Delta Lake Performance

This post explains why Delta Lake is a performant data storage system for different query engines.

It also describes the changes made to Delta Lake over time to help it execute data operations faster.

MERGE Improvements in Delta Lake 3.0

This section reviews the MERGE performance improvements in Delta Lake 3.0. These changes improve the execution speed of MERGE operations by up to 56%. We’ll explore these enhancements and demonstrate the substantial performance gains they provide.

Our earlier post Demystifying MERGE in Delta Lake highlighted the power and versatility of the MERGE command:

MERGE is the most powerful operation you can do with Delta Lake. With merge, you can apply all three standard data manipulation language operations (INSERT, UPDATE, and DELETE) in a single transaction. You can also add multiple conditions to each of these operations for more complex scenarios and datasets.

The recent enhancements to the MERGE command were incorporated in Delta Lake PR 1827. The key improvements include:

  • Data Skipping for MATCHED-Only Merges
  • Enhanced Handling of INSERT-Only Merges
  • Efficient Row Change Writing
  • Efficient Metrics Counter

Data Skipping for MATCHED Only Merges

We now employ data skipping when a MERGE statement includes only MATCHED clauses. This accelerates the search for matches by using target-only MATCHED conditions for data skipping. For example, in the following query, predicates target.value = 5 OR target.value = 7 can exclude unnecessary scans of any file that is known not to contain those values:

Enhanced Handling of Insert-Only Merges

In Delta Lake 3.0 insert-only MERGE statements support any number of NOT MATCHED clauses, whereas previously only a single NOT MATCHED clause was allowed. This improvement allows consolidation of diverse MERGE … INSERT operations into fewer steps, increasing overall efficiency. We now also switch to insert-only MERGE if the MATCHED clause(s) do not have any matches.

Efficient Row Change Writing

We’ve refined the row change writing process for improved efficiency. Rather than processing individual partitions separately, we construct a single dataframe with all the rows to be written, using a comprehensive expression that applies the correct MERGE action to each row.

Efficient Metrics Counter

When updating metrics during MERGE/UPDATE/DELETE actions we replaced the UDFs previously used with dedicated native expressions. This change optimizes code generation, resulting in a more efficient and streamlined method of incrementing metrics.

Benchmarking MERGE Performance

We created a small new benchmark to test the improvement to MERGE. The increased efficiency of MERGE means operations are now faster overall. Here is the summary by statement type:

  • DELETE improves 27%
  • INSERT (with multiple NOT MATCHED clauses) improves 35%
  • INSERT (with a single NOT MATCHED clause) is unchanged
  • UPSERT (combined INSERT and UPDATE) improves 56%.

The new MERGE benchmark has been added to the existing benchmarks in delta-io / benchmarks and you can easily run the tests yourself to see the impact.


In Delta Lake 3.0 we've improved the performance for MERGE statements by up to 56%, providing a speed boost to your Lakehouse data pipelines. We saw that the performance improvements are the combined effect of several changes that increase the efficiency of MERGE.

You can learn more about using MERGE by reading the Merge — Delta Lake documentation, watching the Tech Talk | Diving into Delta Lake Part 3: How do DELETE, UPDATE, and MERGE work video on YouTube, or by reviewing our earlier blog Delta Lake Merge.