def get_archive(self, archive_metadata):
"""
Downloads the archive specified by an archive metadata object, and converts it into a valid list of Message
or Run objects.
:param archive_metadata: Metadata for the archive. To obtain these, see `RapidProClient.list_archives`.
:type archive_metadata: temba_client.v2.types.Archive
:return: Data downloaded from the archive.
:rtype: list of temba_client.v2.Message | list of temba_client.v2.Run
"""
# Download the archive, which is in a gzipped JSONL format, and decompress.
log.info(f"Downloading {archive_metadata.record_count} records from {archive_metadata.period} archive "
f"{archive_metadata.start_date} ({archive_metadata.download_url})...")
archive_response = urllib.request.urlopen(archive_metadata.download_url)
raw_file = BytesIO(archive_response.read())
decompressed_file = gzip.GzipFile(fileobj=raw_file)
# Convert each of the decompressed results to a Run or Message object, depending on what the archive contains.
results = []
if archive_metadata.archive_type == "run":
for line in decompressed_file.readlines():
serialized_run = json.loads(line)
# Set the 'start' field to null if it doesn't exist.
# This field is required to be present in order to be able to deserialize runs, but is often not
# present in the downloaded data (possibly because the archiving process removes null fields, but
# I haven't verified this). Since this field is often null in the data that comes out of the runs
# API directly, and we don't use this in the pipelines, just set missing entries to None.
if "start" not in serialized_run:
serialized_run["start"] = None
results.append(Run.deserialize(serialized_run))
else:
assert archive_metadata.archive_type == "message", "Unsupported archive type, must be either 'run' or 'message'"
for line in decompressed_file.readlines():
serialized_msg = json.loads(line)
results.append(Message.deserialize(serialized_msg))
assert len(results) == archive_metadata.record_count
return results
def fetch_from_rapid_pro(user, google_cloud_credentials_file_path,
raw_data_dir, phone_number_uuid_table,
rapid_pro_source):
log.info("Fetching data from Rapid Pro...")
log.info("Downloading Rapid Pro access token...")
rapid_pro_token = google_cloud_utils.download_blob_to_string(
google_cloud_credentials_file_path,
rapid_pro_source.token_file_url).strip()
rapid_pro = RapidProClient(rapid_pro_source.domain, rapid_pro_token)
# Load the previous export of contacts if it exists, otherwise fetch all contacts from Rapid Pro.
raw_contacts_path = f"{raw_data_dir}/{rapid_pro_source.contacts_file_name}_raw.json"
contacts_log_path = f"{raw_data_dir}/{rapid_pro_source.contacts_file_name}_log.jsonl"
try:
log.info(f"Loading raw contacts from file '{raw_contacts_path}'...")
with open(raw_contacts_path) as raw_contacts_file:
raw_contacts = [
Contact.deserialize(contact_json)
for contact_json in json.load(raw_contacts_file)
]
log.info(f"Loaded {len(raw_contacts)} contacts")
except FileNotFoundError:
log.info(
f"File '{raw_contacts_path}' not found, will fetch all contacts from the Rapid Pro server"
)
with open(contacts_log_path, "a") as contacts_log_file:
raw_contacts = rapid_pro.get_raw_contacts(
raw_export_log_file=contacts_log_file)
# Download all the runs for each of the radio shows
for flow in rapid_pro_source.activation_flow_names + rapid_pro_source.survey_flow_names:
runs_log_path = f"{raw_data_dir}/{flow}_log.jsonl"
raw_runs_path = f"{raw_data_dir}/{flow}_raw.json"
traced_runs_output_path = f"{raw_data_dir}/{flow}.jsonl"
log.info(f"Exporting flow '{flow}' to '{traced_runs_output_path}'...")
flow_id = rapid_pro.get_flow_id(flow)
# Load the previous export of runs for this flow, and update them with the newest runs.
# If there is no previous export for this flow, fetch all the runs from Rapid Pro.
with open(runs_log_path, "a") as raw_runs_log_file:
try:
log.info(f"Loading raw runs from file '{raw_runs_path}'...")
with open(raw_runs_path) as raw_runs_file:
raw_runs = [
Run.deserialize(run_json)
for run_json in json.load(raw_runs_file)
]
log.info(f"Loaded {len(raw_runs)} runs")
raw_runs = rapid_pro.update_raw_runs_with_latest_modified(
flow_id,
raw_runs,
raw_export_log_file=raw_runs_log_file,
ignore_archives=True)
except FileNotFoundError:
log.info(
f"File '{raw_runs_path}' not found, will fetch all runs from the Rapid Pro server for flow '{flow}'"
)
raw_runs = rapid_pro.get_raw_runs_for_flow_id(
flow_id, raw_export_log_file=raw_runs_log_file)
# Fetch the latest contacts from Rapid Pro.
with open(contacts_log_path, "a") as raw_contacts_log_file:
raw_contacts = rapid_pro.update_raw_contacts_with_latest_modified(
raw_contacts, raw_export_log_file=raw_contacts_log_file)
# Convert the runs to TracedData.
traced_runs = rapid_pro.convert_runs_to_traced_data(
user, raw_runs, raw_contacts, phone_number_uuid_table,
rapid_pro_source.test_contact_uuids)
log.info(f"Saving {len(raw_runs)} raw runs to {raw_runs_path}...")
with open(raw_runs_path, "w") as raw_runs_file:
json.dump([run.serialize() for run in raw_runs], raw_runs_file)
log.info(f"Saved {len(raw_runs)} raw runs")
log.info(
f"Saving {len(traced_runs)} traced runs to {traced_runs_output_path}..."
)
IOUtils.ensure_dirs_exist_for_file(traced_runs_output_path)
with open(traced_runs_output_path, "w") as traced_runs_output_file:
TracedDataJsonIO.export_traced_data_iterable_to_jsonl(
traced_runs, traced_runs_output_file)
log.info(f"Saved {len(traced_runs)} traced runs")
log.info(
f"Saving {len(raw_contacts)} raw contacts to file '{raw_contacts_path}'..."
)
with open(raw_contacts_path, "w") as raw_contacts_file:
json.dump([contact.serialize() for contact in raw_contacts],
raw_contacts_file)
log.info(f"Saved {len(raw_contacts)} contacts")
# Download all the runs for each of the radio shows
for flow in pipeline_configuration.activation_flow_names + pipeline_configuration.survey_flow_names:
runs_log_path = f"{raw_data_dir}/{flow}_log.jsonl"
raw_runs_path = f"{raw_data_dir}/{flow}_raw.json"
traced_runs_output_path = f"{raw_data_dir}/{flow}.json"
log.info(f"Exporting flow '{flow}' to '{traced_runs_output_path}'...")
flow_id = rapid_pro.get_flow_id(flow)
# Load the previous export of runs for this flow, and update them with the newest runs.
# If there is no previous export for this flow, fetch all the runs from Rapid Pro.
with open(runs_log_path, "a") as raw_runs_log_file:
try:
log.info(f"Loading raw runs from file '{raw_runs_path}'...")
with open(raw_runs_path) as raw_runs_file:
raw_runs = [Run.deserialize(run_json) for run_json in json.load(raw_runs_file)]
log.info(f"Loaded {len(raw_runs)} runs")
raw_runs = rapid_pro.update_raw_runs_with_latest_modified(
flow_id, raw_runs, raw_export_log_file=raw_runs_log_file)
except FileNotFoundError:
log.info(f"File '{raw_runs_path}' not found, will fetch all runs from the Rapid Pro server for flow '{flow}'")
raw_runs = rapid_pro.get_raw_runs_for_flow_id(flow_id, raw_export_log_file=raw_runs_log_file)
# Fetch the latest contacts from Rapid Pro.
with open(contacts_log_path, "a") as raw_contacts_log_file:
raw_contacts = rapid_pro.update_raw_contacts_with_latest_modified(raw_contacts,
raw_export_log_file=raw_contacts_log_file)
# Convert the runs to TracedData.
traced_runs = rapid_pro.convert_runs_to_traced_data(
user, raw_runs, raw_contacts, phone_number_uuid_table, pipeline_configuration.rapid_pro_test_contact_uuids)
def fetch_from_rapid_pro(user, google_cloud_credentials_file_path, raw_data_dir, phone_number_uuid_table,
rapid_pro_source):
log.info("Fetching data from Rapid Pro...")
log.info("Downloading Rapid Pro access token...")
rapid_pro_token = google_cloud_utils.download_blob_to_string(
google_cloud_credentials_file_path, rapid_pro_source.token_file_url).strip()
rapid_pro = RapidProClient(rapid_pro_source.domain, rapid_pro_token)
# Load the previous export of contacts if it exists, otherwise fetch all contacts from Rapid Pro.
raw_contacts_path = f"{raw_data_dir}/{rapid_pro_source.contacts_file_name}_raw.json"
contacts_log_path = f"{raw_data_dir}/{rapid_pro_source.contacts_file_name}_log.jsonl"
try:
log.info(f"Loading raw contacts from file '{raw_contacts_path}'...")
with open(raw_contacts_path) as raw_contacts_file:
raw_contacts = [Contact.deserialize(contact_json) for contact_json in json.load(raw_contacts_file)]
log.info(f"Loaded {len(raw_contacts)} contacts")
except FileNotFoundError:
log.info(f"File '{raw_contacts_path}' not found, will fetch all contacts from the Rapid Pro server")
with open(contacts_log_path, "a") as contacts_log_file:
raw_contacts = rapid_pro.get_raw_contacts(raw_export_log_file=contacts_log_file)
# Download all the runs for each of the radio shows
for flow in rapid_pro_source.activation_flow_names + rapid_pro_source.survey_flow_names:
runs_log_path = f"{raw_data_dir}/{flow}_log.jsonl"
raw_runs_path = f"{raw_data_dir}/{flow}_raw.json"
traced_runs_output_path = f"{raw_data_dir}/{flow}.jsonl"
log.info(f"Exporting flow '{flow}' to '{traced_runs_output_path}'...")
flow_id = rapid_pro.get_flow_id(flow)
# Load the previous export of runs for this flow, and update them with the newest runs.
# If there is no previous export for this flow, fetch all the runs from Rapid Pro.
with open(runs_log_path, "a") as raw_runs_log_file:
try:
log.info(f"Loading raw runs from file '{raw_runs_path}'...")
with open(raw_runs_path) as raw_runs_file:
raw_runs = [Run.deserialize(run_json) for run_json in json.load(raw_runs_file)]
log.info(f"Loaded {len(raw_runs)} runs")
raw_runs = rapid_pro.update_raw_runs_with_latest_modified(
flow_id, raw_runs, raw_export_log_file=raw_runs_log_file, ignore_archives=True)
except FileNotFoundError:
log.info(f"File '{raw_runs_path}' not found, will fetch all runs from the Rapid Pro server for flow '{flow}'")
raw_runs = rapid_pro.get_raw_runs_for_flow_id(flow_id, raw_export_log_file=raw_runs_log_file)
# Fetch the latest contacts from Rapid Pro.
with open(contacts_log_path, "a") as raw_contacts_log_file:
raw_contacts = rapid_pro.update_raw_contacts_with_latest_modified(raw_contacts,
raw_export_log_file=raw_contacts_log_file)
# Convert the runs to TracedData.
traced_runs = rapid_pro.convert_runs_to_traced_data(
user, raw_runs, raw_contacts, phone_number_uuid_table, rapid_pro_source.test_contact_uuids)
if flow in rapid_pro_source.activation_flow_names:
# Append the Rapid Pro source name to each run.
# Only do this for activation flows because this is the only place where this is interesting.
# Also, demogs may come from either instance, which causes problems downstream.
for td in traced_runs:
td.append_data({
"source_raw": rapid_pro_source.source_name,
"source_coded": CleaningUtils.make_label_from_cleaner_code(
CodeSchemes.SOURCE, CodeSchemes.SOURCE.get_code_with_match_value(rapid_pro_source.source_name),
Metadata.get_call_location()
).to_dict()
}, Metadata(user, Metadata.get_call_location(), TimeUtils.utc_now_as_iso_string()))
log.info(f"Saving {len(raw_runs)} raw runs to {raw_runs_path}...")
with open(raw_runs_path, "w") as raw_runs_file:
json.dump([run.serialize() for run in raw_runs], raw_runs_file)
log.info(f"Saved {len(raw_runs)} raw runs")
log.info(f"Saving {len(traced_runs)} traced runs to {traced_runs_output_path}...")
IOUtils.ensure_dirs_exist_for_file(traced_runs_output_path)
with open(traced_runs_output_path, "w") as traced_runs_output_file:
TracedDataJsonIO.export_traced_data_iterable_to_jsonl(traced_runs, traced_runs_output_file)
log.info(f"Saved {len(traced_runs)} traced runs")
log.info(f"Saving {len(raw_contacts)} raw contacts to file '{raw_contacts_path}'...")
with open(raw_contacts_path, "w") as raw_contacts_file:
json.dump([contact.serialize() for contact in raw_contacts], raw_contacts_file)
log.info(f"Saved {len(raw_contacts)} contacts")
for flow in pipeline_configuration.activation_flow_names + pipeline_configuration.survey_flow_names:
runs_log_path = f"{raw_data_dir}/{flow}_log.jsonl"
raw_runs_path = f"{raw_data_dir}/{flow}_raw.json"
traced_runs_output_path = f"{raw_data_dir}/{flow}.jsonl"
log.info(f"Exporting flow '{flow}' to '{traced_runs_output_path}'...")
flow_id = rapid_pro.get_flow_id(flow)
# Load the previous export of runs for this flow, and update them with the newest runs.
# If there is no previous export for this flow, fetch all the runs from Rapid Pro.
with open(runs_log_path, "a") as raw_runs_log_file:
try:
log.info(f"Loading raw runs from file '{raw_runs_path}'...")
with open(raw_runs_path) as raw_runs_file:
raw_runs = [
Run.deserialize(run_json)
for run_json in json.load(raw_runs_file)
]
log.info(f"Loaded {len(raw_runs)} runs")
raw_runs = rapid_pro.update_raw_runs_with_latest_modified(
flow_id, raw_runs, raw_export_log_file=raw_runs_log_file)
except FileNotFoundError:
log.info(
f"File '{raw_runs_path}' not found, will fetch all runs from the Rapid Pro server for flow '{flow}'"
)
raw_runs = rapid_pro.get_raw_runs_for_flow_id(
flow_id, raw_export_log_file=raw_runs_log_file)
# Fetch the latest contacts from Rapid Pro.
with open(contacts_log_path, "a") as raw_contacts_log_file:
raw_contacts = rapid_pro.update_raw_contacts_with_latest_modified(