Connecting and running queries

This topic provides a walkthrough and examples for how to use the Firebolt Python SDK to connect to Firebolt resources to run commands and query data.

Setting up a connection

To connect to a Firebolt database to run queries or command, you must provide your account credentials through a connection request.

To get started, follow the steps below:

1. Import modules

The Firebolt Python SDK requires you to import the following modules before making any command or query requests to your Firebolt database.

from firebolt.db import connect
from firebolt.client import DEFAULT_API_URL

2. Connect to your database and engine

Your account information can be provided as parameters in a connection() function.

A connection requires the following parameters:

username

The email address associated with your Firebolt user.

password

The password used for connecting to Firebolt.

database

The name of the database you would like to connect to.

engine_name or engine_url

The name or URL of the engine to use for SQL queries.

If the engine is not specified, your default engine is used.

This information can be provided in multiple ways.

  • Set credentials manually

    You can manually include your account information in a connection object in your code for any queries you want to request.

    Replace the values in the example code below with your Firebolt account credentials as appropriate.

    username = "your_username"
    password = "your_password"
    engine_name = "your_engine"
    database_name = "your_database"
    
    connection = connect(
            engine_name=engine_name,
            database=database_name,
            username=username,
            password=password,
    )
    
    cursor = connection.cursor()
    
  • Use an .env file

    Consolidating all of your Firebolt credentials into a .env file can help protect sensitive information from exposure. Create an .env file with the following key-value pairs, and replace the values with your information.

    FIREBOLT_USER="your_username"
    FIREBOLT_PASSWORD="your_password"
    FIREBOLT_ENGINE="your_engine"
    FIREBOLT_DB="your_database"
    

    Be sure to place this .env file into your root directory.

    Your connection script can load these environmental variables from the .env file by using the python-dotenv package. Note that the example below imports the os and dotenv modules in order to load the environmental variables.

    import os
    from dotenv import load_dotenv
    
    load_dotenv()
    
    connection = connect(
        username=os.getenv('FIREBOLT_USER'),
        password=os.getenv('FIREBOLT_PASSWORD'),
        engine_name=os.getenv('FIREBOLT_ENGINE'),
        database=os.getenv('FIREBOLT_DB')
    )
    
    cursor = connection.cursor()
    

3. Execute commands using the cursor

The cursor object can be used to send queries and commands to your Firebolt database and engine. See below for examples of functions using the cursor object.

Server-side synchronous command and query examples

This section includes Python examples of various SQL commands and queries.

Inserting and selecting data

The example below uses cursor to create a new table called test_table, insert rows into it, and then select the table’s contents.

The engine attached to your specified database must be started before executing any queries. For help, see Starting an engine.

cursor.execute(
    """
    CREATE FACT TABLE IF NOT EXISTS test_table (
        id INT,
        name TEXT
    )
    PRIMARY INDEX id;
    """
)

cursor.execute(
    """
    INSERT INTO test_table VALUES
    (1, 'hello'),
    (2, 'world'),
    (3, '!');
    """
)

cursor.execute("SELECT * FROM test_table;")

cursor.close()

Note

For reference documentation on cursor functions, see cursor.

Fetching query results

After running a query, you can fetch the results using a cursor object. The examples below use the data queried from test_table created in the Inserting and selecting data.

print(cursor.fetchone())

Returns: [2, 'world']

print(cursor.fetchmany(2))

Returns: [[1, 'hello'], [3, '!']]

print(cursor.fetchall())

Returns: [[2, 'world'], [1, 'hello'], [3, '!']]

Executing parameterized queries

Parameterized queries (also known as “prepared statements”) format a SQL query with placeholders and then pass values into those placeholders when the query is run. This protects against SQL injection attacks and also helps manage dynamic queries that are likely to change, such as filter UIs or access control.

To run a parameterized query, use the execute() cursor method. Add placeholders to your statement using question marks ?, and in the second argument pass a tuple of parameters equal in length to the number of ? in the statement.

cursor.execute(
    """
    CREATE FACT TABLE IF NOT EXISTS test_table2 (
            id INT,
            name TEXT,
            date_value DATE
    )
        PRIMARY INDEX id;"""
)
cursor.execute(
    "INSERT INTO test_table2 VALUES (?, ?, ?)",
    (1, "apple", "2018-01-01"),
)

cursor.close()

If you need to run the same statement multiple times with different parameter inputs, you can use the executemany() cursor method. This allows multiple tuples to be passed as values in the second argument.

cursor.executemany(
    "INSERT INTO test_table2 VALUES (?, ?, ?)",
    (
        (2, "banana", "2019-01-01"),
        (3, "carrot", "2020-01-01"),
        (4, "donut", "2021-01-01")
    )
)

cursor.close()

Executing multiple-statement queries

Multiple-statement queries allow you to run a series of SQL statements sequentially with just one method call. Statements are separated using a semicolon ;, similar to making SQL statements in the Firebolt UI.

cursor.execute(
    """
    SELECT * FROM test_table WHERE id < 4;
    SELECT * FROM test_table WHERE id > 2;
    """
)
print("First query: ", cursor.fetchall())
assert cursor.nextset()
print("Second query: ", cursor.fetchall())
assert cursor.nextset() is None

cursor.close()

Returns:

First query: [[2, 'banana', datetime.date(2019, 1, 1)],
              [3, 'carrot', datetime.date(2020, 1, 1)],
              [1, 'apple', datetime.date(2018, 1, 1)]]
Second query: [[3, 'carrot', datetime.date(2020, 1, 1)],
               [4, 'donut', datetime.date(2021, 1, 1)]]

Note

Multiple statement queries are not able to use placeholder values for parameterized queries.

Server-side asynchronous query execution

In addition to asynchronous API calls, which allow client-side execution to continue while waiting for API responses, the Python SDK provides server-side asynchronous query execution. When a query is executed asynchronously the only response from the server is a query ID. The status of the query can then be retrieved by polling the server at a later point. This frees the connection to do other queries or even be closed while the query continues to run. And entire service, such as AWS Lamdba, could potentially even be spun down an entire while a long-running database job is still underway.

Note, however, that it is not possible to retrieve the results of a server-side asynchronous query, so these queries are best used for running DMLs and DDLs and SELECTs should be used only for warming the cache.

Executing asynchronous DDL commands

Executing queries server-side asynchronously is similar to executing server-side synchronous queries, but the execute() command receives an extra parameter, async_execution=True. The example below uses cursor to create a new table called test_table. execute(query, async_execution=True) will return a query ID, which can subsequently be used to check the query status.

query_id = cursor.execute(
    """
    CREATE FACT TABLE IF NOT EXISTS test_table (
        id INT,
        name TEXT
    )
    PRIMARY INDEX id;
    """,
    async_execution=True
)

To check the status of a query, send the query ID to `get_status()` to receive a QueryStatus enumeration object. Possible statuses are:

  • RUNNING

  • ENDED_SUCCESSFULLY

  • ENDED_UNSUCCESSFULLY

  • NOT_READY

  • STARTED_EXECUTION

  • PARSE_ERROR

  • CANCELED_EXECUTION

  • EXECUTION_ERROR

Once the status of the table creation is ENDED_SUCCESSFULLY, data can be inserted into it:

from firebolt.async_db.cursor import QueryStatus

query_status = cursor.get_status(query_id)

if query_status == QueryStatus.ENDED_SUCCESSFULLY:
    cursor.execute(
        """
        INSERT INTO test_table VALUES
            (1, 'hello'),
            (2, 'world'),
            (3, '!');
        """
        )

In addition, server-side asynchronous queries can be cancelled calling cancel().

query_id = cursor.execute(
    """
    CREATE FACT TABLE IF NOT EXISTS test_table (
        id INT,
        name TEXT
    )
    PRIMARY INDEX id;
    """,
    async_execution=True
)

cursor.cancel(query_id)

query_status = cursor.get_status(query_id)

print(query_status)

Returns: CANCELED_EXECUTION

Using DATE and DATETIME values

DATE, DATETIME and TIMESTAMP values used in SQL insertion statements must be provided in a specific format; otherwise they could be read incorrectly.

  • DATE values should be formatted as YYYY-MM-DD

  • DATETIME and TIMESTAMP values should be formatted as YYYY-MM-DD HH:MM:SS.SSSSSS

The datetime module from the Python standard library contains various classes and methods to format DATE, TIMESTAMP and DATETIME data types.

You can import this module as follows:

from datetime import datetime