Presto HDFS

Presto is an open-source, distributed SQL query engine designed for fast and interactive data querying across various data sources. While Presto is often associated with querying data stored in Hadoop Distributed File System (HDFS), it is not a storage system itself. Instead, Presto is a query engine that can connect to and query data stored in HDFS and many other data sources. Here’s how Presto can work with HDFS:

1. Data Source Connectivity:

   • Presto provides connectors to various data sources, including HDFS. These connectors allow Presto to read data from and write data to HDFS. When you run a query in Presto, you can specify which data source or catalog to query, including HDFS.

2. Querying Data in HDFS:

   • Once Presto is connected to your HDFS cluster through its HDFS connector, you can write SQL queries to retrieve and analyze data stored in HDFS files. Presto supports standard SQL syntax, which makes it easy to write queries that interact with HDFS data.

3. Metadata Handling:

   • Presto relies on a metastore, which contains information about the structure and location of data in HDFS. It can integrate with various metastore systems like Hive Metastore, MySQL, PostgreSQL, or its built-in Hive metastore connector. This metadata is essential for efficiently querying HDFS data.

4. Distributed Query Processing:

   • Presto uses a distributed query processing model, which means it can distribute query execution across a cluster of machines. This allows Presto to efficiently process large-scale data in HDFS and other data sources in parallel.

5. Optimization and Caching:

   • Presto includes query optimization techniques to minimize data movement and optimize query performance. It can also cache query results in memory for faster retrieval of frequently accessed data.

6. Schema Evolution:

   • Presto can handle schema evolution in HDFS data, allowing you to query data even as its schema evolves over time. This flexibility is important in big data scenarios where data formats may change.

7. Security Integration:

   • Presto integrates with various security mechanisms, including Hadoop’s Kerberos-based authentication and access control, ensuring that data in HDFS is appropriately secured and accessed only by authorized users.

8. Additional Data Sources:

   • Beyond HDFS, Presto can query data from various other sources, including relational databases, cloud data storage (e.g., Amazon S3, Google Cloud Storage), NoSQL databases, and more. This enables Presto to provide a unified SQL interface for querying data across the entire data ecosystem.