Joe Li 9df1de1065 fix(reports): make Slack v2 backoff retries actually fire on transient errors
The @backoff.on_exception decorator on SlackV2Notification.send() was
configured to retry on SlackApiError, but the function's own try/except
catches every SlackApiError and re-raises as NotificationUnprocessableException
before the decorator can see it. As a result, no retries were happening —
a single transient failure (rate limit, connection blip) would fail the
report immediately, defeating the intent of the 5-attempt retry budget.

Switch the decorator to retry on NotificationUnprocessableException, which
is the exception type that send() actually raises for transient Slack
failures (SlackApiError, SlackClientNotConnectedError, and the SlackClientError
catch-all). Mirrors the working pattern already in webhook.py.

Non-transient errors (NotificationParamException, NotificationMalformedException,
NotificationAuthorizationException) still surface immediately — they aren't
retryable and shouldn't be retried.

Test changes:
- Replaces the prior "locks in broken behavior" regression test with
  test_v2_send_retries_on_transient_slack_api_error asserting call_count == 5
- Adds test_v2_send_does_not_retry_param_errors verifying that BotUserAccessError
  → NotificationParamException is NOT retried (call_count == 1)
- Adds an autouse fixture that patches backoff._sync.time.sleep so unit-test
  retries complete in milliseconds rather than the ~150s of real exponential
  backoff. Without this, the parametrized exception-mapping cases that map
  to NotificationUnprocessableException balloon the test runtime by ~75s

The v1 SlackNotification has the same bug but is being deprecated in this
release; not worth fixing there since v1's file_uploads endpoint is already
dead at Slack's side and only the text-only chat_postMessage path still works.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-06 16:11:40 -07:00
2026-04-17 17:21:23 -03:00
2025-12-04 13:18:34 -05:00
2024-04-15 11:21:42 -06:00

Superset

License Latest Release on Github Build Status PyPI version PyPI GitHub Stars Contributors Last Commit Open Issues Open PRs Get on Slack Documentation

Superset logo (light)

A modern, enterprise-ready business intelligence web application.

Documentation

  • User Guide — For analysts and business users. Explore data, build charts, create dashboards, and connect databases.
  • Administrator Guide — Install, configure, and operate Superset. Covers security, scaling, and database drivers.
  • Developer Guide — Contribute to Superset or build on its REST API and extension framework.

Why Superset? | Supported Databases | Release Notes | Get Involved | Resources | Organizations Using Superset

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
  • A powerful, web-based SQL Editor for advanced querying
  • A lightweight semantic layer for quickly defining custom dimensions and metrics
  • 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
  • Lightweight, configurable caching layer to help ease database load
  • Highly extensible security roles and authentication options
  • An API for programmatic customization
  • A cloud-native architecture designed from the ground up for scale

Screenshots & Gifs

Video Overview

superset-video-1080p.webm


Large Gallery of Visualizations


Craft Beautiful, Dynamic Dashboards


No-Code Chart Builder


Powerful SQL Editor


Supported Databases

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.

Installation and Configuration

Try out Superset's quickstart guide or learn about the options for production deployments.

Get Involved

Contributor Guide

Interested in contributing? Check out our Developer Guide to find resources around contributing along with a detailed guide on how to set up a development environment.

Resources

Understanding the Superset Points of View

Repo Activity

Performance Stats of apache/superset - Last 28 days
Languages
TypeScript 40.2%
Python 34.1%
Jupyter Notebook 22.4%
HTML 2.7%
JavaScript 0.3%
Other 0.2%