0.4.0
API Changes
- There is now a new top-level configuration section
storage
which controls whether or not
execution should store intermediate values and the history of pipeline runs on the filesystem,
on S3, or in memory. Thedagster
CLI now includes options to list and wipe pipeline run
history. Facilities are provided for user-defined types to override the default serialization
used for storage. - Similarily, there is a new configuration for
RunConfig
where the user can specify
intermediate value storage via an API. OutputDefinition
now contains an explicitis_optional
parameter and defaults to being
not optional.- New functionality in
dagster.check
:is_list
- New functionality in
dagster.seven
: py23-compatibleFileNotFoundError
,json.dump
,
json.dumps
. - Dagster default logging is now multiline for readability.
- The
Nothing
type now allows dependencies to be constructed between solids that do not have
data dependencies. - Many error messages have been improved.
throw_on_user_error
has been renamed toraise_on_error
in all APIs, public and private
GraphQL
- The GraphQL layer has been extracted out of Dagit into a separate dagster-graphql package.
startSubplanExecution
has been replaced byexecutePlan
.startPipelineExecution
now supports reexecution of pipeline subsets.
Dagit
- It is now possible to reexecute subsets of a pipeline run from Dagit.
- Dagit's
Execute
tab now opens runs in separate browser tabs and a newRuns
tab allows you to
browse and view historical runs. - Dagit no longer scaffolds configuration when creating new
Execute
tabs. This functionality will
be refined and revisited in the future. - Dagit's
Explore
tab is more performant on large DAGs. - The
dagit -q
command line flag has been deprecated in favor of a separate command-line
dagster-graphql
utility. - The execute button is now greyed out when Dagit is offline.
- The Dagit UI now includes more contextual cues to make the solid in focus and its connections
more salient. - Dagit no longer offers to open materializations on your machine. Clicking an on-disk
materialization now copies the path to your clipboard. - Pressing Ctrl-Enter now starts execution in Dagit's Execute tab.
- Dagit properly shows List and Nullable types in the DAG view.
Dagster-Airflow
- Dagster-Airflow includes functions to dynamically generate containerized (
DockerOperator
-based)
and uncontainerized (PythonOperator
-based) Airflow DAGs from Dagster pipelines and config.
Libraries
- Dagster integration code with AWS, Great Expectations, Pandas, Pyspark, Snowflake, and Spark
has been reorganized into a new top-level libraries directory. These modules are now
importable asdagster_aws
,dagster_ge
,dagster_pandas
,dagster_pyspark
,
dagster_snowflake
, anddagster_spark
. - Removed dagster-sqlalchemy and dagma
Examples
- Added the event-pipeline-demo, a realistic web event data pipeline using Spark and Scala.
- Added the Pyspark pagerank example, which demonstrates how to incrementally introduce dagster
into existing data processing workflows.
Documentation
- Docs have been expanded, reorganized, and reformatted.