How does hive work internally?
Hive internally uses a MapReduce framework as a defacto engine for executing the queries. MapReduce is a software framework for writing those applications that process a massive amount of data in parallel on the large clusters of commodity hardware.
What are the main components of hive?
The major components of Hive and its interaction with the Hadoop is demonstrated in the figure below and all the components are described further:
- User Interface (UI) – As the name describes User interface provide an interface between user and hive.
- Driver –
- Compiler –
- Metastore –
- Execution Engine –
How does hive work in Hadoop?
How Does Apache Hive Work? In short, Apache Hive translates the input program written in the HiveQL (SQL-like) language to one or more Java MapReduce, Tez, or Spark jobs. Apache Hive then organizes the data into tables for the Hadoop Distributed File System HDFS) and runs the jobs on a cluster to produce an answer.
What are the components in Hive data model?
Data flow in the Hive contains the Hive and Hadoop system. Underneath the user interface, we have driver, compiler, execution engine, and metastore. All of that goes into the MapReduce and the Hadoop file system.
What happens if you execute a Hive query?
Hive query is received from UI or from thrift server or CLI and it is received by driver. So the driver contacts the compiler to validate the hive query. Those results, execution engines sends back to the driver and driver finally sends back to the client program. This is how Hive communication happens.
Is Hive a data warehouse?
Apache Hive is a distributed data warehouse system that provides SQL-like querying capabilities. SQL-like query engine designed for high volume data stores. Multiple file-formats are supported.
What is yarn architecture?
YARN stands for “Yet Another Resource Negotiator“. YARN architecture basically separates resource management layer from the processing layer. In Hadoop 1.0 version, the responsibility of Job tracker is split between the resource manager and application manager.
Will hive provide data warehousing layer to data over Hadoop?
Apache Hive is a data warehouse system that’s built on top of Hadoop. It provides data summarization, analysis, and query to large pools of Hadoop unstructured data. You can query data stored in Apache HDFS — or even data stored in Apache HBase. MapReduce, Spark, or Tez executes that data.
What is YARN architecture?
What do you Understant by hive Expalin its architecture?
Hive is an ETL and data warehouse tool on top of Hadoop ecosystem and used for processing structured and semi structured data. Hive is a database present in Hadoop ecosystem performs DDL and DML operations, and it provides flexible query language such as HQL for better querying and processing of data.
Can Hadoop replace data warehouse?
Hadoop will not replace a data warehouse because the data and its platform are two non-equivalent layers in Data warehouse architecture. However, there is more probability of Hadoop replacing an equivalent data platform such as a relational database management system.