presto query optimization
Query optimizers are responsible for converting SQL, expressed declaratively, to an efficient sequence of operations that may be performed by the engine on the underlying data. “SQL on everything” is the tagline associated with Presto, the query engine that was initially developed by Facebook to rapidly analyze massive amounts of data — particularly data that lay scattered across multiple formats and sources. This allows Presto to execute some simple queries in O(1) time. 16 technology winners and losers, post-COVID, Download InfoWorld’s ultimate R data.table cheat sheet, COVID-19 crisis accelerates rise of virtual call centers, Q&A: Box CEO Aaron Levie looks at the future of remote work, Rethinking collaboration: 6 vendors offer new paths to remote work, Amid the pandemic, using trust to fight shadow IT, 5 tips for running a successful virtual meeting, Stay up to date with InfoWorld’s newsletters for software developers, analysts, database programmers, and data scientists, Get expert insights from our member-only Insider articles. However, these tips would be equally valid for query optimization on any Presto instance. Presto is an open source distibruted query engine built for Big Data enabling high performance SQL access to a large variety of data sources including HDFS, PostgreSQL, MySQL, Cassandra, MongoDB, Elasticsearch and Kafka among others.. Update 6 Feb 2021: PrestoSQL is now rebranded as Trino. If the GROUP BY columns match, the values are then aggregated together. Starburst Presto distribution delivers fast performance (enabled via cost-based query optimization), enhanced security features, and integration with Azure and HDInsight services such as: Azure Blob Storage. Query tuning. The contents of the Website do not constitute advice and should not be relied upon in making or refraining from making, any decision. This cluster size turned out to be problematic for EMR Presto and for EMR Hive (more details below). ... with the optimizer.join-reordering-strategy configuration property providing the default value. Inspired by the increasingly complex SQL queries run by the Presto user community, engineers at Facebook and Starburst have recently focused on cost-based query optimization. The input to a query optimizer is a “logical plan,” which itself is the result of parsing the input SQL and converting it to a high-level collection of the operations required to execute the query. In the second part, we will discuss concrete optimization rules and transformations. Presto breaks a query into one or more stages, also called fragments, and each stage contains multiple operators. presto_op.optimize_query(query). We’ll discuss a couple here. Presto generally performs the join in the declared order (when cost-based optimizations are off), but it tries to avoid cross joins if possible. When you understand how Presto functions you can better optimize queries when you run them. From there, the planner compiles the AST into a query plan , optimizing it for a fragmenter that then segments the plan into tasks. In its early version, Presto’s query optimizer was a set of rules that would operate on, and mutate, the logical plan until a fixed point is reached. This section details the following best practices: Optimize ORDER BY. External Hive Metastore. Query engines like Presto work well in this auto-scaling context, and they are seeing increased adoption as more enterprises move data to the cloud. The aspect of join ordering that has the largest impact on performance is the size of the data being processed and transferred over the network. Aerospike has announced that Aerospike Connect for Presto is out of beta and now generally available. Figure 6. You will not use the Website for any purpose that is unlawful or prohibited by these Terms and Conditions. I used EMR release emr-5.16.0 for all EMR tests. DoordaHost uses Presto for its main query engine, to help get the most out of it we’ve listed some tips below on how to get the best Query Optimization for Presto when you’re connected to DoordaHost. Get optimization hints using optimize_query(query) e.g. Our setup for running TPC-DS benchmark was as follows: TPC-DS Scale: 3000 Format: ORC (Non Partitioned) Scheme: HDFS Cluster: 16 c3.4xlarge in AWS us-east region. To improve on the RBO, it would be useful to determine the size of the inputs to the joins in order to decide which input should be used to build the hash table. Enable optimization of some aggregations by using values that are stored as metadata. This decoupling of storage and compute allows users to seamlessly resize their compute resources. Adaptive query execution typically takes as input a query plan that is produced as the result of heuristic/rule-based optimization, and then reorders operators or replans subqueries based on run-time performance. All content on the Website is for non-commercial use only and is issued under the Creative Commons Attribution-Non Commercial 4.0 International Licence . To do so, DoordaHost consolidates the results from multiple worker nodes into a single node and then sorts them. Your access to and use of Doorda.com (the Website) , the property of Doorda Ltd (“Doorda”), is subject exclusively to these Terms and Conditions. As such, these databases can analyze and store all the relevant statistics about their datasets. On-The-Fly Query Optimization (CBO & Dynamic Filtering) Presto has several features that greatly speed up query planning and execution. Trimming the number of columns reduces the amount of data that needs to be processed through the entire query execution pipeline. The following information may help you if your cluster is facing a specific performance problem. In this blog post series, we investigate internals of Presto optimizer. Presto and Apache Spark have its own resource manager, but Apache Spark is generally run on top of Hadoops’ YARN resource manager. Today, a strong worldwide community contributes to its ongoing development. Presto optimizes a query using QuadTree. Presto query optimization for Kafka connector. The next step in the evolution of query optimizers was the advent of cost-based optimization. For a typical SQL query, there exists one logical plan but many strategies for implementing and executing that logical plan to produce the desired results. However, the freedom Presto provides to connector is limited to only act as a data source. Data was stored in HDFS inst… Let us walk through this with the TPC-H Q3 benchmark query discussed above. Config Properties; JVM Settings; Tuning Presto# The default Presto settings should work well for most workloads. I've a kafka topic with timestamp as message key and the topic is partitioned by hash of year-month. So it is no surprise that Presto’s query optimizer is unable to improve queries that contain many LIKE clauses. This sequence of operations, while guaranteed to produce accurate results, will not work for even a moderate size dataset in most hardware. But the problem starts when the project goes live and enormous data starts flooding the database. The first is well known in other databases, Cost Based Optimization. The ORDER BY clause returns the results of a query … Doorda will never ask for Credit Card details and requests that you do not enter it on any of the forms on the Website. Use numbers instead of strings within GROUP BY clause. Most optimizers (Presto included) skip cross-joins during join enumeration. Today, we are excited to announce the general availability (GA) of BigQuery materialized views. This already reduces the size of the intermediate result set by several orders of magnitude. Originally developed by Facebook, and now used widely among leading digital and enterprise companies, Presto is the fastest growing distributed SQL query engine within the industry. View our Privacy Policy, 1. ORDER BY clause returns results of a query in a sort order. Adaptive query execution is a paradigm that removes the architectural distinction between query planning and query execution. I'd like to know if the Kafka connector for Presto will do partition/offset related optimization? Now, Presto needs to create an execution plan for this query. Conceptually, Presto’s Cost-Based Optimizer is very simple; alternative query plans are considered, and the best plan is chosen and executed. Any link to other websites is not an endorsement of such websites and you acknowledge and agree that we are not responsible for the content or availability of any such sites. 8.1 The Website is provided on an AS IS and AS AVAILABLE basis without any representation or endorsement made and without warranty of any kind whether express or implied, including but not limited to the implied warranties of satisfactory quality, fitness for a particular purpose, non-infringement, compatibility, security and accuracy. For example, if key, key1 and key2 are partition keys, the following queries … Tuning Presto. To avoid this problem, you have to understand how to configure these parameters in the config.properties and jvm.properties files: Presto memory; Query optimization Using approximate algorithms (approx_distinct() instead of COUNT(DISTINCT …)) Selecting the columns the user wants explicitly, rather than using (SELECT *) Filtering on partitioned columns; Try to extract nested subqueries using a WITH clause. Optimize GROUP BY. The first advantage is the greatly reduced memory required to compute this join since it aggressively applies filters to prune out tuples that are not of interest. The valid values are: Presto has an extensible, federated design that allows it to read and process data seamlessly from disparate data sources and file formats. DoordaHost uses Presto for its main query engine, to help get the most out of it we’ve listed some tips below on how to get the best Query Optimization for Presto when you’re connected to DoordaHost. The role of query optimization is to transform and evolve the initial plan into an equivalent plan that can be executed as fast as possible, at least in a reasonable amount of time, given finite resources of the Presto cluster. 7.1 All copyright, trademarks and all other intellectual property rights in the Website and its content (including without limitation the Website design, text, graphics and all software and source codes connected with the Website) are owned by or licensed to Doorda or otherwise used by Doorda as permitted by law. White Paper: Presto Cost-Based Query Optimization. The RBO would also never suggest a Cartesian product of all three tables for the intermediate result in this case. If you do not accept these Terms and Conditions you must immediately stop using the Website. These suboptimal plan fragments may be reoptimized several times throughout query execution, with a careful tradeoff between opportunistic re-optimization and the risk of producing an even less optimal plan. However, these tips would be equally valid for query optimization on any Presto instance. (1) Change or remove (temporarily or permanently) the Website or any part of it without notice and you confirm that Doorda shall not be liable to you for any such change or removal I'd like to know if the Kafka connector for Presto will do partition/offset related optimization? Presto on the other hand uses its own coordinator within the cluster to schedule queries … It is also inefficient to read all the data from disk for all three tables while the query is only interested in specific tuples that satisfy the constraints described in the predicates. Filter statistics. InfoWorld – By combining machine learning and adaptive query execution, query optimization in Presto could become smarter and more efficient over repeated use. Solving query optimization in Presto “SQL on everything” is the tagline associated with Presto , the query engine that was initially developed by Facebook to rapidly analyze massive amounts of data — particularly data that lay scattered across multiple formats and sources. Therefore, even though the customer table may be smaller than the orders table, once the filters are applied, the number of records flowing into the join from the orders table may actually be fewer. The numbers represent the location of the grouped column in the SELECT statement. We will only send you relevant information and you can unsubscribe at any time. Metadata-Only Query Optimization# We now support an optimization that rewrites aggregation queries that are insensitive to the cardinality of the input (e.g., max(), min(), DISTINCT aggregates) to execute against table metadata. He is currently developing solutions to help low latency queries in Presto at Facebook.. Yutian “James” Sun is a Software Engineer at Facebook working on large-scale distributed database systems. You can capture the rest behind the scenes. When joining 2 tables, specify the larger table on the left side of the join and the smaller table on the right side of the join.