QuestDB Tutorial for Python
Getting Started
To get started, you'll need a few things installed and set up. This should be quick.
- QuestDB: To install Questdb you can see Installation for complete instructions in case you want to use Docker, or
brew
on MacOS, but the easiest way is to download the binaries and run it directly. Instructions for that are Here. - Jupyter Notebooks: That's what this is. To run it, you should:
- make sure you are running Python 3.x and not Python 2.7. If you're in doubt,
python --version
will tell you. - install Jupyter Notebooks with
pip3 install --upgrade ipython jupyter
- make sure that the libraries we use in this tutorial are also installed with
pip3 install requests urlib matplotlib pandas
- clone this repository (
git clone https://github.com/davidgs/QuestNotebook
) - in the repository directory run
jupyter notebook
- make sure you are running Python 3.x and not Python 2.7. If you're in doubt,
That will get you right back to a page like this that is interactive, allowing you to run the code and interact with the database yourself.
If you get errors like ModuleNotFoundError: No module named 'requests'
for any of the libraries you installed above, double-check to make sure that you are actually using Python 3.x jupytper --path
will let you know if Jupyter is using 2.7 or 3.x
Create A Database
We will need someplace to store our data, so let's create a test database where we can put some random data.
We will create a simple table with 5 columns, one of which is a timestamp
The Create operation in QuestDB appends records to bottom of a table. If the table has a designated timestamp
, new record timestamps must be superior or equal to the latest timestamp. Attempts to add a timestamp in middle of a table will result in a timestamp out of order error.
-
cust_id
is the customer identifier. It uniquely identifies customer. -
balance_ccy
balance currency. We useSYMBOL
here to avoid storing text against each record to save space and increase database performance. -
balance
is the current balance for customer and currency tuple. -
inactive
is used to flag deleted records. -
timestamp
timestamp in microseconds of the record. Note that if you receive the timestamp data as a string, it could also be inserted usingto_timestamp
.
This should return a 200
status the first time you run it. If you run it more than once, subsequent runs will return 400
because the database already exists.
import requests
import urllib.parse as par
q = 'create table balances'\
'(cust_id int,'\
' balance_ccy char,'\
'balance double,'\
'inactive boolean,'\
'timestamp timestamp)'\
'timestamp(timestamp)'
r = requests.get("http://localhost:9000/exec?query=" + q)
print(r.status_code)
Generate some Data
Since we have a new setup, we should add some data to QuestDB so that we can have something to query. We will add some random data, for now.
You can re-run this section as many times as you want to add 100 entries at a time, or simply change the range(100)
to add as many datapoints as you wish.
import requests
import random
from datetime import datetime
success = 0
fail = 0
currency = ["$", "€", "£", "¥"]
random.seed()
for x in range(1000):
cust = random.randint(20, 42)
cur = random.choice(currency)
bal = round(random.uniform(10.45, 235.15), 2)
act = bool(random.getrandbits(1))
query = "insert into balances values("\
+ str(cust) + ",'"\
+ cur + "'," \
+ str(bal) + "," \
+ str(act) + ",systimestamp())"
r = requests.get("http://localhost:9000/exec?query=" + query)
if r.status_code == 200:
success += 1
else:
fail += 1
print("Rows inserted: " + str(success))
if fail > 0:
print("Rows Failed: " + str(fail))
Query Data from QuestDB
Now that we have data available, let's try querying some of it to see what we get back!
import requests
import io
r = requests.get("http://localhost:9000/exp?query=select * from balances")
rawData = r.text
print(rawData)
Read the content into Pandas Dataframe
So you'll notice that the returned data is just a massive csv
string. If you'd rather have json
data, then you would change the endpoint to http://localhost:9000/exec ...
But since we're going to use Pandas to frame our data, we'll stick with csv
.
We are also telling pandas to parse the timestamp
field as a date. This is important since we're dealing with Time Series data.
import pandas as pd
pData = pd.read_csv(io.StringIO(rawData), parse_dates=['timestamp'])
print(pData)
Narrow the search
That's just getting us all the data, but let's narrow the search using some SQL clauses. Let's look for a specific cust_id
and only balances of that customer that are in $s. We are also only interested in times the customer was active
Since this is SQL, you can make this query as simple, or as complex, as you'd like.
Since all of the data was generated randomly, this exact query may return no results, so you may have to adjust the cust_id
below until you get results back.
**Note:* The query string must be URL-encoded before it is sent.
import urllib.parse
q = "select cust_id,"\
" balance,"\
" balance_ccy,"\
" inactive,"\
" timestamp"\
" from balances"\
" where cust_id = 26"\
" and balance_ccy = '$'"\
" and not inactive"
query = urllib.parse.quote(q)
r = requests.get("http://localhost:9000/exp?query=" + query)
queryData = r.content
rawData = pd.read_csv(io.StringIO(queryData.decode('utf-8')), parse_dates=['timestamp'])
print(rawData)
Plot the data
We will use matplotlib
to plot the data
from matplotlib import pyplot as plt
rawData.plot("timestamp", ["balance"], subplots=True)
And, with any luck, you'll get a great little plot like this:
Clean up
Now we will clean everything up for the next time.
r = requests.get("http://localhost:9000/exec?query=drop table balances")
if r.status_code == 200:
print("Database Table dropped")
else:
print("Database Table not Dropped: " + str(r.status_code))
And you're done! If you liked this tutorial, please give us a ⭐ on Github and be sure to follow us on twitter!
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