International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
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Optimizing Query Performance through Partitioning in Presto Ajay Krishnan Prabhakaran Data Engineer, Meta Inc ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - As data grows exponentially, optimizing query
efficiently. HDFS’s architecture is built on two main components:
performance in distributed SQL engines like Presto becomes increasingly crucial. Partitioning, bucketing, and sorting strategies allow for efficient data querying and management. This paper explores these techniques in the context of Presto, providing insights into Presto's integration with Apache Spark and Hadoop Distributed File System (HDFS). Through a detailed examination of partitioning, bucketing, sorting, and other optimization techniques, the study highlights best practices to improve query performance. A series of diagrams and real-world case studies illustrate how organizations can enhance their data warehousing capabilities.
NameNode: Manages the metadata of the file system (e.g., directory structure, file ownership, and block locations).
DataNodes: Store the actual data blocks and perform read/write operations as directed by the NameNode
Key Words: Presto, partitioning, query optimization, HDFS, Spark, bucketing, distributed SQL, data warehousing
1.INTRODUCTION Distributed SQL engines like Presto have become essential for large-scale data querying in modern data ecosystems. Developed by Facebook and widely adopted by companies like Netflix and Airbnb, Presto enables real-time queries across distributed data sources without requiring data movement . One of the key techniques that Presto employs to optimize query performance is partitioning. Fig -1: HDFS Architecture Overview
Partitioning divides large datasets into smaller, manageable pieces based on specific column criteria, such as dates or geographical regions, which Presto uses to reduce the scope of data scanned during a query. Additional techniques such as bucketing and sorting work alongside partitioning to further enhance query performance by improving data locality and reducing the complexity of data scans.
2.2 Presto Integration with HDFS Presto natively supports querying data stored in HDFS, allowing users to execute SQL queries across massive datasets without needing to move the data. Presto achieves this by abstracting the data storage layer, enabling it to read from HDFS while distributing the query processing across multiple nodes.
2. HADOOP DISTRIBUTED FILE SYSTEME (HDFS) 2.1 Overview of HDFS
Presto’s ability to work with HDFS enables it to benefit from HDFS’s fault-tolerant and distributed storage architecture. When Presto queries a dataset stored in HDFS, it accesses the relevant data blocks from DataNodes based on the query's requirements, leveraging partitioning and bucketing techniques to minimize the amount of data read.
The Hadoop Distributed File System (HDFS) is a scalable, fault-tolerant, distributed file system designed for storing large datasets across clusters of commodity hardware. HDFS is a core component of the Apache Hadoop ecosystem and is the backbone of many large-scale data processing applications, including Presto and Apache Spark.
3. PRESTO AND APACHE SPARK INTEGRATION
HDFS breaks files into smaller blocks (typically 64MB or 128MB) and distributes these blocks across a cluster of nodes. This block-level distribution allows for parallel data processing, which is essential for handling large datasets
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3.1 Overview of Apache Spark Apache Spark is a distributed data processing framework known for its speed, ease of use, and support for
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