Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome. The complete code for the demo is available on Github: https://github.com/RobGaud/PervasiveSystemsPersonal You can also find me on LinkedIn: https://www.linkedin.com/in/roberto-gaudenzi-4b0422116

1. INTRODUCTION
TO

2. Hello!
I am Roberto Gaudenzi
You can find me at
robgaudenzi@gmail.com
MS in Engineering in Computer Science
Pervasive Systems
a.y. 2015-16
Prof. Chatzigiannakis

3. TIME-SERIES DATA

4. WHAT IS TIME-SERIES DATA?
A time-series is a sequence of data points,
consisting of successive measurements made
over a time interval.
Two major types: Regular and Irregular
They are used in statistics, mathematical
finance, pattern recognition, IoT, …
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5. WHAT IS TIME-SERIES DATA? (2)
Examples of time-series data:
◦ Stock price evolution
◦ Bank transactions
◦ ...
Example of non time-series data:
◦ Facebook relationships
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6. INFLUXDB

7. WHAT IS INFLUXDB?
InfluxDB is an open-source database
specifically designed to handle time-series
data.
Ideal for any use case involving large amounts
of timestamped data, including IoT sensor data
and real-time analytics.
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8. Points are written to InfluxDB according to the
Line Protocol.
Three parts:
◦ The key, composed by the measurement
name and a set of tags
◦ The fields
◦ The timestamp
Example(CLI):
DATA FORMAT: THE LINE PROTOCOL
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INSERT cpu,host=server02,region=uswest value=3
1434055562000010000

9. DATA FORMAT: THE LINE PROTOCOL (2)
Example(InfluxDB-Python):
client = InfluxDBClient(host, port, user, password, dbname)
json_body = [
{
"measurement": "cpu",
"tags": {
"host": "server02",
"region": "uswest"
},
"time": "2009-11-10T23:00:00Z",
"fields": {
"value": 3
}
}
]
client.write_points(json_body)
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10. InfluxQL is an SQL-like query language for
interacting with data in InfluxDB.
Example (CLI):
QUERY LANGUAGE: INFLUXQL
SELECT mean(water_level) AS mean_level
FROM h2o_feet
WHERE time >= '2015-08-18'
GROUP BY time(10d)
ORDER BY time DESC
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11. QUERY LANGUAGE: INFLUXQL (2)
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Example(InfluxDB-Python):
query = “””select mean(water_level) as mean_level
from h2o_feet where time >= ’2015-08-18’
group by time(10d)
order by time DESC”””
client = InfluxDBClient(host, port, user, pwd, dbname)
# Returns an array of JSONs containing results
# from all matching series
result = client.query(query)
print("Result: {0}".format(result))

12. Working with much data over a long period of
time can create storage concerns.
Solutions: downsample or expire the data.
InfluxDB offers two mechanisms for dealing
with this issue: Continuous Queries and
Retention Policies.
DOWNSAMPLING AND DATA RETENTION
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13. A retention policy (RP) describes:
◦ for how long InfluxDB keeps data
◦ how many copies of those data are stored
RETENTION POLICIES
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CREATE RETENTION POLICY two_hours
ON food_data
DURATION 2h
REPLICATION 1
Example(InfluxDB-Python):
client.create_retention_policy('two_hours', '2h', 1,
'food_data',default=False)
Example (CLI):

14. A continuous query (CQ) is an InfluxQL query
that runs automatically and periodically within
a database.
Example (CLI):
CONTINUOUS QUERIES
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CREATE CONTINUOUS QUERY cq_30m
ON food_data
BEGIN SELECT mean(website) AS mean_website,
mean(phone) AS mean_phone
INTO food_data."default".downsampled_orders
FROM orders
GROUP BY time(30m)
END

15. CONTINUOUS QUERIES (2)
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Example(InfluxDB-Python):
query = “””
create continuous query cq_30m
on food_data
begin
select mean(website) as mean_website,
mean(phone) as mean_phone
into food_data."default".downsampled_orders
from orders
group by time(30m)
end”””
# An InfluxDBClientError will be returned in case of error
try:
result = client.query(query)
except:
print("Note: continuous query already exists.")

16. The built-in web admin interface is a simple
way to interact with InfluxDB.
MANAGE YOUR DATA: THE WEB INTERFACE
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◦ Write Data
◦ Query Data
◦ Access remote
instances

17. INFLUXDB IN A SLIDE
“SQL-like”
HTTP APIs and
Built-in interface
Choose your
language
SQL
<>
New
Designed for time series
Flexible and scalable
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18. DEMO

19. INFLUXDB EXAMPLE: INFLUXCHAT
◦ Simple room-chat application in Python
◦ When a user types a message, this is sent to
the server, that forwards it to all connected
users
◦ The server also stores messages into InfluxDB
◦ Continuous Query and Retention Policy for
collecting statistics about message traffic
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20. INFLUXDB EXAMPLE: INFLUXCHAT (2)
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# Set up the InfluxDB client
DB_NAME = "PervSystPers“
DB_ADDRESS = "localhost“
DB_PORT = 8086
MEASUREMENT_NAME ="influxchat“
influxdb_client = InfluxDBClient(host=DB_ADDRESS,
port=DB_PORT,
database=DB_NAME)

21. INFLUXDB EXAMPLE: INFLUXCHAT (3)
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# Set up the continuous query
CONT_QUERY = “””create continuous query cq_30m on PervSystPers
begin select count(value) as num_msg
into PervSystPers."default".downsampled_msg
from influxchat
group by time(30m)
end”””
try:
result_db = influxdb_client.query(CONT_QUERY)
except:
print("Note: continuous query already exists.")
# Set up the retention policy
RET_POL_NAME = 'del_4_weeks‘
RET_POL_PERIOD = ‘4w’
RET_POL_N_COPY = 1
resultdb = influxdb_client.create_retention_policy(RET_POL_NAME,
RET_POL_PERIOD,
RET_POL_N_COPY
DB_NAME,
default=TRUE)

22. INFLUXDB EXAMPLE: INFLUXCHAT (4)
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# Send the incoming message to InfluxDB
json_body = [
{
"measurement": MEASUREMENT_NAME,
"tags": {
"username": sender_name
},
"fields": {
"value": msg_content
}
}
]
influxdb_client.write_points(json_body)

23. Thanks!
ANY QUESTIONS?
You can find me at
robgaudenzi@gmail.com

24. CREDITS AND LINKS
◦ Presentation template by SlidesCarnival
◦ Code available on Github
◦ Presentation available on Slideshare
◦ You can find me also on LinkedIn
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