sadpandajoe de985f7cf1 fix(datasets): drop colliding child uuid on the overwrite path too
Addresses review findings on the metric/column uuid export fix.

- helpers.py: the collision guard only ran in the `not obj` (INSERT)
  branch, but the UPDATE branch still did `setattr(obj, "uuid", incoming)`
  unconditionally. Re-importing a clone bundle with overwrite=True matched
  the child by (table_id, name) and wrote the original's uuid onto the
  clone's child, failing with `UNIQUE constraint failed: sql_metrics.uuid`.
  Move the guard above both branches so an incoming uuid owned by another
  row is dropped on INSERT and UPDATE alike.
- helpers.py: also drop an explicit `uuid: null`. The child schemas accept
  it, and on the overwrite UPDATE it would persist a literal NULL over an
  existing child's uuid — silently, since the column is nullable and
  `unique` permits repeated NULLs — orphaning every folder leaf pointing
  at that child.
- helpers.py: document why an ambiguous `MultipleResultsFound` child match
  stays a hard error. Preferring the uuid match would rename the
  uuid-matched row while the name-matched row still holds that name,
  trading a clear failure for an opaque `(table_id, <name>)` violation at
  the next flush; callers own the recovery contract.
- import_test.py: regression tests for the overwrite re-import and the
  null-uuid paths, and stop overclaiming what the `folders` equality
  assertion proves (that JSON round-trips either way; the uuid assertions
  are the real gate).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-08-01 06:19:31 +00:00
2024-04-15 11:21:42 -06:00

Superset

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Why Superset?

Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

Superset provides:

  • A no-code interface for building charts quickly
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  • Out of the box support for nearly any SQL database or data engine
  • A wide array of beautiful visualizations to showcase your data, ranging from simple bar charts to geospatial visualizations
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Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, and more) that has a Python DB-API driver and a SQLAlchemy dialect.

Here are some of the major database solutions that are supported:

Amazon Athena   Amazon DynamoDB   Amazon Redshift   Apache Doris   Apache Drill   Apache Druid   Apache Hive   Apache Impala   Apache Kylin   Apache Pinot   Apache Solr   Apache Spark SQL   Ascend   Aurora MySQL (Data API)   Aurora PostgreSQL (Data API)   Azure Data Explorer   Azure Synapse   ClickHouse   Cloudflare D1   CockroachDB   Couchbase   CrateDB   Databend   Databricks   Denodo   Dremio   DuckDB   Elasticsearch   Exasol   Firebird   Firebolt   Google BigQuery   Google Sheets   Greenplum   Hologres   IBM Db2   IBM Netezza Performance Server   MariaDB   Microsoft SQL Server   MonetDB   MongoDB   MotherDuck   OceanBase   Oracle   Presto   RisingWave   SAP HANA   SAP Sybase   Shillelagh   SingleStore   Snowflake   SQLite   StarRocks   Superset meta database   TDengine   Teradata   TimescaleDB   Trino   Vertica   YDB   YugabyteDB

A more comprehensive list of supported databases along with the configuration instructions can be found here.

Want to add support for your datastore or data engine? Read more here about the technical requirements.

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