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Apache Parquet Files in Google GCS

This connector materializes delta updates of Flow collections into a GCS bucket in the Apache Parquet format.

The delta updates are batched within Flow, converted to CSV files, and then pushed to the S3 bucket at a time interval that you set. Files are limited to a configurable maximum size. Each materialized Flow collection will produce many separate files.

ghcr.io/estuary/materialize-gcs-parquet:dev provides the latest connector image. You can also follow the link in your browser to see past image versions.

Prerequisites

To use this connector, you'll need:

Configuration

Use the below properties to configure the materialization, which will direct one or more of your Flow collections to your bucket.

Properties

Endpoint

PropertyTitleDescriptionTypeRequired/Default
/bucketBucketBucket to store materialized objects.stringRequired
/credentialsJsonService Account JSONThe JSON credentials of the service account to use for authorization.stringRequired
/uploadIntervalUpload IntervalFrequency at which files will be uploaded.string5m
/prefixPrefixOptional prefix that will be used to store objects.string
/fileSizeLimitFile Size LimitApproximate maximum size of materialized files in bytes. Defaults to 10737418240 (10 GiB) if blank.integer
/parquetConfig/rowGroupRowLimitRow Group Row LimitMaximum number of rows in a row group. Defaults to 1000000 if blank.integer
/parquetConfig/rowGroupByteLimitRow Group Byte LimitApproximate maximum number of bytes in a row group. Defaults to 536870912 (512 MiB) if blank.integer

Bindings

PropertyTitleDescriptionTypeRequired/Default
/pathPathThe path that objects will be materialized to.stringRequired

Sample

materializations:
${PREFIX}/${mat_name}:
endpoint:
connector:
image: "ghcr.io/estuary/materialize-gcs-parquet:dev"
config:
bucket: bucket
credentialsJson: <credentialsJson>
uploadInterval: 5m
bindings:
- resource:
path: ${COLLECTION_NAME}
source: ${PREFIX}/${COLLECTION_NAME}

Parquet Data Types

Flow collection fields are written to Parquet files based on the data type of the field. Depending on the field data type, the Parquet data type may be a primitive Parquet type, or a primitive Parquet type extended by a logical Parquet type.

Collection Field Data TypeParquet Data Type
arrayJSON (extends BYTE_ARRAY)
objectJSON (extends BYTE_ARRAY)
booleanBOOLEAN
integerINT64
numberDOUBLE
string with {contentEncoding: base64}BYTE_ARRAY
string with {format: date}DATE (extends BYTE_ARRAY)
string with {format: date-time}TIMESTAMP (extends INT64, UTC adjusted with microsecond precision)
string with {format: time}TIME (extends INT64, UTC adjusted with microsecond precision)
string with {format: date}DATE (extends INT32)
string with {format: duration}INTERVAL (extends FIXED_LEN_BYTE_ARRAY with a length of 12)
string with {format: uuid}UUID (extends FIXED_LEN_BYTE_ARRAY with a length of 16)
string (all others)STRING (extends BYTE_ARRAY)

File Names

Materialized files are named with monotonically increasing integer values, padded with leading 0's so they remain lexically sortable. For example, a set of files may be materialized like this for a given collection:

bucket/prefix/path/v0000000000/00000000000000000000.parquet
bucket/prefix/path/v0000000000/00000000000000000001.parquet
bucket/prefix/path/v0000000000/00000000000000000002.parquet

Here the values for bucket and prefix are from your endpoint configuration. The path is specific to the binding configuration. v0000000000 represents the current backfill counter for binding and will be increased if the binding is re-backfilled, along with the file names starting back over from 0.

Eventual Consistency

In rare circumstances, recently materialized files may be re-written by files with the same name if the materialization shard is interrupted in the middle of processing a Flow transaction and the transaction must be re-started. Files that were committed as part of a completed transaction will never be re-written. In this way, eventually all collection data will be written to files effectively-once, although inconsistencies are possible when accessing the most recently written data.