As a data engineer managing batch file processing with Databricks, I recently encountered a storage issue that many teams face: rapidly increasing storage volume. In this blog, I’ll share the challenge I faced with my Delta Lake storage, how I resolved it, and the benefits I gained by implementing Databricks’ VACUUM command to manage storage more efficiently.
The Challenge: Rising Storage Volume
I use Databricks to process batch files daily, which involves a significant number of merge operations throughout the day. Every time new data arrives, I merge it into the existing datasets stored in Delta Lake tables. Over time, this continuous merging generated a massive amount of data, as Delta Lake retains previous versions of data files to allow for time travel and rollback features.
Because of this, the Delta Lake storage volume began increasing at an alarming rate, with nearly 1 TB of data being added daily. Soon, I faced a serious problem: my total storage volume had ballooned to more than 150 TB. This not only raised concerns around cloud storage costs but also created long-term concerns about maintaining optimal performance.
The Solution: VACUUM to Reclaim Space
Faced with this rapid data growth, I realized I needed to reduce the storage volume without compromising data integrity or disrupting business processes. That led me to explore Databricks documentation, where I learned about the VACUUM command for Delta Lake.
What is VACUUM in Databricks?
According to Databricks, the VACUUM command removes data files that are no longer referenced by a Delta table. These files are typically old versions or unused files that Delta retains to enable time travel and restore operations. By using VACUUM, I could clean up these obsolete data files, directly reducing the storage footprint of my Delta tables.
Here’s why VACUUM is essential:
- Reducing Cloud Storage Costs: By removing unused files, storage requirements decrease, cutting cloud storage expenses significantly.
- Ensuring Data Compliance: VACUUM ensures that files containing deleted or modified records are completely removed, preventing unwanted access to outdated data.
Testing and Results
Before running VACUUM in my production environment, I decided to test it in a lower environment. After executing the VACUUM command, I was amazed to see a 90% reduction in file size! This massive space savings proved that I was on the right track.
Running VACUUM in Production
With confidence in hand, I executed the VACUUM command in my production environment. The results were astonishing—my Delta Lake storage size reduced by 90%! This drop had a significant impact on cloud storage costs, helping me save a substantial amount of money while ensuring that my Delta Lake tables were clean and compliant with data retention policies.
Automating the VACUUM and OPTIMIZE Process
To maintain these benefits, I decided to automate the VACUUM process, along with Databricks’ OPTIMIZE command, to run at regular intervals. The OPTIMIZE command helps further improve query performance by compacting small files into larger, more efficient ones.
I scheduled both VACUUM and OPTIMIZE to run every weekend. This ensures that my storage footprint remains under control and helps me keep costs low on an ongoing basis, without the need for manual intervention.
Conclusion: Managing Delta Lake Efficiently
By leveraging Databricks’ VACUUM command, I was able to address my growing storage needs effectively. Not only did I reduce my Delta Lake storage volume by 90%, but I also implemented a long-term solution to manage my cloud storage costs and keep my data environment optimized.
If you’re using Databricks and Delta Lake, I highly recommend incorporating VACUUM into your regular maintenance routines. It’s a simple but powerful command that can prevent unnecessary data bloat and keep your storage costs in check, all while maintaining compliance and data efficiency.
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