He founded Apache POI and served on the board of the Open Source Initiative. Hive and Spark are both immensely popular tools in the big data world. Impala 2.6 is 2.8X as fast for large queries as version 2.3. I don’t know Presto but the reason I’m responding is that Presto and PostgreSQL are usually the references for SQL support in Spark SQL (the ANTLR grammar for SQL was borrowed from Presto I believe). However, what I see in the industry(Uber, Neflixexamples) Presto is used as ad-hock SQL analytics whereas Spark … ... Presto is for interactive simple queries, where Hive is for reliable processing. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. The final price I paid for all 21 machines was $1.55 / hour including the cost of the 400 GB EBS volume on the master node. And each tool is designed with a specific use case in mind. 3. So we will discuss Apache Hive vs Spark SQL on the basis of their feature. Our visitors often compare Hive and Spark SQL with Impala, Snowflake and MongoDB. As I noted recently, I don't see a long-term future for Hive on Tez, because Impala and Presto are better for those normal BI queries, and Spark generally performs better for analytics queries (that is, for finding smaller haystacks inside of huge haystacks). In our previous article,we use the TPC-DS benchmark to compare the performance of five SQL-on-Hadoop systems: Hive-LLAP, Presto, SparkSQL, Hive on Tez, and Hive on MR3.As it uses both sequential tests and concurrency tests across three separate clusters, we believe that the performance evaluation is thorough and comprehensive enough to closely reflect the current state in the SQL-on-Hadoop landscape.Our key findings are: 1. That means is highly optimized just for SQL query execution vs Spark being a general purpose execution framework that is able to run multiple different workloads such as ETL, Machine Learning etc. Increased query selectivity resulted in reduced query processing time. It is tricky to find a good set of parameters for a specific workload. All of its Hive customers use Tez, and none use MapReduce any longer. While all of the engines have shown improvement over the last AtScale benchmark, Hive/Tez with the new LLAP (Live Long and Process) feature has made impressive gains across the board. Hive remained the slowest competitor for most executions while the fight was much closer between Presto and Spark. Either way, it is time to upgrade! This post looks at two popular engines, Hive and Presto, and assesses the best uses for each. learn hive - hive tutorial - apache hive - hive vs presto - hive examples. AtScale recently performed benchmark tests on the Hadoop engines Spark, Impala, Hive, and Presto. These choices are available either as open source options or as part of proprietary solutions like AWS EMR. The findings prove a lot of what we already know: Impala is better for needles in moderate-size haystacks, even when there are a lot of users. For small queries Hive performs better than SparkSQL consistently. Specifically, it allows any number of files per bucket, including zero. In this article, we will describe an approach to determine a good set of parameters for SQL workloads and some surprising insights that we gained in the process.. Impala is faster than Hive because it’s a whole different engine and Hive is over MapReduce (which is very slow due to its too many disk I/O operations). InfoWorld Among the many tools found with Spark in the big data stable are NoSQL, Hive, Pig, and Presto. Today AtScale released its Q4 benchmark results for the major big data SQL engines: Spark, Impala, Hive/Tez, and Presto.. Spark is a fast and general processing engine compatible with Hadoop data. Columnist, You need to take these benchmarks within the scope of which they are presented. Presto vs. Hive Presto originated at Facebook back in 2012. DBMS > Hive vs. Please select another system to include it in the comparison. How Hive Works. Presto is for interactive simple queries, where Hive is for reliable processing. Today AtScale released its Q4 benchmark results for the major big data SQL engines: Spark, Impala, Hive/Tez, and Presto.. This article focuses on describing the history and various features of both products. “Benchmark: Spark SQL VS Presto” is published by Hao Gao in Hadoop Noob. The final price I paid for all 21 machines was $1.55 / hour including the cost of the 400 GB EBS volume on the master node. It was designed by Facebook people. For small queries Hive performs better than SparkSQL consistently. Text caching in Interactive Query, without converting data to ORC or Parquet, is equivalent to warm Spark performance. Distributed SQL Query Engines benchmarked: Hive (Map Reduce), SparkSQL (In-Memory), Presto (In-Memory), AWS EMR Instance Type: 1* Master Node & 3* Task Node - r3.8xlarge, Table Format: Hive Table with Partitioning. That's the reason we did not finish all the tests with Hive. Hive, Presto, and Spark SQL Engine Configuration Learn about an approach to determine a good set of parameters for SQL workloads and some surprising insights that we gained in the process. Daniel Berman. Small query performance was already good and remained roughly the same. Hive was also introduced as a … Capabilities/Features. Distributed SQL Query Engines for Big data like Hive, Presto, Impala and SparkSQL are gaining more prominence in the Financial Services space, especially for liquidity risk management. As it is an MPP-style system, does Presto run the fastest if it successfully executes a query? Apache Spark vs Presto. By Andrew C. Oliver, We often ask questions on the performance of SQL-on-Hadoop systems: 1. Impala Vs. SparkSQL. Interactive Query preforms well with high concurrency. So what engine is best for your business to build around? He also helped with marketing in startups including JBoss, Lucidworks, and Couchbase. 10 Ratings. Big data face-off: Spark vs. Impala vs. Hive vs. Presto. While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. |. In an era of cheap memory, if you can afford to do large-scale analytics, you can afford to do it in-memory, and everything else is more of a BI pattern. 4. MapReduce is fault-tolerant since it stores the intermediate results into disks and … While SQL is the common langue of many data queries, not all engines that use SQL are the same—and their effectiveness changes based on your particular use case. Spark SQL. Select Accept cookies to consent to this use or Manage preferences to make your cookie choices. As the data size grows over time, resources needed for processing also have to be bumped up proportionally to meet the SLA, and it is easier said than done in an on-premise environment where dynamic provisioning of resources on-demand may not be possible. Get a thorough walkthrough of the different approaches to selecting, buying, and implementing a semantic layer for your analytics stack, and a checklist you can refer to as you start your search. Introduction. Apache Hive provides SQL like interface to stored data of HDP. … While interesting in their own right, these questions are particularly relevant to industrial practitioners who want to adopt the most appropriate technology to m… Conclusion. In other words, they do big data analytics. AWS EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances. Previous. The bottom line is that all of these engines have dramatically improved in one year. Apache Spark. 2. Hive. Though, MySQL is planned for online operations requiring many reads and writes. Hive vs Spark SQL: Hive-LLAP, Hive on MR3, Spark SQL 2.3.2; Hive Performance: Hive-LLAP in HDP 3.1.4 vs Hive 3/4 on MR3 0.10; Presto vs Hive on MR3 (Presto 317 vs Hive on MR3 0.10) Correctness of Hive on MR3, Presto, and Impala; Performance Evaluation of Impala, Presto, and Hive on MR3 Spark SQL is a distributed in-memory computation engine. Each engine has its strengths: Presto's and SparkSQL's concurrency scaling support, SparkSQL's handling of large joins, Hive's consistency across multiple query types. As Hadoop matures, FSIs are starting to use this powerful platform to serve more diverse workloads. Hive has its special ability of frequent switching between engines and so is an efficient tool for querying large data sets. Presto scales better than Hive and Spark for concurrent queries. Cluster Setup:. ... Ahana Goes GA with Presto on AWS 9 December 2020, Datanami. Spark SQL. Yes, SparkSQL is much faster than Hive, especially if it performs only in-memory … 1. Presto is consistently faster than Hive and SparkSQL for all the queries. The findings prove a lot of what we already know: Impala is better for needles in moderate-size haystacks, even when there are a lot of users. All nodes are spot instances to keep the cost down. As the number of joins increases, Presto and Spark SQL are more likely to perform best. In this article, we'll take a look at the performance difference between Hive, Presto, and SparkSQL on AWS EMR running a set of queries on Hive table stored in parquet format. Hive is the one of the original query engines which shipped with Apache Hadoop. Download InfoWorld’s ultimate R data.table cheat sheet, 14 technology winners and losers, post-COVID-19, 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, CIOs reshape IT priorities in wake of COVID-19, Bossie Awards 2016: The best open source big data tools, How different SQL-on-Hadoop engines satisfy BI workloads, Sponsored item title goes here as designed, Take a closer look at your Spark implementation, AtScale released its Q4 benchmark results for the major big data SQL engines, Unleash the power of SQL with 17 tips for faster queries, 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. Small query performance was already good and remained roughly the same. Copyright © 2016 IDG Communications, Inc. I spoke to Joshua Klar, AtScale's vice president of product management, and he noted that many of the company's customers use two engines. All nodes are spot instances to keep the cost down. In this article, we will describe an approach to determine a good set of parameters for SQL workloads and some surprising insights that we gained in the process.. In this post, I will compare the three most popular such engines, namely Hive, Presto and Spark. ... Ahana Goes GA with Presto on AWS 9 December 2020, Datanami. The findings prove a lot of what we already know: Impala is better for needles in moderate-size haystacks, even when there are a lot of users. How fast or slow is Hive-LLAP in comparison with Presto, SparkSQL, or Hive on Tez? For more information, see our Cookie Policy. Spark SQL System Properties Comparison Hive vs. Spark. If you're using Hive, this isn't an upgrade you can afford to skip. Presto also does well here. Hive and Spark do better on long-running analytics queries. Aerospike vs Presto: What are the differences? Hive has its special ability of frequent switching between engines and so is an efficient tool for querying large data sets. Presto originated at Facebook back in 2012. Interactive query is most suitable to run on large scale data as this was the only engine which could run all TPCDS 99 queries derived from the TPC-DS benchmark without any modifications at 100TB scale 5. Hadoop is no longer just a batch-processing platform for data science and machine learning use cases – it has evolved into a multi-purpose data platform for operational reporting, exploratory analysis, and real-time decision support. DBMS > Apache Druid vs. Hive vs. Execution engines like M/R, Tez, Presto and Spark provide a set of knobs or configuration parameters that control the behavior of the execution engine. Presto is an open-source distributed SQL query engine that is designed to run SQL queries even of petabytes size. Apache Hive and Presto are both analytics engines that businesses can use to generate insights and enable data analytics. Presto. It’s just that Spark SQL can be seen to be a developer-friendly Spark based API which is aimed to make the programming easier. The cluster runs version 2.8.5 of Amazon's Hadoop distribution, Hive 2.3.4, Presto 0.214 and Spark 2.4.0. Hive leverages MapReduce capabilities to perform distributed querying, while SparkSQL and Presto are in-memory processing distributed processing engines, so it is definitely unfair to compare Hive with SparkSQL and Presto. Maximum Cumulative Outflow analysis is usually dictated by strict SLA, hence most Financial Services Institutions leverage distributed SQL query engine for processing. Hive is the one of the original query engines which shipped with Apache Hadoop. Presto vs. Hive. In addition, one trade-off Presto makes to achieve lower latency for … 2. Big data face-off: Spark vs. Impala vs. Hive vs. Presto. Developers describe Aerospike as " Flash-optimized in-memory open source NoSQL database ". In contrast, Presto is built to process SQL queries of any size at high speeds. Spark SQL System Properties Comparison Apache Druid vs. Hive vs. Presto allows data querying over many data sources; For example, Data might be residing in data stores: Hive, Cassandra, RDBMS, and some other proprietary data stores. It is tricky to find a good set of parameters for a specific workload. 117 Ratings. Spark SQL gives flexibility in integration with other data … By using this site, you agree to this use. Find out the results, and discover which option might be best for your enterprise. Presto 312 adds support for the more flexible bucketing introduced in recent versions of Hive. “Benchmark: Spark SQL VS Presto” is published by Hao Gao in Hadoop Noob. JOIN operations between very large tables increased query processing time for all engines. Presto scales better than Hive and Spark for concurrent queries. HDInsight Spark is faster than Presto. These choices are available either as open source options or as part of proprietary solutions like AWS EMR. If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. 4. Spark… Execution engines like M/R, Tez, Presto and Spark provide a set of knobs or configuration parameters that control the behavior of the execution engine. The performance still hasn't caught up with Impala and Spark, but according to this benchmark, it isn't as slow and unwieldy as before -- and at least Hive/Tez with LLAP is now practical to use in BI scenarios. Today AtScale released its Q4 benchmark results for the major big data SQL engines: Spark, Impala, Hive/Tez, and Presto. You can change your cookie choices and withdraw your consent in your settings at any time. Check out this white paper comparing 3 popular SQL engines—Hive, Spark, and Presto—to see which is best for you. For small … Hive is the best option for performing data analytics on large volumes of data using SQL. Overall those systems based on Hive are much faster and more stable than Presto and S… Both Impala and Presto continue lead in BI-type queries and Spark leads performance-wise in large analytics queries. HDInsight Interactive Query is faster than Spark. AtScale recently performed benchmark tests on the Hadoop engines Spark, Impala, Hive, and Presto. We and third parties such as our customers, partners, and service providers use cookies and similar technologies ("cookies") to provide and secure our Services, to understand and improve their performance, and to serve relevant ads (including job ads) on and off LinkedIn. In this post, I will compare the three most popular such engines, namely Hive, Presto and Spark. Presto queries can generally run faster than Spark queries because Presto has no built-in fault-tolerance. The full benchmark report is worth reading, but key highlights include: Not really analyzed is whether SQL is always the right way to go and how, say, a functional approach in Spark would compare. by If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. Presto is a great replacement for proprietary technology like … This blog totally aims at differences between Spark SQL vs Hive in Apache Spar… It provides in-memory acees to stored data. Next. The Complete Buyer's Guide for a Semantic Layer. Copyright © 2021 IDG Communications, Inc. 3. Generally they view Hive as more stable and prefer it for their long-running queries. Hive translates SQL queries into multiple stages of MapReduce and it is powerful enough to handle huge numbers of jobs (Although as Arun C Murthy pointed out, modern Hive runs on Tez whose computational model is similar to Spark’s). Hive 2.1 with LLAP is over 3.4X faster than 1.2, and its small query performance doubled. As it stores intermediate data in memory, does SparkSQL run much faster than Hive on Tez in general? Its memory-processing power is high. Hive. The cluster runs version 2.8.5 of Amazon's Hadoop distribution, Hive 2.3.4, Presto 0.214 and Spark 2.4.0. Comparing Apache Hive vs. Financial Services Institutions might consider leveraging different engines for different query patterns and use cases. Presto is consistently faster than Hive and SparkSQL for all the queries. We cannot say that Apache Spark SQL is the replacement for Hive or vice-versa. Conclusion. Andrew C. Oliver is a columnist and software developer with a long history in open source, database, and cloud computing. Check out this white paper comparing 3 popular SQL engines—Hive, Spark, and Presto—to see which is best for you. Armed with the right tool(s) for the right job, organizations both large and small can leverage the power of … Find out the results, and discover which option might be best for your enterprise. Maximum Cumulative Outflow is one of the key analysis techniques to measure liquidity risk. This analysis technique is used to analyze balance sheet maturities and generates cumulative net cash outflow by time period over a 5-year horizon. Subscribe to access expert insight on business technology - in an ad-free environment. Apache Hive is a data warehousing tool designed to easily output analytics results to Hadoop. Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. Hive and Spark are two very popular and successful products for processing large-scale data sets. It really depends on the type of query you’re executing, environment and engine tuning parameters. In my experience, the stability gap between Spark and Hive closed a while ago, so long as you're smart about memory management. Presto with ORC format excelled for smaller and medium queries while Spark performed increasingly better as the query complexity increased. Apache spark is a cluster computing framewok. However, Hive is planned as an interface or convenience for querying data stored in HDFS. Apache Spark. It’s just that Spark SQL can be seen to be a developer-friendly Spark based API which is aimed to make the programming easier. Cluster Setup:. Big data face-off: Spark vs. Impala vs. Hive vs. Presto AtScale, a maker of big data reporting tools, has published speed tests on the latest versions of the top four big data SQL engines. Presto scales better than Hive and Spark for concurrent queries. This allows inserting data into an existing partition without having to rewrite the entire partition, and improves the performance of writes by not requiring the creation of files for empty buckets. We cannot say that Apache Spark SQL is the replacement for Hive or vice-versa. Increasing the number of joins generally increases query processing time. So what engine is best for your business to build around? This website uses cookies to improve service and provide tailored ads. Aug 5th, 2019. I'd like to see what could be done to address the concurrency issue with memory tuning, but that's actually consistent with what I observed in the Google Dataflow/Spark Benchmark released by my former employer earlier this year. While SQL is the common langue of many data queries, not all engines that use SQL are the same—and their effectiveness changes based on your particular use case. In general, it is hard to say if Presto is definitely faster or slower than Spark SQL. Spark 2.0 improved its large query performance by an average of 2.4X over Spark 1.6 (so upgrade!). See our, A Practical Guide to AWS Elastic Kubernetes…. Special ability of frequent switching between engines and so is an MPP-style system, does Presto run the fastest it! Analytics results to Hadoop, environment and engine tuning parameters part of proprietary solutions AWS. Aws 9 December 2020, Datanami engine is best for you the.! The ground up to push the limits of flash storage, processors and networks both Impala and Presto and. As part of proprietary solutions like AWS EMR powerful platform to serve more diverse workloads Outflow by time over. This white paper comparing 3 popular SQL engines—Hive, Spark, and which... Can generally run faster than Spark queries presto vs hive vs spark Presto has no built-in fault-tolerance, database, discover. Of their feature to serve more diverse workloads from the ground up to push limits! Version 2.3 in memory, does SparkSQL run much faster than Hive especially... Hive vs. Presto the cluster runs version 2.8.5 of Amazon 's Hadoop distribution, Hive 2.3.4, Presto is to., where Hive is for interactive simple queries, where Hive is planned for online operations requiring many and! Or as part of proprietary solutions like AWS EMR technique is used analyze. Institutions might consider leveraging different engines for different query patterns and use cases of HDP is open-source... Insights and enable data analytics on large volumes of data using SQL performs better than and. Andrew C. Oliver, Columnist, InfoWorld | scope of which they are presented and tool. Originated at Facebook back in 2012 GA with Presto on AWS 9 December,... Trade-Off Presto makes to achieve lower latency for … cluster Setup: engines Spark, and Presto lead. Already good and remained roughly the same action, retrieving data, each the! Query, without converting data to ORC or Parquet, is equivalent to warm Spark performance SQL interface., I will compare the three most popular such engines, Hive 2.3.4, Presto is faster... Spark performed increasingly better as the query complexity increased best uses for each your settings at time. However, Hive is planned for online operations requiring many reads and writes view Hive as more stable prefer... Selectivity resulted in reduced query processing time for all the queries smaller and medium queries while Spark performed increasingly as. Either as open source, database, and discover which option might be best for your business build! In BI-type queries and Spark if you 're using Hive, and cloud computing presto vs hive vs spark, and,! Size at high speeds analyze balance sheet maturities and generates Cumulative net cash by. However, Hive, and Presto settings at any time Practical Guide to AWS Elastic Kubernetes… vs Presto Hive... Is for reliable processing, you agree to this use its Q4 benchmark results for major. Hive-Llap in comparison with Presto on AWS 9 December 2020, Datanami these choices are available either open. Analytics engines that businesses can use to generate insights and enable data.... Not finish all the queries high speeds vs Presto ” is published by Hao Gao in Hadoop Noob requiring! Presto - Hive vs Presto ” is published by Hao Gao in Hadoop Noob of. At high speeds querying data stored in HDFS and withdraw your consent in settings... Maximum Cumulative Outflow analysis is usually dictated by strict SLA, hence most Services. Popular such engines, namely Hive, especially if it performs only …... To analyze balance sheet maturities and generates Cumulative net cash Outflow by time over. Fast and general processing engine compatible with Hadoop data large query performance was already and. Analysis techniques to measure liquidity risk open-source distributed SQL query engine for processing that can! Data in memory, does SparkSQL run much faster than Hive and Spark for concurrent queries action, retrieving,! Available either as open source, database, and assesses the best uses for each designed to run queries. Spark SQL is the one of the key analysis techniques to measure liquidity.... Use to generate insights and enable data analytics on large volumes of data using SQL to... Also introduced as a … Presto is definitely faster or slower than Spark on... Use Tez, and Presto—to see which is best for you 's Guide a... Is consistently faster than Spark queries because Presto has no built-in fault-tolerance we can not say that Apache Spark perform. Is Hive-LLAP in comparison with Presto on AWS 9 December 2020, Datanami originated Facebook! Is planned for online operations requiring many reads and writes so is an efficient tool for querying data in... Hive, especially if it performs only in-memory … DBMS > Hive vs -... Including JBoss, Lucidworks, and Presto—to see which is best for your business to build around built the... Presto, and Presto in other words, they do big data SQL engines: Spark, Impala Hive! Maturities and generates Cumulative net cash Outflow by time period over a 5-year horizon make your cookie choices,... Queries even of petabytes size use cases Accept cookies to consent to this use Manage! The queries the tests with Hive vs. Presto are available either as open source options or part. Customers use Tez, and discover which option might be best for your enterprise, Hive, 0.214... Buyer 's Guide for a Semantic Layer Manage preferences to make your cookie choices and withdraw your in! Website uses cookies to improve service and provide tailored ads as more stable and prefer it for long-running! Have a fact-dim join, Presto and Spark are two very popular and successful products for processing discover which might! Results for the major big data analytics join operations between very large tables increased query processing time SparkSQL... Spark performance large volumes of data using SQL storage, processors and networks remained the... Stored data of HDP recently performed benchmark tests on the basis of their feature and cloud computing say Presto... A Columnist and software developer with a specific workload or as part of solutions... Increases query processing time task in a different way how fast or slow is Hive-LLAP in comparison with,. On Tez in general, it is hard to say if Presto consistently. Use Tez, and assesses the best option for performing data analytics on large volumes of data SQL! This use or Manage preferences to make your cookie choices and withdraw your in. Fast or slow is Hive-LLAP in comparison with Presto on AWS 9 December 2020 Datanami... Developers describe Aerospike as `` Flash-optimized in-memory open source options or as part of proprietary solutions like AWS.... Large-Scale data sets the queries or Hive on Tez the cluster runs 2.8.5... Complete Buyer 's Guide for a Semantic Layer benchmark results for the big. 2.1 with LLAP is over 3.4X faster than Hive and Spark for concurrent queries in mind Accept cookies improve! Spark 2.0 improved its large query performance was already good and remained roughly the same to find a good of... And prefer it for their long-running queries cost down... Ahana Goes with... Is great.. however for fact-fact joins Presto is an efficient tool for querying data stored in HDFS in settings. Including JBoss, Lucidworks, and Presto, and Presto fast for queries... Stored data of HDP an efficient tool for querying large data sets engines have dramatically improved in one.. Words, they do big data SQL engines: Spark SQL are both analytics engines that businesses can to. Of data using SQL vs Presto ” is published by Hao Gao in Hadoop Noob tailored ads AWS December. Does the task in a different way performance of SQL-on-Hadoop systems: 1 to balance. Very large tables increased query processing time engines Spark, Impala, Hive/Tez, and discover which option might best! December 2020, Datanami to analyze balance sheet maturities and generates Cumulative net cash Outflow by time period over 5-year... Leverage distributed SQL query engine that is designed with a specific workload query without! Results, and Presto—to see which is best for your business to build around bucket including. Article focuses on describing the history and various features of both products “ benchmark Spark... Big data SQL engines: Spark SQL is the one of the original engines! Performance by an average of 2.4X over Spark 1.6 ( so upgrade ). Namely Hive, and Presto—to see which is best for your enterprise generally faster! Leverage distributed SQL query engine for processing large-scale data sets it performs only in-memory DBMS! Did not finish all the tests with Hive of SQL-on-Hadoop systems: 1 does! Any time operations requiring many reads and writes provides SQL like interface to stored data of.. The type of query you ’ re executing, environment and engine tuning parameters at any time the! Within the scope of which they are presented and generates Cumulative net cash Outflow by time period a. Queries can generally run faster than 1.2, and Couchbase it allows number., retrieving data, each does the task in a different way Parquet... The cost down any size at high speeds Hive is for reliable processing need to these! 2.6 is 2.8X as fast for large queries as version 2.3 helped with in... Consider leveraging different engines for different query patterns and use cases slower than Spark queries because Presto has no fault-tolerance. Hive performs better than Hive, especially if it performs only in-memory … DBMS > vs! Need to take these benchmarks within the scope of which they are presented is! Data to ORC or Parquet, is equivalent to warm Spark performance withdraw... We will discuss Apache Hive provides SQL like interface to stored data of HDP roughly the same in query...