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December 16, 2020
Time Series Virtual Meetup
Obtaining the Perfect Smoke
By Monitoring Your BBQ with
InfluxDB and Telegraf

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Agenda
●Introductions
●Our talk today
●Q&A
●Open Jobs
●Be a speaker

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Speakers
Will Cooke
Engineering Manager,
Storage Team
InfluxData
Scott Anderson
Senior Technical Writer & Technical Lead
Docs Team
InfluxData

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Method 1: Python + MQTT +
Telegraf + InfluxDB Cloud 2.0
I can make it at home for nothing…
Will Cooke

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$200

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Free!

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$120

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Free!

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Do It Yourself is probably not better, but it is a great way to learn.

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COTS

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COTSish

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Inkbird IBT-4XS
● 4 probes
● Rechargeable battery
● $40

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● Low power
○ Maximum 50% power usage of normal Bluetooth, usually a
lot less
● Low speed
○ Theoretical Max 2Mbps, usually a lot less
● Lower range
○ Claims < 100 meters, usually a lot less
○ Simplified modulation scheme
● Well supported under Linux
● Fairly standard architecture
Bluetooth Low Energy

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Descriptors
Getcharacteristics
Bluetooth LE device
Raspberry Pi
Characteristic
AuthenticatePSK
Getdescriptors
Characteristics
Characteristic
Subscribe&enablerealtime
Ihavedataforyou
Readdada
Senddata
Subscribe&enablerealtime

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Python code
CREDENTIALS_MESSAGE = bytearray.fromhex("21 07 06 05 04 03 02 01 b8 22 00 00 00 00 00")
REALTIME_DATA_ENABLE = bytearray.fromhex("0B 01 00 00 00 00")
UNITS_FAHRENHEIT = bytearray.fromhex("02 01 00 00 00 00")
UNITS_CELSIUS = bytearray.fromhex("02 00 00 00 00 00")
BATTERY_LEVEL = bytearray.fromhex("08 24 00 00 00 00")
# iBBQ static service
MAIN_SERVICE = 0xFFF0 # Service which provides the characteristics
CCCD_UUID = 0x2902
# iBBQ static characteristics
SETTINGS_RESULTS = 0xFFF1
PAIR_UUID = 0xFFF2
HISTORY_UUID = 0xFFF3 # Don't know how this works, here for completeness
REALTIMEDATA_UUID = 0xFFF4
CMD_UUID = 0xFFF5
# Static hex little endian ones and zeros
ON = bytearray.fromhex("01 00")
OFF = bytearray.fromhex("00 00")

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Transports
● Line Protocol
○ DIY
● InfluxDB Client Libraries
○ Clients for most languages
○ Someone has done a lot of the hard work for you
● Telegraf
○ No code option
○ Queuing
○ Pre processing
○ Plugin driven
○ Ready made binaries
○ Already using it

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MQTT
● Brokers
○ Mosquitto
● Client Libraries
○ Every platform and every language
○ Python - Paho MQTT
● Telegraf
○ No code option
○ Already using it!

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MQTT
● Message Queuing Telemetry Transport
○ pub/sub model
○ Doesn’t actually queue
● Works well on low powered devices
○ Simple protocol = low CPU & memory footprint
● Hierarchy of topics
○ bbq/temperature/1
○ bbq/temperature/2
○ bbq/temperature/3
○ home/sensors/bathroom
○ home/sensors/#
● Payloads
○ bbq/temperature/1 - 106
○ home/sensors/bathroom - {“humidity”:65, “temperature”:25,
“fan_running”:true}

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Telegraf
● Python sends MQTT message
○ send_mqtt("bbq/temperature/"+str(idx+1), int(temperature))
● Telegraf config
[[inputs.mqtt_consumer]]
servers = ["tcp://192.168.42.100:1883"]
topics = [
"bbq/+/#"
]
data_format = "value"
data_type = "integer"
[[outputs.influxdb_v2]]
urls = ["https://us-west-2-1.aws.cloud2.influxdata.com"]
token =”YOUR_TOKEN”
organization = "wcooke@influxdata.com"
bucket = "My Monitoring Bucket"

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The dreaded stall

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Features & Future Plans
● https://github.com/8none1/pybq
○ Contributions welcome!
● Currently working:
○ Reporting temperatures
○ Reporting battery levels
○ Automatically connect to thermometer and start logging
● Alerts
○ Send instant messenger alerts when certain thresholds are hit
○ Leverage Influx Cloud 2 tasks and alerts
● Low Power Mode
○ Only poll the temperature every minute?
● More ribs

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Method 2: FireBoard + Telegraf +
InfluxDB 2.0
Scott Anderson

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Fireboard 1

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Fireboard 2

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[[outputs.influxdb_v2]]
urls = ["$INFLUX_HOST"]
token = "$INFLUX_TOKEN"
organization = "$INFLUX_ORG"
bucket = "fireboard"
[[inputs.fireboard]]
## Specify auth token for your account
auth_token = "$FIREBOARD_TOKEN"

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Passive BBQ
Monitor & Alert

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import "influxdata/influxdb/monitor"
meatProbes = [2,3]
checkData = {...}
from(bucket:"fireboard")
|> range(start: -15m)
|> filter(fn: (r) => r._measurement == "fireboard" and contains(set: meatProbes, value: int(v: r.channel)))
|> difference(nonNegative: false)
|> aggregateWindow(every: 5m, fn: mean)
|> fill(usePrevious: true)
|> map(fn: (r) => ({ r with
status:
if r._value <= 0.0 then "cooling"
else if r._value <= 0.02 then "stalling"
else "cooking"
}))
|> monitor.check(
crit: (r) => r.status == "cooling",
warn: (r) => r.status == "stalling",
ok: (r) => r.status <= "cooking",
messageFn: (r) => "Probe${r.channel} appears to be ${r.status}",
data: checkData
)

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import "influxdata/influxdb/monitor"
meatProbes = [2,3]
checkData = {...}
from(bucket:"fireboard")
|> range(start: -15m)
|> filter(fn: (r) => r._measurement == "fireboard" and contains(set: meatProbes, value: int(v: r.channel)))
|> difference(nonNegative: false)
|> aggregateWindow(every: 5m, fn: mean)
|> fill(usePrevious: true)
|> map(fn: (r) => ({ r with
status:
if r._value <= 0.0 then "cooling"
else if r._value <= 0.02 then "stalling"
else "cooking"
}))
|> monitor.check(
crit: (r) => r.status == "cooling",
warn: (r) => r.status == "stalling",
ok: (r) => r.status <= "cooking",
messageFn: (r) => "Probe${r.channel} appears to be ${r.status}",
data: checkData
)

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import "influxdata/influxdb/monitor"
meatProbes = [2,3]
checkData = {...}
from(bucket:"fireboard")
|> range(start: -15m)
|> filter(fn: (r) => r._measurement == "fireboard" and contains(set: meatProbes, value: int(v: r.channel)))
|> difference(nonNegative: false)
|> aggregateWindow(every: 5m, fn: mean)
|> fill(usePrevious: true)
|> map(fn: (r) => ({ r with
status:
if r._value <= 0.0 then "cooling"
else if r._value <= 0.02 then "stalling"
else "cooking"
}))
|> monitor.check(
crit: (r) => r.status == "cooling",
warn: (r) => r.status == "stalling",
ok: (r) => r.status <= "cooking",
messageFn: (r) => "Probe${r.channel} appears to be ${r.status}",
data: checkData
)

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import "influxdata/influxdb/monitor"
meatProbes = [2,3]
checkData = {...}
from(bucket:"fireboard")
|> range(start: -15m)
|> filter(fn: (r) => r._measurement == "fireboard" and contains(set: meatProbes, value: int(v: r.channel)))
|> difference(nonNegative: false)
|> aggregateWindow(every: 5m, fn: mean)
|> fill(usePrevious: true)
|> map(fn: (r) => ({ r with
status:
if r._value <= 0.0 then "cooling"
else if r._value <= 0.02 then "stalling"
else "cooking"
}))
|> monitor.check(
crit: (r) => r.status == "cooling",
warn: (r) => r.status == "stalling",
ok: (r) => r.status <= "cooking",
messageFn: (r) => "Probe${r.channel} appears to be ${r.status}",
data: checkData
)

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Comparative Analysis

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import "experimental"
session1 = from(bucket: "fireboard")
|> range(start: 2020-07-22T23:34:10Z, stop: 2020-07-24T14:50:00Z)
|> filter(fn: (r) => r["_measurement"] == "fireboard")
session2 = from(bucket: "fireboard")
|> range(start: 2020-04-11T21:36:10Z, stop: 2020-04-12T20:22:00Z)
|> filter(fn: (r) => r["_measurement"] == "fireboard")
union(tables: [session1, session2])
|> experimental.alignTime()

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Other things on my punch list
● Forecasting
○ Estimate remaining cook time
● Enhanced visualizations
○ Status-determined visualization color schemes
● More notifications
○ “Done” alert
○ “Cooling” alert

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Fireboard InfluxDB Template
github.com/influxdata/community-templates/tree/master/fireboard

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Fireboard InfluxDB Template

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NEXT MEETUP - January 6, 2021
Monitor Your Homebrew Using
InfluxDB Cloud, Telegraf and Raspberry Pi
https://bit.ly/3lUYJrS
Thanks for coming!