Understanding Parquet, Apache ORC, and Avro: Key Differences for Big Data
What big Data format shoul you use?…Choosing the right data format can have a huge impact on performance, storage efficiency, and overall data processing capabilities. Three of the most popular data formats in the big data ecosystem are Parquet, Apache ORC, and Avro. Each of these formats has its strengths, weaknesses, and specific use cases. In this post, we’ll break down the differences between these formats, helping you choose the right one for your next big data project.
1. Parquet
Type: Columnar storage format
Compression: Highly efficient columnar compression (supports Snappy, Gzip, etc.)
Schema: Self-describing schema (embedded in the file)
Use Case: Best for analytical workloads, such as data warehousing and business intelligence (BI) tools.
Parquet is a columnar storage format, which makes it highly efficient for read-heavy analytical queries. Since it stores data by columns rather than rows, it allows systems to read only the relevant columns needed for a query, thus reducing I/O and improving query performance.
Parquet also supports advanced compression techniques, leading to lower storage costs and faster data retrieval. It is widely supported across the Hadoop ecosystem, making it a natural choice for use with tools like Apache Hive, Apache Impala, and Apache Spark.
Strengths:
- Efficient column-based access: Perfect for analytical workloads where only specific columns are queried.
- Compression and storage efficiency: Reduces storage costs and improves performance with advanced compression.
- Interoperability: Works seamlessly with popular Hadoop-based tools (e.g., Hive, Impala, Spark).
- Parallel processing: Suitable for distributed systems, making it easy to process large datasets in parallel.
Weaknesses:
- Write-heavy operations: Not as efficient for write-heavy use cases, especially compared to row-based formats like Avro.
- Small data reads: May incur overhead when reading small datasets due to its columnar structure.
Typical Use Cases:
- Analytical workloads, such as interactive querying, OLAP, and data exploration.
- Big data processing frameworks like Apache Hive, Apache Impala, and Apache Spark.
- Data warehousing and data lakes where query performance and efficient storage are essential.
2. Apache ORC (Optimized Row Columnar)
Type: Columnar storage format
Compression: Highly optimized compression
Schema: Self-describing schema (embedded in the file)
Use Case: Primarily used with Apache Hive for OLAP (Online Analytical Processing) workloads.
Apache ORC is another columnar storage format, designed specifically to optimize data processing within the Hadoop ecosystem. It is especially popular with Apache Hive for handling large-scale data warehousing tasks. ORC excels in both read and write performance and is known for providing even better compression ratios than Parquet in some cases.
Strengths:
- Optimized compression: ORC generally provides superior compression compared to Parquet, reducing storage needs even further.
- Performance: It delivers faster read and write operations, particularly in Hadoop environments.
- Indexing and predicate pushdown: Built-in indexing allows for faster query execution by reducing the amount of data read.
- Lightweight for large datasets: Excellent for high-performance processing of large-scale datasets involving filtering, aggregation, and joins.
Weaknesses:
- Limited support outside the Hadoop ecosystem: While ORC is great for Hive, it is not as widely supported in non-Hadoop environments.
- Hive-centric: Primarily optimized for use with Apache Hive, which might limit its flexibility for other use cases.
Typical Use Cases for ORC:
- Analytical workloads involving complex queries and aggregations.
- Apache Hive-based data warehousing and data lakes.
- Batch processing and ETL (Extract, Transform, Load) pipelines.
3. Avro: A Row-Based Format for Serialization
Type: Row-based storage format
Compression: Supports multiple compression formats (e.g., Snappy, Gzip)
Schema: Schema stored separately in JSON format
Use Case: Ideal for event streaming, data serialization, and messaging systems (e.g., Kafka).
Unlike Parquet and ORC, Avro is a row-based format, making it more suited for write-heavy workloads. It’s a great choice for real-time data streaming, where entire rows need to be serialized and transmitted efficiently. Avro also supports schema evolution, meaning you can modify the schema without breaking compatibility with older data.
Strengths:
- Optimized for writes: Great for high-throughput scenarios, such as real-time data ingestion and event streaming (e.g., Kafka).
- Schema evolution: Avro supports schema changes over time, allowing for flexibility in systems where data structures evolve.
- Compact size: Avro files tend to be more compact, making them ideal for use in data serialization and transmission.
- Interoperability: Avro is commonly used in event-driven architectures and messaging systems (e.g., Kafka, Flink).
Weaknesses:
- Less efficient for analytics: As a row-based format, Avro is not ideal for analytical workloads that require selective column access.
- Not optimized for querying: While it’s great for serialization, querying Avro data can be slower than columnar formats like Parquet and ORC.
Typical Use Cases for Avro:
- Real-time streaming applications, such as Apache Kafka, where low latency and schema evolution are crucial.
- Data serialization in distributed systems.
- Communication between different components of a big data ecosystem.
Storage Data Row based vs Column Based
Of course, each of these models has its advantages and disadvantages, but I can already tell you that row storage is better for transactional systems like SQL Server, Oracle, and Postgres, while column storage is better for analytical data, data warehouses, or tabular data stored in detail.
In row storage, each row represents a tuple, meaning the data is stored together on disk. What happens is that you’ll retrieve the entire row easily, but you’ll always retrieve the entire row. So, even if you perform a select query for just one field, and not use an asterisk, the database management system will still retrieve the entire row. It will have to read the whole row because it’s stored together.
This type of system is great for transactional systems, where rows are updated and inserted constantly. Keep in mind that in a data warehouse, updates rarely happen, though deletions and updates may occur occasionally.
Since each row can have different data types, it’s less efficient. As the entire row is stored together, we can have different data types: an integer, a text field, a date, and a real number. You can compress the data, but each type is different, making it less effective.
In column storage, the column is stored entirely on disk, and it’s stored separately. When you query, only the necessary columns are read. So, when you perform a select query for one field, only that field will be retrieved from the table. This storage type is great for queries and analysis.
Since the column data is all of the same type, compression is much more efficient, so we can use a more efficient compression algorithm or technique, making it much more efficient.
Also, remember that formats like Parquet use column storage, which is why it’s more efficient for data analysis. Transactional databases, on the other hand, store data in rows, like in popular data warehouse systems such as Redshift and Snowflake.”
Table Comparison: Row Storage vs Column Storage
Key Differences: Parquet vs. Apache ORC vs. Avro
Conclusion
When deciding which format to use, consider the nature of your workload.
- Parquet is ideal for analytical processing and data warehousing, where you’ll need to query specific columns in large datasets.
- Apache ORC is highly optimized for use with Apache Hive, offering superior compression and performance for large-scale data processing in Hadoop.
- Avro is perfect for real-time event streaming and data serialization, especially in systems like Apache Kafka, where you need efficient row-level writes and schema evolution.
Choosing the right data format depends on your specific use case — whether you’re focused on data storage, querying performance, or streaming. By understanding these formats and their strengths, you can optimize both your data storage and processing workflows for better scalability and performance.
