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Nate Kupp
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Update changelog for 0.7.11 bugfix release
Test Plan: changelog only Reviewers: prha, schrockn Reviewed By: prha Differential Revision: https://dagster.phacility.com/D2841
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# Changelog
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## 0.7.11
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**Bugfix**
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- Fixed an issue with strict snapshot ID matching when loading historical snapshots, which caused
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errors on the Runs page when viewing historical runs.
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- Fixed an issue where `dagster_celery` had introduced a spurious dependency on `dagster_k8s`
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(#2435)
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- Fixed an issue where our Airflow, Celery, and Dask integrations required S3 or GCS storage and
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prevented use of filesystem storage. Filesystem storage is now also permitted, to enable use of
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these integrations with distributed filesystems like NFS (#2436).
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## 0.7.10
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**New**
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- Mark published modules as python 3.8 compatible.
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- The dagster-airflow package supports loading all Airflow DAGs within a directory path, file path,
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or Airflow DagBag.
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- The dagster-airflow package supports loading all 23 DAGs in Airflow example_dags folder and execution
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of 17 of them (see: `make_dagster_repo_from_airflow_example_dags`).
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- The dagster-airflow package supports loading all 23 DAGs in Airflow example_dags folder and
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execution of 17 of them (see: `make_dagster_repo_from_airflow_example_dags`).
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- The dagster-celery CLI tools now allow you to pass additional arguments through to the underlying
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celery CLI, e.g., running `dagster-celery worker start -n my-worker -- --uid=42` will pass the
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`--uid` flag to celery.
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**Breaking Changes**
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- Runs created prior to 0.7.8 will no longer render their execution plans as DAGs. We are only rendering
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execution plans that have been persisted. Logs are still available.
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- A `dagster instance migrate` is required for this release to support the new experimental assets
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view.
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- Runs created prior to 0.7.8 will no longer render their execution plans as DAGs. We are only
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rendering execution plans that have been persisted. Logs are still available.
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- `Path` is no longer valid in config schemas. Use `str` or `dagster.String` instead.
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- Removed the `@pyspark_solid` decorator - its functionality, which was experimental, is subsumed by
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requiring a StepLauncher resource (e.g. emr_pyspark_step_launcher) on the solid.

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