Presto is an open-source distributed SQL query engine designed for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. It is a parallel processing engine that leverages Apache Hadoop YARN and Apache Mesos for resource management.
How Presto Works:
Query Parsing and Planning: When a SQL query is submitted to Presto, it first goes through a parsing phase where it is checked for syntax errors and transformed into an abstract syntax tree (AST). Then, it is optimized into a logical plan which outlines how the data will be accessed and processed.
Data Access: Presto does not store data itself but reads data from various data sources like Hadoop Distributed File System (HDFS), Apache Cassandra, Apache HBase, relational databases, and more. It uses connectors to interface with these data sources.
Parallel Processing: Presto distributes the query processing across multiple nodes in a cluster. Each node processes a part of the data in parallel, which significantly speeds up query execution for large datasets.
Result Aggregation: After processing, each node sends its partial results back to the coordinator node, which aggregates these results and returns the final output to the user.
Example:
Imagine you have a large dataset stored in HDFS and you want to find out the total sales per product category for the year 2023. When you submit this query to Presto, it will:
Recommendation:
For deploying Presto in a cloud environment, Tencent Cloud offers services like Tencent Cloud Data Lake Analytics (DLA), which simplifies the management and operation of big data analytics, supporting Presto for running SQL queries on large datasets stored in Tencent Cloud Object Storage (COS) or other data lakes.