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Tasks for loading GA data into Snowflake (PART 2)
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This is part 2 of the GA loading pipeline which DOES depend on a Luigi
upgrade.

DE-1374 (PART 2)
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pwnage101 committed Apr 24, 2019
1 parent 12686d0 commit 026c7d8
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318 changes: 318 additions & 0 deletions edx/analytics/tasks/common/bigquery_extract.py
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"""
Collection of abstract tasks for extracting tables from BigQuery.
BigQuery data can ONLY dump into Google Cloud Storage (GCS), not S3. Therefore, this module also
provides GCSToS3Task.
"""
import errno
import json
import logging
import subprocess
import time

import luigi

from edx.analytics.tasks.util.overwrite import OverwriteOutputMixin
from edx.analytics.tasks.util.s3_util import canonicalize_s3_url
from edx.analytics.tasks.util.url import ExternalURL, GCSMarkerTarget, get_target_from_url, url_path_join

log = logging.getLogger(__name__)

try:
from google.cloud import bigquery
from google.oauth2 import service_account
bigquery_available = True # pylint: disable=invalid-name
except ImportError:
log.warn('Unable to import Bigquery libraries')
# On hadoop slave nodes we don't have bigquery libraries installed,
# so just fail noisily if we attempt to use these libraries there.
bigquery_available = False # pylint: disable=invalid-name


class BigQueryExtractDownstreamMixin(OverwriteOutputMixin):
"""
Common luigi parameters for all BigQuery extract tasks and wrapper tasks.
"""
credentials = luigi.Parameter()
project = luigi.Parameter()
dataset = luigi.Parameter()


class BigQueryExtractTask(BigQueryExtractDownstreamMixin, luigi.Task):
"""
Abstract Task to extract one BigQuery table into Google Cloud Storage (GCS).
This task outputs a GCSMarkerTarget which represents the destination prefix for the dumped table
which gets broken up (automatically) into several 1GB JSON gzip'd files.
"""

output_target = None
required_tasks = None

# For example, output_url might be:
#
# gs://bucket/table
#
# in which case the following output files could be generated:
#
# gs://bucket/table/_SUCCESS
# gs://bucket/table/_metadata
# gs://bucket/table/0000001.json.gz
# gs://bucket/table/0000002.json.gz
# gs://bucket/table/0000003.json.gz
output_url = luigi.Parameter(
description='The GCS URL prefix of the output files, NOT including the filename pattern or trailing slash.'
)

def requires(self): # pylint: disable=missing-docstring
if self.required_tasks is None:
self.required_tasks = {
'credentials': ExternalURL(url=self.credentials),
}
return self.required_tasks

def output(self): # pylint: disable=missing-docstring
if self.output_target is None:
self.output_target = GCSMarkerTarget(
self.output_url,
credentials_file=self.input()['credentials'],
)
return self.output_target

@property
def table(self):
"""
Name (str) of the table in BQ to extract.
If this is a sharded table, do not include the date suffix, e.g. return
"ga_sessions_" instead of "ga_sessions_20190408".
"""
raise NotImplementedError

@property
def output_compression_type(self):
"""
The type of compression to use for the output files.
Return None for no compression.
All available options are listed here:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.extract.compression
"""
return 'GZIP'

@property
def output_format(self):
"""
The file format to use for the output files.
All available options are listed here:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.extract.destinationFormat
"""
return bigquery.DestinationFormat.NEWLINE_DELIMITED_JSON

def _get_table_identifier(self):
"""
Construct a table identifier for the table to be extracted.
Override this method to include partition or shard suffixes. By default, this simply
returns the passed-in table without any suffixes.
Returns:
str: The table to extract.
"""
return self.table

def _get_destination_url_pattern(self):
"""
Generate the BQ-compatible GCS destination URL pattern including a single asterisk to
specify where the table dumps fan-out.
For example, if self.output_url is:
gs://bucket/table
then this function might return:
gs://bucket/table/*.json.gz
Returns:
str: GCS URL pattern in BQ extract_table format.
"""
if self.output_compression_type == 'GZIP':
filename_extension = 'json.gz'
else:
filename_extension = 'json'

# This will cause the leaf files themselves to just be named like "0000001.json.gz".
filename_pattern = '*.{}'.format(filename_extension)

return url_path_join(
self.output_url,
filename_pattern,
)

def _get_job_id(self):
"""
Returns:
str: Unique identifier to assign to the BQ job.
"""
return 'extract_{table}_{timestamp}'.format(table=self._get_table_identifier(), timestamp=int(time.time()))

def _make_bq_client(self):
"""
Construct a BigQuery client using the credentials file input target.
"""
with self.input()['credentials'].open('r') as credentials_file:
json_creds = json.load(credentials_file)
self.project_id = json_creds['project_id']
credentials = service_account.Credentials.from_service_account_info(json_creds)
return bigquery.Client(credentials=credentials, project=self.project_id)

def run(self): # pylint: disable=missing-docstring
self.check_bigquery_availability()

client = self._make_bq_client()
dataset_ref = client.dataset(self.dataset, project=self.project)
table_name = self._get_table_identifier()
table_ref = dataset_ref.table(table_name)

job_config = bigquery.job.ExtractJobConfig()
job_config.destination_format = self.output_format
if self.output_compression_type:
job_config.compression = self.output_compression_type
job = client.extract_table(
table_ref,
self._get_destination_url_pattern(),
job_config=job_config,
location='US',
job_id=self._get_job_id(),
)
log.info("Starting BigQuery Extract job.")
job.result() # Waits for job to complete.

self.output().touch_marker()

def check_bigquery_availability(self):
"""
Call to ensure fast failure if this machine doesn't have the Bigquery libraries available.
"""
if not bigquery_available:
raise ImportError('Bigquery library not available.')


class BigQueryExtractShardedTask(BigQueryExtractTask): # pylint: disable=abstract-method
"""
Abstract class for extracting BigQuery tables to GCS, specifically for tables sharded by date.
Be sure to include a trailing underscore when overriding the self.table property, if the table
name needs one.
"""
date = luigi.DateParameter()

def _get_date_suffix(self):
"""
Generate a date sharding suffix in BQ's typical format (YYYYMMDD).
Returns:
str: BQ date suffix.
"""
iso_date_string = self.date.isoformat()
return iso_date_string.replace('-', '')

def _get_table_identifier(self):
"""
Override super's method to add the shard date suffix to the table identifier.
"""
return '{}{}'.format(self.table, self._get_date_suffix())


class CopyGCSToS3Task(OverwriteOutputMixin, luigi.Task):
"""
Abstract Task to copy data from Google Cloud Storage (GCS) to Amazon S3.
Input to this task is a GCSMarkerTarget, and output is an S3MarkerTarget.
For example, if input and output URLs were as follows:
input = gs://bucket/foo
output = s3+https://bucket/bar
and the contents of the input were:
gs://bucket/foo/_SUCCESS
gs://bucket/foo/_metadata
gs://bucket/foo/0000001.json.gz
gs://bucket/foo/0000002.json.gz
gs://bucket/foo/0000003.json.gz
then, the following output files would be generated:
s3://bucket/bar/_SUCCESS
s3://bucket/bar/_metadata
s3://bucket/bar/0000001.json.gz
s3://bucket/bar/0000002.json.gz
s3://bucket/bar/0000003.json.gz
"""

output_url = luigi.Parameter(
description='The S3 URL prefix of the output files, NOT including the filename pattern or extension.'
)

output_target = None
required_tasks = None

def requires(self): # pylint: disable=missing-docstring
if self.required_tasks is None:
self.required_tasks = {
'source': self.insert_source_task,
}
return self.required_tasks

def output(self): # pylint: disable=missing-docstring
if self.output_target is None:
self.output_target = get_target_from_url(self.output_url, marker=True)
return self.output_target

@property
def insert_source_task(self):
"""
Override this to define the source task that outputs a GCSMarkerTarget to copy from.
"""
raise NotImplementedError

def _copy_data_from_gcs_to_s3(self, source_path, destination_path):
"""
Recursively copy a "directory" from GCS to S3.
"""
# Exclude any luigi marker files which should not be copied. The pattern is a Python
# regular expression.
exclusion_pattern = r'.*_SUCCESS$|.*_metadata$'
command = [
'gsutil', '-m', 'rsync', '-x', exclusion_pattern,
source_path,
canonicalize_s3_url(destination_path),
]
log.info(
'Invoking the following command to copy from GCS to S3: %s',
' '.join(command),
)
try:
return_code = subprocess.call(command)
except OSError as err:
if err.errno == errno.ENOENT:
log.error('Check that gsutil is installed.')
raise
if return_code != 0:
raise RuntimeError('Error {code} while syncing {source} to {destination}'.format(
code=return_code,
source=source_path,
destination=destination_path,
))

def run(self): # pylint: disable=missing-docstring
log.debug("Starting GCS Extract job.")
self._copy_data_from_gcs_to_s3(
self.input()['source'].path,
self.output_url,
)
self.output().touch_marker()
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