Optimizing Data Processing with ORC, Parquet, Avro
Many Data Professionals deal with massive volumes of data that need to be processed, analyzed, and stored efficiently. In the world of big data, traditional row-based formats like CSV and Excel quickly become bottlenecks, struggling to handle the sheer size and complexity of modern datasets.
The limitations of conventional data formats like CSV and Excel become more noticeable as data volumes continue to grow exponentially. These row-based formats can be slow and ineffective, particularly when working with big datasets.
Fortunately, modern data processing solutions have given rise to more efficient and optimized data formats, such as ORC (Optimized Row Columnar), Parquet, and Avro.
These formats are designed to handle big data workloads more effectively, offering significant performance and storage benefits.
Lets go through all of them:
ORC (Optimized Row Columnar)
ORC is a columnar storage format designed specifically for Hadoop workloads. Unlike row-based formats, ORC stores data column-by-column, allowing for efficient compression and encoding. This approach can lead to significant space savings and faster query processing times, especially for queries that only access a subset of columns.
employee_id | name | age | department | salary
----------------------------------------------------
1 | Will Smith | 30 | HR | 50000
2 | Jon Doe | 35 | Engineering| 60000
3 | Alice Dawson | 28 | Marketing | 55000In an ORC file, data is organized into strips, which are groups of rows. Each strip contains an index that maps column values to their locations within the strip, enabling efficient data retrieval.
ORC also supports a variety of encoding techniques, such as run-length encoding and dictionary encoding, further improving compression and performance.
ORC is particularly well-suited for batch processing workloads, where data is typically read sequentially. It is widely used in conjunction with Apache Hive, a data warehouse solution built on top of Hadoop, and is supported by various big data processing engines, including Apache Spark and Apache Impala.
Parquet
Parquet is another columnar storage format that shares many similarities with ORC. Like ORC, Parquet stores data column-by-column, enabling efficient compression and encoding.
employee_id | name | age | department | salary
----------------------------------------------------
1 | Will Smith | 30 | HR | 50000
2 | John Doe | 35 | Engineering| 60000
3 | Alice Dawson | 28 | Marketing | 55000However, Parquet is designed to be more broadly applicable across various data processing frameworks and engines, including Apache Spark, Apache Hadoop, Apache Impala, and more.
Parquet files are organized into row groups, which are similar to ORC’s strips. Each row group contains column chunks that store the data for each column. Parquet supports a variety of compression codecs, including Snappy, Gzip, and Brotli, allowing users to choose the most appropriate compression technique for their use case.
Parquet is particularly well-suited for analytics and data warehousing workloads, where efficient data retrieval and querying are crucial. It is widely adopted in the Apache Spark ecosystem and is often used as a storage format for data lakes and data warehouses.
Difference between Parquet and ORC:
- Parquet offers better support for complex data types(ORC offers only Dictionary)
- More efficient column encoding options, and superior query performance, especially for laccessing a subset of columns.
- It also has broader ecosystem integration and is the preferred choice for most new big data projects.
- ORC, on the other hand, is more tightly coupled with the Hadoop ecosystem, particularly Apache Hive, and can be more efficient for queries that scan a majority of columns.
However, Parquet generally outperforms ORC in terms of compression, data skipping, and overall query performance, making it a more versatile and widely adopted columnar storage format.
Avro
While ORC and Parquet are columnar formats, Avro takes a different approach. Avro is a row-based storage format that offers several advantages over traditional formats like CSV and JSON.
[
{"employe_id": 1, "name": "Will Smith", "age": 30, "department": "HR", "salary": 50000},
{"employee_id": 2, "name": "John Doe", "age": 35, "department": "Engineering", "salary": 60000},
{"employee_id": 3, "name": "Alice Dawson", "age": 28, "department": "Marketing", "salary": 55000}
]One of Avro’s key features is its support for schemas. Each Avro file contains a schema that describes the structure of the data, including the data types and field names.
This schema is stored alongside the serialized data, enabling efficient data processing and interoperability across different systems. Avro’s schema evolution capabilities allow for the schema to evolve over time without breaking compatibility with existing data.
Avro supports a rich set of data types, including primitive types (e.g., string, int, long, float, double) and complex types (e.g., record, array, map, union). It also allows users to define custom data types using schemas. Avro data is serialized in a binary format, which can be more efficient for storage and transmission compared to text-based formats like JSON.
Avro is widely used in distributed systems and streaming data pipelines, such as Apache Kafka. It is particularly well-suited for data exchange between different systems and programming languages, as it provides language-agnostic serialization and deserialization capabilities.
When to Use Each Format?
The choice of data format largely depends on the specific use case and requirements. Here are some general guidelines:
- ORC: Best suited for batch processing workloads in the Hadoop ecosystem, where data is typically read sequentially and queries access a subset of columns.
- Parquet: Ideal for analytics and data warehousing workloads, especially in the Apache Spark ecosystem, where efficient data retrieval and querying are crucial.
- Avro: Recommended for distributed systems and streaming data pipelines, where data needs to be exchanged between different systems and programming languages, and schema evolution is important.
Make sure you know that these formats are not mutually exclusive, and they can be used in combination within a data processing pipeline. For example, data might be ingested in Avro format, stored in a data lake using Parquet format, and then processed using Apache Spark or other big data processing engines.
By understanding the strengths and use cases of each format, data engineers and analysts can make informed decisions and build robust data processing pipelines tailored to their specific needs.
“Talk is cheap. Show me the code.” — Linus Torvalds
