Lakehouse Engines Compared: Spark, DuckDB, and Query Federation

When you’re evaluating lakehouse engines, you’ll quickly notice key differences between Spark, DuckDB, and query federation methods. Each brings its own strengths to the table, from Spark’s distributed prowess to DuckDB’s nimble single-node performance and the flexibility of federated queries. But how do these engines stack up in real-world scenarios, and what should you watch out for when choosing the right fit? Let’s break down the practical trade-offs that might impact your next decision.

Benchmarking Methodology and Setup

While performance is a critical aspect of any benchmarking process, this evaluation also considers execution costs, development effort, maturity of the solutions, and compatibility.

The benchmarking utilizes TPC-DS tables with approximately 100 GB of generated data, formatted as Parquet and stored on S3. Both Spark and DuckDB are assessed through core Extract, Load, Transform (ELT) operations, which include reading Parquet files, writing Delta tables, creating fact tables, and optimizing storage efficiency.

The tests are conducted on m6.2xlarge AWS instances to ensure uniformity in hardware specifications. Queries are evaluated at both 10 GB and 100 GB scales to provide a comprehensive comparison of execution costs.

Notably, DuckDB's self-contained binary often exhibits higher efficiency, frequently outperforming Spark under comparable resource conditions.

Integrating Dbt With Duckdb

Integrating DuckDB with dbt offers a combination of operational simplicity and effective performance for data processing.

Utilizing DuckDB as an in-process OLAP engine can streamline both integration and querying processes. The dbt-duckdb adapter allows data engineers to run models effectively on a single node, which can help bypass the complexities often associated with distributed database systems.

This setup typically provides solid performance, particularly when dealing with medium-sized datasets during model execution. However, it's important to note that transitioning to this pipeline may require adjustments to approximately half of the SQL syntax used, in order to ensure compatibility.

Integrating Dbt With Trino for Query Federation

Integrating dbt with Trino facilitates advanced query federation capabilities. Trino allows analytics teams to execute queries across multiple data sources, effectively handling the challenges posed by differing SQL dialects.

The dbt-trino adapter is essential for integrating dbt with Trino’s architecture, which operates on a master-slave model, thus optimizing execution and the model development process.

Trino's adherence to ANSI SQL standards aids in integrating various analytical tools. However, users transitioning models from DuckDB may encounter necessary syntax adjustments, such as explicit casting, to ensure compatibility.

This integration supports scaling analytics and processing across federated datasets, thereby enhancing flexibility in data analysis.

Integrating Dbt With Spark

Integrating dbt with Spark offers expanded capabilities for handling large-scale datasets in distributed analytics.

By utilizing the dbt-spark adapter, users can execute dbt models leveraging Spark’s distributed processing engines, which allows for scaling the processing of raw datasets across various architectures, including cloud, on-premise, and Kubernetes environments.

The integration process is facilitated by dbt 1.5.0’s functional API, which simplifies the management of Spark sessions during execution.

Users can invoke the dbtRunner with specific arguments to execute queries effectively.

While using this integration, users will continue to utilize familiar SQL syntax, albeit with some modifications that are specific to Spark.

This approach enables comprehensive model transformations across substantial datasets, making it a practical choice for organizations looking to enhance their data processing capabilities.

User Experience and SQL Compatibility Challenges

Switching between different lakehouse engines in dbt presents a variety of user experience and SQL compatibility challenges. Notably, there are differences in SQL dialects, particularly between DuckDB and Trino, which can complicate implementation. Migrating dbt models often necessitates substantial alterations to the SQL code—often requiring updates to approximately half of the code—primarily due to data type mismatches and the need for explicit casting.

Trino's handling of data types is stricter when compared to DuckDB, which may lead to additional conversion efforts during migration.

Furthermore, users may encounter errors related to missing column aliases, which can disrupt the migration process. While DuckDB offers a more straightforward integration, thus enhancing the overall user experience, Trino and Spark present challenges, particularly when utilizing query federation, which can complicate workflows and introduce additional compatibility issues for teams.

These factors should be considered when evaluating the switch between different lakehouse engines within the dbt framework.

Performance Insights and Benchmarking Results

Recent benchmarking results have highlighted notable performance differences among various lakehouse engines when handling common analytical workloads.

In tests involving TPC-DS queries, DuckDB demonstrated superior performance in 75% of the scenarios analyzed, attributed to its optimizations for single-node processing on smaller datasets. Specifically, at a data size of 100GB, DuckDB outperformed Spark in several critical operations, such as reading Parquet files and constructing fact tables, which suggests it may offer lower operational costs for certain tasks.

However, it's important to note that Spark exhibited better performance guarantees and scalability for larger datasets, particularly during merging operations.

Spark's live monitoring user interface also provides valuable insights into execution, which can be beneficial for users looking to optimize their workloads. Conversely, the limited feedback from DuckDB and other query engines can impede transparency when evaluating benchmarking results and real-world performance.

This nuanced understanding of each engine’s strengths and weaknesses can guide users in selecting the appropriate tool for their specific use cases.

Choosing the Right Engine for Your Lakehouse

Selecting the appropriate engine for a lakehouse requires a careful assessment of data scale, workload complexity, and specific development needs. DuckDB is a suitable choice for lakehouses managing medium workloads on a single machine, as it offers performance that's approximately 1.6 times faster than Spark at a configuration of 4 virtual cores, while also being around 50% less expensive at the 10GB data scale.

On the other hand, for those dealing with more complex or larger-scale data processing tasks, Apache Spark's scalability, ability to support a wide range of workloads, and comprehensive monitoring tools can provide substantial advantages.

Integration capabilities also play a critical role in this decision-making process. While DuckDB has benefits, it may necessitate additional steps for working with Delta tables, which could affect the overall simplicity of deployment.

Therefore, it's essential to weigh the trade-offs related to performance, cost, integration capabilities, and scalability in order to choose the most fitting engine that aligns with the dynamic requirements of your lakehouse.

Conclusion

When choosing your lakehouse engine, consider your use case first. If you need lightning-fast analytics on small datasets, DuckDB’s your best bet. For massive, distributed workloads, Spark excels with strong scalability and reliability. If you want to unify queries across varied sources, query federation with dbt-trino offers unmatched flexibility. Ultimately, the right choice balances performance, scalability, and compatibility with your existing workflows—so weigh your needs and pick the engine that fits best.