![]() ![]() Snowflake can execute queries against data held locally using the Data Cache, avoiding slower remote storage access. The Metadata Cache maintains the count of rows, and distinct and null values, while the Results Cache contains the result set of every query executed during the past 24 hours.ĭata Warehouses: Use a Least Recently Used (LRU) algorithm to cache raw data in fast SSD. In summary, Snowflake maintains a cache at the following layers:Ĭloud Services: This holds the Metadata and Results Cache. The diagram below illustrates how Snowflake caches data. One of the techniques Snowflake uses to maximize Snowflake query performance involves caching results in both the Virtual Warehouse and Cloud Services layer. Typically held on a hard disk, Cloud Storage is called Remote Storage in Snowflake. The Cloud Storage Layer: Physically stores the data in blob storage. Virtual Warehouse storage, which is considerably faster than disk, is called Local Storage. Each node in the cluster is a computer with eight CPUs, memory, and Solid State Disk (SSD) for temporary storage. The Compute Services Layer: Executes the query on a Virtual Warehouse, a cluster of machines ranging from 1 to 512 times the compute processing. The Cloud Services Layer: This accepts the connection, and the Snowflake query optimizer tunes the query, potentially re-writing the code to maximize SQL query performance. The diagram above illustrates the layers, including: Snowflake is not just a single machine but three interconnected computer systems, each with its own auto-scaling hardware and software. The Snowflake Architecture Explainedīefore diving into specific Snowflake SQL tuning techniques, the architecture illustrated in the diagram is worth considering. In summary, Snowflake query tuning prioritizes tuning SQL statements, whereas Snowflake cost control needs a more strategic approach paying attention to virtual warehouse size and deployment. However, both articles work together to deliver the same end goal: Faster queries for less spend. Reduce Cost: If the priority is to reduce Snowflake cost rather than maximize query performance, the article on Snowflake cost management may be more helpful. Reduce query elapsed times: A frequent priority for end-user queries, it can also be vital for fast data ingestion and transformation to quickly deliver data for analysis. Unlike on-premises databases, where you purchase the hardware and licenses up front, Snowflake charges you per second while the compute resources (a Virtual Warehouse) are active.įor successful performance tuning in Snowflake, you, therefore, need to decide on a priority: I'm so confident I think there's a potential role for system tuning experts whose job is to help maximize performance and reduce costs. One of the most significant differences between Snowflake and on-premises databases like Oracle or SQL Server is that query optimization in Snowflake will deliver both faster results AND save money. ![]() Why Does Snowflake Performance Tuning Matter? If you're already an expert on Snowflake, feel free to skip to the summary and conclusion listing query optimization techniques. This article explains precisely how, with a list of the top 10 Snowflake optimization tips. However, Snowflake Indexes are not supported on default tables, raising an important question: How on Earth can you tune SQL queries on a database without indexes? The Snowflake query optimizer implements many advanced query-tuning techniques. Top 10 Tips for Snowflake Query Optimization ![]()
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