pushpendra-tripathi

/* Useful celery config.

app = Celery('tasks',
             broker='redis://localhost:6379',
             backend='redis://localhost:6379')

app.conf.update(
    CELERY_TASK_RESULT_EXPIRES=3600,
    CELERY_QUEUES=(
        Queue('default', routing_key='tasks.#'),

pushpendra-tripathi

import json
from influxdb_client.client.flux_table import FluxStructureEncoder
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')
output = json.dumps(tables, cls=FluxStructureEncoder, indent=2)

pushpendra-tripathi

data_frame = query_api.query_data_frame('from(bucket:"my-bucket") '
                                        '|> range(start: -10m) '
                                        '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") '
                                        '|> keep(columns: ["location", "temperature"])')
print(data_frame.to_string())

pushpendra-tripathi

csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)',
                                 dialect=Dialect(header=False, delimiter=",", comment_prefix="#", annotations=[],
                                                 date_time_format="RFC3339"))
for csv_line in csv_result:
    if not len(csv_line) == 0:
        print(f'Temperature in {csv_line[9]} is {csv_line[6]}')

pushpendra-tripathi

p = {"_start": datetime.timedelta(hours=-1),
     "_location": "Prague",
     "_desc": True,
     "_floatParam": 25.1,
     "_every": datetime.timedelta(minutes=5)
     }

tables = query_api.query('''
    from(bucket:"my-bucket") |> range(start: _start)
        |> filter(fn: (r) => r["_measurement"] == "my_measurement")

pushpendra-tripathi

tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for table in tables:
    print(table)
    for record in table.records:
        print(record.values)

pushpendra-tripathi

 _now = datetime.now(UTC)
_data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
                           index=[_now, _now + timedelta(hours=1)],
                           columns=["location", "water_level"])

_write_client.write("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
                    data_frame_tag_columns=['location'])

pushpendra-tripathi

_write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                    "fields": {"water_level": 1.0}, "time": 1})
_write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                             "fields": {"water_level": 2.0}, "time": 2},
                                            {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                             "fields": {"water_level": 3.0}, "time": 3}])

pushpendra-tripathi

_write_client.write("my-bucket", "my-org",
                            Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
        _write_client.write("my-bucket", "my-org",
                            [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
                             Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])

pushpendra-tripathi

"""
Write Line Protocol formatted as string
"""
_write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
_write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
                                            "h2o_feet,location=coyote_creek water_level=3.0 3"])

"""
Write Line Protocol formatted as byte array
"""