Amazon SageMaker Feature Store offline store data format
Amazon SageMaker Feature Store supports the AWS Glue and Apache Iceberg table formats for the offline store. You can choose the table format when you’re creating a new feature group. AWS Glue is the default format.
Amazon SageMaker Feature Store offline store data is stored in an Amazon S3 bucket within your account. When you
call PutRecord, your data is buffered, batched, and written into Amazon S3
within 15 minutes. Feature Store only supports the Parquet file format when writing your data to your
offline store. Specifically, when your data is written to your offline store, the data can
be retrieved from your Amazon S3 bucket in Parquet format. Each file can contain multiple
Records.
For the Iceberg format, Feature Store saves the table’s metadata in the same Amazon S3 bucket that
you’re using to store the offline store data. You can find it under the
metadata prefix.
Feature Store also exposes the OfflineStoreConfig.S3StorageConfig.ResolvedOutputS3Uri field, which can be found from in the DescribeFeatureGroup API call. This is the S3 path under which the files for the specific feature group are written.
The following additional fields are added by Feature Store to each record when they persist in the offline store:
-
api_invocation_time – The timestamp when the service receives the
PutRecordorDeleteRecordcall. If using managed ingestion (e.g. Data Wrangler), this is the timestamp when data was written into the offline store. -
write_time – The timestamp when data was written into the offline store. Can be used for constructing time-travel related queries.
-
is_deleted –
Falseby default. IfDeleteRecordis called, a newRecordis inserted intoRecordIdentifierValueand set toTruein the offline store.
Amazon SageMaker Feature Store offline store URI structures
In the following examples amzn-s3-demo-bucket is the Amazon S3 bucket within
your account, is your example
prefix, example-prefix is your account
ID, 111122223333 is your region,
AWS Region is the name of your
feature group. feature-group-name
AWS Glue table format
Records in the offline store stored using the AWS Glue table format are partitioned by event time into hourly partitions. You can’t configure the partitioning scheme. The following URI structure shows the organization of a Parquet file using the AWS Glue format:
s3://amzn-s3-demo-bucket/example-prefix/111122223333/sagemaker/AWS Region/offline-store/feature-group-name-feature-group-creation-time/data/year=year/month=month/day=day/hour=hour/timestamp_of_latest_event_time_in_file_16-random-alphanumeric-digits.parquet
The following example is the output location of a Parquet file for a file with
as
feature-group-namecustomer-purchase-history-patterns:
s3://amzn-s3-demo-bucket/example-prefix/111122223333/sagemaker/AWS Region/offline-store/customer-purchase-history-patterns-1593511200/data/year=2020/month=06/day=31/hour=00/20200631T064401Z_108934320012Az11.parquet
Iceberg table format
Records in the offline store stored in the Iceberg table format are partitioned by event time into daily partitions. You can’t configure the partitioning scheme. The following URI structure shows the organization of the data files saved in the Iceberg table format:
s3://amzn-s3-demo-bucket/example-prefix/111122223333/sagemaker/AWS Region/offline-store/feature-group-name-feature-group-creation-time/data/8-random-alphanumeric-digits/event-time-feature-name_trunc=event-time-year-event-time-month-event-time-day/timestamp-of-latest-event-time-in-file_16-random-alphanumeric-digits.parquet
The following example is the output location of a Parquet file for a file with
as
feature-group-namecustomer-purchase-history-patterns, and the
is
event-time-feature-nameEventTime:
s3://amzn-s3-demo-bucket/example-prefix/111122223333/sagemaker/AWS Region/offline-store/customer-purchase-history-patterns-1593511200/data/0aec19ca/EventTime_trunc=2022-11-09/20221109T215231Z_yolTtpyuWbkaeGIl.parquet
The following example is the location of a metadata file for data files saved in the Iceberg table format.
s3://amzn-s3-demo-bucket/example-prefix/111122223333/sagemaker/AWS Region/offline-store/feature-group-name-feature-group-creation-time/metadata/