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Version: 0.10.4

Google Cloud Storage

Incubating

Important Capabilities​

CapabilityStatusNotes
Asset Containers✅Enabled by default
Data Profiling❌Not supported
Schema Metadata✅Enabled by default

This connector extracting datasets located on Google Cloud Storage. Supported file types are as follows:

  • CSV
  • TSV
  • JSON
  • Parquet
  • Apache Avro

Schemas for Parquet and Avro files are extracted as provided.

Schemas for schemaless formats (CSV, TSV, JSON) are inferred. For CSV and TSV files, we consider the first 100 rows by default, which can be controlled via the max_rows recipe parameter (see below) JSON file schemas are inferred on the basis of the entire file (given the difficulty in extracting only the first few objects of the file), which may impact performance.

This source leverages Interoperability of GCS with S3 and uses DataHub S3 Data Lake integration source under the hood.

Prerequisites​

  1. Create a service account with "Storage Object Viewer" Role - https://cloud.google.com/iam/docs/service-accounts-create
  2. Make sure you meet following requirements to generate HMAC key - https://cloud.google.com/storage/docs/authentication/managing-hmackeys#before-you-begin
  3. Create an HMAC key for service account created above - https://cloud.google.com/storage/docs/authentication/managing-hmackeys#create .

To ingest datasets from your data lake, you need to provide the dataset path format specifications using path_specs configuration in ingestion recipe. Refer section Path Specs for examples.

CLI based Ingestion​

Install the Plugin​

pip install 'acryl-datahub[gcs]'

Starter Recipe​

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
  type: gcs
  config:
    path_specs:
      - include: gs://gcs-ingestion-bucket/parquet_example/{table}/year={partition[0]}/*.parquet
    credential:
      hmac_access_id: <hmac access id>
      hmac_access_secret: <hmac access secret>

Config Details​

Note that a . is used to denote nested fields in the YAML recipe.

FieldDescription
credential ✅
HMACKey
Google cloud storage HMAC keys
credential.hmac_access_id ❓
string
Access ID
credential.hmac_access_secret ❓
string(password)
Secret
max_rows
integer
Maximum number of rows to use when inferring schemas for TSV and CSV files.
Default: 100
number_of_files_to_sample
integer
Number of files to list to sample for schema inference. This will be ignored if sample_files is set to False in the pathspec.
Default: 100
platform_instance
string
The instance of the platform that all assets produced by this recipe belong to
env
string
The environment that all assets produced by this connector belong to
Default: PROD
path_specs
array(object)
path_specs.include ❓
string
Path to table. Name variable {table} is used to mark the folder with dataset. In absence of {table}, file level dataset will be created. Check below examples for more details.
path_specs.default_extension
string
For files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.
path_specs.enable_compression
boolean
Enable or disable processing compressed files. Currently .gz and .bz files are supported.
Default: True
path_specs.exclude
array(string)
path_specs.file_types
array(string)
path_specs.sample_files
boolean
Not listing all the files but only taking a handful amount of sample file to infer the schema. File count and file size calculation will be disabled. This can affect performance significantly if enabled
Default: True
path_specs.table_name
string
Display name of the dataset.Combination of named variables from include path and strings
stateful_ingestion
StatefulStaleMetadataRemovalConfig
Base specialized config for Stateful Ingestion with stale metadata removal capability.
stateful_ingestion.enabled
boolean
The type of the ingestion state provider registered with datahub.
Default: False
stateful_ingestion.remove_stale_metadata
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

Path Specs​

Example - Dataset per file

Bucket structure:

test-gs-bucket
├── employees.csv
└── food_items.csv

Path specs config

path_specs:
    - include: gs://test-gs-bucket/*.csv

Example - Datasets with partitions

Bucket structure:

test-gs-bucket
├── orders
│   └── year=2022
│       └── month=2
│           ├── 1.parquet
│           └── 2.parquet
└── returns
    └── year=2021
        └── month=2
            └── 1.parquet

Path specs config:

path_specs:
    - include: gs://test-gs-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet

Example - Datasets with partition and exclude

Bucket structure:

test-gs-bucket
├── orders
│   └── year=2022
│       └── month=2
│           ├── 1.parquet
│           └── 2.parquet
└── tmp_orders
    └── year=2021
        └── month=2
            └── 1.parquet


Path specs config:

path_specs:
    - include: gs://test-gs-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet
      exclude:
        - **/tmp_orders/**

Example - Datasets of mixed nature

Bucket structure:

test-gs-bucket
├── customers
│   ├── part1.json
│   ├── part2.json
│   ├── part3.json
│   └── part4.json
├── employees.csv
├── food_items.csv
├── tmp_10101000.csv
└──  orders
    └── year=2022
        └── month=2
            ├── 1.parquet
            ├── 2.parquet
            └── 3.parquet

Path specs config:

path_specs:
    - include: gs://test-gs-bucket/*.csv
      exclude:
        - **/tmp_10101000.csv
    - include: gs://test-gs-bucket/{table}/*.json
    - include: gs://test-gs-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet

Valid path_specs.include

gs://my-bucket/foo/tests/bar.avro # single file table
gs://my-bucket/foo/tests/*.* # mulitple file level tables
gs://my-bucket/foo/tests/{table}/*.avro #table without partition
gs://my-bucket/foo/tests/{table}/*/*.avro #table where partitions are not specified
gs://my-bucket/foo/tests/{table}/*.* # table where no partitions as well as data type specified
gs://my-bucket/{dept}/tests/{table}/*.avro # specifying keywords to be used in display name
gs://my-bucket/{dept}/tests/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.avro # specify partition key and value format
gs://my-bucket/{dept}/tests/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.avro # specify partition value only format
gs://my-bucket/{dept}/tests/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # for all extensions
gs://my-bucket/*/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # table is present at 2 levels down in bucket
gs://my-bucket/*/*/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # table is present at 3 levels down in bucket

Valid path_specs.exclude

  • **/tests/**
  • gs://my-bucket/hr/**
  • */tests/.csv
  • gs://my-bucket/foo/*/my_table/**

Notes

  • {table} represents folder for which dataset will be created.
  • include path must end with (. or *.[ext]) to represent leaf level.
  • if *.[ext] is provided then only files with specified type will be scanned.
  • /*/ represents single folder.
  • {partition[i]} represents value of partition.
  • {partition_key[i]} represents name of the partition.
  • While extracting, “i” will be used to match partition_key to partition.
  • all folder levels need to be specified in include. Only exclude path can have ** like matching.
  • exclude path cannot have named variables ( {} ).
  • Folder names should not contain {, }, *, / in their names.
  • {folder} is reserved for internal working. please do not use in named variables.

If you would like to write a more complicated function for resolving file names, then a {transformer} would be a good fit.

caution

Specify as long fixed prefix ( with out /*/ ) as possible in path_specs.include. This will reduce the scanning time and cost, specifically on Google Cloud Storage.

caution

If you are ingesting datasets from Google Cloud Storage, we recommend running the ingestion on a server in the same region to avoid high egress costs.

Code Coordinates​

  • Class Name: datahub.ingestion.source.gcs.gcs_source.GCSSource
  • Browse on GitHub

Questions

If you've got any questions on configuring ingestion for Google Cloud Storage, feel free to ping us on our Slack.