in python/dataproc_templates/elasticsearch/elasticsearch_to_bigtable.py [0:0]
def run(self, spark: SparkSession, args: Dict[str, Any]) -> None:
logger: Logger = self.get_logger(spark=spark)
# Arguments
es_node: str = args[constants.ES_BT_INPUT_NODE]
es_index: str = args[constants.ES_BT_INPUT_INDEX]
es_user: str = args[constants.ES_BT_NODE_USER]
es_password: str = args[constants.ES_BT_NODE_PASSWORD]
es_api_key: str = args[constants.ES_BT_NODE_API_KEY]
flatten_struct = args[constants.ES_BT_FLATTEN_STRUCT]
flatten_array = args[constants.ES_BT_FLATTEN_ARRAY]
catalog: str = ''.join(args[constants.ES_BT_CATALOG_JSON].split())
project_id: str = args[constants.ES_BT_PROJECT_ID]
instance_id: str = args[constants.ES_BT_INSTANCE_ID]
create_new_table: bool = args[constants.ES_BT_CREATE_NEW_TABLE]
batch_mutate_size: int = args[constants.ES_BT_BATCH_MUTATE_SIZE]
ignore_keys = {constants.ES_BT_NODE_PASSWORD, constants.ES_BT_NODE_API_KEY}
filtered_args = {key:val for key,val in args.items() if key not in ignore_keys}
logger.info(
"Starting Elasticsearch to BigTable Spark job with parameters:\n"
f"{pprint.pformat(filtered_args)}"
)
# Read
input_data = ingest_dataframe_from_elasticsearch(
spark, es_node, es_index, es_user, es_password, es_api_key, args, "es.bt.input."
)
if flatten_struct:
# Flatten the Struct Fields
input_data = flatten_struct_fields(input_data)
if flatten_array:
# Flatten the n-D array fields to 1-D array fields
input_data = flatten_array_fields(input_data)
if not input_data.head(1):
logger.info("No records in dataframe, Skipping the BigTable Load")
return
# Write
input_data.write \
.format(constants.FORMAT_BIGTABLE) \
.options(catalog=catalog) \
.option(constants.ES_BT_PROJECT_ID, project_id) \
.option(constants.ES_BT_INSTANCE_ID, instance_id) \
.option(constants.ES_BT_CREATE_NEW_TABLE, create_new_table) \
.option(constants.ES_BT_BATCH_MUTATE_SIZE, batch_mutate_size) \
.save()