In today’s world, data is incredibly important, and being able to quickly search through and analyze huge amounts of data is key for making smart business decisions. SQL Query Engines like Presto and Trino have changed the game by making it easier for companies to work with big data. This article delves into the differences between Presto and Trino, their evolution, and how they cater to the needs of modern data-driven enterprises.
Let’s start by the SQL Query Engine concept, which stands for the tools for querying data across various sources, handling terabytes and petabytes of data. Unlike search queries, SQL query engines analyze queries against their structures to retrieve information, designed for data selection, insertion and update.
SQL Query Engine Benefits
SQL Query Engines simplify Big Data analysis. They address common challenges like varied query languages, data silos, and slow query times by offering a unified approach to querying data across different sources.
PS: It’s important to note that SQL Query Engines are primarily concerned with the processing of data. They do not handle data storage; instead, storage is managed by various data sources, including databases, data warehouses, or object storage services. This distinction highlights the engines’ role in facilitating access and analysis without the complexities of data management.
Benefits and Use Cases:
- Single Point of Access: Facilitates data access across silos, enabling hidden insights and faster decision-making.
- SQL-Based Access: Connects to numerous data systems, offering a simplified approach to accessing complex data.
- Semantic Layer: Translates complex data into familiar business terminology, aiding in a consolidated view across the organization.
- Faster Time-to-Insight: Significantly reduces query times, enhancing productivity and enabling real-time analytics.
- Support for ML and AI: Opens up data sets for advanced analytics, improving algorithm accuracy and recommendations.
Evolution of Presto
Developed at Facebook in 2012, Presto (formerly PrestoDB) was designed to accelerate data warehouse queries, enabling fast access to large-scale data across multiple databases and data warehouses. It provided a solution for companies to analyze petabytes of data efficiently, timely, and cost-effectively.
The key takeaway is that queries that take one or two map-reduce (MR) phases in Hadoop run 10 to 100 times faster in Presto.
- Using Presto in our Big Data Platform on AWS
Trino: A New Era
Trino, initially PrestoSQL, reflects the vision of Presto’s original creators to serve a broader data analytics audience. The 2020 rebrand to Trino marked a commitment to community-driven innovation, scalability, and performance.
Starburst, established by these founders, significantly contributed to Trino’s growth, enhancing its enterprise appeal. By integrating added security, performance optimizations, and broader data source connectivity, Starburst has transformed Trino into a more powerful query engine. Its Enterprise edition further amplifies this, offering advanced management tools and features that exceed the open-source version. Starburst’s efforts have not only maintained Trino’s core efficiency and scalability but also expanded its capabilities to meet the evolving demands of modern data analytics, establishing Trino as a crucial tool for organizations in the complex landscape of big data.
Key Differences and Considerations
While Presto and Trino originate from the same foundational project, their paths have diverged significantly, reflecting in their community support, feature sets, and performance optimizations. This divergence is crucial for organizations considering which engine best fits their data analytics needs.
Community Support and Development Pace: Trino benefits from a vibrant, rapidly growing community, led by its original creators. This has translated into a fast-paced development cycle, introducing new features and optimizations regularly. Presto, while also enjoying strong community support, operates at a different pace, focusing on stability and incremental improvements.
Feature Set and SQL Support: Trino is known for its extensive SQL support, including advanced query capabilities, dynamic filtering, and fault-tolerant execution modes that enhance performance and reliability for complex analytics tasks. Moreover, Trino’s compatibility with Kubernetes enhances its scalability and flexibility, allowing for efficient resource management in diverse operational environments. These features have been instrumental in Trino’s adoption for demanding data analytics environments.
Presto distinguishes itself with features tailored to specific analytics scenarios, such as Project Aria for more efficient processing of ORC files and Presto-on-Spark, which allows Presto queries to be executed using Spark’s execution engine. These unique features make Presto particularly well-suited to environments where these specific capabilities are required.
Performance Optimizations: Both engines have made significant strides in performance optimizations; however, their focuses differ. Trino’s architecture and ongoing enhancements aim to improve query execution speed and resource efficiency across a wide array of data sources. This makes Trino ideal for scenarios requiring fast analytics on diverse data sets.
Presto’s optimizations often target specific use cases or data formats, aiming to provide the most efficient execution for those scenarios. The introduction of features like Project Aria demonstrates Presto’s commitment to optimizing data processing for specific file formats, enhancing performance for workloads that heavily rely on those formats.
Enterprise Features and Support: Trino, particularly through Starburst’s Enterprise offering, has made significant advances in security, connectivity, and manageability, catering to the rigorous demands of enterprise environments. This includes enhanced authentication, authorization, and encryption capabilities, alongside performance features designed to scale efficiently in large deployments.
Presto, through various distributions and support services, also offers enterprise features but with different emphases, such as integration capabilities with other big data technologies, including Hadoop and Spark. This ensures that organizations deeply invested in these ecosystems can leverage Presto effectively.
The Future of SQL Query Engines
As the world of big data keeps growing — getting bigger, more varied, and faster by the day — the need for tools that can keep up is on the rise. Presto and Trino have already shown us how important these tools are for understanding our data. But as the landscape evolves rapidly, new players like StarRocks are stepping in with new ideas and abilities.
When we’re picking the best SQL query engine for us, it’s really important to think about what kind of data it can work with and how well it fits with the other tech tools we use. Different engines work better with certain types of data storage or analysis tools, and finding the right match can make a big difference in how smoothly our data work goes and how much we can discover from our data.
In this ever-changing world, we need to keep our eyes open and regularly check out what’s new in the SQL query engine landscape. We should think about more than just what these engines can do right now. It’s about seeing how they fit with what we already use and how they can meet our unique needs for data analysis. Even though newcomers like StarRocks seem promising, we should carefully look at what features they offer, what data they work best with, and if they’re a good match for what we aim to achieve.
The future of SQL query engines isn’t just about the new tech coming out; it’s also about how well we can adapt and bring these new tools into our work. Staying up-to-date with the latest developments and being willing to try out different options will help make sure we stay ahead in the fast-moving world of data analytics.
Conclusion
Choosing between Presto and Trino is about more than just weighing their differences; it’s about considering how each option aligns with our journey as a company. We need to reflect on the variety of data landscapes we navigate, the complexities of the queries we undertake, how these engines will blend with the technologies we’ve already embraced, and the advanced features our business might require. This decision is a step toward finding a partner that not only fits seamlessly into our current setup but also grows with us, supporting our data-driven ambitions now and in the future. It’s about selecting a tool that becomes an integral part of our team’s success in unlocking the full potential of our data.