Big data has changed the perspective of modern history as digital information has taken the CenterStage. All businesses are running towards capturing more data from their customers, machines, and operations to develop smart planning and strategies for the future.
And Apache Hadoop is among the largest and most popular software frameworks for Data Analysis in the industry. With Hadoop hive acting as one of the essential tools for processing and analyzing big data.
Let us start with a simple Hive definition before we go on to explain different aspects surrounding it.
1. What is Hive? A Definition
Per the Original Definition from their creators, Apache Hive is a data warehouse software that allows reading, writing, and managing large datasets within a distributed storage using SQL.
And for a simple answer to What is the meaning of Hive?, we can say Hive in a Datawarehouse ETL tool or software for managing Big data to leverage analyzing and querying.
2. The History of Hive
Though it was started and developed in the beginning by Facebook, Hive was part of the project under the Apache Hadoop for some time. But with more than a billion users, it became too large to stand on its own.
Now, Hive is one of the leading open house technologies developed by Apache Software Foundation. But now, Hadoop Hive has progressed at an unprecedented level, with top organizations using Hive for Data Analysis. These include Financial Industry Regulation Authority (FINRA), Netflix, Amazon Elastic MapReduce, etc.
3. What is Hive in Hadoop?
In Hadoop, Hive is built on top of the data framework to process structured data. Thus, Apache Hive acts as a platform for Hadoop Distributed File System (HDFS) and MapReduce, allowing professionals to write and analyze large data sets. In Hive, you can do this by writing Hive Query Language (HQL) statements that are quite similar to SQL statements only.
4. Hive Architecture
There are three parts of Hive Architecture in general:
In Hive, professionals can write in multiple languages such as C++, Python, Java, etc. It can be done using the following clients:
Thrift Server: Acts as a cross-language platform helping professionals to support multiple programming languages.
JDBC Driver: Acts as a medium between the Java and Hive applications with official support from both.
ODBC Driver: Adds ODBC protocol allowing several applications to communicate with Hadoop Hive.
6. Hive Services
This section consists of the following services:
Hive CLI: It acts as a Command Line Interface shell that gives professionals a way to write and execute Hive command, queries, etc.
Web User Interface: It is a direct user interface for professionals acting as an alternative way for CLI to perform queries and commands to analyze data.
Hive Metastore: It is the central repository that procures tables and partitions within a warehouse – all related metadata information, serializers, de-serializers, and columns for HDFS system files to read and modify data at any time.
Hive Server or Apache Thrift Server: It can take requests from multiple clients and forward them to Hive Driver, respectively.
Hive Driver: It accepts queries from all sources such as CLI, Web UI, JDBC/ODBC, and Thrift to forward them to the Compiler.
Hive Compiler: It has the responsibility to change HQL statements into MapReduce, respectively.
Hive Execution Engine: It performs the task execution in a specific order for their dependencies.
7. Hive Storage and Computing
Now moving further from Hive services, storage and computing take into account the following tasks:
Data information is stored in the Hive at Meta Storage database.
While data loaded and query results are stored at Hadoop clusters across HDFS.
8. Hive Features
Essential features of Hive include:
Hive’s primary function is to query and manage structured data inside the tables.
Hive represents a fast, scalable, and Datawarehouse ETL tool.
In Hive, you will find the support for multiple file formats that include ORC, TEXTFILE, RCFILE (Record Columnar File), and the SEQUENCE FILE.
Hive follows the same concepts for storing data as columns, rows, tables, sequences, and schema such as SQL, thus lowering the complexities of MapReduce programming.
Processed data is stored in the Hadoop Distributed file system (HDFS) during schema in the database only.
Hive also has built-in support for user-defined functions (UDFs) to change the data and another tool.
Hive allows the use of compressed data stored within the Hadoop ecosystem.
In Hive, data partitioning allows for quick query retrieval and enhances performance.
Hive also supports using different commands through Data Manipulation Language (DML), Data Definition Language (DDL), and UDFs.
Being the main difference from the SQL that performs queries on any traditional database, Hive Query Language (HQL) works specifically on the Hadoop system. There are external plugins available that support the querying for Bitcoin Blockchain as well.
9. How Data Flows in the Hive
You must also understand how data flows in the Hive. Here are the simple steps:
First of all, a professional or user writes a query for execution via CLI or any user interface (UI).
Then the driver checks the query and parses it for specific requirements as well as syntax. Then the driver connects with the query compiler to understand the execution process and metadata information.
Then the Compiler has the desired plan to be executed while communicating to fetch the information requested.
After that, Metastore takes the information to the Compiler back.
The Compiler then, in turn, forwards the query execution process to the driver.
After, this driver then again forwards the execution process to the engine respectively.
This execution engine (EE) acts as a medium between the Hadoop and Hive. Here, the tasks are performed in the MapReduce. Now the task forwards to the job tracker from the Name node to Data node, respectively. In the whole procedure, the execution engine performs the metadata operations.
Results are then fetched from the data nodes and forwarded to the execution engine, sending it to the driver and then to the front end (UI).
Here learners must understand that Hive is not a relational database or any language to get real-time queries. Hive acts as a platform or tool to write and execute queries from Hadoop.
Limitations of Hive
While there are significant Hive Benefits, there are still some limitations that exist in operating Hive:
Hive does not process the ability to handle real-time data and analysis.
Hive Queries possess a high level of latency.
Hive architecture does not support the use of online transaction processing.
10. Hive Modes
In general, Hive works in two different modes as Local mode and Map-reduce mode.
For a Hive local mode:
Manages one data node only
Hadoop installation via the pseudo mode with data size effectively lower and subjects to a single local machine only
Performs at quicker processing
For a Map-reduce mode:
It is the default mode for Hive that contains multiple data nodes
Each of these nodes has varying data sizes
So, users can deal with a large number of data sets
11. Pig vs. Hive
Pig and Hive both represent tools to manage data precisely in Hadoop. Hive is a standardized platform for building SQL-type scripts, while Pig acts as a procedural language platform while performing the MapReduce function’s uniform tasks. Here are the main differences between Pig and Hive.
HQL – A variant of SQL
Pig Latin – a specific procedural language
Structured and Unstructured data
Works on Server-side
Works on Client-side
Fully supports partitioning
Hive takes time in loading while execution is quicker
Pig, on the other hand, loads instantly
Both Pig and Hive have their ideal user base. Hive is suited for data analysts and SQL to perform analytical queries from the historical data. In contrast, programmers prefer Pig to deal with scripting languages while avoiding schema.
12. Hive vs. HBase
Here are the main Hive and HBase differences that you should know in grasping the fundamentals of Apache software:
Hive is built and runs on top of the Hadoop, while HBase operates on top of the HDFS.
HBase is an open-source database management system with more inclined towards unstructured data, while Hive operates mainly towards structured data.
Hive prefers and is suited for high latency operations, whereas HBase is more inclined towards low-latency operations.
Hive’s primary function is directed towards analytical querying, while Hbase follows real-time querying.
Hive has its use in batch processing, whereas Hbase is predominantly transactional processing.
Hive prefers a schema model while HBase doesn’t.
HBase supports NoSQL databases, while Hive is an ETL tool for databases with no support for databases.
These differences between Hive and HBase lays out the different aspects of the Apache frameworks.
13. Hive Optimization Techniques
Here are some of the remarkable ways for optimizing Hive queries in performing them at their optimal:
Create multiple partitions in the directory to lower the read time.
Follow best file formats to enhance query performance that can lower the data size up to 75%. Optimized Row Columnar (ORC) is the standard.
Use a separate index table for enhanced reference from the original table data.
Use a bucketing method for dividing table sets for better control and management.
To perform queries in bulk by using filters, joins, scans and more.
14. What is Hive used as?
Hive’s primary use lies in analyzing huge files and processing structured data.
Writing and executing queries as SQL-like statements by HQL.
Perform tasks at an unprecedented pace with improved results.
Hive for Data Analysis has shown tremendous results in several industry aspects.
Built on top of the Apache Hadoop, Hive definition represents a data software interface for querying and analysis for catering to large datasets. Hadoop hive history provides the growth story of this highly essential Datawarehouse tool. And with specifically suited for Hive big data and easy in executing queries, professionals prefer it over other programs, tools & software.
Hive benefits include quick query results, less time to write HQL queries providing a structure for data formats, and simple learning and implementation. Hive features and data flow provides the concept for the functioning of the tool.
Hive optimization techniques provide multiple tricks and steps to add more performance in accomplishing tasks. And with companies looking toward continuous growth by procuring data, Hive for Data Analysis will play an essential role in reaching their goals and targets.