Hive-Compatible Metadata Repository for ML Feature Stores

Question

Pulumi · Accepted Answer

When setting up a metadata repository for ML feature stores compatible with Apache Hive, you are essentially creating a managed service or resource that can communicate with Hive's metadata store. This metadata store will then manage schema and operations metadata for your machine learning features.

Google Cloud Platform (GCP) offers such services which we can define and manage with Pulumi in Python. Specifically, Pulumi allows you to provision Google Cloud's Dataproc Metastore, which is a fully managed, serverless technical metadata repository based on the open-source Apache Hive Metastore.

Here's how we can achieve this with Pulumi:

1. We use `gcp.dataproc.MetastoreService`, which provides a managed Hive metastore service to connect with ML feature stores or other data analytics workloads.
2. We configure the service according to the desired specifications like network, port, and version of the Hive metastore.
3. We also provide necessary labels for resource identification and encryption configuration if sensitive data is being handled via KMS keys.

The following Python program uses Pulumi to set up such a metadata repository.

```python
import pulumi
import pulumi_gcp as gcp

# We will create a Metastore Service, which is compatible with Hive, for managing metadata of ML feature stores.
# Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/dataproc/metastoreservice/

# First, we set up the network configuration. In a real-world scenario, this would be your VPC network.
network = gcp.compute.Network("network")
subnetwork = gcp.compute.Subnetwork("subnetwork",
    network=network.id,
    ip_cidr_range="10.0.0.0/24",
    region="us-central1")

# Then, we provision the Metastore Service.
metastore_service = gcp.dataproc.MetastoreService("metastore-service",
    network=network.self_link,
    port=9083,  # Default port used by Hive and other metastore clients.
    tier="DEVELOPER",  # Tier selection based on needed capacity and performance.
    location="us-central1",
    project=gcp.config.project,
    hive_metastore_config=gcp.dataproc.MetastoreServiceHiveMetastoreConfigArgs(
        version="3.0.0",  # Specifying the Hive metastore service version.
        config_overrides={
            "hive.metastore.cache.pinobjtypes": "Table,Database,Partition,StorageDescriptor,SerDeInfo",
            "hive.metastore.warehouse.dir": "/mystore"
        },
        # Note: Kerberos and encryption configs are not added for simplicity, but they should be considered for production.
    ),
    network_config=gcp.dataproc.MetastoreServiceNetworkConfigArgs(
        consumers=[
            gcp.dataproc.MetastoreServiceNetworkConfigConsumersArgs(
                subnetwork=subnetwork.id
            )
        ]
    )
)

# Make sure to export the metadata repository URL for later access by ML feature stores or any client services.
pulumi.export("endpoint_uri", metastore_service.endpoint_uri)
```

In this program:

- We set up a Google Cloud VPC network and subnetwork. Typically, you would have a VPC already created for your workload, into which you would deploy this service.
- `MetastoreService` is defined to run on that network. For the `tier`, you might need to choose a more capable offering depending on the scale of your application.
- `hive_metastore_config` includes versioning and specific Hive configuration overrides. These settings are use-case dependent and should be determined according to your requirements.
- We set some overrides within `hive_metastore_config` to fine-tune the behavior of the Hive metastore, such as caching options and warehouse directory.
- `network_config` specifies the subnetwork created just before the MetastoreService to ensure it resides within our defined network for security and connectivity.
- The `endpoint_uri` is exported as a Pulumi stack output to allow access to the metastore service by client applications or ML feature stores.

Please note this is a very basic setup and doesn't include considerations like encryption and security configurations, which would be essential for a production environment, especially one handling sensitive data.