How does Hive support data compression and indexing?

Hive supports data compression and indexing to enhance query performance and reduce storage space utilization. Below are the methods supported by Hive for data compression and indexing:

1. Data compression: Hive supports various data compression formats such as Snappy, Gzip, LZO, and Deflate. By specifying the compression format in the CREATE TABLE statement, data can be compressed when writing. Compressing data can reduce storage space usage and improve performance during queries, as compressed data can be read and transferred more quickly.
2. Columnar storage format: Hive supports columnar storage formats such as ORC (Optimized Row Columnar) and Parquet, which compress and encode columns while storing data to reduce storage space usage and improve query performance.
3. Indexing in Hive allows for faster query speeds by creating indexes on columns within a table. By specifying the columns to create indexes on in the CREATE TABLE statement, indexes can be created while writing data. When querying, Hive will use these indexes to speed up data retrieval and improve query performance.

In conclusion, by using data compression and indexing, Hive can improve query performance, reduce storage space usage, and enhance data processing efficiency.