Data lakes have become a staple for organizations seeking to harness the power of their data at scale. However, traditional data lake architectures are typically built on cloud object storage and in formats like Apache Iceberg or Parquet, which are limited in flexibility and come with a hidden cost: the accumulation of small files. This seemingly innocuous issue can snowball into significant performance bottlenecks and operational headaches. Fortunately, architecting a system to avoid these small files can elevate the performance and scalability of your data pipeline to a surprising degree.
In this blog, we’ll discuss the provenance of small files, the problems they cause, and how to design a next-generation data ecosystem with the VAST DataBase that delivers best-in-class performance at exabyte scale. (For more detail on the VAST DataBase, a key part of the VAST Data Platform, and how it provides streamlined data pipelines, check out this previous blog or the VAST Data Platform white paper.)
This blog is the first in a series that we’ll present to help data engineers and architects think through the design, tradeoffs, and implementation of data lakes, and how the VAST Data Platform can help you get the most out of your data architecture.
Small File Syndrome
Imagine your data lake as a library. If every piece of information were a single page, you’d have a chaotic mess of loose papers, rather than well-organized books that each exist in an intentional order. Similarly, data lakes can become inundated with tiny files, each containing fragments of data. This phenomenon arises from:
Streaming ingestion: Continuous data streams often bring a continuous stream of small files. In our library analogy, imagine a constant stream of new pages coming in, representing additional storylines and material for myriad books. Making sense of this streaming input is not impossible, but is incredibly complex.
Micro-batching: Even with batching, you can still end up with numerous small files, especially if your data volume is high. In the library, consider pages landing in small pre-organized, stapled groups, rather than individual pages. Yes, this is slightly better, but still quite challenging to organize.
Data lake design: Cloud object storage systems like S3 are optimized for large objects, not the multitude of tiny files that data lakes often produce. This is like being optimized for books and expecting to receive complete volumes, yet still receiving individual pages.
The Consequences: A Cascade of Challenges
The proliferation of small files in your data lake triggers a chain reaction of problems:
Query performance woes: Query engines have to open, read, and process each file, leading to excessive overhead and slower query response times.
Metadata overload: Managing metadata for countless files becomes a burden, straining system resources and slowing down metadata operations.
Storage inefficiency: Small files can lead to storage fragmentation, inefficient use of space, and increased storage costs.
Operational complexity: Managing, compacting, and optimizing a data lake riddled with small files becomes a complex and time-consuming task. While auto-compaction can help, it’s not a silver bullet. (Stay tuned for our next blog post where we’ll dive deeper into why.)
To alleviate these challenges, organizations often will architect data pipelines to avoid streaming ingestion directly into tables. Instead, in a clunky process, they will stream into an intermediate store (e.g., Kafka), and then periodically flush data into the data lake to ensure the file count doesn’t escalate to an unmanageable level. However, adding that level of complexity to the data pipeline increases latency, cost, and ultimately degrades performance and time to value for data over time.
Enter the VAST Data Platform: A Paradigm Shift
The VAST Data Platform reimagines the data lake architecture, tackling the small file problem head-on via thehe VAST DataBase, which offers:
A uniquely integrated namespace: The VAST DataBase combines an exabyte-scale namespace for diverse data types with a tabular database for metadata, eliminating the need for separate systems and complex data movement.
Efficient data ingestion and transformation: The VAST DataBase ingests data row-by-row and seamlessly transforms it into optimized columnar chunks, ideal for query performance while maintaining transactional consistency.
Dynamic data management: Unlike immutable object stores, the VAST DataBase allows efficient updates and modifications, eliminating the need for complex metadata layers and file versioning.
Unified metadata: The VAST DataBase’s integrated metadata eliminates the need for external metadata stores, simplifying management and accelerating operations.
Seamless integration: The VAST DataBase supports standard SQL and natively integrates Apache Spark. The VAST DataBase also connects seamlessly with popular query engines like Trino and Dremio, offering a familiar interface and streamlined data access.
Tackling the Small File Challenge with the VAST DataBase
The VAST DataBase is optimized for both write and read performance - in other words, serving both OLTP and OLAP use cases. Transactions are written as rows and then pushed in a columnar format to a highly scalable flash tier known as the VAST DataStore for optimized reads. This results in:
Lightning-fast queries: Columnar storage and optimized data chunks enable rapid query execution, even on massive datasets
Operational simplicity: Streamlined metadata management and native support for updates reduce operational overhead
Optimal storage utilization: Reduces file count and eliminates unnecessary compute processes like compaction and vacuuming to maximize storage efficiency and lower costs
Scalability: The VAST DataBase scales effortlessly to exabytes of data, ensuring your data lake can grow with your business
Unleashing the True Potential of your Data
The accumulation of small files in traditional data lakes can cripple performance and create operational nightmares. The innovative architecture of the VAST Data Platform offers a new paradigm for storing and accessing data, eliminating legacy limitations such as the small file problem and unleashing the true potential of your data lake.
No longer is your library a collection of random pages; now the library is organized for both storage and access. Think of the VAST DataBase as having the features of a data warehouse (an organized library) at the scale of a data lake. This is truly the next step toward true data management and access at the scale of AI, and a key piece of the VAST Data Platform.
Ready to experience the power of a data lake without the small file burden? Want to discuss this more? Contact us!
