12/25/2022 0 Comments Snowflake json query![]() ![]() Nevertheless, fast performance for large scans is not enough for users and environments requiring a robust cloud data warehouse. MPP query engines excel with extremely large table and log file scans. To boost performance, new varieties of query engines may include dynamic query optimizations, with data shards and in-memory shuffling of shards across stages. A root server rewrites queries and passes them to a tree structure of intermediate servers and worker nodes. ![]() Transparent to the user, an infrastructure-wide cluster manager balances the workload. Typical Serverless Query Engine Block Diagram ![]() The infrastructure may contain hundreds of thousands of disks and thousands of CPUs and CPU cores. These are multi-tenant platforms, so users load data, launch queries, and share a slice of massive infrastructure with other users. Architectureįor serverless query engines, the cloud provider deploys and manages the entire architecture infrastructure (Figure 1). Similar to Snowflake, serverless query engines require no infrastructure management to run queries. In this blog, we compare Snowflake, at a platform-architecture level, to serverless query services or query engines. In previous blogs, we compared Snowflake to both on-premises distributed processing data warehousing approaches and cloud-migrated versions of the same technologies, both of which require a great deal of hands-on management. Prospective customers frequently ask how Snowflake compares to other technologies. ![]()
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