Interacting with Pipeline Snapshots

Capturing a Snapshot

To generate a snapshot for an existing pipeline, you could use the following steps:

# Connect to a DataCollector instance and set the pipeline you wish to generate a snapshot for
sdc = DataCollector("https://localhost:18630")
pipeline = sdc.pipelines.get(id='testpipel1c35d2ff-5e49-4f99-ba56-00051fd7845f')

# Capture the snapshot
snapshot = sdc.capture_snapshot(pipeline).snapshot


<Snapshot (name=New Snapshot, time_stamp=1616718217886, batch_number=1)>

Reading from a Snapshot

Once a snapshot is captured, it’s possible to inspect what data was captured.

Let’s assume the above pipeline consisted of a Dev Data Generator origin writing to a Trash destination, with the origin generating datetime data.

To read output values from a stage, you would execute the following:

# Select the stage you're interested in reading the output values for. In this case, the Origin stage
stage = pipeline.stages[0]

# Read the field attribute from output[0], which is the first record captured for the stage specified
field = snapshot[stage].output[0].field

which returns a dictionary of the output:

{'random_value': 2018-11-05 04:31:05.953000}

To check a specific field’s value from the output record you’ve just retrieved, you can index it directly by providing the name of the field:



datetime.datetime(2018, 11, 5, 4, 31, 5, 953000)

Note that the field value is coerced into the appropriate type, but the underlying raw value is stored along with its type.



# field['random_value'].raw_value

# field['random_value'].type

Retrieving an existing Snapshot

Perhaps you had generated a snapshot for the same pipeline previously, either via the DataCollector UI or Python SDK, and now you’d like to retrieve it. The following would allow you to pick a snapshot based on a number of unique attributes such as id, name, or time_stamp:

snapshot = sdc.get_snapshots(pipeline).get(name='Earlier Snapshot')


# snapshot
<Snapshot (name=Earlier Snapshot, time_stamp=1616716207032, batch_number=1)>


Deleting a Snapshot

If you’ve successfully generated a snapshot for a pipeline and no longer need to retain it, it can be deleted using the streamsets.sdk.DataCollector.delete_snapshot() method. The method expects to receive a streamsets.sdk.sdc_models.Snapshot instance as an argument, exactly like the one returned by the streamsets.sdk.DataCollector.get_snapshots() method used previously:

# Check the list of current snapshots for the pipeline

# Get the snapshot object that corresponds to the snapshot you wish to delete
snapshot = sdc.get_snapshots(pipeline).get(name='New Snapshot')


# The snapshot has been deleted


# sdc.get_snapshots(pipeline)
[<Snapshot (name=New Snapshot, time_stamp=1617379142414, batch_number=1)>,
 <Snapshot (name=Earlier Snapshot, time_stamp=1617379338310, batch_number=1)>]

# snapshot
<Snapshot (name=New Snapshot, time_stamp=1617379142414, batch_number=1)>

# sdc.delete_snapshot(snapshot)
<streamsets.sdk.sdc_api.Command object at 0x7f02c29eac88>

# sdc.get_snapshots(pipeline)
[<Snapshot (name=Earlier Snapshot, time_stamp=1617379338310, batch_number=1)>]