Database engine specifications

Superset uses SQLAlchemy as an abstraction layer for running queries and fetching metadata from tables (like column names and types). Unfortunately, while SQLAlchemy offers enough functionality to allow connecting Superset to dozens of databases, there are still implementation details that differ across them. Because of this, Superset has an additional abstraction on top of SQLAlchemy, called a "database engine specification" or, simply, "DB engine spec".

DB engine specs were created initially because there's no SQL standard for computing aggregations at different time grains. For example, to compute a daily metric in Trino or Postgres we could run a query like this:

SELECT
  date_trunc('day', CAST(time_column) AS TIMESTAMP) AS day,
  COUNT(*) AS metric
FROM
  some_table
GROUP BY
  1

For MySQL, instead of using the date_trunc function, we would need to write:

SELECT
  DATE(time_column) AS day,
  COUNT(*) AS metric
FROM
  some_table
GROUP BY
  1

Over time, more and more functionality was added to DB engine specs, including validating SQL, estimating the cost of queries before they are run, and understanding the semantics of error messages. These are all described in detail in this document, and in the table below you can see a summary of what features are supported by each database.

Note that DB engine specs are completely optional. Superset can connect to any database supported by SQLAlchemy (or 3rd party dialects) even if there's no DB engine spec associated with it. But DB engine specs greatly improve the experience of working with a database in Superset.

Features

The tables below (generated via python superset/db_engine_specs/lib.py) summarize the status of all DB engine specs in Superset, organized by feature category for easier navigation (note that this excludes 3rd party DB engine specs).

Quick Navigation

• Feature Overview - High-level summary of support across all databases
• Database Information - Module paths and core metadata
• SQL Capabilities - SQL language features and capabilities
• Time Grains – Common - Standard time granularity support
• Time Grains – Extended - Sub-hour and week variant time grains
• Core Platform & Metadata Features - Platform integration and metadata capabilities
• Operational & Advanced Features - Advanced operational capabilities

Feature Overview

Database	Score	SQL Basics	Advanced SQL	Common Time Grains	Extended Time Grains	Integrations	Advanced Features
Presto	159	Supported	Partial	Supported	Partial	Partial	Supported
Trino	149	Supported	Partial	Supported	Partial	Partial	Partial
Apache Hive	140	Supported	Not supported	Supported	Partial	Partial	Partial
Apache Spark SQL	140	Supported	Not supported	Supported	Partial	Partial	Partial
Databricks Interactive Cluster	140	Supported	Not supported	Supported	Partial	Partial	Partial
base	109	Supported	Partial	Supported	Partial	Partial	Partial
Aurora PostgreSQL (Data API)	104	Supported	Partial	Supported	Partial	Partial	Partial
CockroachDB	94	Supported	Partial	Supported	Partial	Partial	Partial
RisingWave	94	Supported	Partial	Supported	Partial	Partial	Partial
Google BigQuery	83	Supported	Partial	Supported	Partial	Partial	Partial
Apache Doris	79	Supported	Partial	Supported	Partial	Partial	Not supported
Snowflake	72	Supported	Partial	Supported	Partial	Partial	Not supported
Databricks	70	Supported	Partial	Supported	Partial	Partial	Not supported
Databricks (legacy)	70	Supported	Partial	Supported	Partial	Partial	Not supported
StarRocks	69	Supported	Partial	Supported	Partial	Partial	Partial
SingleStore	68	Supported	Partial	Supported	Not supported	Partial	Not supported
ClickHouse Connect (Superset)	61	Supported	Partial	Partial	Partial	Partial	Not supported
Google Sheets	61	Supported	Partial	Supported	Supported	Partial	Partial
Aurora MySQL (Data API)	59	Supported	Partial	Supported	Partial	Partial	Not supported
MariaDB	59	Supported	Partial	Supported	Partial	Partial	Not supported
MySQL	59	Supported	Partial	Supported	Partial	Partial	Not supported
OceanBase	59	Supported	Partial	Supported	Partial	Partial	Not supported
MotherDuck	58	Supported	Partial	Supported	Not supported	Partial	Not supported
KustoSQL	54	Supported	Partial	Supported	Partial	Partial	Not supported
ClickHouse	51	Supported	Partial	Partial	Partial	Partial	Not supported
Databend	51	Supported	Partial	Supported	Partial	Partial	Not supported
Apache Drill	50	Supported	Partial	Supported	Partial	Partial	Partial
Apache Druid	47	Partial	Partial	Supported	Partial	Partial	Not supported
Amazon Redshift	44	Supported	Partial	Supported	Partial	Partial	Not supported
Azure Synapse	44	Partial	Partial	Supported	Partial	Partial	Not supported
Microsoft SQL Server	44	Partial	Partial	Supported	Partial	Partial	Not supported
SQLite	41	Supported	Partial	Supported	Supported	Not supported	Not supported
Shillelagh	41	Supported	Partial	Supported	Supported	Not supported	Not supported
KustoKQL	40	Supported	Partial	Partial	Partial	Partial	Not supported
Ascend	38	Supported	Partial	Supported	Not supported	Partial	Not supported
DuckDB	38	Supported	Partial	Supported	Not supported	Partial	Not supported
IBM Db2	38	Supported	Partial	Supported	Not supported	Partial	Not supported
IBM Db2 for i	38	Supported	Partial	Supported	Not supported	Partial	Not supported
Ocient	38	Partial	Partial	Partial	Partial	Partial	Not supported
Apache Impala	37	Supported	Partial	Partial	Not supported	Partial	Not supported
ElasticSearch (SQL API)	37	Partial	Partial	Partial	Not supported	Partial	Not supported
PostgreSQL	34	Supported	Partial	Supported	Partial	Partial	Not supported
Vertica	34	Supported	Partial	Supported	Partial	Partial	Not supported
Amazon DynamoDB	32	Supported	Partial	Supported	Partial	Partial	Not supported
Apache Pinot	32	Partial	Partial	Supported	Partial	Partial	Not supported
Superset meta database	31	Supported	Partial	Supported	Supported	Not supported	Not supported
Databricks SQL Endpoint	30	Supported	Partial	Supported	Partial	Partial	Not supported
Apache Kylin	28	Supported	Partial	Supported	Not supported	Partial	Not supported
CrateDB	28	Supported	Partial	Supported	Not supported	Partial	Not supported
Dremio	28	Supported	Partial	Supported	Not supported	Partial	Not supported
Exasol	28	Supported	Partial	Supported	Not supported	Partial	Not supported
Firebolt	28	Supported	Partial	Supported	Not supported	Partial	Not supported
IBM Netezza Performance Server	28	Supported	Partial	Supported	Not supported	Partial	Not supported
Oracle	28	Supported	Partial	Supported	Not supported	Partial	Not supported
Parseable	28	Supported	Partial	Supported	Not supported	Partial	Not supported
Couchbase	27	Partial	Partial	Partial	Not supported	Partial	Not supported
Denodo	27	Supported	Partial	Partial	Not supported	Partial	Not supported
SAP HANA	27	Supported	Partial	Partial	Not supported	Partial	Not supported
Teradata	27	Supported	Partial	Partial	Not supported	Partial	Not supported
ElasticSearch (OpenDistro SQL)	26	Partial	Partial	Partial	Not supported	Partial	Not supported
Firebird	26	Supported	Partial	Partial	Not supported	Partial	Not supported
TDengine	25	Supported	Partial	Partial	Not supported	Partial	Not supported
YDB	23	Supported	Partial	Supported	Partial	Partial	Not supported
Amazon Athena	20	Supported	Partial	Supported	Partial	Not supported	Not supported
Apache Solr	20	Partial	Partial	Not supported	Not supported	Partial	Not supported

Database Information

Database	Module	Limit Method	Limit Clause	Max Column Name
Amazon Athena	superset.db_engine_specs.athena	FORCE_LIMIT	True	None
Amazon DynamoDB	superset.db_engine_specs.dynamodb	FORCE_LIMIT	True	None
Amazon Redshift	superset.db_engine_specs.redshift	FORCE_LIMIT	True	127
Apache Doris	superset.db_engine_specs.doris	FORCE_LIMIT	True	64
Apache Drill	superset.db_engine_specs.drill	FORCE_LIMIT	True	None
Apache Druid	superset.db_engine_specs.druid	FORCE_LIMIT	True	None
Apache Hive	superset.db_engine_specs.hive	FORCE_LIMIT	True	767
Apache Impala	superset.db_engine_specs.impala	FORCE_LIMIT	True	None
Apache Kylin	superset.db_engine_specs.kylin	FORCE_LIMIT	True	None
Apache Pinot	superset.db_engine_specs.pinot	FORCE_LIMIT	True	None
Apache Solr	superset.db_engine_specs.solr	FORCE_LIMIT	True	None
Apache Spark SQL	superset.db_engine_specs.spark	FORCE_LIMIT	True	767
Ascend	superset.db_engine_specs.ascend	FORCE_LIMIT	True	None
Aurora MySQL (Data API)	superset.db_engine_specs.aurora	FORCE_LIMIT	True	64
Aurora PostgreSQL (Data API)	superset.db_engine_specs.aurora	FORCE_LIMIT	True	63
Azure Synapse	superset.db_engine_specs.mssql	FORCE_LIMIT	True	128
ClickHouse	superset.db_engine_specs.clickhouse	FORCE_LIMIT	True	None
ClickHouse Connect (Superset)	superset.db_engine_specs.clickhouse	FORCE_LIMIT	True	None
CockroachDB	superset.db_engine_specs.cockroachdb	FORCE_LIMIT	True	63
Couchbase	superset.db_engine_specs.couchbase	FORCE_LIMIT	True	None
CrateDB	superset.db_engine_specs.crate	FORCE_LIMIT	True	None
Databend	superset.db_engine_specs.databend	FORCE_LIMIT	True	None
Databricks	superset.db_engine_specs.databricks	FORCE_LIMIT	True	None
Databricks (legacy)	superset.db_engine_specs.databricks	FORCE_LIMIT	True	None
Databricks Interactive Cluster	superset.db_engine_specs.databricks	FORCE_LIMIT	True	767
Databricks SQL Endpoint	superset.db_engine_specs.databricks	FORCE_LIMIT	True	None
Denodo	superset.db_engine_specs.denodo	FORCE_LIMIT	True	None
Dremio	superset.db_engine_specs.dremio	FORCE_LIMIT	True	None
DuckDB	superset.db_engine_specs.duckdb	FORCE_LIMIT	True	None
ElasticSearch (OpenDistro SQL)	superset.db_engine_specs.elasticsearch	FORCE_LIMIT	True	None
ElasticSearch (SQL API)	superset.db_engine_specs.elasticsearch	FORCE_LIMIT	True	None
Exasol	superset.db_engine_specs.exasol	FORCE_LIMIT	True	128
Firebird	superset.db_engine_specs.firebird	FETCH_MANY	True	None
Firebolt	superset.db_engine_specs.firebolt	FORCE_LIMIT	True	None
Google BigQuery	superset.db_engine_specs.bigquery	FORCE_LIMIT	True	128
Google Sheets	superset.db_engine_specs.gsheets	FORCE_LIMIT	True	None
IBM Db2	superset.db_engine_specs.db2	WRAP_SQL	True	30
IBM Db2 for i	superset.db_engine_specs.ibmi	WRAP_SQL	True	128
IBM Netezza Performance Server	superset.db_engine_specs.netezza	FORCE_LIMIT	True	None
KustoKQL	superset.db_engine_specs.kusto	FORCE_LIMIT	True	None
KustoSQL	superset.db_engine_specs.kusto	WRAP_SQL	True	None
MariaDB	superset.db_engine_specs.mariadb	FORCE_LIMIT	True	64
Microsoft SQL Server	superset.db_engine_specs.mssql	FORCE_LIMIT	True	128
MotherDuck	superset.db_engine_specs.duckdb	FORCE_LIMIT	True	None
MySQL	superset.db_engine_specs.mysql	FORCE_LIMIT	True	64
OceanBase	superset.db_engine_specs.oceanbase	FORCE_LIMIT	True	128
Ocient	superset.db_engine_specs.ocient	FORCE_LIMIT	True	30
Oracle	superset.db_engine_specs.oracle	FORCE_LIMIT	True	128
Parseable	superset.db_engine_specs.parseable	FORCE_LIMIT	True	None
PostgreSQL	superset.db_engine_specs.postgres	FORCE_LIMIT	True	None
Presto	superset.db_engine_specs.presto	FORCE_LIMIT	True	None
RisingWave	superset.db_engine_specs.risingwave	FORCE_LIMIT	True	63
SAP HANA	superset.db_engine_specs.hana	WRAP_SQL	True	30
SQLite	superset.db_engine_specs.sqlite	FORCE_LIMIT	True	None
Shillelagh	superset.db_engine_specs.shillelagh	FORCE_LIMIT	True	None
SingleStore	superset.db_engine_specs.singlestore	FORCE_LIMIT	True	256
Snowflake	superset.db_engine_specs.snowflake	FORCE_LIMIT	True	256
StarRocks	superset.db_engine_specs.starrocks	FORCE_LIMIT	True	64
Superset meta database	superset.db_engine_specs.superset	FORCE_LIMIT	True	None
TDengine	superset.db_engine_specs.tdengine	FORCE_LIMIT	True	64
Teradata	superset.db_engine_specs.teradata	FORCE_LIMIT	True	30
Trino	superset.db_engine_specs.trino	FORCE_LIMIT	True	None
Vertica	superset.db_engine_specs.vertica	FORCE_LIMIT	True	None
YDB	superset.db_engine_specs.ydb	FORCE_LIMIT	True	None
base	superset.db_engine_specs.presto	FORCE_LIMIT	True	None

SQL Capabilities

Database	JOINs	Subqueries	Aliases in SELECT	Aliases in ORDER BY	CTEs	Comments	Escaped Colons	Inline Time Groupby	Source Column When Aliased	Aggregations in ORDER BY	Expressions in ORDER BY
Amazon Athena	True	True	True	True	True	True	False	False	False	True	False
Amazon DynamoDB	True	True	True	True	True	True	True	False	False	True	False
Amazon Redshift	True	True	True	True	True	True	True	False	False	True	False
Apache Doris	True	True	True	True	True	True	True	False	False	True	False
Apache Drill	True	True	True	True	True	True	True	False	False	True	False
Apache Druid	False	True	True	True	True	True	True	False	False	True	False
Apache Hive	True	True	True	True	True	True	True	False	False	False	False
Apache Impala	True	True	True	True	True	True	True	False	False	True	False
Apache Kylin	True	True	True	True	True	True	True	False	False	True	False
Apache Pinot	False	False	False	False	True	True	True	False	False	True	False
Apache Solr	False	False	True	True	True	True	True	False	False	True	False
Apache Spark SQL	True	True	True	True	True	True	True	False	False	False	False
Ascend	True	True	True	True	True	True	True	False	False	True	False
Aurora MySQL (Data API)	True	True	True	True	True	True	True	False	False	True	False
Aurora PostgreSQL (Data API)	True	True	True	True	True	True	True	False	False	True	False
Azure Synapse	True	True	True	True	False	True	True	False	False	True	False
ClickHouse	True	True	True	True	True	True	True	True	False	True	False
ClickHouse Connect (Superset)	True	True	True	True	True	True	True	True	False	True	False
CockroachDB	True	True	True	True	True	True	True	False	False	True	False
Couchbase	False	False	True	True	True	True	True	False	False	True	False
CrateDB	True	True	True	True	True	True	True	False	False	True	False
Databend	True	True	True	True	True	True	True	True	False	True	False
Databricks	True	True	True	True	True	True	True	False	False	True	False
Databricks (legacy)	True	True	True	True	True	True	True	False	False	True	False
Databricks Interactive Cluster	True	True	True	True	True	True	True	False	False	False	False
Databricks SQL Endpoint	True	True	True	True	True	True	True	False	False	True	False
Denodo	True	True	True	True	True	True	True	False	False	True	False
Dremio	True	True	True	True	True	True	True	False	False	True	False
DuckDB	True	True	True	True	True	True	True	False	False	True	False
ElasticSearch (OpenDistro SQL)	False	True	True	True	True	False	True	True	False	True	False
ElasticSearch (SQL API)	False	True	True	True	True	False	True	True	False	True	False
Exasol	True	True	True	True	True	True	True	False	False	True	False
Firebird	True	True	True	True	True	True	True	False	False	True	False
Firebolt	True	True	True	True	True	True	True	False	False	True	False
Google BigQuery	True	True	True	True	True	True	True	False	False	True	True
Google Sheets	True	True	True	True	True	True	True	False	False	True	False
IBM Db2	True	True	True	True	True	True	True	False	False	True	False
IBM Db2 for i	True	True	True	True	True	True	True	False	False	True	False
IBM Netezza Performance Server	True	True	True	True	True	True	True	False	False	True	False
KustoKQL	True	True	True	True	True	False	True	True	False	True	False
KustoSQL	True	True	True	True	True	False	True	True	False	True	False
MariaDB	True	True	True	True	True	True	True	False	False	True	False
Microsoft SQL Server	True	True	True	True	False	True	True	False	False	True	False
MotherDuck	True	True	True	True	True	True	True	False	False	True	False
MySQL	True	True	True	True	True	True	True	False	False	True	False
OceanBase	True	True	True	True	True	True	True	False	False	True	False
Ocient	True	True	True	True	False	True	True	False	False	True	False
Oracle	True	True	True	True	True	True	True	False	False	True	False
Parseable	True	True	True	True	True	True	True	False	False	True	False
PostgreSQL	True	True	True	True	True	True	True	False	False	True	False
Presto	True	True	True	True	True	True	True	False	True	True	False
RisingWave	True	True	True	True	True	True	True	False	False	True	False
SAP HANA	True	True	True	True	True	True	True	False	False	True	False
SQLite	True	True	True	True	True	True	True	False	False	True	False
Shillelagh	True	True	True	True	True	True	True	False	False	True	False
SingleStore	True	True	True	True	True	True	True	False	False	True	False
Snowflake	True	True	True	True	True	True	True	False	False	True	False
StarRocks	True	True	True	True	True	True	True	False	False	True	False
Superset meta database	True	True	True	True	True	True	True	False	False	True	False
TDengine	True	True	True	True	True	True	True	False	False	True	False
Teradata	True	True	True	True	True	True	True	False	False	True	False
Trino	True	True	True	True	True	True	True	False	True	True	False
Vertica	True	True	True	True	True	True	True	False	False	True	False
YDB	True	True	True	True	True	True	True	False	False	True	False
base	True	True	True	True	True	True	True	False	False	True	False

Time Grains – Common

Database	SECOND	MINUTE	HOUR	DAY	WEEK	MONTH	QUARTER	YEAR
Amazon Athena	True	True	True	True	True	True	True	True
Amazon DynamoDB	True	True	True	True	True	True	True	True
Amazon Redshift	True	True	True	True	True	True	True	True
Apache Doris	True	True	True	True	True	True	True	True
Apache Drill	True	True	True	True	True	True	True	True
Apache Druid	True	True	True	True	True	True	True	True
Apache Hive	True	True	True	True	True	True	True	True
Apache Impala	False	True	True	True	True	True	True	True
Apache Kylin	True	True	True	True	True	True	True	True
Apache Pinot	True	True	True	True	True	True	True	True
Apache Solr	False	False	False	False	False	False	False	False
Apache Spark SQL	True	True	True	True	True	True	True	True
Ascend	True	True	True	True	True	True	True	True
Aurora MySQL (Data API)	True	True	True	True	True	True	True	True
Aurora PostgreSQL (Data API)	True	True	True	True	True	True	True	True
Azure Synapse	True	True	True	True	True	True	True	True
ClickHouse	False	True	True	True	True	True	True	True
ClickHouse Connect (Superset)	False	True	True	True	True	True	True	True
CockroachDB	True	True	True	True	True	True	True	True
Couchbase	True	True	True	True	False	True	True	True
CrateDB	True	True	True	True	True	True	True	True
Databend	True	True	True	True	True	True	True	True
Databricks	True	True	True	True	True	True	True	True
Databricks (legacy)	True	True	True	True	True	True	True	True
Databricks Interactive Cluster	True	True	True	True	True	True	True	True
Databricks SQL Endpoint	True	True	True	True	True	True	True	True
Denodo	False	True	True	True	True	True	True	True
Dremio	True	True	True	True	True	True	True	True
DuckDB	True	True	True	True	True	True	True	True
ElasticSearch (OpenDistro SQL)	True	True	True	True	False	True	False	True
ElasticSearch (SQL API)	True	True	True	True	True	True	False	True
Exasol	True	True	True	True	True	True	True	True
Firebird	True	True	True	True	False	True	False	True
Firebolt	True	True	True	True	True	True	True	True
Google BigQuery	True	True	True	True	True	True	True	True
Google Sheets	True	True	True	True	True	True	True	True
IBM Db2	True	True	True	True	True	True	True	True
IBM Db2 for i	True	True	True	True	True	True	True	True
IBM Netezza Performance Server	True	True	True	True	True	True	True	True
KustoKQL	True	True	True	True	True	True	False	True
KustoSQL	True	True	True	True	True	True	True	True
MariaDB	True	True	True	True	True	True	True	True
Microsoft SQL Server	True	True	True	True	True	True	True	True
MotherDuck	True	True	True	True	True	True	True	True
MySQL	True	True	True	True	True	True	True	True
OceanBase	True	True	True	True	True	True	True	True
Ocient	True	True	True	True	True	True	False	True
Oracle	True	True	True	True	True	True	True	True
Parseable	True	True	True	True	True	True	True	True
PostgreSQL	True	True	True	True	True	True	True	True
Presto	True	True	True	True	True	True	True	True
RisingWave	True	True	True	True	True	True	True	True
SAP HANA	True	True	True	True	False	True	True	True
SQLite	True	True	True	True	True	True	True	True
Shillelagh	True	True	True	True	True	True	True	True
SingleStore	True	True	True	True	True	True	True	True
Snowflake	True	True	True	True	True	True	True	True
StarRocks	True	True	True	True	True	True	True	True
Superset meta database	True	True	True	True	True	True	True	True
TDengine	True	True	True	True	True	False	False	False
Teradata	False	True	True	True	True	True	True	True
Trino	True	True	True	True	True	True	True	True
Vertica	True	True	True	True	True	True	True	True
YDB	True	True	True	True	True	True	True	True
base	True	True	True	True	True	True	True	True

Time Grains – Extended

Database	FIVE_SECONDS	THIRTY_SECONDS	FIVE_MINUTES	TEN_MINUTES	FIFTEEN_MINUTES	THIRTY_MINUTES	HALF_HOUR	SIX_HOURS	WEEK_STARTING_SUNDAY	WEEK_STARTING_MONDAY	WEEK_ENDING_SATURDAY	WEEK_ENDING_SUNDAY	QUARTER_YEAR
Amazon Athena	False	False	False	False	False	False	False	False	True	False	True	False	False
Amazon DynamoDB	False	False	False	False	False	False	False	False	True	True	True	True	False
Amazon Redshift	True	True	True	True	True	True	False	False	False	False	False	False	False
Apache Doris	False	False	False	False	False	False	False	False	False	True	False	False	False
Apache Drill	False	False	False	False	True	True	False	False	False	False	False	False	False
Apache Druid	True	True	True	True	True	True	False	True	True	False	True	False	False
Apache Hive	False	False	False	False	False	False	False	False	True	False	True	False	False
Apache Impala	False	False	False	False	False	False	False	False	False	False	False	False	False
Apache Kylin	False	False	False	False	False	False	False	False	False	False	False	False	False
Apache Pinot	False	False	True	True	True	True	False	False	False	False	False	False	False
Apache Solr	False	False	False	False	False	False	False	False	False	False	False	False	False
Apache Spark SQL	False	False	False	False	False	False	False	False	True	False	True	False	False
Ascend	False	False	False	False	False	False	False	False	False	False	False	False	False
Aurora MySQL (Data API)	False	False	False	False	False	False	False	False	False	True	False	False	False
Aurora PostgreSQL (Data API)	True	True	True	True	True	True	False	False	False	False	False	False	False
Azure Synapse	False	False	True	True	True	True	False	False	True	True	False	False	False
ClickHouse	False	False	True	True	True	True	False	False	False	False	False	False	False
ClickHouse Connect (Superset)	False	False	True	True	True	True	False	False	False	False	False	False	False
CockroachDB	True	True	True	True	True	True	False	False	False	False	False	False	False
Couchbase	False	False	False	False	False	False	False	False	False	False	False	False	False
CrateDB	False	False	False	False	False	False	False	False	False	False	False	False	False
Databend	False	False	True	True	True	False	False	False	False	False	False	False	False
Databricks	False	False	False	False	False	False	False	False	True	False	True	False	False
Databricks (legacy)	False	False	False	False	False	False	False	False	True	False	True	False	False
Databricks Interactive Cluster	False	False	False	False	False	False	False	False	True	False	True	False	False
Databricks SQL Endpoint	False	False	False	False	False	False	False	False	True	False	True	False	False
Denodo	False	False	False	False	False	False	False	False	False	False	False	False	False
Dremio	False	False	False	False	False	False	False	False	False	False	False	False	False
DuckDB	False	False	False	False	False	False	False	False	False	False	False	False	False
ElasticSearch (OpenDistro SQL)	False	False	False	False	False	False	False	False	False	False	False	False	False
ElasticSearch (SQL API)	False	False	False	False	False	False	False	False	False	False	False	False	False
Exasol	False	False	False	False	False	False	False	False	False	False	False	False	False
Firebird	False	False	False	False	False	False	False	False	False	False	False	False	False
Firebolt	False	False	False	False	False	False	False	False	False	False	False	False	False
Google BigQuery	False	False	True	True	True	True	False	False	False	True	False	False	False
Google Sheets	True	True	True	True	True	True	True	True	True	True	True	True	True
IBM Db2	False	False	False	False	False	False	False	False	False	False	False	False	False
IBM Db2 for i	False	False	False	False	False	False	False	False	False	False	False	False	False
IBM Netezza Performance Server	False	False	False	False	False	False	False	False	False	False	False	False	False
KustoKQL	False	True	True	False	False	True	False	False	False	False	False	False	False
KustoSQL	False	False	True	True	True	False	True	False	True	True	False	False	False
MariaDB	False	False	False	False	False	False	False	False	False	True	False	False	False
Microsoft SQL Server	False	False	True	True	True	True	False	False	True	True	False	False	False
MotherDuck	False	False	False	False	False	False	False	False	False	False	False	False	False
MySQL	False	False	False	False	False	False	False	False	False	True	False	False	False
OceanBase	False	False	False	False	False	False	False	False	False	True	False	False	False
Ocient	False	False	False	False	False	False	False	False	False	False	False	False	True
Oracle	False	False	False	False	False	False	False	False	False	False	False	False	False
Parseable	False	False	False	False	False	False	False	False	False	False	False	False	False
PostgreSQL	True	True	True	True	True	True	False	False	False	False	False	False	False
Presto	True	True	True	True	True	False	True	True	True	True	True	True	False
RisingWave	True	True	True	True	True	True	False	False	False	False	False	False	False
SAP HANA	False	False	False	False	False	False	False	False	False	False	False	False	False
SQLite	True	True	True	True	True	True	True	True	True	True	True	True	True
Shillelagh	True	True	True	True	True	True	True	True	True	True	True	True	True
SingleStore	False	False	False	False	False	False	False	False	False	False	False	False	False
Snowflake	False	False	True	True	True	True	False	False	False	False	False	False	False
StarRocks	False	False	False	False	False	False	False	False	False	True	False	False	False
Superset meta database	True	True	True	True	True	True	True	True	True	True	True	True	True
TDengine	False	False	False	False	False	False	False	False	False	False	False	False	False
Teradata	False	False	False	False	False	False	False	False	False	False	False	False	False
Trino	True	True	True	True	True	False	True	True	True	True	True	True	False
Vertica	True	True	True	True	True	True	False	False	False	False	False	False	False
YDB	False	True	True	True	True	True	False	False	False	False	False	False	False
base	True	True	True	True	True	False	True	True	True	True	True	True	False

Core Platform & Metadata Features

Integration with platform features and metadata handling.

Database	Masked Encrypted Extra	Column Type Mappings	Function Names	File Upload	Dynamic Schema	Catalog	Dynamic Catalog	SSH Tunneling	Latest Partition	Query Cancellation	Get Metrics	Extra Table Metadata	Exception Mapping	Custom Errors
Amazon Athena	False	False	False	True	False	False	False	False	False	False	False	False	False	False
Amazon DynamoDB	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Amazon Redshift	False	False	False	True	False	False	False	True	False	True	False	False	False	False
Apache Doris	False	True	False	True	True	True	True	True	False	True	False	False	False	False
Apache Drill	False	False	False	True	True	False	False	True	False	False	False	False	False	False
Apache Druid	False	False	False	True	False	False	False	True	False	False	False	False	True	False
Apache Hive	False	True	True	True	True	True	True	True	True	True	False	True	False	False
Apache Impala	False	False	False	True	False	False	False	True	False	True	False	False	False	False
Apache Kylin	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Apache Pinot	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Apache Solr	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Apache Spark SQL	False	True	True	True	True	True	True	True	True	True	False	True	False	False
Ascend	False	False	False	True	False	False	False	True	False	True	False	False	False	False
Aurora MySQL (Data API)	False	True	False	True	True	False	False	True	False	True	False	False	False	False
Aurora PostgreSQL (Data API)	False	True	False	True	True	True	True	True	False	True	False	False	False	False
Azure Synapse	False	True	False	True	False	False	False	True	False	False	False	False	False	False
ClickHouse	False	True	True	False	False	False	False	True	False	False	False	False	True	False
ClickHouse Connect (Superset)	False	True	True	False	True	False	False	True	False	False	False	False	True	False
CockroachDB	False	True	False	True	True	True	True	True	False	True	False	False	False	False
Couchbase	False	False	False	True	False	False	False	True	False	False	False	False	False	False
CrateDB	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Databend	False	True	True	False	False	False	False	True	False	False	False	False	True	False
Databricks	False	False	False	True	True	True	True	True	False	False	False	False	False	True
Databricks (legacy)	False	False	False	True	True	True	True	True	False	False	False	False	False	True
Databricks Interactive Cluster	False	True	True	True	True	True	True	True	True	True	False	True	False	False
Databricks SQL Endpoint	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Denodo	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Dremio	False	False	False	True	False	False	False	True	False	False	False	False	False	False
DuckDB	False	True	False	True	False	False	False	True	False	False	False	False	False	False
ElasticSearch (OpenDistro SQL)	False	False	False	True	False	False	False	True	False	False	False	False	False	False
ElasticSearch (SQL API)	False	False	False	True	False	False	False	True	False	False	False	False	True	False
Exasol	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Firebird	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Firebolt	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Google BigQuery	False	False	False	True	False	True	True	False	True	False	False	True	True	False
Google Sheets	False	False	True	True	False	False	False	False	False	False	False	True	False	False
IBM Db2	False	False	False	True	True	False	False	True	False	False	False	False	False	False
IBM Db2 for i	False	False	False	True	True	False	False	True	False	False	False	False	False	False
IBM Netezza Performance Server	False	False	False	True	False	False	False	True	False	False	False	False	False	False
KustoKQL	False	False	False	True	False	False	False	True	False	False	False	False	True	False
KustoSQL	False	True	False	True	False	False	False	True	False	False	False	False	True	False
MariaDB	False	True	False	True	True	False	False	True	False	True	False	False	False	False
Microsoft SQL Server	False	True	False	True	False	False	False	True	False	False	False	False	False	False
MotherDuck	False	True	False	True	False	True	True	True	False	False	False	False	False	False
MySQL	False	True	False	True	True	False	False	True	False	True	False	False	False	False
OceanBase	False	True	False	True	True	False	False	True	False	True	False	False	False	False
Ocient	False	False	False	True	False	False	False	True	False	True	False	False	False	False
Oracle	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Parseable	False	False	False	True	False	False	False	True	False	False	False	False	False	False
PostgreSQL	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Presto	False	True	True	True	True	True	True	True	True	True	False	True	False	False
RisingWave	False	True	False	True	True	True	True	True	False	True	False	False	False	False
SAP HANA	False	False	False	True	False	False	False	True	False	False	False	False	False	False
SQLite	False	False	True	True	False	False	False	False	False	False	False	False	False	False
Shillelagh	False	False	True	True	False	False	False	False	False	False	False	False	False	False
SingleStore	False	True	True	True	True	False	False	True	False	True	False	False	False	False
Snowflake	False	False	False	True	True	True	True	True	False	True	False	False	False	False
StarRocks	False	True	False	True	True	False	False	True	False	True	False	False	False	False
Superset meta database	False	False	True	False	False	False	False	False	False	False	False	False	False	False
TDengine	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Teradata	False	False	False	True	False	False	False	True	False	False	False	False	False	False
Trino	False	True	True	True	True	True	True	True	True	True	False	True	True	False
Vertica	False	False	False	True	False	False	False	True	False	False	False	False	False	False
YDB	False	False	False	False	False	False	False	True	False	False	False	False	False	False
base	False	True	True	True	True	True	True	True	True	False	False	False	False	False

Operational & Advanced Features

Database	User Impersonation	Expand Data	Cost Estimation	SQL Validation
Amazon Athena	False	False	False	False
Amazon DynamoDB	False	False	False	False
Amazon Redshift	False	False	False	False
Apache Doris	False	False	False	False
Apache Drill	True	False	False	False
Apache Druid	False	False	False	False
Apache Hive	True	True	True	False
Apache Impala	False	False	False	False
Apache Kylin	False	False	False	False
Apache Pinot	False	False	False	False
Apache Solr	False	False	False	False
Apache Spark SQL	True	True	True	False
Ascend	False	False	False	False
Aurora MySQL (Data API)	False	False	False	False
Aurora PostgreSQL (Data API)	False	False	True	True
Azure Synapse	False	False	False	False
ClickHouse	False	False	False	False
ClickHouse Connect (Superset)	False	False	False	False
CockroachDB	False	False	True	False
Couchbase	False	False	False	False
CrateDB	False	False	False	False
Databend	False	False	False	False
Databricks	False	False	False	False
Databricks (legacy)	False	False	False	False
Databricks Interactive Cluster	True	True	True	False
Databricks SQL Endpoint	False	False	False	False
Denodo	False	False	False	False
Dremio	False	False	False	False
DuckDB	False	False	False	False
ElasticSearch (OpenDistro SQL)	False	False	False	False
ElasticSearch (SQL API)	False	False	False	False
Exasol	False	False	False	False
Firebird	False	False	False	False
Firebolt	False	False	False	False
Google BigQuery	False	False	True	False
Google Sheets	True	False	False	False
IBM Db2	False	False	False	False
IBM Db2 for i	False	False	False	False
IBM Netezza Performance Server	False	False	False	False
KustoKQL	False	False	False	False
KustoSQL	False	False	False	False
MariaDB	False	False	False	False
Microsoft SQL Server	False	False	False	False
MotherDuck	False	False	False	False
MySQL	False	False	False	False
OceanBase	False	False	False	False
Ocient	False	False	False	False
Oracle	False	False	False	False
Parseable	False	False	False	False
PostgreSQL	False	False	False	False
Presto	True	True	True	True
RisingWave	False	False	True	False
SAP HANA	False	False	False	False
SQLite	False	False	False	False
Shillelagh	False	False	False	False
SingleStore	False	False	False	False
Snowflake	False	False	False	False
StarRocks	True	False	False	False
Superset meta database	False	False	False	False
TDengine	False	False	False	False
Teradata	False	False	False	False
Trino	True	False	True	False
Vertica	False	False	False	False
YDB	False	False	False	False
base	False	False	True	False

Database information

A DB engine spec has attributes that describe the underlying database engine, so that Superset can know how to build and run queries. For example, some databases don't support subqueries, which are needed for some of the queries produced by Superset for certain charts. When a database doesn't support subqueries the query is run in two-steps, using the results from the first query to build the second query.

These attributes and their default values (set in the base class, BaseEngineSpec) are described below:

limit_method = LimitMethod.FORCE_LIMIT

When running user queries in SQL Lab, Superset needs to limit the number of rows returned. The reason for that is cost and performance: there's no point in running a query that produces millions of rows when they can't be loaded into the browser.

For most databases this is done by parsing the user submitted query and applying a limit, if one is not present, or replacing the existing limit if it's larger. This is called the FORCE_LIMIT method, and is the most efficient, since the database will produce at most the number of rows that Superset will display.

For some databases this method might not work, and they can use the WRAP_SQL method, which wraps the original query in a SELECT * and applies a limit via the SQLAlchemy dialect, which should get translated to the correct syntax. This method might be inefficient, since the database optimizer might not be able to push the limit to the inner query.

Finally, as a last resource there is the FETCH_MANY method. When a DB engine spec uses this method the query runs unmodified, but Superset fetches only a certain number of rows from the cursor. It's possible that a database using this method can optimize the query execution and compute rows as they are being read by the cursor, but it's unlikely. This makes this method the least efficient of the three.

Note that when Superset runs a query with a given limit, say 100, it always modifies the query to request one additional row (LIMIT 101, in this case). This extra row is dropped before the results are returned to the user, but it allows Superset to inform the users that the query was indeed limited. Otherwise a query with LIMIT 100 that returns exactly 100 rows would seem like it was limited, when in fact it was not.

allows_joins = True

Not all databases support JOINs. When building complex charts, Superset will try to join the table to itself in order to compute top_n groups, for example. If the database doesn't support joins Superset will instead run a prequery, and use the results to build the final query.

allows_subqueries = True

Similarly, not all databases support subqueries. For more complex charts Superset will build subqueries if possible, or run the query in two-steps otherwise.

allows_alias_in_select = True

Does the DB support aliases in the projection of a query, eg:

SELECT COUNT(*) AS cnt

Superset will try to use aliases whenever possible, in order to give friendly names to expressions.

allows_alias_in_orderby = True

Does the DB support referencing alias in the GROUP BY, eg:

SELECT
  UPPER(country_of_origin) AS country
  COUNT(*) AS cnt
FROM
  some_table
GROUP BY
  country

Otherwise the query is written as:

SELECT
  UPPER(country_of_origin) AS country
  COUNT(*) AS cnt
FROM
  some_table
GROUP BY
  UPPER(country_of_origin)

time_groupby_inline = False

In theory this attribute should be used to omit time filters from the self-joins. When the attribute is false the time attribute will be present in the subquery used to compute limited series, eg:

SELECT DATE_TRUNC('day', ts) AS ts,
       team AS team,
       COUNT(*) AS count
FROM public.threads
JOIN
  (SELECT team AS team__,
          COUNT(*) AS mme_inner__
   FROM public.threads
   -- this is added when `time_groupby_inline = False`
   WHERE ts >= TO_TIMESTAMP('2022-07-27 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
     AND ts < TO_TIMESTAMP('2023-07-27 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
   --
   GROUP BY team
   ORDER BY mme_inner__ DESC
   LIMIT 5) AS anon_1 ON team = team__
WHERE ts >= TO_TIMESTAMP('2022-07-27 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
  AND ts < TO_TIMESTAMP('2023-07-27 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
GROUP BY DATE_TRUNC('day', ts),
         team
ORDER BY count DESC
LIMIT 10000;

In practice, the attribute doesn't seem to be working as of 2023-07-27.

allows_alias_to_source_column = True

When this is true the database allows queries where alias can overshadow existing column names. For example, in this query:

SELECT
  foo + 1 AS foo
FROM
  some_table
ORDER BY
  foo  -- references the alias `foo + 1`, not the column `foo`

allows_hidden_orderby_agg = True

If set to true the database allows expressions in the GROUP BY that are not present in the projection (SELECT), eg:

SELECT
  country,
  COUNT(*)
FROM
  some_table
GROUP BY
  country
ORDER BY
  SUM(population)  -- not present in the `SELECT`

allows_hidden_cc_in_orderby = False

This the opposite of allows_alias_in_orderby, for databases that require aliases in the ORDER BY. For example, BigQuery doesn't like this query:

SELECT
  CASE
    WHEN type = 'feature' THEN 'f'
    WHEN type = 'bug' THEN 'b'
    ELSE 'o'
  END AS cc_type
FROM
  some_table
GROUP BY
  cc_type
ORDER BY
  CASE
    WHEN type = 'feature' THEN 'f'
    WHEN type = 'bug' THEN 'b'
    ELSE 'o'
  END

Instead, it must be written as:

SELECT
  CASE
    WHEN type = 'feature' THEN 'f'
    WHEN type = 'bug' THEN 'b'
    ELSE 'o'
  END AS cc_type
FROM
  some_table
GROUP BY
  cc_type
ORDER BY
  cc_type

allows_cte_in_subquery = True

When a virtual dataset is used in a chart the original query is converted into a subquery, and is wrapped in an outer query that is generated based on the chart controls. The virtual dataset query might have a CTE, and some databases don't like subqueries with CTEs in them.

When this attribute is false Superset will extract the CTE and move it outside of the subquery when generating SQL for charts. The name of the new CTE will be cte_alias, also defined in the DB engine spec.

allow_limit_clause = True

Allows for the LIMIT clause. Otherwise, the database probably uses TOP to limit rows.

max_column_name_length: int | None = None

Most databases have a well defined limit for the maximum length of a column name (SQLite is probably the one exception). While the can be set (and defaults) to None, it's highly recommended to set a value to prevent errors.

allows_sql_comments = True

Are comments supported in the DB? In general SQL in comments are defined by double dashes:

-- this is a comment
SELECT *  -- we need everything
FROM some_table

allows_escaped_colons = True

SQLAlchemy recommends escaping colons to prevent them from being interpreted as bindings to parameters. Because of this, when building queries from virtual datasets Superset will escape all colons with \:.

This works for most databases except Athena. The allows_escaped_colons attribute specifies if the database supports the escape colon.

Basic features

These are features that all DB engine specs should support, as the name suggests. They provide a much better user experience for the user.

Time grains

The most basic feature that DB engine specs need to support is defining time grain expressions. These are dialect-specific SQL expressions that are used to compute metrics on a given time grain when building charts. For example, when computing the metric COUNT(*) on a daily basis, Superset will generate the following query:

SELECT
  <DB engine spec expression for TimeGrain.DAY>,
  COUNT(*)
...
GROUP BY
  <DB engine spec expression for TimeGrain.DAY>

For some databases with support for DATE_TRUNC or TIME_FLOOR this is easy. Here's how Apache Druid computes 15 minute aggregations:

TIME_FLOOR(CAST({col} AS TIMESTAMP), 'PT15M')

Where {col} is the time column being aggregated — the expression is actually a Jinja2 template. Druid uses the ISO standard for durations, with PT15M representing 15 minutes.

On the other and, here's the same for SQLite:

DATETIME(
  STRFTIME(
    '%Y-%m-%dT%H:%M:00',
    {col}
  ),
  printf(
    '-%d minutes',
    CAST(strftime('%M', {col}) AS INT) % 15
  )
)

The SQLite version has to truncate the column down to the minute, and then subtract a number of minutes equals to the modulo 15.

Time grain expressions are defined in the _time_grain_expressions class attribute, which maps from a superset.constants.TimeGrain to the SQL expression. The dictionary has a special key None, that should map to the column directly, for when no time grain is specified.

Note that it's possible to add new time grains via configuration. For example, if you want to add a "2 seconds" time grain to your installation you can add it to TIME_GRAIN_ADDONS, and implement it in TIME_GRAIN_ADDON_EXPRESSIONS:

# superset_config.py
TIME_GRAIN_ADDONS = {"PT2S": "2 second"}

TIME_GRAIN_ADDON_EXPRESSIONS = {
    "clickhouse": {
        "PT2S": "toDateTime(intDiv(toUInt32(toDateTime({col})), 2)*2)",
    }
}

Column type mapping

Column type mapping, defined in the column_type_mappings class attribute, is just a way of mapping type names from the database to types Superset understand. The default values in BaseEngineSpec are sane:

_default_column_type_mappings: tuple[ColumnTypeMapping, ...] = (
    (
        re.compile(r"^string", re.IGNORECASE),
        types.String(),
        GenericDataType.STRING,
    ),
    (
        re.compile(r"^float", re.IGNORECASE),
        types.Float(),
        GenericDataType.NUMERIC,
    ),
    (
        re.compile(r"^date", re.IGNORECASE),
        types.Date(),
        GenericDataType.TEMPORAL,
    ),
    (
        re.compile(r"^bool(ean)?", re.IGNORECASE),
        types.Boolean(),
        GenericDataType.BOOLEAN,
    ),
    ...
)

But you might want to implement more specific types in the DB engine spec, or complex types. For example, for MSSQL we have:

from sqlalchemy.dialects.mssql.base import SMALLDATETIME

class MssqlEngineSpec(BaseEngineSpec):
    ...
    column_type_mappings = (
        (
            re.compile(r"^smalldatetime.*", re.IGNORECASE),
            SMALLDATETIME(),
            GenericDataType.TEMPORAL,
        ),
    )

Function names

DB engine specs should implement a class method called get_function_names that returns a list of strings, representing all the function names that the database supports. This is used for autocomplete in SQL Lab.

Masked encrypted extra

Superset does a good job in keeping credentials secure. When you add a database with a password, for example:

postgresql://admin:password123@db.example.org:5432/db

The password is sent over the network only when the database is created. When you edit the database later, Superset will return this as the SQLAlchemy URI:

postgresql://admin:XXXXXXXXXX@db.example.org:5432/db

The password will be masked in the API response; it's not just masked in the browser UI. This is done in order to avoid sending the password unnecessarily over the network. Also, if a non-admin user has access to the API response, they won't be able to know the database password.

When the database is edited, the Superset backend is smart enough to replace the masked password with the actual password, unless the password has changed. That is, if you change the database in the URI from db to db2 the SQLAlchemy URI will be stored in the backend as:

postgresql://admin:password123@db.example.org:5432/db2

The password is not the only piece of information where security is critical. For many databases (like BigQuery), sensitive information is stored in the credentials JSON payload. For example:

{
  "type": "service_account",
  "project_id": "dbt-tutorial-347100",
  "private_key_id": "4bc71f06990c864a590fad8b94be6a5904fc171f",
  "private_key": "<SENSITIVE INFORMATION>",
  "client_email": "dbt-user-278@dbt-tutorial-347100.iam.gserviceaccount.com",
  "client_id": "115666988796889519425",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/dbt-user-278%40dbt-tutorial-347100.iam.gserviceaccount.com"
}

Similarly to password, we don't want to send private_key to the client when a database is edited; the Superset API should never return its actual contents. Instead, Superset should return a masked value, and users should be able to edit the JSON without having to type in the private_key on every edit.

To do this, DB engine specs and implement 2 methods, mask_encrypted_extra and unmask_encrypted_extra. They have these names because the credentials are stored in an encrypted column called encrypted_extra. Here's how these methods look like for BigQuery:

from superset.constants import PASSWORD_MASK


class BigQueryEngineSpec(BaseEngineSpec):

    @classmethod
    def mask_encrypted_extra(cls, encrypted_extra: str | None) -> str | None:
        if encrypted_extra is None:
            return encrypted_extra

        try:
            config = json.loads(encrypted_extra)
        except (json.JSONDecodeError, TypeError):
            return encrypted_extra

        try:
            config["credentials_info"]["private_key"] = PASSWORD_MASK
        except KeyError:
            pass

        return json.dumps(config)

    @classmethod
    def unmask_encrypted_extra(
        cls,
        old: str | None,
        new: str | None
    ) -> str | None:
        if old is None or new is None:
            return new

        try:
            old_config = json.loads(old)
            new_config = json.loads(new)
        except (TypeError, json.JSONDecodeError):
            return new

        if "credentials_info" not in new_config:
            return new

        if "private_key" not in new_config["credentials_info"]:
            return new

        if new_config["credentials_info"]["private_key"] == PASSWORD_MASK:
            new_config["credentials_info"]["private_key"] = old_config[
                "credentials_info"
            ]["private_key"]

        return json.dumps(new_config)

This way, when a user edits an existing BigQuery connection, the private_key is shown as XXXXXXXXXX. Everything else in the JSON is still displayed, and the user can change any of the fields without having to provide the private key.

Note that while this is a basic feature that should be implemented for security reasons, it only makes sense in DB engine specs that use encrypted_extra to store connection information.

Nice to have features

The next set of features are nice to have. They don't apply to all databases, and are not strictly needed for security or usability.

User impersonation

In general there's no user-level granularity when accessing a database in Superset. A single database connection is shared by all users who have access to that database. There are many use cases when this is not desirable, and some databases implement mechanisms in which they can impersonate users, potentially reducing the scope of permissions available to run the query.

For example, the Google Sheets DB engine spec implements this via the get_url_for_impersonation class method:

class GSheetsEngineSpec(ShillelaghEngineSpec):

    @classmethod
    def get_url_for_impersonation(
        cls,
        url: URL,
        impersonate_user: bool,
        username: str | None,
        access_token: str | None,
    ) -> URL:
        if impersonate_user and username is not None:
            user = security_manager.find_user(username=username)
            if user and user.email:
                url = url.update_query_dict({"subject": user.email})

        return url

The method get_url_for_impersonation updates the SQLAlchemy URI before every query. In this particular case, it will fetch the user's email and add it to the subject query argument. The driver will then lower the permissions to match that given user. This allows the connection to be configured with a service account that has access to all the spreadsheets, while giving users access to only the spreadsheets they own are have been shared with them (or with their organization — Google will handle the authorization in this case, not Superset).

Alternatively, it's also possible to impersonate users by implementing the update_impersonation_config. This is a class method which modifies connect_args in place. You can use either method, and ideally they should be consolidated in a single one.

OAuth2

Support for authenticating to a database using personal OAuth2 access tokens was introduced in SIP-85. The Google Sheets DB engine spec is the reference implementation.

Note that this API is still experimental and evolving quickly, subject to breaking changes. Currently, to add support for OAuth2 to a DB engine spec, the following attributes are needed:

class BaseEngineSpec:

    supports_oauth2 = True
    oauth2_exception = OAuth2RedirectError

    oauth2_scope = " ".join([
        "https://example.org/scope1",
        "https://example.org/scope2",
    ])
    oauth2_authorization_request_uri = "https://example.org/authorize"
    oauth2_token_request_uri = "https://example.org/token"

The oauth2_exception is an exception that is raised by cursor.execute when OAuth2 is needed. This will start the OAuth2 dance when BaseEngineSpec.execute is called, by returning the custom error OAUTH2_REDIRECT to the frontend. If the database driver doesn't have a specific exception, it might be necessary to overload the execute method in the DB engine spec, so that the BaseEngineSpec.start_oauth2_dance method gets called whenever OAuth2 is needed.

The DB engine should implement logic in either get_url_for_impersonation or update_impersonation_config to update the connection with the personal access token. See the Google Sheets DB engine spec for a reference implementation.

Currently OAuth2 needs to be configured at the DB engine spec level, ie, with one client for each DB engien spec. The configuration lives in superset_config.py:

# superset_config.py
DATABASE_OAUTH2_CLIENTS = {
    "Google Sheets": {
        "id": "XXX.apps.googleusercontent.com",
        "secret": "GOCSPX-YYY",
        "scope": " ".join(
            [
                "https://www.googleapis.com/auth/drive.readonly",
                "https://www.googleapis.com/auth/spreadsheets",
                "https://spreadsheets.google.com/feeds",
            ],
        ),
        "authorization_request_uri": "https://accounts.google.com/o/oauth2/v2/auth",
        "token_request_uri": "https://oauth2.googleapis.com/token",
    },
}
DATABASE_OAUTH2_JWT_ALGORITHM = "HS256"
DATABASE_OAUTH2_REDIRECT_URI = "http://localhost:8088/api/v1/database/oauth2/"
DATABASE_OAUTH2_TIMEOUT = timedelta(seconds=30)

When configuring a client only the ID and secret are required; the DB engine spec should have default values for the scope and endpoints. The DATABASE_OAUTH2_REDIRECT_URI attribute is optional, and defaults to /api/v1/databases/oauth2/ in Superset.

In the future we plan to support adding custom clients via the Superset UI, and being able to manually assign clients to specific databases.

File upload

When a DB engine spec supports file upload it declares so via the supports_file_upload class attribute. The base class implementation is very generic and should work for any database that has support for CREATE TABLE. It leverages Pandas and the df_to_sql method.

For some databases the df_to_sql classmethod needs to be implemented. For example, for BigQuery the DB engine spec implements a custom method that uses the to_gbq method.

Extra table metadata

DB engine specs can return additional metadata associated with a table. This is done via the get_extra_table_metadata class method. Trino uses this to return information about the latest partition, for example, and Bigquery returns clustering information. This information is then surfaced in the SQL Lab UI, when browsing tables in the metadata explorer (on the left panel).

DB API exception mapping

Different DB API 2.0 drivers implement different exceptions, even if they have the same name. The get_dbapi_exception_mapping class method returns a dictionary mapping these custom exceptions to Superset exceptions, so that Superset can return more specific errors when an exception is raised by the underlying driver.

For example, for ClickHouse we have:

from urllib3.exceptions import NewConnectionError

from superset.db_engine_specs.exceptions import SupersetDBAPIDatabaseError


class ClickHouseEngineSpec(ClickHouseBaseEngineSpec):

    @classmethod
    def get_dbapi_exception_mapping(cls) -> dict[type[Exception], type[Exception]]:
        return {NewConnectionError: SupersetDBAPIDatabaseError}

This way, if the ClickHouse driver raises a NewConnectionError it would get wrapped in a SupersetDBAPIDatabaseError.

Custom errors

Queries can fail in many different ways. For example, in SQLite:

sqlite> CREATE TABLE a (b INT);
sqlite> SELECT c FROM a;
Error: no such column: c
sqlite>

When a query fails, Superset will return the message, "Error: no such column: c", to the user as a generic error.

Since ideally we want to return specific and actionable error messages, DB engine specs can implement methods that map error messages to more specific errors. For example, the SQLite DB engine specs defines:

COLUMN_DOES_NOT_EXIST_REGEX = re.compile("no such column: (?P<column_name>.+)")


class SqliteEngineSpec(BaseEngineSpec):

    custom_errors: dict[Pattern[str], tuple[str, SupersetErrorType, dict[str, Any]]] =
        COLUMN_DOES_NOT_EXIST_REGEX: (
            __('We can\'t seem to resolve the column "%(column_name)s"'),
            SupersetErrorType.COLUMN_DOES_NOT_EXIST_ERROR,
            {},
        ),
    }

This way, when a user selects a column that doesn't exist Superset can return a more informative error.

Dynamic schema

In SQL Lab it's possible to select a database, and then a schema in that database. Ideally, when running a query in SQL Lab, any unqualified table names (eg, table, instead of schema.table) should be in the selected schema. For example, if the user selects dev as the schema and then runs the following query:

SELECT * FROM my_table

The table my_table should live in the dev schema. In order to do that, it's necessary to modify the SQLAlchemy URI before running the query. Since different databases have different ways of doing that, this functionality is implemented via the adjust_engine_params class method. The method receives the SQLAlchemy URI and connect_args, as well as the schema in which the query should run. It then returns a potentially modified URI and connect_args to ensure that the query runs in the specified schema.

When a DB engine specs implements adjust_engine_params it should have the class attribute supports_dynamic_schema set to true. This is critical for security, since it allows Superset to know to which schema any unqualified table names belong to. For example, in the query above, if the database supports dynamic schema, Superset would check to see if the user running the query has access to dev.my_table. On the other hand, if the database doesn't support dynamic schema, Superset would use the default database schema instead of dev.

Implementing this method is also important for usability. When the method is not implemented selecting the schema in SQL Lab has no effect on the schema in which the query runs, resulting in a confusing results when using unqualified table names.

Catalog

In general, databases support a hierarchy of one-to-many concepts:

1. Database
2. Catalog
3. Namespace
4. Table
5. Column

These concepts have different names depending on the database. For example, Postgres uses the following terminology:

1. Cluster (database)
2. Database (catalog)
3. Schema (namespace)
4. Table
5. Column

BigQuery, on the other hand:

1. BigQuery (database)
2. Project (catalog)
3. Schema (namespace)
4. Table
5. Column

Hive and Trino:

1. Database
2. Catalog
3. Schema
4. Table
5. Column

If the database supports catalogs, then the DB engine spec should have the supports_catalog class attribute set to true. It should also implement the get_default_catalog method, so that the proper permissions can be created when datasets are added.

Dynamic catalog

Superset support for multiple catalogs. Since, in general, a given SQLAlchemy URI connects only to a single catalog, it requires DB engine specs to implement the adjust_engine_params method to rewrite the URL to connect to a different catalog, similar to how dynamic schemas work. Additionally, DB engine specs should also implement the get_catalog_names method, so that users can browse the available catalogs.

SSH tunneling

Superset can connect to databases via an SSH tunnel. For databases where this doesn't make sense (eg, SQLite or BigQuery) the DB engine spec should have disable_ssh_tunneling set to true.

Query cancelation

Superset will try to cancel running queries if the users wants so, but it's up to the DB engine spec to handle this.

Some databases have an implicit query cancelation. When a cursor stops being polled it will cancel the query. For databases that behave like this, the class method has_implicit_cancel (which should really be a class attribute) should return true.

For other databases, DB engine specs can implement query cancelation via the prepare_cancel_query and cancel_query methods. Implementation of query cancelation is usually heavily dependent on the database, but the DB engine specs that support it can serve as an example.

Get metrics on dataset creation

When a physical dataset is first created, the get_metrics class method is called on the table. The base implementation returns the COUNT(*) metric, but DB engine specs can override get_metrics to return other metrics. This method is useful for semantic layers that contain their own metrics definitions; when Superset connect to them it can automatically create those metrics when a dataset is added.

This feature is still experimental, and ideally there would be a mechanism for calling it periodically or when a dataset is explored, in order to sync new metric definitions to the dataset.

WHERE on latest partition

In some databases, running SELECT * can be a very expensive operation, since the query might scan all partitions for a given table. Because of that, some DB engine specs implement the where_latest_partition method, which returns a modified SQLAlchemy query with an additional predicate that filters on the latest partition.

Advanced features

Expand complex types

Some databases will visually expand complex types (arrays and structures) when displaying results from queries. For example, the BigQuery UI is able to expand objects into columns and array into rows, so that this:

array	struct
[1, 2, 3]	{a: one, b: two}

Is shown as:

array	struct	struct.a	struct.b
1	{a: one, b: two}	one	two

A similar behavior has been implemented in Superset for Presto, and can be enabled via the PRESTO_EXPAND_DATA feature flag. To implement this feature a DB engine spec should implement the expand_data method, which takes the columns and rows and returns modified columns and rows.

Note that despite being implemented only for Presto, this behavior has nothing that is Presto specific, and in theory could be implemented in a generic way for all database without requiring custom DB engine spec implementations (that is, the Presto expand_data method could be moved to the base class, after being cleaned up, and we could then enable the feature per DB in the configuration).

Query cost estimation

Some databases allow uses to estimate the cost of running a query before running it. This is done via the estimate_query_cost method in DB engine specs, which receives the SQL and returns a list of "costs". The definition of what "cost" is varies from database to database (in the few that support this functionality), and it can be formatted via the query_cost_formatter.

The query_cost_formatter can be overridden with an arbitrary function via the config QUERY_COST_FORMATTERS_BY_ENGINE. This allows custom deployments of Superset to format the results in different ways. For example, at some point in Lyft the cost for running Presto queries would also show the carbon footprint (in trees).

SQL validation

A few databases support validating the syntax of the SQL as the user is typing it, indicating in SQL Lab any errors. This is usually done using an EXPLAIN query and, because it gets called every few seconds as the user types, it's important that the database returns the result quickly.

This is currently implement for Presto and Postgres, via custom classes in superset/sql_validators that should be enabled in the configuration. Implementing this as custom classes, instead of a validate_sql method in the DB engine spec offers no advantages, and ideally in the future we should move the logic to DB engine specs.

Testing DB engine specs

Superset has a command to test the connection to a given database, as well as checking if the SQLAlchemy dialect implements all necessary methods used by Superset, and checking which features are supported by the DB engine spec (if one exists). To run the tool just call the test-db command with the SQLAlchemy URI to be tested:

superset test-db sqlite://

If the connection needs additional arguments they can be passed when the command runs.