What is a Hive?

The data warehouse system used to summarize, analyze and query the data of larger amounts in the Hadoop platform is called Hive. SQL queries are converted into other forms such as MapReduce so that the jobs are reduced to a larger extent. It is a form of Extract-Transform-Load process to analyze as well as process the structured and unstructured data. It performs DDL and DML operations with other query languages such as HQL, which is provided for querying and processing of data. Hive is built over Hadoop to process data. It translates the input into SQL language to compile and produce results of data.

Understanding Hive

Below are the major components of the hive:

Meta Store

The repository that stores the metadata is called the hive meta store. The metadata consists of the different data about the tables like their location, schema, information about the partitions, which helps to monitor variously distributed data progress in the cluster. It also keeps track of the data and replicates the data, which provides a backup in case of emergencies like data loss. The metadata information is present in relational databases and not in the Hadoop file system.

Driver

On execution of the Hive query language statement, the driver receives the statement, and it controls it for the full execution cycle. Along with the execution of the statement, the driver also stores the metadata generated from the execution. It also creates sessions to monitor the progress and life cycle of different executions. After the completion of the reducing operation by the MapReduce job, the driver collects all the data and results of the query

Compiler

It is used for translating the Hive query language into MapReduce input. It invokes a method that executes the steps and tasks that are needed to read the HiveQL output as needed by MapReduce.

Optimizer

The main task of the optimizer is to improve the efficiency and scalability, creating a task while transforming the data before the reduced operation. It also performs transformations like aggregation, pipeline conversion by a single join for multiple joins.

Executor

After the compilation and optimization step, the main task of the executor is to execute the tasks. The main task of the executor is to interact with the Hadoop job tracker for scheduling tasks ready to run.

UI, Thrift server, and CLI

Thrift server is used by other clients to interact with the Hive engine. The user interface and the command-line interface helps to submit the queries as well as process monitoring and instructions so that external users can interact with the hive.

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Below are the steps showing hive interaction with the Hadoop framework:

Executing the query

The query is sent to the driver from hive interfaces such as the command line or web UI. A driver may be any database driver like JDB or ODBC, etc.

Getting the plan

The syntax for the requirement of the query or query plan can be checked with the help of a query compiler which passes through the query and is invoked by the driver.

Getting the metadata

The meta store can be residing in any database, and the compiler makes a request to access the metadata.

Sending the metadata

At the request of the compiler, the meta store sends the metadata.

Sending the plan

The compiler sends the plan to the driver on verifying the requirements sent by the compiler. This step completes the parsing and compiling of a query.

Executing the plan

The execution plan is sent to the execution engine by the driver.

Executing the job

An executing the job is a MapReduce job that runs in the backend. Then it follows the normal convention of the Hadoop framework – the execution engine will send a job to the job tracker which is residing on the name node, and the name node, in turn, will assign the job to the task tracker which is in data note. The MapReduce job is executed here.

Metadata ops

While executing the job, the execution engine can execute metadata operations with the meta store.

Fetching the result

The data nodes, after the completion of the processing, pass on the result to the execution engine.

Sending the result

The driver receives the result from the execution engine.

Result Sending

Finally, the Hive interfaces receive the result from the driver.

Thus, by the execution of the above steps, a complete query execution in Hive takes place.

How does the Hive make working so easy?

Hive is a data warehousing framework built on top of Hadoop, which helps users for performing data analysis, querying on data, and data summarization on large volumes of data sets. HiveQL is a unique feature that looks like SQL data stored in the database and performs an extensive analysis. I was capable of reading data at a very high speed and writing the data into the data warehouses, as well as it can manage large data sets distributed across multiple locations. Along with this, the hive also provides structure to the data that is stored in the database, and users are able to connect to the hive using a command-line tool or JDBC driver.

Top Companies

Major organizations working with big data used hive-like Facebook, Amazon, Walmart, and many others.

What can you do with Hive?

There are a lot of functionalities of the hive-like data query, data summarization, and data analysis. It supports a query language called HiveQL or Hive Query Language. The Hive query language queries are translated into MapReduce job, which is processed on the Hadoop cluster. Apart from this, Hiveql also reduces scripts that can be added to the queries. In this way, HiveQL increases the schema design flexibility, which also supports data deserialization and data serialization.

Working with Hive

Below are some of the operational details in Hive. Hive data types are broadly classified into four types as given below:

Column Types
Literals
Null Values
Complex Types

1. Column Types

These are the column data types of the hive. These are classified as below:

Integral types: Integer data is represented using integral data type. The symbol is INT. Any data exceeding the upper limit of INT has to be assigned a datatype of BIGINT. In the same way, any data below the lower limit of INT needs to be assigned SMALLINT. There is another datatype called TINYINT, which is even smaller than SMALLINT.
String types: The string data type is represented in the hive by a single quote(‘) or double quotes(“). It can be of two types – VARCHAR or CHAR.
Timestamp: Hive timestamp supports java.sql.Timestamp format “yyyy-mm-dd hh:mm:ss.ffffffffff” and format “YYYY-MM-DD HH:MM:SS.fffffffff”.
Date: Date is represented in the hive in the format YYYY-MM-DD representing year-month-day.
Decimals: Decimals in a hive is represented in the java big decimal format and is used to represent immutable arbitrary precision. It is represented in the format Decimal(precision, scale).
Union types: Union is used in the hive to create a collection of a heterogeneous datatype. It can be created using create a union.

Below is an example:

UNIONTYPE<int, double, array<string>, struct<a:int,b:string>> {0:1} {1:2.0} {2:["three","four"]} {3:{"a":5,"b":"five"}} {2:["six","seven"]} {3:{"a":8,"b":"eight"}} {0:9} {1:10.0}

2. Literals

There are few literals used in the hive. They are as below:

Floating point type: They are represented as numbers with a decimal point. These are pretty similar to a double data type.
Decimal type: This type of data contains decimal type data only but with a higher range of floating-point value than the double data type. The range of decimal type is approximate -10^-308 to 10^308.

3. Null Value

The special value NULL represents missing values in the hive.

4. Complex Types

Below are the different complex types found in the hive:

Arrays: Arrays are represented in a hive in the same form as of java. The syntax is like ARRAY<datatype>.
Maps: Maps are represented in the hive in the same form as of java. The syntax is like MAP
<primitivetype, datatype>.
Structs: Structs in the hive are represented like complex data with comments. The syntax is like STRUCT<columnname : datatype [COMMENT columncomment], ...>.

Besides all these, we can create databases, tables, partition them and lots of other functions.

Databases: They are the namespaces containing a collection of tables. Below is the syntax to create a database in a hive.

CREATE DATABASE [IF NOT EXISTS] sampled;

The databases can also be dropped if not needed anymore. Below is the syntax to drop a database.

DROP DATABASE [IF EXISTS] sampled;

Tables: They can also be created in the hive to store data. Below is the syntax for creating a table.

CREATE [TEMPORARY] [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.] table_nam [(col_name data_type [COMMENT col_comment], ...)] [COMMENT table_comment [ROW FORMAT row_format] [STORED AS file_format]

A table can also be dropped if not needed anymore. Below is the syntax to drop a table.

DROP TABLE [IF EXISTS] table_name;

Advantages

The main advantage of Apache Hive is for data querying, summarization, and analysis. It is designed for better productivity of the developer and also comes with the cost of increasing latency and decreasing efficiency. Apache Hive provides for a wide range of user-defined functions that can be interlinked with other Hadoop packages like RHipe, Apache Mahout, etc. It helps developers to a great extent when working with complex analytical processing and multiple data formats. It is mainly used for data warehousing, which means a system used for reporting and data analysis.

It involves cleansing, transforming, and modeling data to provide useful information about various business aspects which will help in producing a benefit to an organization. Data analysis has a lot of different aspects and approaches, which encompass diverse techniques with a variety of names in different business models, social science domains, etc. It is much user-friendly and allows users to simultaneously access the data, increasing the response time. Compared to the other type of queries on huge data sets, the hive’s response time is much faster than others. It is also much flexible in terms of performance when adding more data and by increasing the number of nodes in the cluster.

Why should we use the Hive?

Along with data analysis, hive provides a wide range of options to store the data into HDFS. It supports different file systems like a flat-file or text file, sequence file consisting of binary key-value pairs, RC files that stores column of a table in a columnar database. Nowadays, the file that is most suitable with Hive is known as ORC files or Optimized Row Columnar files.

Why do we need Hive?

In today’s world Hadoop is associated with the most spread technologies that are used for big data processing. The very rich collection of tools and technologies that are used for data analysis and other big data processing.

Who is the right audience for learning Hive technologies?

Majorly people having a background as developers, Hadoop analytics, system administrators, data warehousing, SQL professionals, and Hadoop administration can master the hive.

How will this technology help you in career growth?

It is one of the hot skills in the market nowadays, and it is one of the best tools for data analysis in the big data Hadoop world. Big enterprises doing analysis over large data sets are always looking for people with the right to skills so they can manage and query huge volumes of data. It is one of the best tools available in the market in big data technologies in recent days that can help an organization around the world with its data analysis.

Conclusion

Apart from the above-given functions, the hive has much more advanced capabilities. The power of hive to processing a large number of datasets with great accuracy makes hive one best tools used for analytics in the big data platform. Besides, it also has great potential to emerge as one of the leading big data analytics tools in the coming days due to periodic improvement and ease of use for the end-user.

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