E-MapReduce:Use DuckDB in a Notebook

Background information

DuckDB is a lightweight, high-performance, embedded analytical database engine optimized for Online Analytical Processing (OLAP) scenarios.

• Features

  • Embedded architecture: Embeds directly into an application as a library, similar to SQLite, without requiring a separate server. It supports both in-memory and on-disk modes.

  • Columnar storage: Stores data by column to optimize the performance of aggregate queries and scans.

  • Vectorized execution: Uses SIMD instructions to process data batches in parallel, which reduces CPU overhead.

  • Standard compatibility: Supports SQL-92 and SQL:2011 standards, including Common Table Expressions (CTEs), window functions, JOINs (including ASOF JOIN), and subqueries.

  • Direct read for multiple formats: Query CSV, Parquet, and JSON files directly without importing them.

  • Zero-copy integration: Seamlessly integrates with in-memory data structures, such as Pandas and Arrow, to avoid data migration overhead.

  • Federated query: Uses the httpfs extension to access remote files, such as files on S3, or connect to external databases, such as PostgreSQL, for federated queries.

• Scenarios

  • Interactive analytics: Processes datasets that range from gigabytes to terabytes. It can be used as a replacement for Pandas or Excel when handling big data.

  • Edge computing: Can be deployed on edge devices for local data analytics.

  • Data science: Seamlessly integrates with the Python and R ecosystems to serve as a pre-processing engine for machine learning (ML).

  • Real-time OLAP: Supports analytical needs that require both frequent updates and complex queries.

Limits

Only the following engine versions support the operations in this topic:

• esr-4.x: esr-4.4.0 and later.

• esr-3.x: esr-3.4.0 and later.

• esr-2.x: esr-2.8.0 and later.

Procedure

1. On the EMR Serverless Spark page, click Data Development in the navigation pane on the left.

2. Create a Notebook.

   1. On the Development tab, click the icon.

   2. In the dialog box that appears, enter a name, set **Type** to Interactive Development > Notebook, and then click OK.

3. In the upper-right corner, select a Notebook session instance that has been created and started.

   You can also select Create Notebook Session from the drop-down list to create a new Notebook session instance. For more information, see Manage Notebook sessions.

   Note

   DuckDB is a single-node SQL engine that uses only the resources of the Notebook Spark driver. Therefore, you may need to increase the resource configurations for the Spark driver's spark.driver.cores and spark.driver.memory parameters.

4. To use DuckDB in the Python code of a Notebook, run import duckdb. The following examples demonstrate common use cases.

   • View the content of Parquet, CSV, and JSON files.

     import duckdb
     
     # View the content of a Parquet file
     duckdb.sql("SELECT * FROM 'oss://bucket/path/to/test.parquet'")
     # View the content of a CSV file
     duckdb.sql("SELECT * FROM 'oss://bucket/path/to/test.csv'")
     # View the content of a JSON file
     duckdb.sql("SELECT * FROM 'oss://bucket/path/to/test.json'")

     

   • View the schema of a Parquet file.

     import duckdb
     
     # View the schema of a Parquet file
     duckdb.sql("SELECT * FROM parquet_schema('oss://bucket/path/to/test.parquet')")

     

   • Export the content of a Parquet file to a CSV file.

     import duckdb
     
     # Export the content of a Parquet file to a CSV file
     duckdb.sql("COPY (SELECT * FROM 'oss://bucket/path/to/test.parquet') TO 'oss://bucket/path/to/output.csv' (HEADER, DELIMITER ',');")

   Note

   Before you read from or write to OSS files, you must activate OSS, create a bucket, and confirm that you have the required access permissions for the bucket.

For more information, see the official DuckDB documentation.