{"id":183201,"date":"2024-03-18T08:38:00","date_gmt":"2024-03-18T07:38:00","guid":{"rendered":"https:\/\/liora.io\/en\/?p=183201"},"modified":"2026-02-06T08:22:59","modified_gmt":"2026-02-06T07:22:59","slug":"apache-presto-everything-you-need-to-know-about-this-distributed-sql-query-engine","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/apache-presto-everything-you-need-to-know-about-this-distributed-sql-query-engine","title":{"rendered":"Apache Presto: everything you need to know about this distributed SQL query engine"},"content":{"rendered":"<style>\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><p><strong>The ability to efficiently manage large datasets has become an unavoidable necessity. Apache Presto, a distributed SQL query engine designed for high-speed performance on huge data volumes, is the answer to this challenge.<\/strong><\/p>\t\t\n\t\t<p>Initially developed by Facebook to meet their own massive <a href=\"https:\/\/liora.io\/en\/modernising-data-processing-with-new-tools\">data processing<\/a> needs, Presto quickly evolved into the industry&#8217;s solution of choice, offering remarkable flexibility and efficiency.<\/p>\t\t\n\t\t\t<h3>Key features of Apache Presto<\/h3>\t\t\n\t\t<p><strong>Apache Presto<\/strong> offers a range of features that set it apart from other <a href=\"https:\/\/liora.io\/en\/apache-kafka-the-real-time-data-processing-platform\">data processing technologies<\/a>, making it particularly well-suited for fast, efficient analysis of large volumes of data.<\/p>\t\t\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Find out more about our training courses<\/a><\/div><\/div>\n\n\t\t\t<style>\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image6.png\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t<h4>Support for multiple data sources<\/h4>\t\t\n\t\t<p><strong>Extensive connectivity<\/strong><\/p><p>Presto can connect to a variety of data sources, such as distributed file systems (like <a href=\"https:\/\/liora.io\/en\/decoding-hdfs-unveiling-the-core-of-hadoop-distributed-file-system\">HDFS<\/a>), relational databases, and even cloud storage services. This capability enables users to perform queries on data from heterogeneous sources, without the need to first move or transform the data.<\/p><p><strong>Data federation<\/strong><\/p><p>With this feature, users can run queries involving multiple data sources in a single SQL query, greatly simplifying the analysis of disparate data.<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image1.png\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t<h4>Query performance and optimization<\/h4>\t\t\n\t\t<p><strong>Fast execution<\/strong><\/p><p>Designed for high query performance, even on very large datasets, it uses an <a href=\"https:\/\/liora.io\/en\/ram-or-random-access-memory-what-is-it\">in-memory processing model<\/a> and parallelizes queries across the cluster to accelerate response times.<\/p><p><strong>Advanced optimizations<\/strong><\/p><p>The engine incorporates sophisticated optimizations such as distributed query planning, predicate pushdown and other optimization techniques to maximize query efficiency.<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image7.png\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t<h4>Flexibility and scalability<\/h4>\t\t\n\t\t<p><strong>Horizontal scalability<\/strong><\/p><p>Presto can be easily scaled to handle load increases simply by adding more nodes to the cluster. This makes it ideal for environments where data volumes and compute requirements can fluctuate.<\/p><p><strong>Ad hoc and analytical query support<\/strong><\/p><p>Flexible in terms of the types of queries it can execute, from simple ad hoc queries to complex analyses, making it useful for a wide range of analytical applications.<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image5.png\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t<h4>SQL support and extensions<\/h4>\t\t\n\t\t<p><strong>SQL compatibility<\/strong><\/p><p>It supports much of the <a href=\"https:\/\/liora.io\/en\/sql-learn-all-about-the-programming-language-for-databases\">SQL standard<\/a>, including complex functions, joins, aggregations and subqueries, making it easy to learn for those familiar with SQL.<\/p><p><strong>Extensions and customization<\/strong><\/p><p>It also offers the possibility of extending its capabilities with user-defined functions and plug-ins, enabling advanced customization according to specific needs.<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image10.png\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t<h4>Ease of use and maintenance<\/h4>\t\t\n\t\t<p><strong>Simple configuration<\/strong><\/p><p>Presto is relatively simple to configure and maintain, with a minimum of external dependencies. This ease of configuration makes it attractive to teams with limited resources.<\/p><p><strong>Active community and support<\/strong><\/p><p>With an active open-source community and growing support from major technology companies, Presto benefits from constant evolution and strong user support.<\/p>\t\t\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-scientist\">Discover our training courses<\/a><\/div><\/div>\n\n\t\t\t<h3>Comparison with other tools<\/h3>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image3.png\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t<h4>Versus Hive<\/h4>\t\t\n\t\t<p><strong>Performance<\/strong><\/p><p>Presto is generally faster than Hive for most queries. Presto is designed for rapid analysis and ad hoc queries, while Hive is better suited to batch data processing tasks.<\/p><p><strong>Processing model<\/strong><\/p><p>Hive uses <a href=\"https:\/\/liora.io\/en\/mapreduce-how-to-use-it-for-big-data\">MapReduce<\/a> for batch processing, which can be slower for some queries. Presto, on the other hand, uses an in-memory processing model, which speeds up query processing.<\/p><p>SQL on Hadoop<\/p><p>While Hive was one of the first tools to enable SQL queries to be written on Hadoop, Presto offers a more modern approach with better performance.<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image2.png\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t<h4>Versus Apache Spark<\/h4>\t\t\n\t\t<p><strong>Data processing<\/strong><\/p><p>Spark is primarily designed for batch processing and in-memory calculations, while Presto is optimized for ad hoc queries on large datasets.<\/p><p><strong>Ecosystem and integration<\/strong><\/p><p>Spark is part of a wider ecosystem, including Spark Streaming, MLlib for machine learning, and GraphX for graph processing. Presto is more specialized in <a href=\"https:\/\/liora.io\/en\/open-sql-file-complete-tutorial\">SQL query execution.<\/a><\/p><p><strong>Programming languages<\/strong><\/p><p>Spark supports several programming languages (Scala, Java, Python, R), offering greater flexibility for application development. Presto focuses primarily on SQL.<\/p>\t\t\n\t\t\t<h3>Advantages and disadvantages<\/h3>\t\t\n\t\t\t<style type=\"text\/css\">\n.tg  {border-collapse:collapse;border-spacing:0;}\n.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n  overflow:hidden;padding:10px 5px;word-break:normal;}\n.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\n.tg .tg-0x09{background-color:#9b9b9b;text-align:left;vertical-align:top}\n.tg .tg-t724{background-color:#D9D9D9;font-family:Tahoma, Geneva, sans-serif !important;font-size:18px;font-weight:bold;\n  text-align:left;vertical-align:middle}\n.tg .tg-6qw1{background-color:#c0c0c0;text-align:center;vertical-align:top}\n.tg .tg-08l1{background-color:#F3F3F3;font-family:Tahoma, Geneva, sans-serif !important;font-size:18px;text-align:left;\n  vertical-align:top}\n<\/style>\n<table style=\"undefined;table-layout: fixed; width: 700px\">\n<colgroup>\n<col style=\"width: 100px\">\n<col style=\"width: 300px\">\n<col style=\"width: 300px\">\n<\/colgroup>\n<thead>\n  <tr>\n    <th><\/th>\n    <th><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image4.png\" width=\"30\" height=\"30\"><\/th>\n    <th><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/01\/image8.png\" width=\"30\" height=\"30\"><\/th>\n  <\/tr>\n<\/thead>\n<tbody>\n  <tr>\n    <td>Presto<\/td>\n    <td>Rapidity of queries, support for multiple data sources, and ease of use for those familiar with SQL.<\/td>\n    <td>Less suitable for batch processing and intensive computations.<\/td>\n  <\/tr>\n  <tr>\n    <td>Hive<\/td>\n    <td>Better suited for batch processing and ETL tasks, and widely adopted in the industry.<\/td>\n    <td>Slower performance for ad hoc queries.<\/td>\n  <\/tr>\n  <tr>\n    <td>Spark<\/td>\n    <td>Fast batch processing, support for real-time streaming, and flexibility with multiple programming languages.<\/td>\n    <td>May be more complex to configure and optimize, especially for simple SQL queries.<\/td>\n  <\/tr>\n<\/tbody>\n<\/table>\n\t\t\t<h3>Conclusion<\/h3>\t\t\n\t\t<p><strong>Apache Presto<\/strong> stands out as a fast and flexible distributed SQL query engine, ideal for ad hoc analysis of large datasets. Its ability to query a variety of data sources and its efficient architecture make it a valuable choice in the <strong>Big Data ecosystem.<\/strong><\/p>\t\t\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Find out about our training courses<\/a><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The ability to efficiently manage large datasets has become an unavoidable necessity. Apache Presto, a distributed SQL query engine designed for high-speed performance on huge data volumes, is the answer to this challenge. Initially developed by Facebook to meet their own massive data processing needs, Presto quickly evolved into the industry&#8217;s solution of choice, offering [&hellip;]<\/p>\n","protected":false},"author":76,"featured_media":183208,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-183201","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/183201","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/76"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=183201"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/183201\/revisions"}],"predecessor-version":[{"id":205972,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/183201\/revisions\/205972"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/183208"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=183201"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=183201"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}