{"id":16461,"date":"2018-05-26T06:10:51","date_gmt":"2018-05-26T06:10:51","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=16461"},"modified":"2021-05-09T13:10:47","modified_gmt":"2021-05-09T07:40:47","slug":"impala-sql","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/impala-sql\/","title":{"rendered":"Impala SQL Tutorial – Basics of Impala Query Language"},"content":{"rendered":"

In this\u00a0Impala<\/strong>\u00a0SQL Tutorial, we are going to study Impala\u00a0Query\u00a0Language Basics. It\u00a0<\/span>offers a high degree of compatibility with the Hive Query Language (HiveQL). However, there is much more to learn about Impala SQL, which we will explore, here. In addition, we will also discuss Impala Data-types.<\/span><\/p>\n

So, let’s start Impala SQL – Basic Introduction to Impala Query Langauge.<\/p>\n

What is Impala SQL?<\/span><\/h2>\n

While providing a great degree of compatibility with HiveQL, the Impala Query Language is also based on SQL. Since both\u00a0Hive<\/strong> and Impala statements are based on SQL, there are various statements in both Hive and Impala are identical and some of the statements are different as well. <\/span><\/p>\n

So, some points regarding how Impala statements are based on SQL are:<\/span><\/p>\n

    \n
  • Both Impala and Hive use the Data Definition Language (DDL).<\/span><\/li>\n
  • Moreover, Hive uses metastore<\/strong> to store table structures and their properties and to record the information, Impala uses the same metastore.<\/span><\/li>\n<\/ul>\n

    Let’s Discuss Hive DDL in detail<\/strong>
    \nAlso, see the following key points that show how the Impala Query Language supports HiveQL:<\/span><\/p>\n

      \n
    • There are some statements and clauses which are similar for both Impala and HiveQL, like JOIN, UNION ALL, ORDERBY, LIMIT, DISTINCT, and AGGREGATE<\/span><\/li>\n<\/ul>\n
        \n
      • As same as Data Manipulation Language (DML), Impala statements support data manipulation statements.<\/span><\/li>\n<\/ul>\n
          \n
        • Statements like SELECT <\/strong>and INSERT are same in Impala as well as HiveQL<\/span><\/li>\n<\/ul>\n
            \n
          • Also, Impala supports statements like INSERT INTO and INSERT OVERWRITE.<\/span><\/li>\n<\/ul>\n
              \n
            • In Impala and HiveQL, various built-in functions<\/strong> in several categories like mathematical, conditional, or string are the same. Also, they use the same name and parameter types.<\/span><\/li>\n<\/ul>\n

              Impala Data Types<\/span><\/h2>\n

              Moreover, both Impala and Hive support data types<\/strong> with the same names and semantics, they are:<\/span><\/p>\n

                \n
              • String<\/span><\/li>\n
              • TINYINT<\/span><\/li>\n
              • SMALLINT<\/span><\/li>\n
              • INT<\/span><\/li>\n
              • BIGINT<\/span><\/li>\n
              • FLOAT<\/span><\/li>\n
              • DOUBLE<\/span><\/li>\n
              • BOOLEAN<\/span><\/li>\n
              • STRING<\/span><\/li>\n
              • TIMESTAMP<\/span><\/li>\n<\/ul>\n

                To, learn all these Data Types in detail, follow the link:\u00a0Impala Data Types: Usage, Syntax, and Examples<\/strong>
                \nSo, this was all about\u00a0Impala SQL- Impala Query Language. Hope you like our explanation.<\/p>\n

                Conclusion – Impala SQL<\/h2>\n

                As a result, we have seen the whole concept of Impala SQL – Structured Query Language. Also, we have seen how both Hive and Impala are identical on the basis of SQL language. However, if any doubt occurs, feel free to ask in the comment tab.<\/p>\n","protected":false},"excerpt":{"rendered":"

                In this\u00a0Impala\u00a0SQL Tutorial, we are going to study Impala\u00a0Query\u00a0Language Basics. It\u00a0offers a high degree of compatibility with the Hive Query Language (HiveQL). However, there is much more to learn about Impala SQL, which we...<\/p>\n","protected":false},"author":6,"featured_media":16613,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[27],"tags":[3486,6506,6552,6561,6563],"class_list":["post-16461","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-impala","tag-data-types-in-impala","tag-impala-data-types","tag-impala-query-language","tag-impala-sql","tag-impala-structured-query-language"],"yoast_head":"\nImpala SQL Tutorial - Basics of Impala Query Language - DataFlair<\/title>\n<meta name=\"description\" content=\"Impala SQL-Basics of Impala Query Language, how Impala statements are based on SQL, how Impala Query Language supports HiveQL, Impala Data-types\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/impala-sql\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Impala SQL Tutorial - 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