MindsDB

MindsDB provides Machine Learning capabilities to enable predictive questions about your data. With MindsDB:

Developers can quickly add AI capabilities to their applications.
Data scientists can streamline MLOps by deploying ML models as AI Tables.
Data analysts can easily make forecasts on complex data, such as multivariate time-series with high cardinality, and visualize these in BI tools like Grafana, and Tableau.

Combining both MindsDB and QuestDB provides unbound prediction ability with SQL.

This guide describes how to pre-process data in QuestDB and then access these data from MindsDB to produce powerful ML models.

Prerequisites

docker: To create an image and run the container.
mysql: The client we use to interact with MindsDB (mysql -h 127.0.0.1 --port 47335 -u mindsdb -p). Alternatively, use MindsDB web console at http://localhost:47334/ instead.
Curl: To upload data to QuestDB from a local CSV file.

Instructions

The following are the overall steps to connect MindsDB and QuestDB:

Build a Docker image and spawn a container to run MindsDB and QuestDB together.
Add QuestDB as a datasource to MindsDB using a SQL Statement.
Create a table and add data for a simple ML use case using QuestDB's web console.
Connect to MindsDB using mysql as a client and write some SQL.

We have put together all the files needed in GH.

Running the Docker container

Clone the repository for this tutorial. The Dockerfile allows us to build an image with the following command:

docker build -t questdb/mindsdb:latest .

Then, start the service container qmdb with the following command:

docker run --rm \
    -p 8812:8812 \
    -p 9009:9009 \
    -p 9000:9000 \
    -p 8888:8888 \
    -p 47334:47334 \
    -p 47335:47335 \
    -d \
    --name qmdb \
    questdb/mindsdb:latest

The container is run as user quest. It takes about 10 seconds to become responsive, logs can be followed in the terminal:

docker logs -f qmdb
...
http API: starting...
mysql API: starting...
mongodb API: starting...
...
mongodb API: started on 47336
mysql API: started on 47335
http API: started on 47334

The container has these mount points:

/home/quest: User home directory.
~/questdb/: QuestDB's root directory.
~/questdb/db/: QuestDB's data root directory.
~/backups/: Directory for backups.
~/csv/: Directory for the COPY operation.
~/mindsdb/storage/: MindsDB's data root directory.

The container is running Debian GNU/Linux 11 (bullseye) and exposes these ports:

9000: QuestDB Web Console
8812: QuestDB pg-wire
9009: QuestDB InfluxDB Line Protocol ingress line protocol
47334: MindsDB WebConsole
47335: MindsDB mysql API
47336: MindsDB mongodb API

Adding data to QuestDB

There are different ways to insert data to QuestDB.

SQL

We can access QuestDB's web console at http://localhost:9000.

Run the following SQL query to create a simple table:

CREATE TABLE IF NOT EXISTS house_rentals_data (
    number_of_rooms INT,
    number_of_bathrooms INT,
    sqft INT,
    location SYMBOL,
    days_on_market INT,
    initial_price FLOAT,
    neighborhood SYMBOL,
    rental_price FLOAT,
    ts TIMESTAMP
) TIMESTAMP(ts) PARTITION BY YEAR;

We could populate table house_rentals_data with random data:

INSERT INTO house_rentals_data SELECT * FROM (
    SELECT
        rnd_int(1,6,0),
        rnd_int(1,3,0),
        rnd_int(180,2000,0),
        rnd_symbol('great', 'good', 'poor'),
        rnd_int(1,20,0),
        rnd_float(0) * 1000,
        rnd_symbol('alcatraz_ave', 'berkeley_hills', 'downtown', 'south_side', 'thowsand_oaks', 'westbrae'),
        rnd_float(0) * 1000 + 500,
        timestamp_sequence(
            to_timestamp('2021-01-01', 'yyyy-MM-dd'),
            14400000000L
        )
    FROM long_sequence(100)
);

CURL command

The data CSV file can be downloaded to a local folder and uploaded to QuestDB using the following command:

curl -F data=@sample_house_rentals_data.csv "http://localhost:9000/imp?forceHeader=true&name=house_rentals_data"

Either way, this gives us 100 data points, one every 4 hours, from 2021-01-16T12:00:00.000000Z (QuestDB's timestamps are UTC with microsecond precision).

Connect to MindsDB

We can connect to MindsDB with a standard mysql-wire-protocol compliant client (no password, hit ENTER):

mysql -h 127.0.0.1 --port 47335 -u mindsdb -p

Alternatively, we can use MindsDB web console at http://localhost:47334:

From the terminal or the MindsDB web console, run the following command to check the available databases:

SHOW DATABASES;

QuestDB is not shown in the result:

+--------------------+
| Database           |
+--------------------+
| mindsdb            |
| files              |
| information_schema |
+--------------------+

To see QuestDB as a database we need to add it to MindsDB:

CREATE DATABASE questdb
    WITH ENGINE = "questdb",
    PARAMETERS = {
        "user": "admin",
        "password": "quest",
        "host": "questdb",
        "port": "8812",
        "database": "questdb"
    };

Then, run SHOW DATABASES; should display both MindsDB and QuestDB:

+--------------------+
| Database           |
+--------------------+
| mindsdb            |
| files              |
| questdb            |
| information_schema |
+--------------------+

`questdb`

This is a read-only view on our QuestDB instance. We can query it leveraging the full power of QuestDB's unique SQL syntax because statements are sent from MindsDB to QuestDB without interpreting them. It only works for SELECT statements:

SELECT * FROM questdb(
  SELECT
        ts, neighborhood,
            sum(days_on_market) DaysLive,
            min(rental_price) MinRent,
            max(rental_price) MaxRent,
            avg(rental_price) AvgRent
    FROM house_rentals_data
    WHERE ts BETWEEN '2021-01-08' AND '2021-01-10'
    SAMPLE BY 1d FILL (0, 0, 0, 0)
);

The result should be something like this:

+--------------+----------------+----------+----------+----------+--------------------+
| ts           | neighborhood   | DaysLive | MinRent  | MaxRent  | AvgRent            |
+--------------+----------------+----------+----------+----------+--------------------+
| 1610064000.0 | south_side     | 19       | 1285.338 | 1285.338 | 1285.338134765625  |
| 1610064000.0 | downtown       | 7        | 1047.14  | 1047.14  | 1047.1396484375    |
| 1610064000.0 | berkeley_hills | 17       | 727.52   | 727.52   | 727.5198974609375  |
| 1610064000.0 | westbrae       | 36       | 1038.358 | 1047.342 | 1042.85009765625   |
| 1610064000.0 | thowsand_oaks  | 5        | 1067.319 | 1067.319 | 1067.318603515625  |
| 1610064000.0 | alcatraz_ave   | 0        | 0.0      | 0.0      | 0.0                |
| 1610150400.0 | south_side     | 10       | 694.403  | 694.403  | 694.4031982421875  |
| 1610150400.0 | downtown       | 16       | 546.798  | 643.204  | 595.0011291503906  |
| 1610150400.0 | berkeley_hills | 4        | 1256.49  | 1256.49  | 1256.4903564453125 |
| 1610150400.0 | westbrae       | 0        | 0.0      | 0.0      | 0.0                |
| 1610150400.0 | thowsand_oaks  | 0        | 0.0      | 0.0      | 0.0                |
| 1610150400.0 | alcatraz_ave   | 14       | 653.924  | 1250.477 | 952.2005004882812  |
| 1610236800.0 | south_side     | 0        | 0.0      | 0.0      | 0.0                |
| 1610236800.0 | downtown       | 9        | 1357.916 | 1357.916 | 1357.9158935546875 |
| 1610236800.0 | berkeley_hills | 0        | 0.0      | 0.0      | 0.0                |
| 1610236800.0 | westbrae       | 0        | 0.0      | 0.0      | 0.0                |
| 1610236800.0 | thowsand_oaks  | 0        | 0.0      | 0.0      | 0.0                |
| 1610236800.0 | alcatraz_ave   | 0        | 0.0      | 0.0      | 0.0                |
+--------------+----------------+----------+----------+----------+--------------------+

Beyond SELECT statements, for instance when we need to save the results of a query into a new table, we need to use QuestDB's web console available at http://localhost:9000:

CREATE TABLE sample_query_results AS (
    SELECT
        ts,
        neighborhood,
        sum(days_on_market) DaysLive,
        min(rental_price) MinRent,
        max(rental_price) MaxRent,
        avg(rental_price) AvgRent
    FROM house_rentals_data
    WHERE ts BETWEEN '2021-01-08' AND '2021-01-10'
    SAMPLE BY 1d FILL (0, 0, 0, 0)
) TIMESTAMP(ts) PARTITION BY MONTH;

`mindsdb`

Contains the metadata tables necessary to create ML models:

USE mindsdb;
SHOW TABLES;

+-------------------+
| Tables_in_mindsdb |
+-------------------+
| models            |
| models_versions   |
+-------------------+

Create a predictor model

We can create a predictor model mindsdb.home_rentals_model_ts to predict the rental_price for a neighborhood considering the past 20 days, and no additional features:

CREATE PREDICTOR mindsdb.home_rentals_model_ts FROM questdb (
    SELECT
        neighborhood,
        rental_price,
        ts
    FROM house_rentals_data
)
PREDICT rental_price ORDER BY ts GROUP BY neighborhood
WINDOW 20 HORIZON 1;

This triggers MindsDB to create/train the model based on the full data available from QuestDB's table house_rentals_data (100 rows) as a time series on the column ts.

When status is complete, the model is ready for use; otherwise, we simply wait while we observe MindsDB's logs. Creating/training a model will take time proportional to the number of features, i.e. cardinality of the source table as defined in the inner SELECT of the CREATE MODEL statement, and the size of the corpus, i.e. number of rows. The model is a table in MindsDB:

SHOW TABLES;

The new table is displayed:

+-----------------------+
| Tables_in_mindsdb     |
+-----------------------+
| models                |
| models_versions       |
| home_rentals_model_ts |
+-----------------------+

Describe the predictor model

We can get more information about the trained model, how was the accuracy calculated or which columns are important for the model by executing the DESCRIBE MODEL statement:

DESCRIBE MODEL mindsdb.home_rentals_model_ts;

*************************** 1. row ***************************
        accuracies: {'complementary_smape_array_accuracy':0.859}
           outputs: ['rental_price']
            inputs: ['neighborhood', 'ts', '__mdb_ts_previous_rental_price']
        datasource: home_rentals_model_ts
             model: encoders --> dtype_dict --> dependency_dict --> model --> problem_definition --> identifiers --> imputers --> accuracy_functions

Or, to see how the model encoded the data prior to training we can execute:

DESCRIBE MODEL mindsdb.home_rentals_model_ts.features;

+--------------+-------------+------------------+---------+
| column       | type        | encoder          | role    |
+--------------+-------------+------------------+---------+
| neighborhood | categorical | OneHotEncoder    | feature |
| rental_price | float       | TsNumericEncoder | target  |
| ts           | datetime    | ArrayEncoder     | feature |
+--------------+-------------+------------------+---------+

Additional information about the models and how they can be customized can be found on the Lightwood docs.

Query MindsDB for predictions

The latest rental_price value per neighborhood in table questdb.house_rentals_data can be obtained directly from QuestDB executing query:

SELECT * FROM questdb (
    SELECT
        neighborhood,
        rental_price,
        ts
    FROM house_rentals_data
    LATEST BY neighborhood
);

+----------------+--------------+--------------+
| neighborhood   | rental_price | ts           |
+----------------+--------------+--------------+
| thowsand_oaks  | 1150.427     | 1610712000.0 |   (2021-01-15 12:00:00.0)
| south_side     | 726.953      | 1610784000.0 |   (2021-01-16 08:00:00.0)
| downtown       | 568.73       | 1610798400.0 |   (2021-01-16 12:00:00.0)
| westbrae       | 543.83       | 1610841600.0 |   (2021-01-17 00:00:00.0)
| berkeley_hills | 559.928      | 1610870400.0 |   (2021-01-17 08:00:00.0)
| alcatraz_ave   | 1268.529     | 1610884800.0 |   (2021-01-17 12:00:00.0)
+----------------+--------------+--------------+

To predict the next value:

SELECT
    tb.ts,
    tb.neighborhood,
    tb.rental_price as predicted_rental_price,
    tb.rental_price_explain as explanation
FROM questdb.house_rentals_data AS ta
JOIN mindsdb.home_rentals_model_ts AS tb
WHERE ta.ts > LATEST;

+---------------------+----------------+------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ts                  | neighborhood   | predicted_rental_price | explanation                                                                                                                                                                              |
+---------------------+----------------+------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2021-01-17 00:00:00 | downtown       |      877.3007391233444 | {"predicted_value": 877.3007391233444, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 379.43294697022424, "confidence_upper_bound": 1375.1685312764646} |
| 2021-01-19 08:00:00 | westbrae       |      923.1387395936794 | {"predicted_value": 923.1387395936794, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 385.8327438509463, "confidence_upper_bound": 1460.4447353364124}  |
| 2021-01-15 16:00:00 | thowsand_oaks  |      1418.678199780345 | {"predicted_value": 1418.678199780345, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 1335.4600013965369, "confidence_upper_bound": 1501.8963981641532} |
| 2021-01-17 12:00:00 | berkeley_hills |      646.5979284300436 | {"predicted_value": 646.5979284300436, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 303.253838410034, "confidence_upper_bound": 989.9420184500532}    |
| 2021-01-18 12:00:00 | south_side     |       1422.69481363723 | {"predicted_value": 1422.69481363723, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 129.97617491441304, "confidence_upper_bound": 2715.413452360047}   |
| 2021-01-18 04:00:00 | alcatraz_ave   |      1305.009073065412 | {"predicted_value": 1305.009073065412, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 879.0232742685288, "confidence_upper_bound": 1730.994871862295}   |
+---------------------+----------------+------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Stop the container

To terminate the container, run:

docker stop qmdb

Prerequisites​

Instructions​

Running the Docker container​

Adding data to QuestDB​

SQL​

CURL command​

Connect to MindsDB​

questdb​

mindsdb​

Create a predictor model​

Describe the predictor model​

Query MindsDB for predictions​

Stop the container​