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Postgres 10, Performance, and You by Gabe Enslein Postgres 10 is out this year, with a whole host of features you won't want to miss. Join the Heroku data team as we take a deep dive into parallel queries, native json
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The video titled "Postgres 10, Performance, and You" presented by Gabe Enslein at RailsConf 2018 focuses on the performance enhancements introduced in Postgres 10, highlighting new features that significantly improve database efficiency. Enslein begins with a brief introduction to Postgres, detailing its widespread popularity due to community-driven features like native JSON support, data integrity, and numerous extensions. Key points discussed in the presentation include: - **Native Table Partitioning**: Postgres 10 introduced efficient table partitioning to minimize bloat and improve query performance. This allows bulk operations to occur on partitions without overhead, enhancing performance and reducing database load. - **Hash Indexes**: The new full support for hash indexes ensures reliability in indexing JSON data and enhances concurrency, resolving previous issues with index corruption and replication failures. - **Full-text Index Searching**: A significant feature added is the ability to perform efficient searches directly on JSON blobs, making it easier to manage unstructured data. - **Parallel Queries**: Enhancements in parallel processing allow better utilization of multiple CPU processors, which includes parallel bitmap heap scans, gather merges, and merge joins. These improvements reduce the need for sequential scans and optimize overall query performance. Enslin illustrates these points through practical examples, such as managing a social interest platform and backend data structures that require handling large datasets efficiently. He notes that querying for quotes and user-generated content is more efficient due to Postgres 10's new capabilities. The conclusion underscores the importance of adopting Postgres 10 for improved performance, especially for applications handling terabytes of data, and the value of leveraging the community's tools and Heroku's enhancements to manage and optimize Postgres databases effectively. Overall, the talk conveys the message that the advancements in Postgres 10 can enable developers to build robust applications that handle complex queries and large datasets more efficiently. ### Main Takeaways: - The shift to native features like table partitioning and enhanced indexing is crucial for managing large data efficiently. - Using Postgres 10's parallel processing capabilities is vital for optimizing query performance. - Leveraging community tools and extensions can significantly aid in maximizing Postgres’s performance capabilities.
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