The QueryResultsTable
"""
query = _get_query_argument(args, cell_body, _utils.notebook_environment())
if args['verbose']:
print query.sql
return query.execute(args['target'], table_mode=args['mode'], use_cache=not args['nocache'],
allow_large_results=args['large']).results
def _execute_cell(args, cell_body):
"""Implements the BigQuery cell magic used to execute BQ queries.
The supported syntax is:
%%bigquery execute [-q|--sql <query identifier>] <other args>
[<YAML or JSON cell_body or inline SQL>]
Args:
args: the arguments following '%bigquery execute'.
cell_body: optional contents of the cell interpreted as YAML or JSON.
Returns:
The QueryResultsTable
"""
query = _get_query_argument(args, cell_body, _utils.notebook_environment())
if args['verbose']:
print query.sql
return query.execute(args['target'], table_mode=args['mode'], use_cache=not args['nocache'],
allow_large_results=args['large']).results
def _dryrun_cell(args, cell_body):
"""Implements the BigQuery cell magic used to dry run BQ queries.
The supported syntax is:
%%bigquery dryrun [-q|--sql <query identifier>]
[<YAML or JSON cell_body or inline SQL>]
Args:
args: the argument following '%bigquery dryrun'.
cell_body: optional contents of the cell interpreted as YAML or JSON.
Returns:
The response wrapped in a DryRunStats object
"""
query = _get_query_argument(args, cell_body, _utils.notebook_environment())
if args['verbose']:
print query.sql
result = query.execute_dry_run()
return gcp.bigquery._query_stats.QueryStats(total_bytes=result['totalBytesProcessed'],
is_cached=result['cacheHit'])
def _dryrun_cell(args, cell_body):
"""Implements the BigQuery cell magic used to dry run BQ queries.
The supported syntax is:
%%bigquery dryrun [-q|--sql <query identifier>]
[<YAML or JSON cell_body or inline SQL>]
Args:
args: the argument following '%bigquery dryrun'.
cell_body: optional contents of the cell interpreted as YAML or JSON.
Returns:
The response wrapped in a DryRunStats object
"""
query = _get_query_argument(args, cell_body, _utils.notebook_environment())
if args['verbose']:
print query.sql
result = query.execute_dry_run()
return gcp.bigquery._query_stats.QueryStats(total_bytes=result['totalBytesProcessed'],
is_cached=result['cacheHit'])
def _udf_cell(args, js):
"""Implements the bigquery_udf cell magic for ipython notebooks.
The supported syntax is:
%%bigquery udf --module <var>
<js function>
Args:
args: the optional arguments following '%%bigquery udf'.
declaration: the variable to initialize with the resulting UDF object.
js: the UDF declaration (inputs and outputs) and implementation in javascript.
Returns:
The results of executing the UDF converted to a dataframe if no variable
was specified. None otherwise.
"""
variable_name = args['module']
if not variable_name:
raise Exception('Declaration must be of the form %%bigquery udf --module <variable name>')
# Parse out the input and output specification
spec_pattern = r'\{\{([^}]+)\}\}'
spec_part_pattern = r'[a-z_][a-z0-9_]*'
specs = re.findall(spec_pattern, js)
if len(specs) < 2:
raise Exception('The JavaScript must declare the input row and output emitter parameters '
'using valid jsdoc format comments.\n'
'The input row param declaration must be typed as {{field:type, field2:type}} '
'and the output emitter param declaration must be typed as '
'function({{field:type, field2:type}}.')
inputs = []
input_spec_parts = re.findall(spec_part_pattern, specs[0], flags=re.IGNORECASE)
if len(input_spec_parts) % 2 != 0:
raise Exception('Invalid input row param declaration. The jsdoc type expression must '
'define an object with field and type pairs.')
for n, t in zip(input_spec_parts[0::2], input_spec_parts[1::2]):
inputs.append((n, t))
outputs = []
output_spec_parts = re.findall(spec_part_pattern, specs[1], flags=re.IGNORECASE)
if len(output_spec_parts) % 2 != 0:
raise Exception('Invalid output emitter param declaration. The jsdoc type expression must '
'define a function accepting an an object with field and type pairs.')
for n, t in zip(output_spec_parts[0::2], output_spec_parts[1::2]):
outputs.append((n, t))
# Look for imports. We use a non-standard @import keyword; we could alternatively use @requires.
# Object names can contain any characters except \r and \n.
import_pattern = r'@import[\s]+(gs://[a-z\d][a-z\d_\.\-]*[a-z\d]/[^\n\r]+)'
imports = re.findall(import_pattern, js)
# Split the cell if necessary. We look for a 'function(' with no name and a header comment
# block with @param and assume this is the primary function, up to a closing '}' at the start
# of the line. The remaining cell content is used as support code.
split_pattern = r'(.*)(/\*.*?@param.*?@param.*?\*/\w*\n\w*function\w*\(.*?^}\n?)(.*)'
parts = re.match(split_pattern, js, re.MULTILINE|re.DOTALL)
support_code = ''
if parts:
support_code = (parts.group(1) + parts.group(3)).strip()
if len(support_code):
js = parts.group(2)
# Finally build the UDF object
udf = gcp.bigquery.UDF(inputs, outputs, variable_name, js, support_code, imports)
_utils.notebook_environment()[variable_name] = udf
def _udf_cell(args, js):
"""Implements the bigquery_udf cell magic for ipython notebooks.
The supported syntax is:
%%bigquery udf --module <var>
<js function>
Args:
args: the optional arguments following '%%bigquery udf'.
js: the UDF declaration (inputs and outputs) and implementation in javascript.
Returns:
The results of executing the UDF converted to a dataframe if no variable
was specified. None otherwise.
"""
variable_name = args['module']
if not variable_name:
raise Exception('Declaration must be of the form %%bigquery udf --module <variable name>')
# Parse out the input and output specification
spec_pattern = r'\{\{([^}]+)\}\}'
spec_part_pattern = r'[a-z_][a-z0-9_]*'
specs = re.findall(spec_pattern, js)
if len(specs) < 2:
raise Exception('The JavaScript must declare the input row and output emitter parameters '
'using valid jsdoc format comments.\n'
'The input row param declaration must be typed as {{field:type, field2:type}} '
'and the output emitter param declaration must be typed as '
'function({{field:type, field2:type}}.')
inputs = []
input_spec_parts = re.findall(spec_part_pattern, specs[0], flags=re.IGNORECASE)
if len(input_spec_parts) % 2 != 0:
raise Exception('Invalid input row param declaration. The jsdoc type expression must '
'define an object with field and type pairs.')
for n, t in zip(input_spec_parts[0::2], input_spec_parts[1::2]):
inputs.append((n, t))
outputs = []
output_spec_parts = re.findall(spec_part_pattern, specs[1], flags=re.IGNORECASE)
if len(output_spec_parts) % 2 != 0:
raise Exception('Invalid output emitter param declaration. The jsdoc type expression must '
'define a function accepting an an object with field and type pairs.')
for n, t in zip(output_spec_parts[0::2], output_spec_parts[1::2]):
outputs.append((n, t))
# Look for imports. We use a non-standard @import keyword; we could alternatively use @requires.
# Object names can contain any characters except \r and \n.
import_pattern = r'@import[\s]+(gs://[a-z\d][a-z\d_\.\-]*[a-z\d]/[^\n\r]+)'
imports = re.findall(import_pattern, js)
# Split the cell if necessary. We look for a 'function(' with no name and a header comment
# block with @param and assume this is the primary function, up to a closing '}' at the start
# of the line. The remaining cell content is used as support code.
split_pattern = r'(.*)(/\*.*?@param.*?@param.*?\*/\w*\n\w*function\w*\(.*?^}\n?)(.*)'
parts = re.match(split_pattern, js, re.MULTILINE|re.DOTALL)
support_code = ''
if parts:
support_code = (parts.group(1) + parts.group(3)).strip()
if len(support_code):
js = parts.group(2)
# Finally build the UDF object
udf = gcp.bigquery.UDF(inputs, outputs, variable_name, js, support_code, imports)
_utils.notebook_environment()[variable_name] = udf