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"""
This example is related to `InfluxDB Cloud <https://docs.influxdata.com/influxdb/cloud/>`_ and not available
on a local InfluxDB OSS instance.
How to manage explicit bucket schemas to enforce column names, tags, fields, and data types for your data.
"""
import datetime
from influxdb_client import InfluxDBClient, BucketSchemasService, PostBucketRequest, SchemaType, \
MeasurementSchemaCreateRequest, MeasurementSchemaColumn, ColumnSemanticType, ColumnDataType, \
MeasurementSchemaUpdateRequest
"""
Define credentials
"""
influx_cloud_url = 'https://us-west-2-1.aws.cloud2.influxdata.com'
influx_cloud_token = '...'
org_name = '...'
with InfluxDBClient(url=influx_cloud_url, token=influx_cloud_token, org=org_name, debug=False) as client:
uniqueId = str(datetime.datetime.now())
org_id = client.organizations_api().find_organizations(org=org_name)[0].id
bucket_schemas_api = BucketSchemasService(api_client=client.api_client)
"""
Create a bucket with the schema_type flag set to explicit
"""
print("------- Create Bucket -------\n")
created_bucket = client \
.buckets_api() \
.create_bucket(bucket=PostBucketRequest(name=f"my_schema_bucket_{uniqueId}",
org_id=org_id,
retention_rules=[],
schema_type=SchemaType.EXPLICIT))
print(created_bucket)
"""
Sets the schema for a measurement: Usage CPU
[
{"name": "time", "type": "timestamp"},
{"name": "service", "type": "tag"},
{"name": "host", "type": "tag"},
{"name": "usage_user", "type": "field", "dataType": "float"},
{"name": "usage_system", "type": "field", "dataType": "float"}
]
"""
print("------- Create Schema -------\n")
columns = [
MeasurementSchemaColumn(name="time",
type=ColumnSemanticType.TIMESTAMP),
MeasurementSchemaColumn(name="service",
type=ColumnSemanticType.TAG),
MeasurementSchemaColumn(name="host",
type=ColumnSemanticType.TAG),
MeasurementSchemaColumn(name="usage_user",
type=ColumnSemanticType.FIELD,
data_type=ColumnDataType.FLOAT),
MeasurementSchemaColumn(name="usage_system",
type=ColumnSemanticType.FIELD,
data_type=ColumnDataType.FLOAT)
]
create_request = MeasurementSchemaCreateRequest(name="usage_cpu", columns=columns)
created_schema = bucket_schemas_api.create_measurement_schema(bucket_id=created_bucket.id,
org_id=org_id,
measurement_schema_create_request=create_request)
print(created_bucket)
"""
Lists the Schemas
"""
print("\n------- Lists the Schemas -------\n")
measurement_schemas = bucket_schemas_api.get_measurement_schemas(bucket_id=created_bucket.id).measurement_schemas
print("\n".join([f"---\n ID: {ms.id}\n Name: {ms.name}\n Columns: {ms.columns}" for ms in measurement_schemas]))
print("---")
"""
Update a bucket schema
"""
print("------- Update a bucket schema -------\n")
columns.append(MeasurementSchemaColumn(name="usage_total",
type=ColumnSemanticType.FIELD,
data_type=ColumnDataType.FLOAT))
update_request = MeasurementSchemaUpdateRequest(columns=columns)
updated_schema = bucket_schemas_api.update_measurement_schema(bucket_id=created_bucket.id,
measurement_id=created_schema.id,
measurement_schema_update_request=update_request)
print(updated_schema)
"""
Delete previously created bucket
"""
print("------- Delete Bucket -------\n")
client.buckets_api().delete_bucket(created_bucket)
print(f" successfully deleted bucket: {created_bucket.name}")
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