{"id":10019,"date":"2018-03-05T13:29:55","date_gmt":"2018-03-05T13:29:55","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=10019"},"modified":"2018-03-05T13:29:55","modified_gmt":"2018-03-05T13:29:55","slug":"impala-vs-hive","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/impala-vs-hive\/","title":{"rendered":"Impala vs Hive &#8211; Difference Between Hive and Impala"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Both <strong>Apache Hive<\/strong> and Impala, used for running queries on HDFS. But there are some differences between Hive and Impala &#8211; \u00a0SQL war in the Hadoop Ecosystem. <\/span><\/p>\n<p><span style=\"font-weight: 400\">So, in this article, &#8220;Impala vs Hive&#8221; we will compare Impala vs Hive performance on the basis of different features and discuss why Impala is faster than\u00a0Hive,\u00a0when to use Impala vs hive. Before comparison, we will also discuss the introduction of both these technologies. \u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400\">Introduction: Impala vs Hive<\/span><\/h2>\n<p><strong>a. What is Hive?<\/strong><br \/>\n<span style=\"font-weight: 400\">Basically, for performing data-intensive tasks we use Hive. Such as querying, analysis, processing, and visualization. It was first developed by Facebook. Also, it is a data warehouse infrastructure build over <strong>Hadoop <\/strong>platform. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Moreover, Hive is versatile in its usage since it supports analysis of huge datasets stored in Hadoop\u2019s <strong>HDFS<\/strong> and other compatible file systems. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Like Amazon S3. Hive offers an SQL \u2013 like language (HiveQL) with schema on reading and transparently converts queries to <strong>MapReduce<\/strong>, Apache Tez, and Spark jobs. Some of the best features of Hive are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Like it offers to index for accelerated processing<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Hive supports several types of storages. Such as Plain Text, RCFIle, HBase, ORC<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Also, it supports Metadata storage in RDBMS<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Hive supports SQL like queries. Though we can get implicitly converted into MapReduce, Tez or Spark jobs<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">To manipulate strings, dates it has Built-in User Defined Functions (UDFs)<\/span><\/li>\n<\/ul>\n<p><strong>b. What is Impala?<\/strong><br \/>\n<span style=\"font-weight: 400\">On defining Impala we can say it is an open source Massively Parallel Processing (MPP) SQL engine. Moreover, \u00a0for running queries on HDFS and Apache HBase, Impala is a wonderful choice. For processing, it doesn\u2019t require the data to be moved or transformed prior. <\/span><\/p>\n<p><span style=\"font-weight: 400\">However, it is easily integrated with the whole of Hadoop ecosystem. Also, for open source interactive business intelligence tasks, Impala\u2019s unified resource management across frameworks makes it the standard. Some of the best features of Impala are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Impala does support for <strong>Hadoop Distributed File System (HDFS)<\/strong> and <strong>Apache HBase<\/strong><\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">However, Impala also recognizes Hadoop file formats like text, LZO, Avro, RCFile, Parquet<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It also Supports Kerberos authentication<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">With Apache Sentry, it also offers Role based authorization.<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400\">Difference between Hive and Impala<\/span><\/h2>\n<p>Following are the featurewise comparison between\u00a0Impala vs Hive:<\/p>\n<div id=\"attachment_10214\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Impala-vs-Hive-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-10214\" class=\"wp-image-10214 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Impala-vs-Hive-01.jpg\" alt=\"Impala vs Hive - SQL war in Hadoop Ecosystem\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Impala-vs-Hive-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Impala-vs-Hive-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Impala-vs-Hive-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Impala-vs-Hive-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Impala-vs-Hive-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-10214\" class=\"wp-caption-text\">Impala vs Hive &#8211; SQL war in Hadoop Ecosystem<\/p><\/div>\n<h3><span style=\"font-weight: 400\">a. Query Process<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Basically, \u00a0in Hive every query has the common problem of a \u201ccold start\u201d.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Impala avoids any possible startup overheads, being a native query language. However, that are very frequently and commonly observed in MapReduce based jobs. Moreover, to process a query always Impala daemon processes are started at the boot time itself, making it ready.`<\/span><\/p>\n<h3><span style=\"font-weight: 400\">b. Intermediate Results<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Basically, Hive materializes all intermediate results. Hence, it enables enabling better scalability and fault tolerance. However, that has an adverse effect on slowing down the data processing.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">However, it&#8217;s streaming intermediate results between executors. Although, that trades off scalability as such.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">c. During the Runtime<\/span><span style=\"font-weight: 400\"> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\"> At Compile time, Hive generates query expressions.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">During the Runtime, Impala generates code for \u201cbig loops\u201d.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">d. Interactive Computing<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">For interactive computing, Hive is not an ideal.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">For interactive computing, Impala is meant.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">e. Type<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Basically, it \u00a0is a batch based Hadoop MapReduce<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">However, it \u00a0is more like MPP database<\/span><\/p>\n<h3><span style=\"font-weight: 400\">f. Complex Types<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Though, it supports complex types<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">However, it does not support complex types<\/span><\/p>\n<h3><span style=\"font-weight: 400\">g. Query Execution<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">The output of the query will be produced as Hive is fault tolerant, while a data node goes down during the query execution.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Impala starts all over again, while a data node goes down during the query execution.<\/span><\/p>\n<h3>h. Performance<\/h3>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Hive<\/strong><\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">while keeping Hive\u2019s ability to perform well at mid to high query complexity, Hive LLAP gets good performance at the low end.<\/p>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Impala<\/strong><\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">Similarly, while Impala struggles as query complexity increases but Impala perform well with less complex queries.<\/p>\n<h3>i.\u00a0 SQL Queries<\/h3>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Hive<\/strong><\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">Hive LLAP allows customers to perform sub-second interactive queries without the need for additional SQL-based analytical tools.<\/p>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Impala<\/strong><\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">Impala offers fast, interactive SQL queries directly on our Apache Hadoop data stored in HDFS or HBase.<\/p>\n<h3>j. Time consumption<\/h3>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Hive<\/strong><\/p>\n<\/li>\n<\/ul>\n<p>The dynamic runtime features of Hive LLAP minimizes the overall work. Hence, we can say working with Hive LLAP consumes less time.<\/p>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Impala \u00a0<\/strong><\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">Impala consumes less time for simpler queries, but for complex queries, it needs more time than Hive LLAP.<\/p>\n<h3>k. Direct interaction<\/h3>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Hive<\/strong><\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">Hive LLAP has Long-Lived Daemons.\u00a0 That replaces direct interaction with HDFS Data Nodes and tightly integrated DAG-based framework.<\/p>\n<ul>\n<li>\n<p dir=\"ltr\"><strong>Impala<\/strong><\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">Impala needs to have the file in Apache Hadoop HDFS storage or HBase (Columnar database).<\/p>\n<h3><span style=\"font-weight: 400\">l. ETL jobs<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">For long running ETL jobs, Hive is an ideal choice, since Hive transforms SQL queries into Apache Spark or Hadoop jobs.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">NA<\/span><\/p>\n<h3><span style=\"font-weight: 400\">m. Speed<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">NA<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">However, Impala is 6-69 times faster than Hive.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">n. When to use<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><strong>Hive<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">The hive will be your ideal choice, if you are considering of taking up an upgradation project then compatibility comes up as an important factor to rely upon.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><strong>Impala<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Impala is the best choice out of the two if you are starting something fresh.<\/span><\/p>\n<p>So, this was all in Impala vs Hive. Hope you likeour explanation.<\/p>\n<h2><span style=\"font-weight: 400\">Conclusion &#8211; Impala vs Hive<\/span><\/h2>\n<p><span style=\"font-weight: 400\">As a result, we have learned about both of these technologies. Apache Hive and Impala. Also, we have covered details about this Impala vs Hive technology in depth. <\/span><\/p>\n<p><span style=\"font-weight: 400\">However, we have shown few differences between Hive and Impala technology but in practice, these are not two different competitors competing to show which one of them is the best. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Although, each complements other in rarely good use cases each of them is known for their characteristics as defined earlier.<\/span><\/p>\n<p><span style=\"font-weight: 400\">But practically we can say both of Apache Hive and Impala need not be competitors competing with each other. Well, to execute queries both Hive and Impala has a strong MapReduce foundation. <\/span><\/p>\n<p><span style=\"font-weight: 400\">However, when we need to use both together, we get the best out of both worlds. Such as compatibility and performance. Well, after learning Impala vs Hive, still if any query occurs feel free to ask in the comment section. \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Both Apache Hive and Impala, used for running queries on HDFS. But there are some differences between Hive and Impala &#8211; \u00a0SQL war in the Hadoop Ecosystem. So, in this article, &#8220;Impala vs Hive&#8221;&#46;&#46;&#46;<\/p>\n","protected":false},"author":7,"featured_media":10208,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[2742,3847,4598,4600,5807,5808],"class_list":["post-10019","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hive","tag-comparison-between-impala-and-hive","tag-difference-between-hive-and-impala","tag-features-of-hive","tag-features-of-impala","tag-hive-vs-impala","tag-hive-vs-impala-feature-wise-comparison"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Impala vs Hive - Difference Between Hive and Impala - DataFlair<\/title>\n<meta name=\"description\" content=\"Impala vs Hive-SQL in Hadoop ecosystem,Difference Between Hive and Impala,features of impala &amp; Hive,feature wise comparison of Impala &amp; 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