[#9622] improvement(docs): Add guide for Lance REST integration with Spark and Ray

docs/lakehouse-generic-lance-table.md

@@ -302,3 +302,31 @@ done
 
 Other table operations (load, alter, drop, truncate) follow standard relational catalog patterns. See [Table Operations](./manage-relational-metadata-using-gravitino.md#table-operations) for details.
 
+### Using Lance table with MinIO
+To use Lance tables stored in MinIO with Gravitino, ensure that the MinIO storage backend is properly configured. Below is an example of how to set up and use Lance tables with MinIO.
+
+```shell
+curl -X POST -H "Accept: application/vnd.gravitino.v1+json" \
+  -H "Content-Type: application/json" -d '{
+  "name": "lance_orders",
+  "comment": "Order table stored in MinIO",
+  "columns": [
+    {
+      "name": "id",
+      "type": "integer",
+      "comment": "Primary identifier",
+      "nullable": false
+    }
+  ],
+  "properties": {
+    "format": "lance",
+    "location": "s3://bucket1/lance_orders",
+    "lance.storage.access_key_id": "ak",
+    "lance.storage.endpoint": "http://minio:9000",
+    "lance.storage.secret_access_key": "sk",
+    "lance.storage.allow_http": "true"
+  }
+}' http://localhost:8090/api/metalakes/test/catalogs/lance_catalog/schemas/sales/tables
+
+```
+

docs/lance-rest-integration.md

@@ -0,0 +1,231 @@
+---
+title: "Lance REST Integration"
+slug: /lance-rest-integration
+keywords:
+  - lance
+  - lance-rest
+  - spark
+  - ray
+  - integration
+license: "This software is licensed under the Apache License version 2."
+---
+
+## Overview
+
+This guide provides comprehensive instructions for integrating the Apache Gravitino Lance REST service with data processing engines that support the Lance format, including Apache Spark via the [Lance Spark connector](https://lance.org/integrations/spark/) and Ray via the [Lance Ray connector](https://lance.org/integrations/ray/).
+
+This documentation assumes familiarity with the Lance REST service setup as described in the [Lance REST Service](./lance-rest-service) documentation.
+
+## Compatibility Matrix
+
+The following table outlines the tested compatibility between Gravitino versions and Lance connector versions:
+
+| Gravitino Version (Lance REST) | Supported lance-spark Versions | Supported lance-ray Versions |
+|--------------------------------|--------------------------------|------------------------------|
+| 1.1.1                          | 0.0.10 – 0.0.15                | 0.0.6 – 0.0.8                |
+
+:::note
+- These version ranges show which versions are expected to work together.
+- Not all versions in these ranges have been tested. Only some versions were tested.
+- Before using in production, please test the exact connector versions in your own environment.
+- The Lance ecosystem is changing quickly, so some versions may introduce breaking changes.
+:::
+
+### Why Maintain a Compatibility Matrix?
+
+The Lance ecosystem is under active development, with frequent updates to APIs and features. Gravitino's Lance REST service depends on specific connector behaviors to ensure reliable operation. Using incompatible versions may result in:
+
+- Runtime errors or exceptions
+- Data corruption or loss
+- Unexpected behavior in query execution
+- Performance degradation
+
+## Prerequisites
+
+Before proceeding, ensure the following requirements are met:
+
+1. **Gravitino Server**: A running Gravitino server instance with the Lance REST service enabled
+    - Default endpoint: `http://localhost:9101/lance`
+
+2. **Lance Catalog**: A Lance catalog created in Gravitino using either:
+    - Lance REST namespace API (`CreateNamespace` operation - see [Lance REST Service documentation](./lance-rest-service.md)
+    - Gravitino REST API, for more, please refer to [lakehouse-generic-catalog](./lakehouse-generic-catalog.md)
+    - Example catalog name: `lance_catalog`
+
+3. **Lance Spark Bundle** (for Spark integration):
+    - Downloaded `lance-spark` bundle JAR matching your Apache Spark version
+    - Note the absolute file path for configuration
+
+4. **Python Dependencies**:
+    - For Spark integration: `pyspark`
+    - For Ray integration: `ray`, `lance-namespace`, `lance-ray`
+
+## Spark Integration
+
+### Configuration
+
+The following example demonstrates how to configure a PySpark session to interact with Lance REST and perform table operations using Spark SQL.
+
+```python
+from pyspark.sql import SparkSession
+import os
+import logging
+
+# Configure logging for debugging
+logging. basicConfig(level=logging.INFO)
+
+# Configure Spark to use the lance-spark bundle
+# Replace /path/to/lance-spark-bundle-3.5_2.12-0.0.15.jar with your actual JAR path
+os.environ["PYSPARK_SUBMIT_ARGS"] = (
+    "--jars /path/to/lance-spark-bundle-3.5_2.12-0.0.15.jar "
+    "--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\" "
+    "--master local[1] pyspark-shell"
+)
+
+# Initialize Spark session with Lance REST catalog configuration
+# Note: The catalog "lance_catalog" must exist in Gravitino before running this code, you can create
+# it via Lance REST API `CreateNameSpace` or Gravitino REST API `CreateCatalog`.
+spark = SparkSession.builder \
+    .appName("lance_rest_integration") \
+    .config("spark.sql.catalog.lance", "com.lancedb.lance. spark.LanceNamespaceSparkCatalog") \
+    .config("spark.sql.catalog.lance.impl", "rest") \
+    .config("spark.sql.catalog.lance.uri", "http://localhost:9101/lance") \
+    .config("spark.sql.catalog.lance.parent", "lance_catalog") \
+    .config("spark.sql.defaultCatalog", "lance") \
+    .getOrCreate()
+
+# Enable debug logging for troubleshooting
+spark.sparkContext.setLogLevel("DEBUG")
+
+# Create schema (database)
+spark.sql("CREATE DATABASE IF NOT EXISTS sales")
+
+# Create Lance table with explicit location
+spark.sql("""
+    CREATE TABLE sales.orders (
+        id INT,
+        score FLOAT
+    )
+    USING lance
+    LOCATION '/tmp/sales/orders.lance/'
+    TBLPROPERTIES ('format' = 'lance')
+""")
+
+# Insert sample data
+spark.sql("INSERT INTO sales.orders VALUES (1, 1.1)")
+
+# Query data
+spark.sql("SELECT * FROM sales.orders").show()
+```
+
+### Storage Location Configuration
+
+The `LOCATION` clause in the `CREATE TABLE` statement is optional. When omitted, lance-spark automatically determines an appropriate storage location based on catalog properties.
+For detailed information on location resolution logic, refer to the [Lakehouse Generic Catalog documentation](./lakehouse-generic-catalog.md#key-property-location).
+
+For cloud storage backends such as Amazon S3 or MinIO, specify credentials and endpoint configuration in the table properties:
+
+```python
+spark.sql("""
+    CREATE TABLE sales.orders (
+        id INT,
+        score FLOAT
+    )
+    USING lance
+    LOCATION 's3://bucket/tmp/sales/orders.lance/'
+    TBLPROPERTIES (
+        'format' = 'lance',
+        'lance.storage.access_key_id' = 'your_access_key',
+        'lance.storage.secret_access_key' = 'your_secret_key',
+        'lance.storage.endpoint' = 'http://minio:9000',
+        'lance.storage.allow_http' = 'true'
+    )
+""")
+```
+
+## Ray Integration
+
+### Installation
+
+Install the required Ray integration packages:
+
+```shell
+pip install lance-ray
+```
+
+:::info
+- Ray will be automatically installed if not already present
+- lance-ray is currently tested with Ray versions 2.41.0 to 2.50.0
+- Ensure Ray version compatibility in your environment before deployment
+:::
+
+### Usage Example
+
+The following example demonstrates reading and writing Lance datasets through the Lance REST namespace using Ray:
+
+```python
+import ray
+import lance_namespace as ln
+from lance_ray import read_lance, write_lance
+
+# Initialize Ray runtime
+ray.init()
+
+# Connect to Lance REST namespace
+namespace = ln.connect("rest", {"uri":  "http://localhost:9101/lance"})
+
+# Create sample dataset
+data = ray.data.range(1000).map(
+    lambda row: {"id": row["id"], "value": row["id"] * 2}
+)
+
+# Write dataset to Lance table
+# Note: Both the catalog "lance_catalog" and schema "sales" must exist in Gravitino, you can create
+# them via Lance REST API `CreateNameSpace` or Gravitino REST API `CreateCatalog` and `CreateSchema`.
+write_lance(
+    data, 
+    namespace=namespace, 
+    table_id=["lance_catalog", "sales", "orders"]
+)
+
+# Read dataset from Lance table
+ray_dataset = read_lance(
+    namespace=namespace, 
+    table_id=["lance_catalog", "sales", "orders"]
+)
+
+# Perform filtering operation
+result = ray_dataset.filter(lambda row: row["value"] < 100).count()
+print(f"Filtered row count: {result}")
+```
+
+## Additional Engine Support
+
+The Lance REST service is compatible with other data processing engines that support the Lance format, including:
+
+- **DuckDB**: For analytical SQL queries
+- **Pandas**: For Python-based data manipulation
+- **DataFusion**: For Rust-based query execution
+
+Note: These three engines does not support Lance REST natively yet, but can still interact with Lance datasets through table location paths retrieved from the Lance REST service.
+
+For engine-specific integration instructions, consult the [Lance Integration Documentation](https://lance.org/integrations).
+
+### General Integration Pattern
+
+Most Lance-compatible engines follow this general pattern:
+
+1. Establish connection to Lance REST service endpoint
+2. Authenticate using appropriate credentials
+3. Reference tables using the hierarchical namespace structure
+4. Execute read/write operations using engine-native APIs
+
+Refer to each engine's the specific documentation for detailed configuration parameters and code examples.
+
+## Additional Resources
+
+- [Lance REST Service Documentation](./lance-rest-service)
+- [Lance Format Specification](https://lance.org/)
+- [Apache Gravitino Documentation](https://gravitino.apache.org/)
+- [Lakehouse Generic Catalog Guide](./lakehouse-generic-catalog.md)

docs/lance-rest-service.md

@@ -233,7 +233,6 @@ URL encoded:        lance_catalog%24schema%24table01
 - Currently supports only **two levels of namespaces** before tables
 - Tables **cannot** be nested deeper than schema level  
 - Parent catalog must be created in Gravitino before using Lance REST API
-- Metadata operations require Gravitino server to be available
 - Namespace deletion is recursive and irreversible
 :::
 
@@ -399,3 +398,7 @@ ns.create_table(create_table_request, body)
 
 </TabItem>
 </Tabs>
+
+## Integration with Lance REST
+
+To use the Lance REST service with Apache Spark, Ray and other engines, please refer to [lance-rest-integration](./lance-rest-integration.md) for more details.