Apache Hive is an open-source data warehousing tool that was developed to provide an SQL-like interface to query and analyze data stored in a distributed computing environment. Hive supports a wide range of storage formats to allow for the efficient processing of different data types. In this article, we will explore the different types of storage formats supported by Hive.
1. TextFile
TextFile is the default storage format for Hive tables. It stores data in a plain text format, with each record separated by a new line. This format is ideal for storing and processing large volumes of unstructured data, such as log files.
2. SequenceFile
SequenceFile is a binary file format that stores key-value pairs. It is optimized for reading and writing large amounts of structured data, such as serialized objects. SequenceFile is highly compressed and splittable, which makes it efficient for processing large datasets.
3. RCFile
RCFile (Record Columnar File) is a columnar storage format that stores data in a compressed and splittable file. It uses a row group-based approach to store data, where each row group contains a set of rows for all columns. This format is ideal for processing large datasets that have a few columns that are frequently accessed.
4. ORCFile
ORCFile (Optimized Row Columnar File) is a highly compressed and splittable file format that was developed by the Hive community. It stores data in a columnar format, with each column compressed and stored separately. This format is highly efficient for processing large datasets, as it reduces I/O and storage requirements.
5. Parquet
Parquet is a columnar storage format that was developed by the Apache Arrow project. It is highly compressed and splittable, which makes it efficient for processing large datasets. Parquet stores data in a nested columnar format, which allows for efficient querying of nested data structures, such as JSON and Avro.
6. Avro
Avro is a data serialization system that is used for efficient data exchange between systems. It stores data in a binary format that is self-describing, meaning that it includes metadata that describes the structure of the data. Avro is highly optimized for reading and writing data, and it supports complex data types, such as nested records and arrays.
7. JSON
JSON (JavaScript Object Notation) is a lightweight data interchange format that is widely used for transmitting data between systems. It stores data in a human-readable text format, with each record represented as a set of key-value pairs. JSON is highly flexible and supports complex data structures, such as nested records and arrays.
8. HBase
HBase is a NoSQL database that provides random access to large volumes of structured and semi-structured data. Hive can be used to query and analyze data stored in HBase tables, using the HBase storage handler.
9. CSV
CSV (Comma-Separated Values) is a plain text format that is used to store tabular data. Hive can read and write CSV files using the CSV SerDe (Serializer/Deserializer) library.
10. XML
XML (Extensible Markup Language) is a markup language that is used to store and exchange structured data. Hive can read and write XML files using the XPath SerDe library.
11. Thrift
Thrift is a serialization framework that is used for cross-language communication between systems. Hive can read and write Thrift files using the Thrift SerDe library.
12. JDBC
JDBC (Java Database Connectivity) is a standard API that is used to access relational databases. Hive can be used to access data stored in JDBC-compliant databases, using the JDBC storage handler.
13. CarbonData
CarbonData is a columnar storage format that is optimized for processing large datasets with complex structures. It supports features such as compression, indexing, and encoding, and is designed to work with Hadoop-based systems such as Hive. [Supported by Hive through third-party libraries and plugins]
14. Cassandra
Cassandra is a distributed NoSQL database that is designed for high scalability and fault tolerance. Hive can be used to query data stored in Cassandra tables, using the Cassandra storage handler. [Supported by Hive through third-party libraries and plugins]
15. MongoDB
MongoDB is a document-oriented NoSQL database that is designed for high performance and scalability. Hive can be used to query data stored in MongoDB collections, using the MongoDB storage handler. [Supported by Hive through third-party libraries and plugins]
16. Kudu
Kudu is a columnar storage format that is optimized for real-time analytics and machine learning. Hive can be used to query data stored in Kudu tables, using the Kudu storage handler. [Supported by Hive through third-party libraries and plugins]
Hive important pages to refer
