influxdb query with large response too slow - influxdb

Question

Our query is taking 20s and we need to substantially reduce this. We're calling it via the python dataframe client, but I reproduced the same query and 20s response time via the CLI client:
influx --host 10.0.5.183 --precision RFC3339 -execute "select * from turbine_ops.permanent.turbine_interval where ((turbine_id = 'NKWF-T15' or turbine_id = 'NKWF-T41' or turbine_id = 'NKWF-T23' or turbine_id = 'NKWF-T19' or turbine_id = 'NKWF-T51' or turbine_id = 'NKWF-T14' or turbine_id = 'NKWF-T42' or turbine_id = 'NKWF-T26' or turbine_id = 'NKWF-T39' or turbine_id = 'NKWF-T49' or turbine_id = 'NKWF-T38') and time >= '2019-05-01')">/dev/null
Influx is running on a r5.large EC2 instance with EBS a general purpose SSD (gp2) volume, the CLI is on an EC2 in the same subnet. The query returns 747120 rows, each having 1 tag (turbine_id) and 5 fields (all decimal values). Does this seem normal?
Via htop on the influx host I see no significant change in RAM usage, a brief CPU spike that lasts ~1s at the start of query, and then no subsequent CPU activity.
Shard duration is set to 1 year.
show series exact cardinality on turbine_ops
name: turbine_interval
count
-----
11
I tried scaling the influxdb host to r5.8xlarge and the query time did not change at all.
explain select * from turbine_ops.permanent.turbine_interval where ((turbine_ = 'NKWF-T15' or turbine_id = 'NKWF-T41' or turbine_id = 'NKWF-T23' or turbine_id = 'NKWF-T19' or turbine_id = 'NKWF-T51' or turbine_id = 'NKWF-T14' or turbine_id = 'NKWF-T42' or turbine_id = 'NKWF-T26' or turbine_id = 'NKWF-T39' or turbine_id = 'NKWF-T49' or turbine_id = 'NKWF-T38') and time >= '2019-05-01')
QUERY PLAN
EXPRESSION:
AUXILIARY FIELDS: active_power::float, “duration”::integer, rotor_rpm::float, turbine_id::tag, wind_speed::float, yaw_direction::float
NUMBER OF SHARDS: 1
NUMBER OF SERIES: 10
CACHED VALUES: 0
NUMBER OF FILES: 150
NUMBER OF BLOCKS: 3515
SIZE OF BLOCKS: 12403470
explain analyze select * from turbine_ops.permanent.turbine_interval where ((turbine_ = 'NKWF-T15' or turbine_id = 'NKWF-T41' or turbine_id = 'NKWF-T23' or turbine_id = 'NKWF-T19' or turbine_id = 'NKWF-T51' or turbine_id = 'NKWF-T14' or turbine_id = 'NKWF-T42' or turbine_id = 'NKWF-T26' or turbine_id = 'NKWF-T39' or turbine_id = 'NKWF-T49' or turbine_id = 'NKWF-T38') and time >= '2019-05-01')
EXPLAIN ANALYZE
.
└── select
├── execution_time: 1.442047426s
├── planning_time: 2.105094ms
├── total_time: 1.44415252s
└── build_cursor
├── labels
│ └── statement: SELECT active_power::float, “duration”::integer, rotor_rpm::float, turbine_id::tag, wind_speed::float, yaw_direction::float FROM turbine_ops.permanent.turbine_interval WHERE turbine_ = ‘NKWF-T15’ OR turbine_id::tag = ‘NKWF-T41’ OR turbine_id::tag = ‘NKWF-T23’ OR turbine_id::tag = ‘NKWF-T19’ OR turbine_id::tag = ‘NKWF-T51’ OR turbine_id::tag = ‘NKWF-T14’ OR turbine_id::tag = ‘NKWF-T42’ OR turbine_id::tag = ‘NKWF-T26’ OR turbine_id::tag = ‘NKWF-T39’ OR turbine_id::tag = ‘NKWF-T49’ OR turbine_id::tag = ‘NKWF-T38’
└── iterator_scanner
├── labels
│ └── auxiliary_fields: active_power::float, “duration”::integer, rotor_rpm::float, turbine_id::tag, wind_speed::float, yaw_direction::float
└── create_iterator
├── labels
│ ├── cond: turbine_ = ‘NKWF-T15’ OR turbine_id::tag = ‘NKWF-T41’ OR turbine_id::tag = ‘NKWF-T23’ OR turbine_id::tag = ‘NKWF-T19’ OR turbine_id::tag = ‘NKWF-T51’ OR turbine_id::tag = ‘NKWF-T14’ OR turbine_id::tag = ‘NKWF-T42’ OR turbine_id::tag = ‘NKWF-T26’ OR turbine_id::tag = ‘NKWF-T39’ OR turbine_id::tag = ‘NKWF-T49’ OR turbine_id::tag = ‘NKWF-T38’
│ ├── measurement: turbine_interval
│ └── shard_id: 1584
├── cursors_ref: 0
├── cursors_aux: 50
├── cursors_cond: 0
├── float_blocks_decoded: 2812
├── float_blocks_size_bytes: 12382380
├── integer_blocks_decoded: 703
├── integer_blocks_size_bytes: 21090
├── unsigned_blocks_decoded: 0
├── unsigned_blocks_size_bytes: 0
├── string_blocks_decoded: 0
├── string_blocks_size_bytes: 0
├── boolean_blocks_decoded: 0
├── boolean_blocks_size_bytes: 0
└── planning_time: 1.624627ms
Please let me know any optimizations we may be able to make.

Answer 1

My suspicions were confirmed that influx itself was not the culprit here, when I curled the HTTP API directly and got a ~3s response. I’m not sure why the CLI or python DataFrameClient are adding so much overhead but I got to a Pandas dataframe in 3.78s using this:
import urllib
import pandas as pd
from io import BytesIO
data = {}
data['db']='turbine_ops'
data['precision']='s'
data['q']="select * from turbine_ops.permanent.turbine_interval where ((turbine_id = 'NKWF-T15' or turbine_id = 'NKWF-T41' or turbine_id = 'NKWF-T23' or turbine_id = 'NKWF-T19' or turbine_id = 'NKWF-T51' or turbine_id = 'NKWF-T14' or turbine_id = 'NKWF-T42' or turbine_id = 'NKWF-T26' or turbine_id = 'NKWF-T39' or turbine_id = 'NKWF-T49' or turbine_id = 'NKWF-T38') and time >= '2019-05-01')"
url_values=urllib.parse.urlencode(data)
url="http://10.0.5.183:8086/query?" + url_values
request = urllib.request.Request(url, headers={'Accept':'application/csv'})
response = urllib.request.urlopen(request)
response_bytestr = response.read()
df = pd.read_csv(BytesIO(response_bytestr), sep=",")
This is a good start, faster would be even better so please submit other solutions.