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Overview
The article discusses Presto, a distributed SQL query engine developed by Facebook to enable interactive analysis of large datasets stored in their data warehouse. It highlights Presto's architecture, performance improvements over traditional systems, and its open-source release.
What You'll Learn
1
How to optimize SQL queries for low latency in large datasets
2
Why Presto is more efficient than Hive/MapReduce for interactive analytics
3
How to integrate Presto with various data sources using connectors
Prerequisites & Requirements
- Understanding of SQL and distributed systems
- Familiarity with Hadoop and data warehousing concepts(optional)
Key Questions Answered
How does Presto improve query performance compared to Hive/MapReduce?
Presto is 10x better than Hive/MapReduce in terms of CPU efficiency and latency for most queries at Facebook. It achieves this by using an in-memory processing model and pipelined execution, which reduces end-to-end latency significantly.
What is the architecture of Presto?
Presto is a distributed SQL query engine that supports ANSI SQL and operates by sending SQL queries to a coordinator that manages execution across nodes. It avoids MapReduce, using a custom execution engine that processes data in memory and streams it between stages.
What are the current capabilities and limitations of Presto?
Presto supports a large subset of ANSI SQL, including joins and aggregations, but has limitations on join table sizes and cannot write output data back to tables. It processes over 30,000 queries daily, handling one petabyte of data.
What future improvements are planned for Presto?
Future enhancements for Presto include removing restrictions on join and aggregation sizes, introducing the ability to write output tables, and developing a query accelerator with a new data format optimized for processing.
Key Statistics & Figures
Data processed daily
1 petabyte
Presto processes this amount of data daily across the company.
CPU efficiency improvement
10x better
Presto outperforms Hive/MapReduce in CPU efficiency and latency for most queries.
Number of queries run daily
30,000 queries
This reflects the high demand and usage of Presto among Facebook employees.
Cluster size
1,000 nodes
Presto has successfully scaled to this size in a single cluster.
Technologies & Tools
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Backend
Presto
A distributed SQL query engine for interactive analytics.
Backend
Hadoop
Used for data storage in Facebook's data warehouse.
Storage
Hdfs
The primary storage system for Facebook's data warehouse.
Programming Language
Java
The language used to implement Presto.
Key Actionable Insights
1Leverage Presto's in-memory processing capabilities to reduce query latency in your data analysis tasks.By using Presto, you can significantly improve the speed of your data queries, especially when working with large datasets, making it ideal for real-time analytics.
2Utilize Presto's extensibility to connect to various data sources beyond HDFS.Presto's design allows for easy integration with different data stores, enabling a unified SQL querying capability across diverse data environments.
3Monitor the performance of your Presto queries to identify bottlenecks and optimize execution plans.Understanding how Presto schedules and executes queries can help you fine-tune performance and improve overall system efficiency.
Common Pitfalls
1
Assuming Presto can replace all functionalities of Hive/MapReduce without understanding its limitations.
While Presto offers significant advantages in speed and efficiency, it currently lacks the ability to write output data back to tables, which may limit its use in certain scenarios.
Related Concepts
Distributed SQL Query Engines
Data Warehousing
Real-time Analytics
Hadoop Ecosystem