def test_run_mysql_executemany_query_execute_error(config):
conn = CMySQLConnection()
conn.cursor = MagicMock()
conn.commit = MagicMock()
conn.rollback = MagicMock()
conn.close = MagicMock()
cursor = conn.cursor.return_value
cursor.executemany = MagicMock(side_effect=Exception("Boom!"))
cursor.close = MagicMock()
with pytest.raises(Exception):
run_mysql_executemany_query(
mysql_conn=conn,
sql_query=SQL_MLWH_MULTIPLE_INSERT,
values=["test"] # type: ignore
)
# check transaction is not committed
assert conn.commit.called is False
# check connection is closed
assert cursor.close.called is True
assert conn.close.called is True
def update_mlwh_filtered_positive_fields(config: Config,
samples: List[SampleDoc]) -> bool:
"""Bulk updates sample filtered positive fields in the MLWH database
Arguments:
config {Config} -- application config specifying database details
samples {List[Dict[str, str]]} -- the list of samples whose filtered positive fields should be updated
Returns:
bool -- whether the updates completed successfully
"""
mysql_conn = create_mysql_connection(config, False)
if mysql_conn is not None and mysql_conn.is_connected():
mlwh_samples = [map_mongo_to_sql_common(sample) for sample in samples]
run_mysql_executemany_query(
mysql_conn, SQL_MLWH_MULTIPLE_FILTERED_POSITIVE_UPDATE,
mlwh_samples)
return True
else:
return False
def test_run_mysql_executemany_query_success(config):
conn = CMySQLConnection()
conn.cursor = MagicMock()
conn.commit = MagicMock()
conn.rollback = MagicMock()
conn.close = MagicMock()
cursor = conn.cursor.return_value
cursor.executemany = MagicMock()
cursor.close = MagicMock()
run_mysql_executemany_query(mysql_conn=conn,
sql_query=SQL_MLWH_MULTIPLE_INSERT,
values=[{}])
# check transaction is committed
assert conn.commit.called is True
# check connection is closed
assert cursor.close.called is True
assert conn.close.called is True
def migrate_all_dbs(config: Config,
s_start_datetime: str = "",
s_end_datetime: str = "") -> None:
if not config:
logger.error("Aborting run: Config required")
return
if not valid_datetime_string(s_start_datetime):
logger.error(
"Aborting run: Expected format of Start datetime is YYMMDD_HHmm")
return
if not valid_datetime_string(s_end_datetime):
logger.error(
"Aborting run: Expected format of End datetime is YYMMDD_HHmm")
return
start_datetime = datetime.strptime(s_start_datetime, MONGO_DATETIME_FORMAT)
end_datetime = datetime.strptime(s_end_datetime, MONGO_DATETIME_FORMAT)
if start_datetime > end_datetime:
logger.error(
"Aborting run: End datetime must be greater than Start datetime")
return
logger.info(
f"Starting DART update process with Start datetime {start_datetime} and End datetime {end_datetime}"
)
try:
mongo_docs_for_sql = []
# open connection to mongo
with create_mongo_client(config) as client:
mongo_db = get_mongo_db(config, client)
samples_collection = get_mongo_collection(mongo_db,
COLLECTION_SAMPLES)
# 1. get samples from mongo between these time ranges
samples = get_samples(samples_collection, start_datetime,
end_datetime)
if not samples:
logger.info("No samples in this time range.")
return
logger.debug(f"{len(samples)} samples to process")
root_sample_ids, plate_barcodes = extract_required_cp_info(samples)
logger.debug(f"{len(plate_barcodes)} unique plate barcodes")
# 2. of these, find which have been cherry-picked and remove them from the list
cp_samples_df = get_cherrypicked_samples(config,
list(root_sample_ids),
list(plate_barcodes))
if cp_samples_df is None: # we need to check if it is None explicitly
raise Exception(
"Unable to determine cherry-picked sample - potentially error connecting to MySQL"
)
# get the samples between those dates minus the cherry-picked ones
if cp_samples_df is not None and not cp_samples_df.empty:
# we need a list of cherry-picked samples with their respective plate barcodes
cp_samples = cp_samples_df[[
FIELD_ROOT_SAMPLE_ID, FIELD_PLATE_BARCODE
]].to_numpy().tolist()
logger.debug(
f"{len(cp_samples)} cherry-picked samples in this timeframe"
)
samples = remove_cherrypicked_samples(samples, cp_samples)
else:
logger.debug("No cherry-picked samples in this timeframe")
logger.info(
f"{len(samples)} samples between these timestamps and not cherry-picked"
)
# 3. add the UUID fields if not present
add_sample_uuid_field(samples)
# update the samples with source plate UUIDs
samples_updated_with_source_plate_uuids(mongo_db, samples)
# 4. update samples in mongo updated in either of the above two steps (would expect the same set of samples
# from both steps)
logger.info("Updating Mongo...")
_ = update_mongo_fields(mongo_db, samples)
logger.info("Finished updating Mongo")
# convert mongo field values into MySQL format
for sample in samples:
mongo_docs_for_sql.append(
map_mongo_sample_to_mysql(sample, copy_date=True))
if (num_sql_docs := len(mongo_docs_for_sql)) > 0:
logger.info(
f"Updating MLWH database for {num_sql_docs} sample documents")
# create connection to the MLWH database
with create_mysql_connection(config, False) as mlwh_conn:
# 5. update the MLWH (should be an idempotent operation)
run_mysql_executemany_query(mlwh_conn,
SQL_MLWH_MULTIPLE_INSERT,
mongo_docs_for_sql)
# 6. add all the plates with non-cherrypicked samples (determined in step 2) to DART, as well as any
# positive samples in these plates
update_dart_fields(config, samples)
else:
def update_mlwh_with_legacy_samples(config: Config,
s_start_datetime: str = "",
s_end_datetime: str = "") -> None:
if not valid_datetime_string(s_start_datetime):
print("Aborting run: Expected format of Start datetime is YYMMDD_HHmm")
return
if not valid_datetime_string(s_end_datetime):
print("Aborting run: Expected format of End datetime is YYMMDD_HHmm")
return
start_datetime = datetime.strptime(s_start_datetime, MONGO_DATETIME_FORMAT)
end_datetime = datetime.strptime(s_end_datetime, MONGO_DATETIME_FORMAT)
if start_datetime > end_datetime:
print("Aborting run: End datetime must be greater than Start datetime")
return
print(
f"Starting MLWH update process with Start datetime {start_datetime} and End datetime {end_datetime}"
)
try:
mongo_docs_for_sql = []
number_docs_found = 0
# open connection mongo
with create_mongo_client(config) as client:
mongo_db = get_mongo_db(config, client)
samples_collection = get_mongo_collection(mongo_db,
COLLECTION_SAMPLES)
print("Selecting Mongo samples")
# this should take everything from the cursor find into RAM memory (assuming you have
# enough memory)
mongo_docs = list(
samples_collection.find({
FIELD_CREATED_AT: {
"$gte": start_datetime,
"$lte": end_datetime
}
}))
number_docs_found = len(mongo_docs)
print(
f"{number_docs_found} documents found in the mongo database between these timestamps"
)
# convert mongo field values into MySQL format
for doc in mongo_docs:
mongo_docs_for_sql.append(
map_mongo_sample_to_mysql(doc, copy_date=True))
if number_docs_found > 0:
print(
f"Updating MLWH database for {len(mongo_docs_for_sql)} sample documents"
)
# create connection to the MLWH database
with create_mysql_connection(config, False) as mlwh_conn:
# execute sql query to insert/update timestamps into MLWH
run_mysql_executemany_query(mlwh_conn,
SQL_MLWH_MULTIPLE_INSERT,
mongo_docs_for_sql)
else:
print(
"No documents found for this timestamp range, nothing to insert or update in MLWH"
)
except Exception:
print_exception()