Part 9: Tuning & Photon Engine

Apache Spark is incredibly fast at scale, but because it runs on the Java Virtual Machine (JVM), it suffers from performance overhead—CPU serialization tax, garbage collection pauses, and a lack of hardware-level optimization. Databricks solved this by rewriting Spark’s execution layer in C++ and changing how data is clustered on disk. Let’s optimize.

The C++ Revolution: The Photon Engine

Photon is Databricks’ next-generation vectorized query engine.

Instead of executing Spark operations inside the JVM using Java code, Photon bypasses the JVM entirely for query execution. It is written in highly optimized C++ and interacts directly with server memory.

[ Spark Driver ] ──( Plan Optimization )──► [ Executor Node (JVM) ]
                                                   │
                                                   ▼ (Hands off execution)
                                            [ Photon Engine (C++) ]
                                            - Vectorized (SIMD)
                                            - JVM GC Bypassed

ELI5: What is Photon? Think of replacing a reliable but heavy family sedan engine (the JVM) with a C++ lightweight carbon-fiber Formula 1 racing engine. Check out ELI5: The Photon Engine for the full breakdown.

The Two Advantages of Photon:

1. Vectorized (SIMD) Execution: Bypasses row-by-row processing. Photon processes rows in columns (vectors) of up to 10,000 values, executing arithmetic across arrays using modern CPU SIMD registers in a single instruction cycle.
2. No JVM Garbage Collection (GC) Pauses: In standard Spark, if you allocate millions of temporary objects during a query, the JVM eventually freezes execution to run a memory garbage collection. This causes random latency spikes. Photon manages its own C++ memory, eliminating GC pauses completely.

To use Photon, you don’t need to change a single line of SQL or Python. You simply enable it in your cluster settings. For compatible operations, consult the Databricks Photon Engine Documentation.

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Data Skipping: Z-Ordering

As we discussed in Part 3, partitioning splits your data into directory folders. But what if you filter by multiple fields that aren’t partitioned?

This is where Z-Ordering (also known as multi-dimensional clustering) comes in.

Z-Ordering rearranges the physical rows inside your Parquet files so that rows with similar values in the Z-ordered columns are written to the same files.

ELI5: What is Z-Ordering? Imagine sorting books in a library by both Author AND Publication Year. Z-Ordering maps them in a grid, clustering books with similar authors and dates in specific corners so you can skip checking 99% of the shelves. See ELI5: Z-Ordering for the full analogy.

To Z-Order a Delta table, run the OPTIMIZE command:

OPTIMIZE prod.sales.orders_silver
ZORDER BY (customer_country, product_sku);

The Rule of Z-Ordering:

• Only Z-Order by columns you filter by in your WHERE clauses.
• Limit your Z-Order columns to 1-3. If you Z-Order by 10 columns, the clustering effect degrades, and the physical sort becomes useless.
• Run OPTIMIZE ZORDER periodically (e.g. daily or weekly) as new incoming inserts will write unclustered files.

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The Modern Alternative: Liquid Clustering

While Z-Ordering is powerful, it has major limitations:

• It is expensive to run because it performs a full rewrite of your files.
• If you insert new data, you have to run OPTIMIZE again, which rewrites the files again, causing high write amplification.
• If you change your query filters, you have to change your Z-Order columns and rewrite the whole table.

To solve this, Databricks introduced Liquid Clustering.

Liquid Clustering replaces both partitioning and Z-Ordering. It dynamically clusters data on disk as it is written, without the overhead of rigid directories or expensive full-table rewrites.

Data engineers looking at Liquid Clustering while holding hands with traditional partitioning and Z-Ordering.

Creating a Table with Liquid Clustering

You define the clustering keys using the CLUSTER BY clause:

CREATE TABLE prod.sales.orders_liquid (
    order_id INT,
    customer_country STRING,
    product_sku STRING,
    revenue DOUBLE
)
USING delta
CLUSTER BY (customer_country, product_sku);

Why Liquid Clustering is Better:

1. Dynamic Clustering: It groups data on the fly. You don’t need to plan a partitioning strategy.
2. Incremental Optimizations: When you run OPTIMIZE, it only rewrites files that need clustering, running 10x faster than a traditional Z-Order.
3. Changeable Keys: You can change your clustering keys at any time using ALTER TABLE ... CLUSTER BY without rewriting your historical data. Newer files will cluster on the new keys.

For detailed performance differences, see the Databricks Liquid Clustering Guide.

Now that our tables are optimized for speed, we need to schedule and run our production pipelines. In the final part, we’ll orchestrate our workflows.