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Getting started tutorial

This tutorial will help you connect your Firebolt account to AWS Marketplace, create a database, ingest a sample dataset from Amazon S3 into Firebolt, and run fundamental analytics queries over the data. If you do not have an active Firebolt account, schedule a call to get set up and register for our hands-on Firebolt workshop to get an interactive, instructor-led tutorial on Firebolt.

This tutorial uses Firebolt’s sample dataset, from the fictional gaming company “Ultra Fast Gaming Inc.” This dataset is publicly available with the access credentials shared below.

  1. Registering for Firebolt through AWS Marketplace
  2. Create your first database
  3. Run your first query
  4. Ingest data
    1. Create an external table
    2. Create a fact table
    3. Use INSERT to ingest data
  5. Query the ingested data
    1. Configure an aggregating index

Registering for Firebolt through AWS Marketplace

This registration is a prerequisite for starting engines and running queries.

To register

  1. On the Firebolt page, navigate to the Configuration menu. Click Billing.

  2. Click Connect to AWS Marketplace. This will take you to the Firebolt page available on AWS Marketplace.

  3. On the AWS Marketplace page, click the View Purchase Options on the top right hand corner of the screen.

  4. Click Setup Your Account.

Your account should now be associated with AWS Marketplace.

Create your first database

To start working with data, you first create a database and a Firebolt engine. An engine represents the compute resources that are attached to a database for a certain workload. A database always has one general purpose engine that can write to the Firebolt file format (F3) for data ingestion and run analytics queries. We use that single-engine set up in this tutorial. Many databases are set up with additional analytics engines that can only query, and are configured to optimize different query workloads. For more information, see Work with engines.

To create a database and engine

  1. From the Databases page, choose New Database.

  2. Enter a Database name (we use Tutorial in this topic).

  3. Under Database engines, leave the default engine selected. Firebolt will name the engine Tutorial_general_purpose.

  4. Choose Create database.

    Firebolt adds your database to the Databases page.

Run your first query

Before we ingest the sample data and run a query over it, we’ll go to the SQL workspace for the database and run a simple query to demonstrate how to start an engine. For more information about the SQL workspace, see Query data.

To open your database, start an engine, and run a query

  1. From the Database page, find the database that you created in the list, and then choose the Open in SQL workspace icon (>_) next to the Database name.

  2. In the Script 1 tab, type the simple query below that returns a list of databases in your account.
    SHOW DATABASES;
    
  3. Choose Run Script and note that the Using list indicates the engine that Firebolt uses to run the script, for example, Tutorial_general_purpose.

  4. When Firebolt prompts you to start the engine, choose Start Engine. The engine will take a few minutes to set up.

Ingest data

Ingesting data into Firebolt is a three-step process. You:

  1. Create an external table.

  2. Create a fact or dimension table.

  3. Run an INSERT command from the external table to the fact or dimension table.

Create an external table

An external table is a special, virtual table that serves as a connector to your data source. After the external table is created, you ingest data by running an INSERT command from that external table into a fact table or dimension table. The INSERT command must run on a general purpose engine. After the data is available in fact and dimension tables, you can run analytics queries over those tables using any engine. Although it’s possible, we don’t recommend running analytics queries on external tables. For more information on external tables, see CREATE EXTERNAL TABLE.

IMPORTANT: To learn more about how to set up AWS roles to access your data on S3, see Use AWS roles to access S3.

To create an external table

  1. Choose the plus symbol (+) next to Script 1 to create a new script tab, Script 2, in the SQL workspace.

  2. Copy and paste the query below into the Script 2 tab.
    CREATE EXTERNAL TABLE IF NOT EXISTS ex_levels (
    -- Column definitions map to data fields
    -- in the source data file and are specified
    -- as sources in the INSERT INTO statement for ingestion. 
     LevelID INTEGER,
     GameID INTEGER,
     Level INTEGER,
     Name TEXT,
     LevelType TEXT,
     NextLevel INTEGER NULL,
     MinPointsToPass INTEGER,
     MaxPoints INTEGER, 
     NumberOfLaps INTEGER,
     MaxPlayers INTEGER,
     MinPlayers INTEGER,
     PointsPerLap REAL,
     MusicTrack TEXT,
     SceneDetails TEXT,
     MaxPlayTimeSeconds INTEGER,
     LevelIcon TEXT
    ) 
    -- The URL specifies the data source in S3.
    -- All files in the location that match the OBJECT_PATTERN
    -- will be processed during ingestion.
    URL = 's3://firebolt-publishing-public/help_center_assets/firebolt_sample_dataset/'
    -- These credentials specify a role or AWS key credentials
    -- with permission to read from the S3 location.
    -- These credentials are commented out for this tutorial because the bucket
    -- is publicly accessible.
    -- CREDENTIALS = ( AWS_KEY_ID = '******' AWS_SECRET_KEY = '******' )
    OBJECT_PATTERN = 'help_center_assets/firebolt_sample_dataset/levels.csv'
    TYPE = (CSV SKIP_HEADER_ROWS = 1);
    
  3. Choose Run Script.
    Firebolt creates the external table. When finished, the external table ex_levels appears on the object panel of the database.

  4. Choose the vertical ellipses next to Script 2, choose Save script, enter a name (for example, MyExTableScript) and then press ENTER to save the script.

Create a fact table

In this step, you’ll create a Firebolt fact table called levels, which you use in the next step as the target for an INSERT command.

When creating a fact or dimension table, you will specify a primary index. Firebolt uses the primary index when it ingests data so that it is saved to S3 for highly efficient pruning and sorting when the data is queried. A primary index is required when creating a fact table, and recommended for dimension tables. For more information, see Using primary indexes.

The fact table that we create in this step specifies the LevelID column for the primary index. For more information about choosing columns for a primary index, see How to choose primary index columns.

To create a fact table

  1. Create a new script tab.

  2. Copy and paste the query below into the script tab.
    CREATE FACT TABLE IF NOT EXISTS levels
    (
     LevelID INTEGER UNIQUE,
     GameID INTEGER,
     Level INTEGER,
     Name TEXT,
     LevelType TEXT,
     NextLevel INTEGER NULL,
     MinPointsToPass INTEGER,
     MaxPoints INTEGER, 
     NumberOfLaps INTEGER,
     MaxPlayers INTEGER,
     MinPlayers INTEGER,
     PointsPerLap REAL,
     MusicTrack TEXT,
     SceneDetails TEXT,
     MaxPlayTimeSeconds INTEGER,
     LevelIcon BYTEA,
     SOURCE_FILE_NAME TEXT,
     SOURCE_FILE_TIMESTAMP TIMESTAMP
    ) 
    PRIMARY INDEX LevelID;
    
  3. Choose Run Script.
    Firebolt creates the fact table. When finished, the table levels appears on the object panel of the database.

Use INSERT to ingest data

You can now use the INSERT command to copy the data from the external table into the fact table. During this operation, Firebolt ingests the data from your source into Firebolt.

Use source_file_name in the WHERE clause to specify which records to load from Amazon S3 and improve the performance of the read from S3.

To run an INSERT command that ingests data

  1. Create a new script tab.
  2. Copy and paste the query below into the script tab.
    INSERT INTO levels
    SELECT
     LevelID,
     GameID,
     Level,
     Name,
     LevelType,
     NextLevel,
     MinPointsToPass,
     MaxPoints, 
     NumberOfLaps,
     MaxPlayers,
     MinPlayers,
     PointsPerLap,
     MusicTrack,
     SceneDetails,
     MaxPlayTimeSeconds,
     DECODE(REPLACE(LevelIcon,'"',''),'BASE64'),
     SOURCE_FILE_NAME, 
     SOURCE_FILE_TIMESTAMP 
    FROM ex_levels WHERE SOURCE_FILE_TIMESTAMP > (SELECT COALESCE(MAX(SOURCE_FILE_TIMESTAMP), '1980-01-01'::TIMESTAMP) FROM levels);
    
  3. Choose Run Script.
    The query results pane indicates a Status of Running as shown below.

    The Status changes to Success when the ingestion is complete as shown below.

Query the ingested data

Now that the data has been ingested into the levels table, you can run analytics queries over the table that benefit from the speed and efficiency of Firebolt.

To verify that you inserted the data into the table, run a simple SELECT query like the one below.

SELECT
  *
FROM
  levels

The values shown in the query results pane should be similar to those shown below.

Configure an aggregating index

An aggregating index enables you to take a subset of table columns and predefine dimensions and measures to aggregate. Many aggregations are supported—from SUM, MAX, and MIN to more complex aggregations such as COUNT and COUNT(DISTINCT). At query runtime, instead of calculating the aggregation on the entire table and scanning all rows, Firebolt uses the pre-calculated values in the aggregating index. For more information, see Aggregating indexes.

From the levels fact table that you created in the previous step, assume you want to run a query to look at the AVG(NumberOfLaps), grouped by LevelType. You can create an aggregating index to speed up these queries by running the statement below.

CREATE AGGREGATING INDEX
  levels_agg_idx
ON levels (
  LevelType 
  , AVG(NumberOfLaps)
  );

After you run the script, you see the levels_agg_idx index listed in the object pane. Any queries that run over the levels table that combine any of these fields and aggregations defined in the index will now use the index instead of reading the entire table.