Version: 1.2.0

Fileset catalog with ADLS

This document describes how to configure a Fileset catalog with ADLS (aka. Azure Blob Storage (ABS), or Azure Data Lake Storage (v2)).

Prerequisites

To set up a Fileset catalog with ADLS, follow these steps:

Download the gravitino-azure-bundle-${gravitino-version}.jar file.
Place the downloaded file into the Gravitino Fileset catalog classpath at ${GRAVITINO_HOME}/catalogs/fileset/libs/.
Start the Gravitino server by running the following command:

$ ${GRAVITINO_HOME}/bin/gravitino-server.sh start

Once the server is up and running, you can proceed to configure the Fileset catalog with ADLS. In the rest of this document we will use http://localhost:8090 as the Gravitino server URL, please replace it with your actual server URL.

Configurations for creating a Fileset catalog with ADLS

Configuration for a ADLS Fileset catalog

Apart from configurations mentioned in fileset-catalog-catalog-configuration, the following properties are required to configure a Fileset catalog with ADLS:

Configuration item	Description	Default value	Required	Since version
`filesystem-providers`	(deprecated) The file system providers to add. Set it to `abs` if it's a Azure Blob Storage fileset, or a comma separated string that contains `abs` like `oss,abs,s3` to support multiple kinds of fileset including `abs`.	(none)	Yes	0.8.0-incubating
`default-filesystem-provider`	(deprecated) The name default filesystem providers of this Fileset catalog if users do not specify the scheme in the URI. Default value is `builtin-local`, for Azure Blob Storage, if we set this value, we can omit the prefix 'abfss://' in the location.	`builtin-local`	No	0.8.0-incubating
`azure-storage-account-name`	The account name of Azure Blob Storage.	(none)	Yes	0.8.0-incubating
`azure-storage-account-key`	The account key of Azure Blob Storage.	(none)	Yes	0.8.0-incubating
`credential-providers`	The credential provider types, separated by comma, possible value can be `adls-token`, `azure-account-key`. As the default authentication type is using account name and account key as the above, this configuration can enable credential vending provided by Gravitino server and client will no longer need to provide authentication information like account_name/account_key to access ADLS by GVFS. Once it's set, more configuration items are needed to make it works, please see adls-credential-vending	(none)	No	0.8.0-incubating

note

default-filesystem-provider and filesystem-providers are deprecated since 1.2.0. The fileset catalog automatically loads filesystem providers on the classpath, including buildin filesystem provider and cloud providers when the corresponding bundle jar is present (for example, gravitino-azure-bundle).

Configurations for a schema

Refer to Schema configurations for more details.

Configurations for a fileset

Refer to Fileset configurations for more details.

Example of creating Fileset catalog with ADLS

This section demonstrates how to create the Fileset catalog with ADLS in Gravitino, with a complete example.

Step1: Create a Fileset catalog with ADLS

First, you need to create a Fileset catalog with ADLS. The following example shows how to create a Fileset catalog with ADLS:

Shell
Java
Python

curl -X POST -H "Accept: application/vnd.gravitino.v1+json" \
-H "Content-Type: application/json" -d '{
  "name": "example_catalog",
  "type": "FILESET",
  "comment": "This is a ADLS fileset catalog",
  "properties": {
    "location": "abfss://container@account-name.dfs.core.windows.net/path",
    "azure-storage-account-name": "The account name of the Azure Blob Storage",
    "azure-storage-account-key": "The account key of the Azure Blob Storage"
  }
}' http://localhost:8090/api/metalakes/metalake/catalogs

GravitinoClient gravitinoClient = GravitinoClient
    .builder("http://localhost:8090")
    .withMetalake("metalake")
    .build();

Map<String, String> adlsProperties = ImmutableMap.<String, String>builder()
    .put("location", "abfss://container@account-name.dfs.core.windows.net/path")
    .put("azure-storage-account-name", "azure storage account name")
    .put("azure-storage-account-key", "azure storage account key")
    .build();

Catalog adlsCatalog = gravitinoClient.createCatalog("example_catalog",
    Type.FILESET,
    "This is a ADLS fileset catalog",
    adlsProperties);
// ...

gravitino_client: GravitinoClient = GravitinoClient(uri="http://localhost:8090", metalake_name="metalake")
adls_properties = {
    "location": "abfss://container@account-name.dfs.core.windows.net/path",
    "azure-storage-account-name": "azure storage account name",
    "azure-storage-account-key": "azure storage account key"
}

adls_properties = gravitino_client.create_catalog(name="example_catalog",
                                                  catalog_type=Catalog.Type.FILESET,
                                                  provider=None,
                                                  comment="This is a ADLS fileset catalog",
                                                  properties=adls_properties)

Step2: Create a schema

Once the catalog is created, you can create a schema. The following example shows how to create a schema:

Shell
Java
Python

curl -X POST -H "Accept: application/vnd.gravitino.v1+json" \
-H "Content-Type: application/json" -d '{
  "name": "test_schema",
  "comment": "This is a ADLS schema",
  "properties": {
    "location": "abfss://container@account-name.dfs.core.windows.net/path"
  }
}' http://localhost:8090/api/metalakes/metalake/catalogs/test_catalog/schemas

Catalog catalog = gravitinoClient.loadCatalog("test_catalog");

SupportsSchemas supportsSchemas = catalog.asSchemas();

Map<String, String> schemaProperties = ImmutableMap.<String, String>builder()
    .put("location", "abfss://container@account-name.dfs.core.windows.net/path")
    .build();
Schema schema = supportsSchemas.createSchema("test_schema",
    "This is a ADLS schema",
    schemaProperties
);
// ...

gravitino_client: GravitinoClient = GravitinoClient(uri="http://localhost:8090", metalake_name="metalake")
catalog: Catalog = gravitino_client.load_catalog(name="test_catalog")
catalog.as_schemas().create_schema(name="test_schema",
                                   comment="This is a ADLS schema",
                                   properties={"location": "abfss://container@account-name.dfs.core.windows.net/path"})

Step3: Create a fileset

After creating the schema, you can create a fileset. The following example shows how to create a fileset:

Shell
Java
Python

curl -X POST -H "Accept: application/vnd.gravitino.v1+json" \
-H "Content-Type: application/json" -d '{
  "name": "example_fileset",
  "comment": "This is an example fileset",
  "type": "MANAGED",
  "storageLocation": "abfss://container@account-name.dfs.core.windows.net/path/example_fileset",
  "properties": {
    "k1": "v1"
  }
}' http://localhost:8090/api/metalakes/metalake/catalogs/test_catalog/schemas/test_schema/filesets

GravitinoClient gravitinoClient = GravitinoClient
    .builder("http://localhost:8090")
    .withMetalake("metalake")
    .build();

Catalog catalog = gravitinoClient.loadCatalog("test_catalog");
FilesetCatalog filesetCatalog = catalog.asFilesetCatalog();

Map<String, String> propertiesMap = ImmutableMap.<String, String>builder()
        .put("k1", "v1")
        .build();

filesetCatalog.createFileset(
    NameIdentifier.of("test_schema", "example_fileset"),
    "This is an example fileset",
    Fileset.Type.MANAGED,
    "abfss://container@account-name.dfs.core.windows.net/path/example_fileset",
    propertiesMap,
);

gravitino_client: GravitinoClient = GravitinoClient(uri="http://localhost:8090", metalake_name="metalake")

catalog: Catalog = gravitino_client.load_catalog(name="test_catalog")
catalog.as_fileset_catalog().create_fileset(ident=NameIdentifier.of("test_schema", "example_fileset"),
                                            type=Fileset.Type.MANAGED,
                                            comment="This is an example fileset",
                                            storage_location="abfss://container@account-name.dfs.core.windows.net/path/example_fileset",
                                            properties={"k1": "v1"})

Accessing a fileset with ADLS

Using the GVFS Java client to access the fileset

To access fileset with Azure Blob Storage(ADLS) using the GVFS Java client, based on the basic GVFS configurations, you need to add the following configurations:

Configuration item	Description	Default value	Required	Since version
`azure-storage-account-name`	The account name of Azure Blob Storage.	(none)	Yes	0.8.0-incubating
`azure-storage-account-key`	The account key of Azure Blob Storage.	(none)	Yes	0.8.0-incubating

note

If the catalog has enabled credential vending, the properties above can be omitted. More details can be found in Fileset with credential vending.

Configuration conf = new Configuration();
conf.set("fs.AbstractFileSystem.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.Gvfs");
conf.set("fs.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.GravitinoVirtualFileSystem");
conf.set("fs.gravitino.server.uri", "http://localhost:8090");
conf.set("fs.gravitino.client.metalake", "test_metalake");
conf.set("azure-storage-account-name", "account_name_of_adls");
conf.set("azure-storage-account-key", "account_key_of_adls");
Path filesetPath = new Path("gvfs://fileset/test_catalog/test_schema/test_fileset/new_dir");
FileSystem fs = filesetPath.getFileSystem(conf);
fs.mkdirs(filesetPath);
...

Similar to Spark configurations, you need to add ADLS (bundle) jars to the classpath according to your environment.

If you want to custom your hadoop version or there is already a hadoop version in your project, you can add the following dependencies to your pom.xml:

  <dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-common</artifactId>
    <version>${HADOOP_VERSION}</version>
  </dependency>

  <dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-azure</artifactId>
    <version>${HADOOP_VERSION}</version>
  </dependency>

  <dependency>
    <groupId>org.apache.gravitino</groupId>
    <artifactId>gravitino-filesystem-hadoop3-runtime</artifactId>
    <version>${GRAVITINO_VERSION}</version>
  </dependency>

note

Since version 1.1.0, the gravitino-azure JAR is no longer required, as it is now included in the gravitino-filesystem-hadoop3-runtime JAR.

Or use the bundle jar with Hadoop environment if there is no Hadoop environment:

  <dependency>
    <groupId>org.apache.gravitino</groupId>
    <artifactId>gravitino-azure-bundle</artifactId>
    <version>${GRAVITINO_VERSION}</version>
  </dependency>

  <dependency>
    <groupId>org.apache.gravitino</groupId>
    <artifactId>gravitino-filesystem-hadoop3-runtime</artifactId>
    <version>${GRAVITINO_VERSION}</version>
  </dependency>

Using Spark to access the fileset

The following code snippet shows how to use PySpark 3.5.0 with Hadoop environment(Hadoop 3.3.4) to access the fileset:

Before running the following code, you need to install required packages:

pip install pyspark==3.5.0
pip install apache-gravitino==${GRAVITINO_VERSION}

Then you can run the following code:

from pyspark.sql import SparkSession
import os

gravitino_url = "http://localhost:8090"
metalake_name = "test"

catalog_name = "your_adls_catalog"
schema_name = "your_adls_schema"
fileset_name = "your_adls_fileset"
# JDK8 as follows, JDK17 will be slightly different, you need to add '--conf \"spark.driver.extraJavaOptions=--add-opens=java.base/sun.nio.ch=ALL-UNNAMED\" --conf \"spark.executor.extraJavaOptions=--add-opens=java.base/sun.nio.ch=ALL-UNNAMED\"' to the submit args.
os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars /path/to/gravitino-filesystem-hadoop3-runtime-{gravitino-version}.jar,/path/to/hadoop-azure-3.3.4.jar,/path/to/azure-storage-7.0.1.jar,/path/to/wildfly-openssl-1.0.7.Final.jar --master local[1] pyspark-shell"
spark = SparkSession.builder
    .appName("adls_fileset_test")
    .config("spark.hadoop.fs.AbstractFileSystem.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.Gvfs")
    .config("spark.hadoop.fs.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.GravitinoVirtualFileSystem")
    .config("spark.hadoop.fs.gravitino.server.uri", "http://localhost:8090")
    .config("spark.hadoop.fs.gravitino.client.metalake", "test")
    .config("spark.hadoop.azure-storage-account-name", "azure_account_name")
    .config("spark.hadoop.azure-storage-account-key", "azure_account_key")
    .config("spark.hadoop.fs.azure.skipUserGroupMetadataDuringInitialization", "true")
    .config("spark.driver.memory", "2g")
    .config("spark.driver.port", "2048")
    .getOrCreate()

data = [("Alice", 25), ("Bob", 30), ("Cathy", 45)]
columns = ["Name", "Age"]
spark_df = spark.createDataFrame(data, schema=columns)
gvfs_path = f"gvfs://fileset/{catalog_name}/{schema_name}/{fileset_name}/people"

spark_df.coalesce(1).write
    .mode("overwrite")
    .option("header", "true")
    .csv(gvfs_path)

If your Spark without Hadoop environment, you can use the following code snippet to access the fileset:

## Replace the following code snippet with the above code snippet with the same environment variables

os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars /path/to/gravitino-azure-bundle-{gravitino-version}.jar,/path/to/gravitino-filesystem-hadoop3-runtime-{gravitino-version}.jar --master local[1] pyspark-shell"

If Spark can't start with the above configuration (no Hadoop environment available and use bundle jar), you can try to set the jars to the classpath directly:

jars_path = (
    "/path/to/gravitino-azure-bundle-{gravitino-version}.jar:"
    "/path/to/gravitino-filesystem-hadoop3-runtime-{gravitino-version}.jar"
)

os.environ["PYSPARK_SUBMIT_ARGS"] = (
    f'--conf "spark.driver.extraClassPath={jars_path}" '
    f'--conf "spark.executor.extraClassPath={jars_path}" '
    '--master local[1] pyspark-shell'
)

gravitino-azure-bundle-${gravitino-version}.jar: A "fat" JAR that includes gravitino-azure functionality and all necessary dependencies like hadoop-azure (3.3.1) and other packages needed to access ADLS. Use this if your Spark environment doesn't have a pre-existing Hadoop setup.
gravitino-filesystem-hadoop3-runtime-${gravitino-version}.jar: A "fat" JAR that bundles Gravitino's virtual filesystem client and includes the functionality of gravitino-azure. It is required for accessing Gravitino filesets.
hadoop-azure-3.3.4.jar, azure-storage-7.0.1.jar, and wildfly-openssl-1.0.7.Final.jar: Standard Hadoop dependencies for ADLS access. If you are running in an existing Hadoop environment, you need to provide these JARs. They are typically located in the ${HADOOP_HOME}/share/hadoop/tools/lib directory.
gravitino-azure-${gravitino-version}.jar: A "thin" JAR that only provides the Azure integration code. Its functionality is already included in the gravitino-azure-bundle and gravitino-filesystem-hadoop3-runtime JARs, so you do not need to add it as a direct dependency unless you want to manage all Hadoop and Azure dependencies manually.

Please choose the correct jar according to your environment.

note

In some Spark versions, a Hadoop environment is necessary for the driver, adding the bundle jars with '--jars' may not work. If this is the case, you should add the jars to the spark CLASSPATH directly.

Accessing a fileset using the Hadoop fs command

The following are examples of how to use the hadoop fs command to access the fileset in Hadoop 3.1.3.

Adding the following contents to the ${HADOOP_HOME}/etc/hadoop/core-site.xml file:

  <property>
    <name>fs.AbstractFileSystem.gvfs.impl</name>
    <value>org.apache.gravitino.filesystem.hadoop.Gvfs</value>
  </property>

  <property>
    <name>fs.gvfs.impl</name>
    <value>org.apache.gravitino.filesystem.hadoop.GravitinoVirtualFileSystem</value>
  </property>

  <property>
    <name>fs.gravitino.server.uri</name>
    <value>http://localhost:8090</value>
  </property>

  <property>
    <name>fs.gravitino.client.metalake</name>
    <value>test</value>
  </property>

  <property>
    <name>azure-storage-account-name</name>
    <value>account_name</value>
  </property>
  <property>
    <name>azure-storage-account-key</name>
    <value>account_key</value>
  </property>

Add the necessary jars to the Hadoop classpath.

For ADLS, you need to add gravitino-filesystem-hadoop3-runtime-${gravitino-version}.jar and hadoop-azure-${hadoop-version}.jar located at ${HADOOP_HOME}/share/hadoop/tools/lib/ to the Hadoop classpath.

Run the following command to access the fileset:

./${HADOOP_HOME}/bin/hadoop dfs -ls gvfs://fileset/adls_catalog/adls_schema/adls_fileset
./${HADOOP_HOME}/bin/hadoop dfs -put /path/to/local/file gvfs://fileset/adls_catalog/adls_schema/adls_fileset

Using the GVFS Python client to access a fileset

In order to access fileset with Azure Blob storage (ADLS) using the GVFS Python client, apart from basic GVFS configurations, you need to add the following configurations:

Configuration item	Description	Default value	Required	Since version
`azure_storage_account_name`	The account name of Azure Blob Storage	(none)	Yes	0.8.0-incubating
`azure_storage_account_key`	The account key of Azure Blob Storage	(none)	Yes	0.8.0-incubating

note

If the catalog has enabled credential vending, the properties above can be omitted.

Please install the gravitino package before running the following code:

pip install apache-gravitino==${GRAVITINO_VERSION}

from gravitino import gvfs
options = {
    "cache_size": 20,
    "cache_expired_time": 3600,
    "auth_type": "simple",
    "azure_storage_account_name": "azure_account_name",
    "azure_storage_account_key": "azure_account_key"
}
fs = gvfs.GravitinoVirtualFileSystem(server_uri="http://localhost:8090", metalake_name="test_metalake", options=options)
fs.ls("gvfs://fileset/{adls_catalog}/{adls_schema}/{adls_fileset}/")

Using fileset with pandas

The following are examples of how to use the pandas library to access the ADLS fileset

import pandas as pd

storage_options = {
    "server_uri": "http://localhost:8090", 
    "metalake_name": "test",
    "options": {
        "azure_storage_account_name": "azure_account_name",
        "azure_storage_account_key": "azure_account_key"
    }
}
ds = pd.read_csv(f"gvfs://fileset/${catalog_name}/${schema_name}/${fileset_name}/people/part-00000-51d366e2-d5eb-448d-9109-32a96c8a14dc-c000.csv",
                 storage_options=storage_options)
ds.head()

For other use cases, please refer to the Gravitino Virtual File System document.

Fileset with credential vending

Since 0.8.0-incubating, Gravitino supports credential vending for ADLS fileset. If the catalog has been configured with credential, you can access ADLS fileset without providing authentication information like azure-storage-account-name and azure-storage-account-key in the properties.

How to create an ADLS Fileset catalog with credential vending

Apart from configuration method in create-adls-fileset-catalog, properties needed by adls-credential should also be set to enable credential vending for ADLS fileset. Take adls-token credential provider for example:

curl -X POST -H "Accept: application/vnd.gravitino.v1+json" \
-H "Content-Type: application/json" -d '{
  "name": "adls-catalog-with-token",
  "type": "FILESET",
  "comment": "This is a ADLS fileset catalog",
  "properties": {
    "location": "abfss://container@account-name.dfs.core.windows.net/path",
    "azure-storage-account-name": "The account name of the Azure Blob Storage",
    "azure-storage-account-key": "The account key of the Azure Blob Storage",
    "credential-providers": "adls-token",
    "azure-tenant-id":"The Azure tenant id",
    "azure-client-id":"The Azure client id",
    "azure-client-secret":"The Azure client secret key"
  }
}' http://localhost:8090/api/metalakes/metalake/catalogs

How to access ADLS fileset with credential vending

When the catalog is configured with credentials and client-side credential vending is enabled, you can access ADLS filesets directly using the GVFS Java/Python client or Spark without providing authentication details.

GVFS Java client:

Configuration conf = new Configuration();
conf.setBoolean("fs.gravitino.enableCredentialVending", true);
conf.set("fs.AbstractFileSystem.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.Gvfs");
conf.set("fs.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.GravitinoVirtualFileSystem");
conf.set("fs.gravitino.server.uri", "http://localhost:8090");
conf.set("fs.gravitino.client.metalake", "test_metalake");
// No need to set azure-storage-account-name and azure-storage-account-name
Path filesetPath = new Path("gvfs://fileset/adls_test_catalog/test_schema/test_fileset/new_dir");
FileSystem fs = filesetPath.getFileSystem(conf);
fs.mkdirs(filesetPath);
...

Spark:

spark = SparkSession.builder
    .appName("adls_fileset_test")
    .config("spark.hadoop.fs.gravitino.enableCredentialVending", "true")
    .config("spark.hadoop.fs.AbstractFileSystem.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.Gvfs")
    .config("spark.hadoop.fs.gvfs.impl", "org.apache.gravitino.filesystem.hadoop.GravitinoVirtualFileSystem")
    .config("spark.hadoop.fs.gravitino.server.uri", "http://localhost:8090")
    .config("spark.hadoop.fs.gravitino.client.metalake", "test")
    # No need to set azure-storage-account-name and azure-storage-account-name
    .config("spark.driver.memory", "2g")
    .config("spark.driver.port", "2048")
    .getOrCreate()

Python client and Hadoop command are similar to the above examples.

Prerequisites​

Configurations for creating a Fileset catalog with ADLS​

Configuration for a ADLS Fileset catalog​

Configurations for a schema​

Configurations for a fileset​

Example of creating Fileset catalog with ADLS​

Step1: Create a Fileset catalog with ADLS​

Step2: Create a schema​

Step3: Create a fileset​

Accessing a fileset with ADLS​

Using the GVFS Java client to access the fileset​

Using Spark to access the fileset​

Accessing a fileset using the Hadoop fs command​

Using the GVFS Python client to access a fileset​

Using fileset with pandas​

Fileset with credential vending​

How to create an ADLS Fileset catalog with credential vending​

How to access ADLS fileset with credential vending​

Prerequisites

Configurations for creating a Fileset catalog with ADLS

Configuration for a ADLS Fileset catalog

Configurations for a schema

Configurations for a fileset

Example of creating Fileset catalog with ADLS

Step1: Create a Fileset catalog with ADLS

Step2: Create a schema

Step3: Create a fileset

Accessing a fileset with ADLS

Using the GVFS Java client to access the fileset

Using Spark to access the fileset

Accessing a fileset using the Hadoop fs command

Using the GVFS Python client to access a fileset

Using fileset with pandas

Fileset with credential vending

How to create an ADLS Fileset catalog with credential vending

How to access ADLS fileset with credential vending