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4peaks file formats5/17/2023 ![]() ![]() ![]() If the size of a file is smaller than the typical block size in Hadoop, we consider it as a small file. We know that Hadoop’s performance is drawn out when we work with a small number of files with big size rather than a large number of files with small size. The data retrieved is as shown in Figure below: SEQUENCEFILE Now we will perform one basic SELECT operation on the data as shown below: select athelete from olympic We have successfully loaded our input file data into our table which is of TEXTFILE format. Load data local inpath ‘path of your file’ into table olympic The data inside the above input file is delimited by tab space. As explained earlier the file format is specified as TEXTFILE at the end. The schema of the table created above can be checked using describe olympic We can load data into the created table as follows: Here we are creating a table with name “olympic” and the schema of the table is as specified above. Creating TEXTFILE create table olympic(athelete STRING,age INT,country STRING,year STRING,closing STRING,sport STRING,gold INT,silver INT,bronze INT,total INT) row format delimited fields terminated by '\t' stored as textfile Let us see one example in Hive about how to create TEXTFILE table format, how to load data into TEXTFILE format and perform one basic select operation in Hive. The TEXTFILE input and TEXTFILE output format are present in the Hadoop package as shown below: .TextInputFormat ![]() If we do not specify anything it will consider the file format as TEXTFILE format. We can create a TEXTFILE format in Hive as follows: create table table_name (schema of the table) row format delimited fields terminated by ',' | stored as TEXTFILE.Īt the end, we need to specify the type of file format. This means fields in each record should be separated by comma or space or tab or it may be JSON(JavaScript Object Notation) data.īy default, if we use TEXTFILE format then each line is considered as a record. In Hive if we define a table as TEXTFILE it can load data of from CSV (Comma Separated Values), delimited by Tabs, Spaces, and JSON data. TEXTFILE format is a famous input/output format used in Hadoop. Let us now discuss the types of file formats in detail. However, it verifies if the file format matches the table definition or not. Hive does not verify whether the data that you are loading matches the schema for the table or not. These file formats mainly vary between data encoding, compression rate, usage of space and disk I/O. How records are encoded in a file defines a file format. As we are dealing with structured data, each record has to be its own structure. In Hive it refers to how records are stored inside the file. There are some specific file formats which Hive can handle such as:īefore going deep into the types of file formats lets first discuss what a file format is! File FormatĪ file format is a way in which information is stored or encoded in a computer file. We can configure Hive with MySQL database. As mentioned HiveQL can handle only structured data. By default, Hive has derby database to store the data in it. Like SQL, HiveQL handles structured data only. Hive provides a language called HiveQL which allows users to query and is similar to SQL. Apache HiveĪpache Hive is an open source data warehouse software that facilitates querying and managing of large datasets residing in distributed storage. Clik on the names of the software to be directed to a link providing information for downloading and installing the same.This Blog aims at discussing the different file formats available in Apache Hive. After reading this Blog you will get a clear understanding of the different file formats that are available in Hive and how and where to use them appropriately. Before we move forward let’s discuss Apache Hive. There are several free software programs available from different providers for viewing trace or chromatogram files and for handling the file formats provided. Quality scores and base call information accessible via word processorįASTA base call information accessible via word processor File Extensionĭata accessible via chromatogram viewers and analysis SoftwareĬhromatogram snapshot formatted for viewing via Adobe Acrobat The table below displays the information contained within each file. After sequencing your sample, we provide you with five file types. ![]()
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