Apache Hive is a data warehouse software project built on top of Apache Hadoop. It allows users to write SQL-like queries to analyze and process large amounts of data stored in the Hadoop Distributed File System (HDFS). Hive was developed by Facebook, but it is now an open-source project maintained by the Apache Software Foundation.
Hive was initially developed by Facebook in 2007 as a way to simplify data querying for their data analysts, who were familiar with SQL but not Hadoop’s native programming languages, such as Java or MapReduce. Later, it was donated to the Apache Software Foundation and became a top-level Apache project.
Hive uses a query language called HiveQL, which is similar to SQL, to define tables and perform queries on data. HiveQL statements are compiled into MapReduce jobs that run on a Hadoop cluster, which allows it to handle large-scale datasets with distributed computing. Hive also supports other data processing engines like Apache Tez and Spark.
Hive supports various data serialization formats such as Avro, Parquet, ORC, and more. It also provides a range of optimizations, including query optimization and query execution plan caching, to improve the performance of queries.
Architecture Of Hive
The Hive consists of the following components :
- Hive Client
- Hive Services
- Hive Storage and Computing
1. Hive Client
Hive Provides supports for the applications written in programming languages like python, java, etc. by using the JDFC, ODBC, and drivers for performing any queries on the drive. And hive client is categorized into three parts.
Thrift Clients: A Hive server is based on the apache thrift, so it can serve the request from a thrift client.
ODBC Client: It is the client application that supports ODBC protocol.
JDBC Client: It is a java application that supports JDBC protocol. Hive allows these applications to connect to it by using the JDBC drivers.
2. Hive Services
Hive CLI: We can execute the hive queries in the Hive CLI (Command Line Interface) is a shell.
Hive Web User Interface: The Hive Web UI is just an alternative to Hive CLI. It provides a web-based GUI for executing Hive queries and commands.
Hive meta store: It is a central repository that stores all the structure information of various tables and partitions in the warehouse. It also includes metadata of column and its type information, the serializers and deserializers which is used to read and write data, and the corresponding HDFS files where the data is stored.
Hive Server: It is referred to as Apache Thrift Server. It accepts the request from different clients and provides it to Hive Driver.
Hive Driver: It receives queries from different sources like web UI, CLI, Thrift, and JDBC/ODBC driver. It transfers the queries to the compiler.
Hive Compiler: The purpose of the compiler is to parse the query and perform semantic analysis on the different query blocks and expressions. It converts HiveQL statements into MapReduce jobs.
Hive Execution Engine: Optimizer generates the logical plan in the form of DAG of map-reduce tasks and HDFS tasks. In the end, the execution engine executes the incoming tasks in the order of their dependencies.
3. Hive Storage and Computing
Hive services such as Meta store, File system, and Job Client, in turn, communicate with Hive storage and performs the following actions
- Metadata information of tables created in Hive is stored in Hive “Meta storage database”.
- Query results and data loaded in the tables are going to be stored in the Hadoop cluster on HDFS.
Features of Hive
Open-Source: It is an open-source tool so we can use it free of cost.
Query large datasets: It is used to manage the datasets that are stored in the Hadoop Distributed File System.
File Formats: It supports various types of file formats such as textfile, ORC, Parquet, LZO Compression, etc.
Hive-Query Language: This language is similar to SQL. Only the basic knowledge of SQL is enough to work with Hive such as tables, rows, columns, and schema, etc.
Fast: Hive is a Fast, scalable, extensible tool and uses familiar concepts.
Table Structure: Hive as data warehouse designed for managing and querying only structured data that is stored in tables that is similar to RDBMS Tables.
Ad-hoc Queries: Hive allows us to run ad-hoc queries which are the commands or queries whose value depends on some variable for the data analysis.
ETL Support: ETL Functionalities such as Extraction, Transformation, and Loading data into tables coupled with joins, partitions, etc.
Limitations:
Apache Hive has some limitations that users should be aware of before using it. Some of these limitations include:
- High Latency
Hive is optimized for batch processing and is not suitable for low-latency processing. The query execution time can be high due to the overhead of converting the HiveQL queries to MapReduce or Tez jobs.
2. Limited support for transactions:
It does not support transactions, which can be a disadvantage for applications that require transactional consistency.
3. Limited support for real-time processing:
Apache Hive is designed for batch processing and is not suitable for real-time processing of data.
4. Limited support for complex data types:
Hive does not support complex data types such as arrays, maps, and structs, which can be a disadvantage when dealing with data that has nested structures.
5. Limited support for updates and deletes:
It does not support update and delete operations on tables, which can be a limitation for applications that require these features.
6. Steep learning curve:
Hive uses its own query language called HiveQL, which is similar to SQL but has some differences. As a result, there can be a steep learning curve for users who are not familiar with HiveQL.
7. Limited support for security:
It has limited support for security and access control, which can be a concern in applications that require strict security measures. However, this can be mitigated by integrating with other security solutions such as Kerberos.
To conclude…
Apache Hive is a versatile and powerful data warehouse system that provides users with a SQL-like interface to query and analyze large datasets stored in Hadoop. Its scalability, support for different data serialization formats, data partitioning, user-defined functions, and integration with the Hadoop ecosystem make it a popular choice for big data processing and analysis. By leveraging the features of Apache Hive, organizations can gain valuable insights into their data and make more informed decisions to drive business success.
