Hive is a managed service that runs on top of Hadoop. Hive abstracts the complexity of the Big Data platform, providing a simple, SQL-like interface for storing and querying large amounts of data.
HQL syntax is similar to SQL
Hive Query Language (HQL) is a powerful and scalable SQL like language for Apache Hive. Like SQL, HQL is case insensitive and is easily written.
There are a variety of operations to perform on your database using HQL. These include modifying your columns, reorganizing the structure of your table, and executing functions.
For example, if you want to change the name of your table or modify its structure, you’ll need to use an ALTER TABLE statement. But be sure to do so in a consistent manner. Otherwise, you’ll be in for an error.
An ALTER TABLE statement can also change the metadata of your table. This can be beneficial if you need to correct a mistake or if you are attempting to optimize a query.
The SELECT clause is another way to insert data into a Hive table. It is similar to the INSERT INTO command, but instead of using the FROM command, you can simply pass the name of another table as a parameter.
One of the benefits of external tables is that they are free from ownership of data. They can read and write data files from a distributed directory.
Using this option is very beneficial in terms of data security. It allows you to separate new and older data, and reduces the risk of tampering with new data.
Aside from INSERT, you can also use the DROP table command to remove all rows in a table. Deleted data cannot be recovered, however. If you don’t want to drop the table, you can also choose to delete the metadata of the table.
Performing other operations on your table can be accomplished using an ALTER TABLE statement. Some of these include removing partitions and moving their locations.
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Hive abstracts the complexity of Hadoop
Apache Hive is an open source data warehouse system that helps in the analysis of massive datasets. It is a great tool for developers, especially those who have been accustomed to working with SQL.
Apache Hive is used by many companies and organizations. Some of its major uses include ad-hoc query, data warehousing, and data summarization.
Using Apache Hive, developers can write simple SQL queries to read petabytes of data. In addition to these, Apache Hive also offers a number of optimizations for speed and fault tolerance.
Hive is built on top of the MapReduce framework. While MapReduce is a powerful framework, it is not easy to write code that is able to handle large data sets. So, Hive was developed to simplify the process.
Hive supports a number of file formats. These formats can be used in both on-premise and cloud environments. Moreover, it can be used for batch jobs as well as interactive queries.
Hive also supports several storage types. For example, it can handle HDFS, Hadoop Distributed File System, and other similar data storage systems. Moreover, it can also perform ETL.
Hive’s UI is user-friendly. Users can create queries and run them on Hadoop infrastructure. The UI includes a CLI and an Executor that interacts with the Hadoop Job Tracker. This interface calls the driver, which is responsible for submitting a query, executing it, and tracking the progress.
Hive’s metadata store helps the driver keep track of data. It contains information about the location, schema, and type of each table in the database. Moreover, Hive allows you to create your own user-defined functions. You can use these functions to filter data, perform data cleansing, or perform other tasks.
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Hive runs on top of Hadoop
Hive is a data warehouse tool built on top of Hadoop Distributed File System. It is used to handle large amounts of structured and semi-structured data. The tool can be run as an interactive or batch mode.
Hive provides a query language called HQL. This is similar to SQL. Query syntax supports arrays, text and binary strings, as well as fixed-point data types.
Hive also offers several aggregation APIs. For instance, it can join data based on columns, MERGE and overwrite. A user can also specify custom serialisation schemes.
In addition to these features, Hive can be deployed on the same cluster as HBase. As such, it can help to simplify Map-Reduce jobs.
When running on HDFS, Hive must be optimized for online transactions. To do this, it can use directory structures to improve performance. And, it supports nested queries and views. However, it does not support deletion or updates.
Hive’s architecture is very similar to other Hadoop components. Specifically, it has a data store and a command line interface.
Hive stores metadata for its tables in a separate “meta storage database”. It also sends metadata to the compiler to produce an execution plan.
A map or reduce job is then generated. During the Map stage, the mapper’s task is to red data from join tables.
After the Map stage, the resultant file goes through a shuffle stage. The resulting output is in the form of key-value pairs.
Hive provides a query planner, which reduces the complexity of the underlying query. However, the tool does not provide an ACID transaction property.
Using Hive, users can write custom scripts. They can then execute them on workstations. An optimizer performs many transformations on the execution plan.
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