What are the most important bits for the Hive?
10. What are the components used in Hive Query Processor?
- Metadata Layer (ql/metadata)
- Type Interfaces (ql/typeinfo)
- Sessions (ql/session)
- Map/Reduce Execution Engine (ql/exec)
- Plan Components (ql/plan)
- Hive Function Framework (ql/udf)
- Tools (ql/tools)
- Optimizer (ql/optimizer)
What are the issues faced in Hive real time?
There are many real time problems where we need nested queries , whereas hive supports only correlated queries. There is no subtract operation available in hive and thus we need to create two tables and perform left outer join on it with condition to accomplish the task.
Which of the following is the commonly used Hive services?
Following are the commonly used Hive services: Command Line Interface (cli) Printing the contents of an RC file with the use of rcfilecat tool. HiveServer (hiveserver)
Why Hive is not suitable for OLTP?
Apache Hive is mainly used for batch processing i.e. OLAP and it is not used for OLTP because of the real-time operations of the database. Instead, hbase is extensively used for transactional processing wherein the response time of the query is not highly interactive i.e. OLTP.
What is Metastore in Hive?
It stores metadata for Hive tables (like their schema and location) and partitions in a relational database. It provides client access to this information by using metastoreservice API. Hive metastore consists of two fundamental units: A service that provides metastore access to other Apache Hive services.
What is partitioning in Hive?
The partitioning in Hive means dividing the table into some parts based on the values of a particular column like date, course, city or country. The advantage of partitioning is that since the data is stored in slices, the query response time becomes faster.
What are characteristics of hive?
Apache Hive Features
| Features | Explanation |
|---|---|
| Supported Computing Engine | Hive supports MapReduce, Tez, and Spark computing engine. |
| Framework | Hive is a stable batch-processing framework built on top of the Hadoop Distributed File system and can work as a data warehouse. |
How can I improve my hive performance?
Types of Performance Tuning Techniques
- 1 Avoid locking of tables.
- 2 Use the Hive execution engine as TEZ.
- 3 Use Hive Cost Based Optimizer (CBO)
- 4 Parallel execution at a Mapper & Reducer level.
- 5 Use STREAMTABLE option.
- 6 Use Map Side JOIN Option.
- 7 Avoid Calculated Fields in JOIN and WHERE clause.
How is data stored in Hive?
It queries data stored in a distributed storage solution, like the Hadoop Distributed File System (HDFS) or Amazon S3. Hive stores its database and table metadata in a metastore, which is a database or file backed store that enables easy data abstraction and discovery.
Why do we need Hive?
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.
Can a table name be changed in Hive?
You can rename the table name in the hive. You need to use the alter command. This command allows you to change the table name as shown below.
What is bucketing in Hive?
Bucketing in hive is the concept of breaking data down into ranges, which are known as buckets, to give extra structure to the data so it may be used for more efficient queries. The range for a bucket is determined by the hash value of one or more columns in the dataset (or Hive metastore table).
What is Beeline in Hive?
Hive comes with HiveServer2 which is a server interface and has its own Command Line Interface(CLI) called Beeline which is used to connect to Hive running on Local or Remove server and run HiveQL queries. Beeline is a JDBC client that is based on the SQLLine CLI.
Why is Hive important?
Hive allows users to read, write, and manage petabytes of data using SQL. Hive is built on top of Apache Hadoop, which is an open-source framework used to efficiently store and process large datasets. As a result, Hive is closely integrated with Hadoop, and is designed to work quickly on petabytes of data.
Why we use bucketing in Hive?
Bucketing in hive is useful when dealing with large datasets that may need to be segregated into clusters for more efficient management and to be able to perform join queries with other large datasets. The primary use case is in joining two large datasets involving resource constraints like memory limits.
What is importance of Hive?
What are the advantages of Hive?
Advantages of Hive
- Keeps queries running fast.
- Takes very little time to write Hive query in comparison to MapReduce code.
- HiveQL is a declarative language like SQL.
- Provides the structure on an array of data formats.
- Multiple users can query the data with the help of HiveQL.
- Very easy to write query including joins in Hive.
What is skew join in Hive?
A skew join is used when there is a table with skew data in the joining column. A skew table is a table that is having values that are present in large numbers in the table compared to other data. Skew data is stored in a separate file while the rest of the data is stored in a separate file.
What type of database is Hive?
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.
What task does a Hive perform?
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.