ORC (Optimized Row Columnar) and Parquet are two popular file formats for storing and processing large datasets in Hadoop-based systems such as Hive. Both formats are designed to optimize query performance, reduce storage costs, and improve data processing efficiency. In this article, we will compare and contrast the ORC and Parquet file formats, and discuss their strengths and weaknesses.
ORC File Format
ORC is a columnar storage format that was developed by the Apache ORC project. It is designed to optimize query performance by storing data in a columnar layout, which allows for efficient compression, indexing, and processing of large datasets. ORC files are binary files that contain multiple columns of data, along with metadata and compression information.
Creating an ORC file in Hive
To create an ORC file in Hive, you can use the following syntax:
CREATE TABLE freshers_in_orc(
col1 INT,
col2 STRING,
col3 DOUBLE
)
STORED AS ORC;
Reading an ORC file in Hive
To read an ORC file in Hive, you can use the following syntax:
SELECT * FROM frehsers_in_orc;
ORC file features
(a) ORC files are optimized for processing large datasets.
(b) ORC files support compression algorithms such as Zlib, Snappy, and LZO.
(c) ORC files support predicate pushdown, which allows Hive to filter rows based on a predicate before reading the entire file.
(d) ORC files support column pruning, which allows Hive to read only the columns that are required for a query.
(e) ORC files support schema evolution, which allows for changes to the table schema without having to rewrite the entire file.
Parquet File Format
Parquet is a columnar storage format that was developed by the Apache Parquet project. It is designed to optimize query performance by storing data in a columnar layout, which allows for efficient compression, indexing, and processing of large datasets. Parquet files are binary files that contain multiple columns of data, along with metadata and compression information.
Creating a Parquet file in Hive
To create a Parquet file in Hive, you can use the following syntax:
CREATE TABLE freshers_in_par(
col1 INT,
col2 STRING,
col3 DOUBLE
)
STORED AS PARQUET;
Reading a Parquet file in Hive
To read a Parquet file in Hive, you can use the following syntax:
SELECT * FROM freshers_in_par;
Parquet file features
(a) Parquet files are optimized for processing large datasets.
(b) Parquet files support compression algorithms such as Snappy, Gzip, and LZO.
(c) Parquet files support predicate pushdown, which allows Hive to filter rows based on a predicate before reading the entire file.
(d) Parquet files support column pruning, which allows Hive to read only the columns that are required for a query.
(e) Parquet files support schema evolution, which allows for changes to the table schema without having to rewrite the entire file.
Differences between ORC and Parquet
While both ORC and Parquet are columnar storage formats that offer similar features and benefits, there are some differences between the two formats that are worth noting:
Compression algorithm support: ORC supports more compression algorithms than Parquet, including Zlib, Snappy, and LZO. Parquet only supports Snappy, Gzip, and LZO.
Encoding support: ORC supports more encoding techniques than Parquet, including Dictionary encoding, Run-length encoding, and Delta encoding. Parquet only supports Dictionary encoding and Run-length encoding.
Predicate pushdown: While both ORC and Parquet support predicate pushdown, ORC supports it for more data types than Parquet. ORC can push down predicates for string, integer, decimal, date, timestamp, and boolean data types, while Parquet only supports predicate pushdown for string and integer data types.
Query performance: ORC is generally considered to have better query performance for complex queries that involve aggregation or filtering of large datasets. This is because ORC stores data in a more compact format, which reduces disk I/O and improves query processing time. Parquet, on the other hand, is better suited for simple queries that involve reading a few columns from a large dataset.
File size: ORC files are generally smaller than Parquet files, due to the way they are compressed and stored on disk. This means that ORC files require less disk space and can be read and processed more quickly than Parquet files.
Schema evolution: ORC supports schema evolution more robustly than Parquet. ORC allows for changes to the table schema without having to rewrite the entire file, while Parquet requires a full rewrite of the file when the schema changes.
ORC and Parquet offer similar benefits and features, but there are some differences in their compression and encoding techniques, query performance, and schema evolution support. It’s important to choose the file format that best suits your data and query requirements.
Hive important pages to refer
