This tutorial shows how to use QuestDB with a Jupyter notebook. We will learn
how to create a database, query data and read table contents into a Pandas
dataframe, and use
matplotlib to create charts of our table data.
To get started, you'll need a few things installed and set up. This should be quick.
Jupyter Notebooks: These are interactive Python environments that will help you run a complete version of this tutorial. To run it, you should:
- make sure you are running Python 3.x and not Python 2.7. If you're in doubt,
python --versionwill tell you.
- install Jupyter Notebooks with
pip3 install --upgrade ipython jupytermake 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
That will get you right back to a page that looks eerily similar to 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
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 the bottom of a table. If the table has a designated new record, timestamps must be greater than, 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_idis the customer identifier. It uniquely identifies a customer.
balance_ccybalance currency. We use char in this example, but we could use
SYMBOLto avoid storing text against each record to save space and increase database performance.
balanceis the current balance for customer and currency tuple.
inactiveis used to flag deleted records.
timestamptimestamp in microseconds of the record. Note that if you receive the timestamp data as a string, it could also be inserted using
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
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
range(100) to add as many datapoints as you wish.
Now that we have data available, let's try querying some of it to see what we get back!
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.
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 USD.
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. All of the data
was generated randomly, so this exact query may return no results, 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.
We will use matplotlib to plot the data
From that query we should get a nice little plot of our data, like this:
Now we will clean everything up for the next time.
You can now stop your QuestDB instance, if you'd like, or leave it running and find some great uses for it! If you enjoyed this tutorial, please follow us on Twitter, and make sure you give our GitHub repository a star!