As modern data systems grow in scale, the need for optimized data storage and fast query performance becomes critical. Spark, along with Delta Lake, provides powerful capabilities for handling massive datasets, but as the volume of data increases, certain optimizations are necessary to maintain performance and manage costs. In this blog, we’ll explore three key optimizations in Spark and Delta Lake:

• Partitioning
• ZORDER
• VACUUM

By understanding these techniques, how they work, and when to use them, you can ensure your data lake remains efficient and cost-effective as it scales.

 

1. Why These Optimizations Came Into Picture

1.1. The Problem: Large-Scale Data and Performance Challenges

With the rapid increase in data generation (from IoT devices, application logs, etc.), traditional approaches to storing and querying data became inefficient. As data lakes grew larger:

• Query performance slowed down as Spark had to scan more files.
• Storage costs increased significantly as old data files accumulated.

Delta Lake, built on top of Parquet, addressed many of these problems by offering ACID transactions and time travel (data versioning). However, as more data was written and updated, new challenges emerged, such as:

• Slow query response times due to inefficient file layouts.
• Increased cloud storage costs due to orphaned files left over from updates and deletes.

This is where Partitioning, ZORDER, and VACUUM come in.

 

2. What Are Partitioning, ZORDER, and VACUUM?

2.1. Partitioning

Partitioning refers to splitting your data into different directories based on column values. It is a logical division of data that enables Spark to perform partition pruning (only reading the relevant partitions during a query).

Example: If you are storing log data with a timestamp, you can partition by year, month, and day.

Benefit: Partitioning helps Spark avoid scanning unnecessary files when querying based on partitioned columns, improving performance and reducing cost.

2.2. ZORDER

ZORDER is a technique that optimizes data within each partition. Unlike partitioning, which divides data at the file level, ZORDER organizes data within the files themselves. It’s a form of multi-dimensional clustering that ensures that similar data values (based on frequently queried columns) are stored together within the same file.

Example:
If you’re frequently filtering on user_id or device_id, ZORDER organizes the data within each partition so that similar user_id records are grouped together, making those queries much faster.

2.3. VACUUM

VACUUM is a clean-up operation that removes obsolete data files from a Delta table. Delta Lake stores old versions of data for ACID compliance and time travel. However, over time, these files accumulate and occupy unnecessary storage.

Benefit: VACUUM helps manage the size of Delta tables by deleting files that are no longer needed, ensuring that your data lake stays optimized and storage costs remain low.

 

3. What Problem Do These Optimizations Solve?

Problem	Before Optimization	After Optimization
Slow query performance	Spark reads large numbers of files even for simple filters	Partitioning + ZORDER reduces file scans, improving performance
High cloud storage costs	Obsolete files accumulate over time	VACUUM removes unused files, saving storage costs
File layout inefficiency	Data is scattered randomly across files	ZORDER groups similar data together for efficient file access
Metadata bloat	Reading Delta logs becomes slow due to many small files	Partitioning + ZORDER reduces metadata load, speeding up operations

 

4. How to Implement Partitioning, ZORDER, and VACUUM

4.1. Implementing Partitioning

Partitioning is implemented when creating the table or writing data. It is especially useful for columns that have a low cardinality (few distinct values). Common examples are date-based columns like year, month, day.

SQL Example:

CREATE TABLE logs

USING DELTA

PARTITIONED BY (year, month, day)

AS

SELECT *, 

      year(timestamp) AS year,

      month(timestamp) AS month,

      day(timestamp) AS day

FROM raw_logs;

PySpark Example:

df.write.format(“delta”)

   .partitionBy(“year”, “month”, “day”)

   .mode(“overwrite”)

   .save(“/mnt/delta/logs/”)

Ideal file size for partitioning:
When partitioning, it’s crucial to keep files large enough to avoid the small file problem. Files should ideally be between 100MB to 1GB. If files are too small (less than 100MB), Spark’s performance may degrade due to too many small files. On the other hand, excessively large files may hurt parallelism and increase query latency. The ideal file size will depend on your cluster’s size and the specific workload, but keeping files in this range is typically a good practice.

When to use:

• Use partitioning when you often query by columns like year, month, or region.
• Avoid partitioning by high cardinality columns (e.g., user_id) because it can result in too many small files, negatively affecting performance.

4.2. Implementing ZORDER

ZORDER is applied after the data has been loaded into the table. It is typically used for columns that are frequently queried but cannot be partitioned due to their high cardinality (e.g., user_id, device_id).

SQL Example:

OPTIMIZE logs

ZORDER BY (user_id, timestamp);

PySpark Example:

spark.sql(“””

   OPTIMIZE logs

   ZORDER BY (user_id, timestamp)

“””)

When to use:

• Use ZORDER after large inserts, updates, or merges to optimize data layout for frequently queried columns.
• Apply ZORDER only on 1-3 columns that are often used for filtering in queries.

4.3. Implementing VACUUM

VACUUM is used to clean up old files that are no longer needed after updates or deletes. The operation deletes files older than a specified retention period (default is 7 days).

SQL Example:

VACUUM logs RETAIN 168 HOURS;  — 7 days

PySpark Example:

spark.conf.set(“spark.databricks.delta.retentionDurationCheck.enabled”, “false”)

spark.sql(“””

   VACUUM logs RETAIN 1 HOURS;  — Aggressive cleanup

“””)

When to use:

• Run VACUUM periodically, especially after heavy updates or deletes, to reclaim storage.
• Avoid running VACUUM with a very short retention period, as it may delete active data files and disrupt ongoing operations.

 

5. Best Practices

5.1. When to Use Partitioning

• Use for columns with low cardinality like year, month, or region.
• Partition by 1-3 columns to avoid too many small files.
• Avoid partitioning by high cardinality columns (like user_id) as this leads to a small file problem.

5.2. When to Use ZORDER

• Use ZORDER for frequently queried columns that are not suitable for partitioning (e.g., user_id, device_id).
• ZORDER should be applied after heavy data loads (e.g., inserts, updates, merges) to ensure efficient file layouts.

5.3. When to Use VACUUM

• Run VACUUM periodically (e.g., daily or weekly) to clean up old data files.
• Ensure you set an appropriate retention period to avoid deleting useful data, especially when using time travel features.

 

6. Conclusion

As data lakes scale, Partitioning, ZORDER, and VACUUM become essential techniques for managing data storage, query performance, and costs.

• Partitioning helps you organize your data into efficient file structures based on columns like year or region.
• ZORDER optimizes data within partitions to improve query speed on frequently filtered columns.
• VACUUM ensures that old, obsolete files are removed, keeping storage costs under control.

By combining these three techniques, you can build high-performance, cost-effective data pipelines that scale with your growing data needs.

Geetha S