ApacheCalciteScannableTableVsAdapter - eellpp/pubScratchpad GitHub Wiki

Apache Calcite is a flexible framework for building data management systems, and it allows you to extend its capabilities by creating Adapters or Custom Tables. The choice between these two approaches depends on your use case and the level of customization required. Below is an explanation of when to use each, along with their use cases, pros, and cons.

Adapter and Custom Table are often used interchangeably in Calcite, but they serve different purposes depending on the level of abstraction and integration.

• A custom table is a specific implementation of a table in Calcite, typically by implementing interfaces like Table, ScannableTable, or FilterableTable.
• It is used when you want to define custom behavior for a single table (e.g., custom data access logic, transformations, or computations).
• It is tightly coupled to the schema and does not provide a reusable way to connect to external data sources.

• A custom adapter is a higher-level abstraction that allows you to connect Calcite to an external data source (e.g., a database, file system, or API).
• It involves creating a reusable framework to expose multiple tables or schemas from the external data source.
• It typically includes:
  • A schema factory to create schemas dynamically.
  • A table factory to create tables dynamically.
  • Integration with Calcite's query planning and optimization.

Examples

1. A custom adapter can be written to read all the files from a directory and expose it as tables .
2. You can implement a custom adapter that reads CSV files from S3 using the AWS SDK for Java. Steps: Use the AWS SDK for Java (aws-java-sdk-s3) to interact with S3 and read the CSV file. Implement a TableFactory or Table interface in Calcite to expose the CSV data as a table. Parse the CSV file and convert it into Calcite's relational data model (e.g., Enumerable or ScannableTable).

Adapters in Calcite are used to connect to external data sources and make them queryable through SQL. They act as a bridge between Calcite and the external system, translating SQL queries into the native API or query language of the data source.

• You want to integrate an external data source (e.g., a database, file system, or API) into Calcite.
• The external data source has a well-defined schema and query interface.
• You want to leverage Calcite's query optimization and planning capabilities for the external data source.

• Connecting to a relational database (e.g., MySQL, PostgreSQL).
• Querying data from a NoSQL database (e.g., MongoDB, Cassandra).
• Integrating with a file-based data source (e.g., CSV, Parquet, JSON).
• Exposing a REST API or custom data source as a SQL-queryable table.

• Reusability: Adapters can be reused across multiple schemas and queries.
• Optimization: Calcite can optimize queries across multiple adapters.
• Standardization: Provides a unified SQL interface for heterogeneous data sources.
• Extensibility: Easy to add new data sources without modifying the core Calcite engine.

• Complexity: Implementing an adapter can be complex, especially for non-relational data sources.
• Performance Overhead: Adapters may introduce performance overhead due to translation between SQL and the native API.
• Limited Customization: Adapters are constrained by the capabilities of the external data source.

Custom tables are used when you need to define a table with custom behavior that cannot be achieved through standard adapters. This approach allows you to implement custom logic for reading, writing, or transforming data.

• You need to implement custom logic for data access or transformation.
• The data source does not have a well-defined schema or query interface.
• You want to create a virtual table that computes data on the fly (e.g., a table that aggregates or joins data from multiple sources).
• You need to extend Calcite's functionality beyond what adapters can provide.

• Creating a virtual table that performs real-time calculations or transformations.
• Implementing a custom join or aggregation logic.
• Exposing a custom data source that does not fit into the adapter model (e.g., a streaming data source).
• Adding custom security or access control logic to a table.

• Flexibility: You have full control over the behavior of the table.
• Custom Logic: Can implement complex transformations or computations.
• No External Dependency: Does not rely on an external data source or API.
• Integration: Can integrate with Calcite's query planning and optimization.

• Complexity: Implementing custom tables requires a deeper understanding of Calcite's internals.
• Maintenance: Custom tables need to be maintained and updated as Calcite evolves.
• Limited Reusability: Custom tables are often specific to a particular use case and may not be reusable.
• Performance: Custom logic may introduce performance bottlenecks if not optimized.

• Use Adapters when you want to integrate an external data source and leverage Calcite's SQL capabilities.
• Use Custom Tables when you need to implement custom logic or behavior that cannot be achieved with adapters.

The choice depends on your specific requirements, the complexity of the data source, and the level of customization needed. Adapters are generally easier to implement and maintain, while custom tables offer greater flexibility at the cost of increased complexity.

To create a custom adapter, you need to implement a schema factory and a table factory. These factories allow Calcite to dynamically discover and create schemas and tables from your data source.

1. Implement a Schema Factory

   • A schema factory creates instances of your custom schema.
   • It implements the org.apache.calcite.schema.SchemaFactory interface.

2. Implement a Custom Schema

   • The schema exposes tables from your data source.
   • It implements the org.apache.calcite.schema.Schema interface.

3. Implement a Table Factory (Optional)

   • A table factory creates instances of your custom table.
   • It implements the org.apache.calcite.schema.TableFactory interface.

4. Register the Adapter

   • Register the schema factory in a Calcite model file (JSON) or programmatically.

Let’s create a custom adapter for querying multiple CSV files. This example demonstrates how to create a reusable adapter that dynamically exposes CSV files as tables.

The schema factory creates instances of the custom schema.

import org.apache.calcite.schema.Schema;
import org.apache.calcite.schema.SchemaFactory;
import org.apache.calcite.schema.SchemaPlus;
import org.apache.calcite.schema.Table;

import java.util.Map;

public class CsvSchemaFactory implements SchemaFactory {
    @Override
    public Schema create(SchemaPlus parentSchema, String name, Map<String, Object> operand) {
        // The "directory" parameter specifies the path to the CSV files
        String directory = (String) operand.get("directory");
        return new CsvSchema(directory);
    }
}

The custom schema reads CSV files from a directory and exposes them as tables.

import org.apache.calcite.schema.Schema;
import org.apache.calcite.schema.Table;

import java.io.File;
import java.util.HashMap;
import java.util.Map;

public class CsvSchema implements Schema {
    private final String directory;

    public CsvSchema(String directory) {
        this.directory = directory;
    }

    @Override
    public Map<String, Table> getTableMap() {
        Map<String, Table> tableMap = new HashMap<>();
        File dir = new File(directory);
        if (dir.isDirectory()) {
            for (File file : dir.listFiles()) {
                if (file.getName().endsWith(".csv")) {
                    String tableName = file.getName().replace(".csv", "");
                    tableMap.put(tableName, new CsvTable(file.getAbsolutePath()));
                }
            }
        }
        return tableMap;
    }
}

The custom table reads data from a CSV file.

import org.apache.calcite.schema.impl.AbstractTable;
import org.apache.calcite.linq4j.Enumerator;
import org.apache.calcite.linq4j.Linq4j;
import org.apache.calcite.DataContext;
import org.apache.calcite.rel.type.RelDataType;
import org.apache.calcite.rel.type.RelDataTypeFactory;
import org.apache.calcite.util.Source;
import org.apache.calcite.util.Sources;

import java.io.File;
import java.util.ArrayList;
import java.util.List;
import java.util.Scanner;

public class CsvTable extends AbstractTable {
    private final String csvFilePath;

    public CsvTable(String csvFilePath) {
        this.csvFilePath = csvFilePath;
    }

    @Override
    public RelDataType getRowType(RelDataTypeFactory typeFactory) {
        return typeFactory.builder()
                .add("id", typeFactory.createSqlType(SqlTypeName.INTEGER))
                .add("name", typeFactory.createSqlType(SqlTypeName.VARCHAR))
                .add("age", typeFactory.createSqlType(SqlTypeName.INTEGER))
                .build();
    }

    @Override
    public Enumerator<Object[]> scan(DataContext root) {
        Source source = Sources.of(new File(csvFilePath));
        List<Object[]> rows = new ArrayList<>();
        try (Scanner scanner = new Scanner(source.file())) {
            if (scanner.hasNextLine()) {
                scanner.nextLine(); // Skip header
            }
            while (scanner.hasNextLine()) {
                String line = scanner.nextLine();
                String[] fields = line.split(",");
                Object[] row = new Object[fields.length];
                row[0] = Integer.parseInt(fields[0]);
                row[1] = fields[1];
                row[2] = Integer.parseInt(fields[2]);
                rows.add(row);
            }
        } catch (Exception e) {
            throw new RuntimeException("Error reading CSV file", e);
        }
        return Linq4j.enumerator(rows);
    }
}

Register the schema factory in a Calcite model file (JSON).

{
  "version": "1.0",
  "defaultSchema": "CSV_SCHEMA",
  "schemas": [
    {
      "name": "CSV_SCHEMA",
      "type": "custom",
      "factory": "com.example.CsvSchemaFactory",
      "operand": {
        "directory": "path/to/csv/files"
      }
    }
  ]
}

Use Calcite's JDBC interface to query the CSV files.

import org.apache.calcite.jdbc.CalciteConnection;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.Statement;

public class CsvAdapterExample {
    public static void main(String[] args) throws Exception {
        Connection connection = DriverManager.getConnection("jdbc:calcite:model=path/to/model.json");
        Statement statement = connection.createStatement();
        ResultSet resultSet = statement.executeQuery("SELECT id, name, age FROM CSV_SCHEMA.employees WHERE age > 30");
        while (resultSet.next()) {
            System.out.println(
                    resultSet.getInt("id") + ", " +
                            resultSet.getString("name") + ", " +
                            resultSet.getInt("age"));
        }
        resultSet.close();
        statement.close();
        connection.close();
    }
}

• Use a custom table when you need to implement custom behavior for a single table.
• Use a custom adapter when you want to expose an external data source (e.g., a directory of CSV files, a database, or an API) as a queryable schema in Calcite.

This example demonstrates how to create a custom adapter that dynamically exposes CSV files as tables. You can extend this approach to support more complex data sources and features.