Data Engineer : What is Presto
Presto is a high-performance, distributed SQL query engine designed for querying large data sets across multiple sources.
It was originally developed by Facebook to run interactive analytic queries against a large internal data warehouse, and it’s now used by many other organizations and is open-source.
Key Features of Presto:
Federated Queries:
Presto can query data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analysis across your entire data ecosystem.
High Performance:
Presto is designed for speed. It’s not only fast at reading large amounts of data; it’s also optimized for complex queries.
Scalability:
Presto is designed to be scalable. It can handle petabyte-scale data warehouses with ease.
In-Memory Processing:
Presto processes data in-memory, which allows for fast query execution. However, it doesn’t store the data, making it different from traditional databases.
Support for SQL:
Presto supports ANSI SQL, including complex queries, aggregations, joins, and window functions.
User-Defined Functions (UDFs):
Presto allows you to write custom functions in Java, which you can then use in your SQL queries.
Open Source:
Presto is open-source under the Apache License, which means it’s free to use and you have access to the source code.
How Presto Works:
Presto’s architecture is fundamentally different from that of traditional database systems. Presto is a distributed system that runs on a cluster of machines. Its architecture includes:
Coordinator Node:
The Coordinator is responsible for parsing queries, creating plans, managing the query execution, and returning results to the client.
Worker Nodes:
Worker nodes are responsible for executing tasks and processing data. The Coordinator sends tasks to the Worker nodes, which then process the data and send the results back to the Coordinator.
Connector Plugins:
Presto connects to data sources through a connector interface. Each data source, such as Hive, Cassandra, or a relational database, has its own connector that knows how to communicate with that specific type of data source.
Use Cases:
Interactive Analytics:
Presto is excellent for interactive analytic queries against big data sources.
Data Lake Queries:
Presto is commonly used to query data lakes where data is stored in a distributed file system like HDFS or S3.
Cross-Platform Data Analysis:
With its ability to query across different data sources, Presto is great for scenarios where you need to analyze data that’s spread across multiple systems.
Getting Started with Presto:
To start using Presto, you would typically:
Set Up Presto Cluster:
Install Presto on a cluster of machines. You’ll need one machine to act as the Coordinator and multiple machines to act as Worker nodes.
Configure Data Sources:
Configure the connectors for the data sources you want to query.
Run Queries:
Use a SQL client to connect to the Presto Coordinator and run your queries.
Presto’s performance and flexibility make it a compelling choice for organizations that need to run fast, interactive queries on large datasets that are spread across multiple systems.
