As more Hadoop workloads move to interactive and user-facing, teams face the unpleasant prospect of using one SQL engine just for interactive while they use Hive for everything else. This hangout is to cover difference between different execution engines available in Hadoop and Spark clusters The first thing we see is that Impala has an advantage on queries that run in less than 30 seconds. Hive Pros: Hive Cons: 1). Thanks. Pre-fetching and caching of column chunks 3. It may have been possible to find Impala-specific workarounds to these gaps, but no attempt was made to do so since these results could not be directly compared. This introduces a lot of cost and complexity to Hadoop because it really means separate specialized teams to tune, troubleshoot and operate two very different SQL systems. Both Apache Hiveand Impala, used for running queries on HDFS. Because of this, Impala is also great when working with ad-hoc queries, like when exploring by iteratively digging into data.  You’ll want to change your query over and over again, at a moment’s notice, and have very fast response times so you’re not waiting forever for each iteration. Â. Hive LLAP has many sophisticated capabilities that may make it a little harder for developers to get started and use effectively.  In Hive LLAP, sometimes a query takes longer to go through the planning and ramp-up for execution.  However, Hive is designed to be very fault-tolerant.  If a fragment of a long-running query fails, Hive will reassign it and try again. Tez was initially an alternative execution engine for Hive. Hive is a data warehouse software project built on top of APACHE HADOOP developed by Jeff’s team at Facebook with a current stable version of 2.3.0 released 7 months ago on 19 July 2017. Apache Hadoop and associated open source project names are trademarks of the Apache Software Foundation. To prepare the Impala environment the nodes were re-imaged and re-installed with Cloudera’s CDH version 5.8 using Cloudera Manager. How fast or slow is Hive-LLAP in comparison with Presto, SparkSQL, or Hive on Tez? Hive supports file format of Optimized row columnar (ORC) format with Zlib compression but Impala supports the Parquet format with snappy compression. Here we will only draw comparison between the queries that ran on both engines with identical syntax. Apache Hive is easily the best SQL engine in the Hadoop ecosystem, with ACID, security, Spark access etc. Only queries that worked in both environments were included. LLAP (Live Long and Process) is the newest query acceleration engine for Hive 2.0, which entered GA in 2017. Pig, Spark, PrestoDB, and other query engines also share the Hive Metastore without communicating though HiveServer. Interactive Query preforms well with high concurrency. LLAP stands for ‘Long Live and Process’ Hortonworks distribution usually supports LLAP as it is a part of their Stinger initiative. The positions change as query times get a bit longer: By the time we reach one minute, Hive has completed 32 queries compared to Impala’s 26 and the relative position does not switch again. TEZ AM query coordinator : TEZ Am which accepts the incoming the request of the user and execute them in executors available inside the LLAP daemons (JVM). But there are some differences between Hive and Impala – SQL war in the Hadoop Ecosystem. Result 1. It is a stable query engine : 2). Before we get to the numbers, an overview of the test environment, query set and data is in order. Impala is shipped by Cloudera, MapR, and Amazon. The x axis in this chart moves in discrete 30 second intervals. Introduction: how does LLAP fit into Hive LLAP is a set of persistent daemons that execute fragments of Hive queries. Data Warehouse – Impala vs. Hive LLAP, , a lively debate among experts, on October 20, 2020, 10:00am US pacific time, 1:00pm US eastern time, complete with customer use case examples, and followed by a live q&a. Â. Query processin… The first thing we see is that Impala has an advantage on queries that run in less than 30 seconds. for enterprise data warehouse, or EDW, use cases.  With an EDW, you are supporting Business Intelligence reports and dashboards, dependent data marts, other enterprise applications, external systems, and more. Aren’t two superheroes better than one? Your email address will not be published. if yes, why does Impala run much faster than Hive in Cloudera? Download the. Contact Us 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 … The defaults from Cloudera Manager were used to setup / configure Impala 2.6.0.