DataSources

from utils.rc.client.requests import Requests
from utils.rc.client.auth import AuthClient

from utils.rc.dtos.project import Project
from utils.rc.dtos.dataset import Dataset
from utils.rc.dtos.recipe import Recipe
from utils.rc.dtos.transform import Transform
from utils.rc.dtos.template import Template
from utils.rc.dtos.template import TemplateTransform
from utils.rc.dtos.template import TemplateInput
from utils.rc.dtos.template_v2 import TemplateV2, TemplateTransformV2
from utils.rc.dtos.segment import Segment, ItemExpression, Operator
from utils.rc.dtos.scenario import Scenario

from utils.rc.dtos.dataSource import DataSource
from utils.rc.dtos.dataSource import DataSourceType
from utils.rc.dtos.dataSource import SnowflakeConfig
from utils.rc.dtos.dataSource import MongoConfig
from utils.rc.dtos.dataSource import S3Config
from utils.rc.dtos.dataSource import GcpConfig
from utils.rc.dtos.dataSource import AzureBlobConfig
from utils.rc.dtos.dataSource import MySQLConfig
from utils.rc.dtos.dataSource import RedshiftConfig
from utils.rc.dtos.dataSource import RedisStorageConfig

import pandas as pd
import logging
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)

# Requests.setRootHost("https://test.dev.rapidcanvas.net/api/")
# Requests.setRootHost("http://localhost:8080/api/")
AuthClient.setToken()

INFO:Authentication successful

Snowflake

Use this code snippet in Notebook to establish a connection with Snowflake data source.

dataSource = DataSource.createDataSource(
    "snowflake-101",
    DataSourceType.SNOWFLAKE,
    {
        SnowflakeConfig.USER: "nikunjrc",
        SnowflakeConfig.PASSWORD: "sZEWA27V86YGs5G",
        SnowflakeConfig.ACCOUNT: "OM82799.us-central1.gcp"
    }
)

The following code snippet is used to create a project.

project = Project.create(
    name="Test Snowflake",
    description="Testing snowflake lib",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

This code is used on the Notebook to fetch the file from Snowflake and upload this onto the canvas.

signup = project.addDataset(
    dataset_name="signup",
    dataset_description="signup golden",
    data_source_id=dataSource.id,
    data_source_options={
        SnowflakeConfig.WAREHOUSE: "COMPUTE_WH",
        SnowflakeConfig.QUERY: "SELECT * FROM rapidcanvas.public.SIGNUP"
    }
)

signup.getData()

The following code snippet allows you to export the output dataset to the Snowflake datasource.

dataset.update_sync_options(
    dataSource.id,
    {
      SnowflakeConfig.TABLE: "table name",
      SnowflakeConfig.DATABASE: "database name",
      SnowflakeConfig.SCHEMA: "schema name",
      SnowflakeConfig.IF_TABLE_EXISTS = "append"
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with a fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to Snowflake.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    SnowflakeConfig.TABLE: "table name",
    SnowflakeConfig.DATABASE: "database name",
    SnowflakeConfig.SCHEMA: "schema name"
  }
)

Mongo

Use this code snippet in Notebook to establish a connection with Mongo data source.

dataSource = DataSource.createDataSource(
    "mongo-101",
    DataSourceType.MONGO,
    {
        MongoConfig.CONNECT_STRING: "mongodb://testuser2:testuser2@34.68.122.18:27017/test"
    }
)

2023-02-02 12:07:09.094 INFO    root: Found existing data source by name: mongo-101
2023-02-02 12:07:09.095 INFO    root: Updating the same

The following code snippet is used to create a project.

project = Project.create(
    name="Test Mongodb",
    description="Testing mongodb lib",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

2023-02-02 12:09:07.010 INFO    root: Found existing project by name: Test Mongodb
2023-02-02 12:09:07.011 INFO    root: Deleting existing project
2023-02-02 12:09:07.123 INFO    root: Creating new project by name: Test Mongodb

The following code snippet is used to upload the dataset that is fetched from Mongo database onto the canvas.

titanic = project.addDataset(
    dataset_name="titanic",
    dataset_description="titanic golden",
    data_source_id=dataSource.id,
    data_source_options={
        MongoConfig.DATABASE: "test",
        MongoConfig.COLLECTION: "titanic",
        MongoConfig.QUERY_IN_JSON_FORMAT: "{}"
    }
)

2023-02-02 12:09:07.300 INFO    root: Creating new dataset by name:titanic

titanic.getData()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.925	nan	S
1	6	0	3	Moran, Mr. James	male	nan	0	0	330877	8.4583	nan	Q
2	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
3	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.075	nan	S
4	14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.275	nan	S
...	...	...	...	...	...	...	...	...	...	...	...	...
95	105	0	3	Gustafsson, Mr. Anders Vilhelm	male	37.0	2	0	3101276	7.925	nan	S
96	106	0	3	Mionoff, Mr. Stoytcho	male	28.0	0	0	349207	7.8958	nan	S
97	107	1	3	Salkjelsvik, Miss. Anna Kristine	female	21.0	0	0	343120	7.65	nan	S
98	78	0	3	Moutal, Mr. Rahamin Haim	male	nan	0	0	374746	8.05	nan	S
99	109	0	3	Rekic, Mr. Tido	male	38.0	0	0	349249	7.8958	nan	S

100 rows × 12 columns

The following code snippet allows you to export the output dataset to the Snowflake datasource.

dataset.update_sync_options(
    dataSource.id,
    {
      MongoConfig.COLLECTION: "collection name",
      MongoConfig.DATABASE: "database name",
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with a fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to Snowflake.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    MongoConfig.COLLECTION: "collection name",
    MongoConfig.DATABASE: "database name",
  }
)

Amazon S3

Use this code snippet in Notebook to establish a connection with Amazon S3 data source.

dataSource = DataSource.createDataSource(
"s3-101",
DataSourceType.S3_STORAGE,
{
    S3Config.BUCKET: "bucket-name",
    S3Config.ACCESS_KEY_ID: "access-key-id",
    S3Config.ACCESS_KEY_SECRET: "access-key-secret"
}
)

The following code snippet is used to create a project.

project = Project.create(
    name="Test Amazon S3",
    description="Testing Amazon S3",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

The following code snippet is used to upload the dataset that is imported from Amazon S3 onto the canvas.

project.addDataset(
    dataset_name="signup",
    dataset_description="signup golden",
    data_source_id=dataSource.id,
    data_source_options={
        S3Config.FILE_PATH: "file-path"
    }
)

signup.getData()

The following code snippet allows you to export the output dataset to the Amazon S3 datasource.

dataset.update_sync_options(
    dataSource.id,
    {
      S3Config.OUTPUT_FILE_DIRECTORY: "files/",
      S3Config.OUTPUT_FILE_NAME: "dataset.parquet"
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with a fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to Snowflake.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    S3Config.OUTPUT_FILE_DIRECTORY: "files/",
    S3Config.OUTPUT_FILE_NAME: "dataset-${RUN_ID}.parquet"
  }
)

Google Cloud Storage (GCS)

Use this code snippet in Notebook to establish a connection with Google Cloud Storage data source.

dataSource = DataSource.createDataSource(
    "gcp-101",
    DataSourceType.GCP_STORAGE,
    {
        GcpConfig.BUCKET: "bucket-name",
        GcpConfig.ACCESS_KEY: "access key path"
    }
)

The following code snippet is used to create a project.

project = Project.create(
    name="Test Google Cloud Storage",
    description="Testing Google Cloud Storage",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

The following code snippet is used to upload the dataset that is imported from Google Cloud Storage onto the canvas.

project.addDataset(
    dataset_name="signup",
    dataset_description="signup golden",
    data_source_id=dataSource.id,
    data_source_options={
        GcpConfig.FILE_PATH: "file-path"
    }
)

signup.getData()

The following code snippet allows you to export the output dataset to the Google Cloud Storage (GCS) datasource.

dataset.update_sync_options(
    dataSource.id,
    {
      GcpConfig.OUTPUT_FILE_DIRECTORY: "files/",
      GcpConfig.OUTPUT_FILE_NAME: "dataset.parquet"
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to Google Cloud Storage.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    GcpConfig.OUTPUT_FILE_DIRECTORY: "files/",
    GcpConfig.OUTPUT_FILE_NAME: "dataset-${RUN_ID}.parquet"
  }
)

Azure Blob Storage

Use this code snippet in Notebook to establish a connection with Azure blob storage data source.

dataSource = DataSource.createDataSource(
    "azure-101",
    DataSourceType.AZURE_BLOB,
    {
        AzureBlobConfig.CONTAINER_NAME: "container-name",
        AzureBlobConfig.CONNECT_STR: "connect-string",

    }
)

The following code snippet is used to create a project.

project = Project.create(
    name="Test Azure Blob Storage",
    description="Testing Azure Blob Storage",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

The following code snippet is used to upload the dataset that is imported from Azure Blob Storage onto the canvas. This has the file path from where the file is located.

project.addDataset(
    dataset_name="signup",
    dataset_description="signup golden",
    data_source_id=dataSource.id,
    data_source_options={
        AzureBlobConfig.FILE_PATH: "file-path"
    }
)

The following code snippet allows you to export the output dataset to the Azure Blob datasource.

dataset.update_sync_options(
    dataSource.id,
    {
      AzureBlobConfig.OUTPUT_FILE_DIRECTORY: "files/",
      AzureBlobConfig.OUTPUT_FILE_NAME: "dataset.parquet"
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with a fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to Azure Blob.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    AzureBlobConfig.OUTPUT_FILE_DIRECTORY: "files/",
    AzureBlobConfig.OUTPUT_FILE_NAME: "dataset-${RUN_ID}.parquet"
  }
)

MySQL/MsSQL

Use this code snippet in Notebook to establish a connection with MySQL data source.

dataSource = DataSource.createDataSource(
    "mysql-101",
    DataSourceType.MYSQL,
    {
        MySQLConfig.CONNECT_STRING: "mysql://root:password@34.170.43.138/azure"
    }
)

The following code snippet is used to create a project.

project = Project.create(
    name="Test MySQL/MsSQL",
    description="Testing MySQL/MsSQL",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

The following code snippet is used to upload the dataset that is imported from MySQL/MsSQL onto the canvas.

dataset = project.addDataset(
    dataset_name="titanic",
    dataset_description="titanic golden",
    data_source_id=dataSource.id,
    data_source_options={
        MySQLConfig.QUERY: "SELECT * FROM titanic limit 100"
    }
)

The following code snippet allows you to export the output dataset to the MySQL/MsSQL datasource.

dataset.update_sync_options(
    dataSource.id,
    {
        MySQLConfig.TABLE: "titanic"
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with a fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to MySQL/MsSQL.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    MySQLConfig.TABLE: "titanic"
  }
)

Redshift

Use this code snippet in Notebook to establish a connection with Redshift data source.

dataSource = DataSource.createDataSource(
    "redshift-101",
    DataSourceType.REDSHIFT,
    {
        RedshiftConfig.CONNECT_STRING: "mysql://root:password@34.170.43.138/azure"
    }
)

The following code snippet is used to create a project.

project = Project.create(
    name="Test Redshift",
    description="Testing Redshift",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

The following code snippet is used to upload the dataset that is imported from Redshift onto the canvas.

dataset = project.addDataset(
    dataset_name="titanic",
    dataset_description="titanic golden",
    data_source_id=dataSource.id,
    data_source_options={
        RedshiftConfig.QUERY: "SELECT * FROM titanic limit 100"
    }
)

The following code snippet allows you to export the output dataset to the Redshift datasource.

dataset.update_sync_options(
    dataSource.id,
    {
        RedshiftConfig.TABLE: "titanic"
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with a fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to Redshift.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    RedshiftConfig.TABLE: "titanic"
  }
)

Redis

Use this code snippet in Notebook to establish a connection with Redshift data source.

dataSource = DataSource.createDataSource(
    "redis-101",
    DataSourceType.REDIS_STORAGE,
    {
        RedisStorageConfig.HOST: "127.0.0.1",
        RedisStorageConfig.PORT: "6379"
    }
)

The following code snippet is used to create a project.

project = Project.create(
    name="Test Redis",
    description="Testing Redis",
    icon="https://rapidcanvas.ai/wp-content/uploads/2022/09/bitcoin_prediction_med.jpg",
    createEmpty=True
)

Note: You cannot import files from Redis to the platform but can export files and store in this data source.

The following code snippet allows you to export the output dataset to the Redis datasource.

dataset.update_sync_options(
    dataSource.id,
    {
        RedisStorageConfig.FEATURE_NAME: "titanic",
        RedisStorageConfig.FEATURE_KEY_COLUMN: "PassengerId",
        RedisStorageConfig.FEATURE_VALUE_COLUMNS: "Sex,Parch"
    }
)
dataset.sync()

When a scheduled job is run, the source dataset updated with a fresh set of records is used in the machine learning flow of a project to generate a new output dataset. Subsequently, this output dataset is exported to Redis.

project_run = ProjectRun.create_project_run(
  project.id, "test-run-v1", "*/2 * * * *"
)

project_run.add_project_run_sync(
  dataset.id,
  dataSource.id,
  {
    RedisStorageConfig.FEATURE_NAME: "titanic",
    RedisStorageConfig.FEATURE_KEY_COLUMN: "PassengerId",
    RedisStorageConfig.FEATURE_VALUE_COLUMNS: "Sex,Parch"
  }
)