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AWS Athena​

PyAthenaJDBC​

PyAthenaJDBC is a Python DB 2.0 compliant wrapper for the Amazon Athena JDBC driver.

The connection string for Amazon Athena is as follows:

awsathena+jdbc://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com/{schema_name}?s3_staging_dir={s3_staging_dir}&...

Note that you'll need to escape & encode when forming the connection string like so:

s3://... -> s3%3A//...

AWS DynamoDB​

PyDynamoDB​

PyDynamoDB is a Python DB API 2.0 (PEP 249) client for Amazon DynamoDB.

The connection string for Amazon DynamoDB is as follows:

dynamodb://{aws_access_key_id}:{aws_secret_access_key}@dynamodb.{region_name}.amazonaws.com:443?connector=superset

Doris​

The sqlalchemy-doris library is used to connect to Apache Doris through SQLAlchemy.

You'll need the following setting values to form the connection string:

  • User: User Name
  • Password: Password
  • Host: Doris FE Host
  • Port: Doris FE port
  • Catalog: Catalog Name
  • Database: Database Name

Here's what the connection string looks like:

doris://<User>:<Password>@<Host>:<Port>/<Catalog>.<Database>

AWS Redshift​

The sqlalchemy-redshift library is used to connect to Redshift through SQLAlchemy.

You'll need to set the following values to form the connection string:

  • User Name: userName
  • Password: DBPassword
  • Database Host: AWS Endpoint
  • Database Name: Database Name
  • Port: default 5439

psycopg2​

Here's what the SQLALCHEMY URI looks like:

redshift+psycopg2://<userName>:<DBPassword>@<AWS End Point>:5439/<Database Name>

StarRocks​

The sqlalchemy-starrocks library is used to connect to StarRocks through SQLAlchemy.

You'll need to the following setting values to form the connection string:

  • User: User Name
  • Password: DBPassword
  • Host: StarRocks FE Host
  • Catalog: Catalog Name
  • Database: Database Name
  • Port: StarRocks FE port

Here's what the connection string looks like:

starrocks://<User>:<Password>@<Host>:<Port>/<Catalog>.<Database>

Apache Drill​

SQLAlchemy​

To connect to Apache Drill through SQLAlchemy. You can use the sqlalchemy-drill connector.

Once that is done, you can connect to Drill in two ways, either via the REST interface or by JDBC. If you are connecting via JDBC, you must have the Drill JDBC Driver installed.

The basic connection string for Drill looks like this:

drill+sadrill://<username>:<password>@<host>:<port>/<storage_plugin>?use_ssl=True

To connect to Drill running on a local machine running in embedded mode you can use the following connection string:

drill+sadrill://localhost:8047/dfs?use_ssl=False

Apache Druid​

A native connector to Druid ships with Skeyecharts (behind the DRUID_IS_ACTIVE flag)

The connection string looks like:

druid://<User>:<password>@<Host>:<Port-default-9088>/druid/v2/sql

Here's a breakdown of the key components of this connection string:

  • User: username portion of the credentials needed to connect to your database
  • Password: password portion of the credentials needed to connect to your database
  • Host: IP address (or URL) of the host machine that's running your database
  • Port: specific port that's exposed on your host machine where your database is running

Customizing Druid Connection​

When adding a connection to Druid, you can customize the connection a few different ways in the Add Database form.

Custom Certificate

You can add certificates in the Root Certificate field when configuring the new database connection to Druid:

When using a custom certificate, pydruid will automatically use https scheme.

Disable SSL Verification

To disable SSL verification, add the following to the Extras field:

engine_params:
{"connect_args":
	{"scheme": "https", "ssl_verify_cert": false}}

Aggregations​

Common aggregations or Druid metrics can be defined and used in Skeyecharts. The first and simpler use case is to use the checkbox matrix exposed in your datasource’s edit view (Sources -> Druid Datasources -> [your datasource] -> Edit -> [tab] List Druid Column).

Clicking the GroupBy and Filterable checkboxes will make the column appear in the related dropdowns while in the Explore view. Checking Count Distinct, Min, Max or Sum will result in creating new metrics that will appear in the List Druid Metric tab upon saving the datasource.

By editing these metrics, you’ll notice that their JSON element corresponds to Druid aggregation definition. You can create your own aggregations manually from the List Druid Metric tab following Druid documentation.

Post-Aggregations​

Druid supports post aggregation and this works in Skeyecharts. All you have to do is create a metric, much like you would create an aggregation manually, but specify postagg as a Metric Type. You then have to provide a valid json post-aggregation definition (as specified in the Druid docs) in the JSON field.

Apache Hive​

The pyhive library is used to connect to Hive through SQLAlchemy.

The expected connection string is formatted as follows:

hive://hive@{hostname}:{port}/{database}

Apache Impala​

The connector library to Apache Impala is impyla.

The expected connection string is formatted as follows:

impala://{hostname}:{port}/{database}

Ascend.io​

The connector library to Ascend.io is impyla.

The expected connection string is formatted as follows:

ascend://{username}:{password}@{hostname}:{port}/{database}?auth_mechanism=PLAIN;use_ssl=true

Skeyecube​

The expected connection string is formatted as follows:

Skeyecube://<username>:<password>@<hostname>:<port>/<project>?<param1>=<value1>&<param2>=<value2>

Apache Pinot​

The connector library for Apache Pinot is pinotdb.

The expected connection string using username and password is formatted as follows:

pinot://<username>:<password>@<pinot-broker-host>:<pinot-broker-port>/query/sql?controller=http://<pinot-controller-host>:<pinot-controller-port>/verify_ssl=true``

Apache Solr​

The sqlalchemy-solr library provides a Python / SQLAlchemy interface to Apache Solr.

The connection string for Solr looks like this:

solr://{username}:{password}@{host}:{port}/{server_path}/{collection}[/?use_ssl=true|false]

Apache Spark SQL​

The connector library for Apache Spark SQL pyhive.

The expected connection string is formatted as follows:

hive://hive@{hostname}:{port}/{database}