Optimizing Data Storage and Querying with Trino, MinIO, and Apache Iceberg

As data continues to grow exponentially, organizations require efficient tools to store, manage, and query massive datasets. In a modern data architecture, these needs are often met by combining powerful querying engines with scalable storage solutions and robust data formats. This article explores how Trino, MinIO, and Apache Iceberg can work together to optimize data storage and querying in a data lakehouse architecture.

What is Trino?

Trino, formerly known as PrestoSQL, is a high-performance distributed SQL engine designed for querying vast datasets spread across multiple data sources. Its unique architecture allows users to run complex SQL queries across large-scale data lakes and warehouses without needing to move data. Trino excels at running interactive queries on petabyte-scale datasets with minimal latency.

Key benefits of Trino include:

• Distributed Execution: Trino’s architecture allows it to query data stored across multiple sources, such as HDFS, S3, and MinIO, simultaneously.
• High Query Performance: Its ability to parallelize query execution ensures minimal response time for even the most complex queries.
• Support for Multiple Data Sources: Trino can seamlessly integrate with various data formats and storage systems, including Apache Iceberg.

MinIO: Scalable Object Storage

MinIO is an open-source object storage solution compatible with the Amazon S3 API, designed to meet the demands of modern cloud-native applications. It offers scalable, high-performance storage for structured and unstructured data. MinIO is particularly well-suited for environments that require fast, resilient storage across hybrid and multi-cloud infrastructures.

Key advantages of MinIO include:

• Cloud-Native Design: MinIO is lightweight and built to work in Kubernetes environments, making it ideal for cloud-native data architectures.
• S3 Compatibility: Its full compatibility with the S3 API allows seamless integration with other tools in the data stack, such as Apache Iceberg.
• High Availability: MinIO is designed with distributed clusters that ensure data availability, fault tolerance, and scalability.

Apache Iceberg: A Modern Table Format for the Data Lake

Apache Iceberg is a high-performance table format for organizing and managing data in a data lake. Iceberg provides a clear structure and enables advanced data management features such as schema evolution, partitioning, and snapshot isolation, which are essential for efficient querying and data operations.

Iceberg solves several challenges commonly associated with large datasets:

• Table Evolution: Iceberg enables dynamic partitioning and schema evolution without rewriting large portions of data.
• Data Integrity: By supporting ACID transactions, Iceberg ensures consistency and data integrity even in highly concurrent environments.
• Efficient Data Reads: Iceberg’s advanced data pruning, filtering, and indexing techniques optimize query performance by reducing the amount of data scanned during queries.

How Trino, MinIO, and Iceberg Work Together

To leverage Trino, MinIO, and Apache Iceberg in an integrated data architecture, several components need to be aligned for seamless operation. While these technologies can work in tandem, certain configurations and metastore setups are necessary to ensure proper functionality.

Trino for Querying

Trino is designed to query large datasets efficiently. It can connect to Iceberg tables stored on MinIO, offering SQL queries on top of the object storage. However, one critical component that must be configured properly is the Iceberg catalog. Iceberg tables require a metastore (e.g., AWS Glue, Hive Metastore), which manages metadata for tables and partitions. Without this metadata catalog, Trino cannot effectively query Iceberg tables, even if the data files are correctly stored in MinIO.

• Key Consideration: Trino’s Iceberg connector requires a properly configured metastore, such as Glue or Hive, to track Iceberg’s metadata, which includes table schemas, partitioning, and snapshots. Misconfigurations can lead to connection errors, as seen with AWS Glue connector misinterpretations when trying to interact with MinIO’s S3-compatible API.

MinIO for Scalable Storage

MinIO provides scalable object storage that is compatible with the S3 API. It can store Iceberg table data, including Parquet or ORC files, but it does not inherently manage metadata. This is where the metastore becomes critical. MinIO serves as a reliable storage layer for Iceberg table data, but path-style access may be required for S3 connectors to function correctly with non-AWS S3 endpoints like MinIO.

• Key Consideration: If MinIO is used as storage, ensure S3-compatible paths are configured properly. Path-style access, rather than virtual-hosted style, might be necessary to handle connections correctly.

Iceberg for Data Management

Iceberg’s table format is key to organizing and managing data efficiently on MinIO, but it relies on an external metastore to track and manage metadata. The Iceberg metastore handles tasks such as partition evolution and snapshots, which are crucial for Trino to execute queries without scanning entire datasets. This separation of metadata and data storage allows Iceberg to manage large datasets more effectively than legacy table formats like Hive.

• Key Consideration: Iceberg requires a separate metastore service (e.g., Hive, AWS Glue) to function properly with Trino. Direct connection between Trino and MinIO without a proper metadata catalog will fail, as the metadata is crucial for query optimization.

Key Use Cases:

1. Data Analytics: Trino, MinIO, and Iceberg enable organizations to run interactive SQL queries across large-scale datasets, making it easier to perform real-time analytics on fresh data.
2. Data Warehousing: The combination of these technologies supports massive data warehousing operations, offering efficient storage with fast query response times.
3. Data Lakes: For organizations building scalable data lakes, Trino provides the querying power, MinIO offers the scalable storage, and Iceberg organizes the data into manageable, performant tables.

Best Practices for Implementing Trino, MinIO, and Iceberg

To get the most out of this architecture, consider the following best practices:

• Optimize Storage: Ensure MinIO is properly configured for your data workload, focusing on replication and high availability for critical data.
• Leverage Partitioning: Use Iceberg’s dynamic partitioning capabilities to organize data efficiently, improving query performance by reducing unnecessary scans.
• Query Optimization: Take advantage of Trino’s query optimization features, such as predicate pushdown and vectorized execution, to minimize query latency.
• Monitor Performance: Regularly monitor the performance of Trino queries and the health of your MinIO storage clusters to ensure optimal operation.

Common Issues and Troubleshooting for Trino, MinIO, and Iceberg Integration

When integrating Trino with MinIO and Apache Iceberg, several common issues may arise, typically related to configuration, metadata management, and the S3-compatible storage layer. Below are key issues and potential solutions based on real-world scenarios.

Common Issues and Troubleshooting for Trino, MinIO, and Iceberg Integration

When integrating Trino with MinIO and Apache Iceberg, several common issues may arise, typically related to configuration, metadata management, and the S3-compatible storage layer. Below are key issues and potential solutions based on real-world scenarios.

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1. Catalog Naming Discrepancy

• Issue: After configuring Trino to query data from an Iceberg table on a MinIO-backed S3 endpoint, users may find that the catalog is labeled as “Iceberg” instead of a custom name, such as “Nessie.”
• Cause: The catalog name in Trino is dictated by the properties file used during configuration. If the file is named iceberg.properties, the catalog will be labeled “Iceberg” regardless of the intended name.
• Solution: Rename the properties file to match the desired catalog name. For example, renaming iceberg.properties to nessie.properties will display the catalog as “Nessie” in Trino.

Example:

mv iceberg.properties nessie.properties

After the change, the catalog will be named “Nessie.”

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2. Trino Writing Data to Iceberg

• Issue: Writing data to Iceberg through Trino (e.g., from Spark to Trino to S3) raises concerns about performance and architecture complexity.
• Cause: Trino was originally designed for reading data, and while it now supports ETL workflows with fault-tolerant execution, its write performance is generally slower compared to Spark, especially for large-scale data operations.
• Solution: Stick to using Spark for writing data to Iceberg, and reserve Trino for querying. Spark provides better support for features like streaming, file size control, and advanced partitioning strategies. Trino can be used for analytical queries once data is ingested via Spark.

Best Practice: Keep Spark for ETL operations and Trino for query performance on large datasets.

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3. Connecting Trino to Multiple MinIO Endpoints

• Issue: When using multiple MinIO (S3-compatible) endpoints in Trino, accessing and joining data from different endpoints may result in errors such as NoSuchBucket or incorrect S3 endpoint usage.
• Cause: The issue often arises from improper configuration of the Hive Metastore or incorrect mapping of MinIO properties across different S3 endpoints. Trino may default to the wrong MinIO endpoint if paths or credentials are misconfigured.
• Solution:
  1. Ensure that each MinIO endpoint is configured separately in its own properties file (minio1.properties, minio2.properties).
  2. Use path-style access in both the Hive Metastore and Trino configurations.
  3. Double-check that the hive-site.xml and each Trino catalog file point to the correct S3 endpoints.

Example:

# minio1.properties
s3.endpoint=http://minio-1:9000
# minio2.properties
s3.endpoint=http://minio-2:9000

4. Iceberg Table Registered But No Data Visible

• Issue: After registering a table in Iceberg (using the register_table procedure), the data files are not visible when querying the table in Trino, even though snapshots are registered correctly.
• Cause: This issue often occurs when Iceberg fails to recognize the existing Parquet files in the S3 bucket. The register_table procedure registers metadata but does not automatically scan or load existing files unless they are explicitly imported.
• Solution: Ensure that the Parquet files follow Iceberg’s data organization standards. If necessary, migrate or reformat the existing data files to be compatible with Iceberg’s table format, or use the add_files procedure to import them into the Iceberg metadata.

Example:

CALL iceberg.system.add_files(
   schema_name => 'public',
   table_name => 'testTable',
   table_location => 's3://myawsbucket/public/testtable/'
);