As we all know, big data processes huge amounts of data and rapidly delivers insights. It is summed up by the four V’s: volume, variety, velocity, and veracity. Data scientists and analysts require some dedicated tools to transform this raw data into significant content, a conceivably overwhelming task.
Hadoop is the most popular software framework designed to process and also store big data information. Hive is a tool designed to use with Hadoop. Let us see more details.
What Is Hive?
Hive is an open-source system that processes structured data in Hadoop. It resides on the top of the latter for summarizing big data and also facilitating analysis and queries. It is an ETL and Data warehousing tool developed on the top of Hadoop Distributed File System(HDFS). Hive makes easy for performing operations like data encapsulation, Ad-hoc queries and analysis of huge datasets.
How Does The Hive Make Working So Easy?
Hive is a data warehousing framework built on Hadoop which helps users to perform data analysis, querying on data and also data summarization on huge volumes of data sets.
HiveQL is a unique feature that resembles SQL data stored in the database and performs an extensive analysis. It can rapidly read data and write into the data warehouses. It can also manage large data sets distributed across multiple locations. Hive provides a structure to the data that is stored in the database and users can connect to hive via a command-line tool or JDBC driver.
Features And Characteristics Of Hive-
The following are Hive’s chief characteristics to keep in mind when using it for data processing:
- It is designed for querying and managing structured data stored in tables.
- Hive uses familiar concepts and is scalable and fast too.
- Tables and databases get created first and then data gets loaded into the proper tables. Table structure is similar to the relational databases.
- It allows access files stored in HDFS. Also, similar to other data storage systems such as Apache HBase.
- Hive supports the following file formats- ORC, SEQUENCEFILE, RCFILE (Record Columnar File), and TEXTFILE.
- It uses an SQL inspired language, sparing the user from dealing with complexity of Mapreduce programming. It makes learning more accessible by using familiar concepts found in relational databases like columns, tables, rows, and schema, etc.
- Hive uses directory structures to “partition” data, improving performance on specific queries
- It supports partition and buckets for fast and simple data retrieval
- It supports custom user-defined functions (UDF) for tasks like data cleansing and filtering. Hive UDFs can be defined according to programmers’ requirements.
- It allows converting the variety of formats from to within Hive. Although, it is very simple and possible.
Need Of Apache Hive–
The main purpose of using apache hive is data querying, analysis and summarization. It also helps to improve developers’ productivity which generally comes at the cost of increasing latency. Hive is a variant of SQL and has many user-defined functions to effectively solve problems. You can easily connect Hive queries to various Hadoop packages like RHive, RHipe, and also even Apache Mahout. It helps developers to work with complex analytical processing and challenging data formats.
Hive allows users to simultaneously access data and increases the response time means the time a system or a functional unit requires to react to a given input. Truth to be told, Hive has faster response time as compared to other types of queries. It is more flexible as more commodities can easily be added so as to add more clusters of data without compromising the performance.
How Data Flows In The Hive?
- The data analyst executes a query with the User Interface (UI).
- To retrieve the plan, the driver interacts with the query compiler which consists of the query execution process and metadata information. The driver parses the query to verify syntax and requirements.
- The compiler creates the job plan (metadata) to be executed and communicates with the metastore to retrieve a metadata request.
- Metastore sends metadata information back to the compiler
- The compiler relays the proposed query execution plan to the driver.
- The driver sends the execution plans to the execution engine.
- EE processes the query. The job process executes in MapReduce. Then execution engine sends the job to the JobTracker found in the Name node, and assigns it to the TaskTracker, in the Data node. At the same time, the execution engine executes metadata operations with the metastore.
- The results are retrieved from the data nodes.
- Then the results are sent to the execution engine. And it sends the results back to the driver and the front end (UI).
Why You Should Learn Hive?
By using hive, you can efficiently work with Hadoop. It is a complete data warehouse infrastructure built on top of hadoop framework. Hive is particularly deployed to come up with querying of data, data analysis and summarizing data while working with large volumes of data. HiveQL is an SQL-like interface used to query data stored in databases.
Hive rapidly reads data and writes within data warehouses while managing huge datasets distributed across multiple locations. It is all possible due to SQL-like features. Hive provides a structure to the data that is already in the database. Users can connect with Hive using a command-line tool and a JDBC driver.
Limitations Of Hive-
- Hive is not designed for Online transaction processing (OLTP). Although, we can use it for Online Analytical Processing (OLAP).
- It does not support updates and deletes. But, it supports overwriting or apprehending data.
- Basically, in Hive, Subqueries are not supported.
Hive Optimization Techniques-
Here we came with hacks to optimize Hive queries and make them run faster in their clusters.
- To reduce read time, split your data within the directory, else all data will get read.
- Make use of proper file formats like Optimized Row Columnar (ORC) so as to increase query performance.
- ORC reduces the original data size up to 75%.
- Divide table sets into more manageable parts by bucketing
- Create a separate index table that performs as a quick reference for the original table.
- Improve aggregations, filters, scans and joins by vectorizing your queries.
Things That You Can Do With Hive–
Hive supports a query language called HiveQL or Hive Query Language. There are many functionalities of the hive like- data query, data summarization, and data analysis also. The Hive queries are translated into MapReduce job which is processed on the Hadoop cluster. Besides this, Hiveql also reduces scripts that can be added into the queries. This way, HiveQL increases schema design flexibility which supports data deserialization and data serialization.
Why Do We Need Hive?
Hadoop is a widely spread technology that is used for big data processing. It is rich in a collection of tools and technologies that are used for data analysis and other big data processing.
This is some basics about the Hive. Are you looking to develop software with Hive? We have dedicated developers to help you through development with new technologies and frameworks. Connect with solace and get a free quote for software development. We will be happy to help you.