Optimize performance with caching

The Delta cache accelerates data reads by creating copies of remote files in nodes’ local storage using a fast intermediate data format. The data is cached automatically whenever a file has to be fetched from a remote location. Successive reads of the same data are then performed locally, which results in significantly improved reading speed.

The Delta cache works for all Parquet files and is not limited to Delta Lake format files. The Delta cache supports reading Parquet files in GCS, Amazon S3, DBFS, HDFS, Azure Blob storage, Azure Data Lake Storage Gen1, and Azure Data Lake Storage Gen2. It does not support other storage formats such as CSV, JSON, and ORC.

Delta and Apache Spark caching

There are two types of caching available in Databricks: Delta caching and Spark caching. Here are the characteristics of each type:

  • Type of stored data: The Delta cache contains local copies of remote data. It can improve the performance of a wide range of queries, but cannot be used to store results of arbitrary subqueries. The Spark cache can store the result of any subquery data and data stored in formats other than Parquet (such as CSV, JSON, and ORC).
  • Performance: The data stored in the Delta cache can be read and operated on faster than the data in the Spark cache. This is because the Delta cache uses efficient decompression algorithms and outputs data in the optimal format for further processing using whole-stage code generation.
  • Automatic vs manual control: When the Delta cache is enabled, data that has to be fetched from a remote source is automatically added to the cache. This process is fully transparent and does not require any action. However, to preload data into the cache beforehand, you can use the CACHE command (see Cache a subset of the data). When you use the Spark cache, you must manually specify the tables and queries to cache.
  • Disk vs memory-based: The Delta cache is stored on the local disk, so that memory is not taken away from other operations within Spark. Due to the high read speeds of modern SSDs, the Delta cache can be fully disk-resident without a negative impact on its performance. In contrast, the Spark cache uses memory.


You can use Delta caching and Apache Spark caching at the same time.


The following table summarizes the key differences between Delta and Apache Spark caching so that you can choose the best tool for your workflow:

Feature Delta cache Apache Spark cache
Stored as Local files on a worker node. In-memory blocks, but it depends on storage level.
Applied to Any Parquet table stored on GCS and other file systems. Any DataFrame or RDD.
Triggered Automatically, on the first read (if cache is enabled). Manually, requires code changes.
Evaluated Lazily. Lazily.
Force cache CACHE and SELECT .cache + any action to materialize the cache and .persist.
Availability Can be enabled or disabled with configuration flags, disabled on certain node types. Always available.
Evicted Automatically on any file change, manually when restarting a cluster. Automatically in LRU fashion, manually with unpersist.

Delta cache consistency

The Delta cache automatically detects when data files are created or deleted and updates its content accordingly. You can write, modify, and delete table data with no need to explicitly invalidate cached data.

The Delta cache automatically detects files that have been modified or overwritten after being cached. Any stale entries are automatically invalidated and evicted from the cache.

Use Delta caching

Delta caching works by default with all instance types that have local SSDs.

On instance types that support Delta caching, Databricks automatically enables Delta caching on that cluster and configures appropriate cache sizes for that instance type.

The Delta cache is configured to use at most half of the space available on the local SSDs provided with the worker nodes. For configuration options, see Configure the Delta cache.

Cache a subset of the data

To explicitly select a subset of data to be cached, use the following syntax:

CACHE SELECT column_name[, column_name, ...] FROM [db_name.]table_name [ WHERE boolean_expression ]

You don’t need to use this command for the Delta cache to work correctly (the data will be cached automatically when first accessed). But it can be helpful when you require consistent query performance.

For examples and more details, see

Monitor the Delta cache

You can check the current state of the Delta cache on each of the executors in the Storage tab in the Spark UI.

Monitor Delta cache

When a node reaches 100% disk usage, the cache manager discards the least recently used cache entries to make space for new data.

Configure the Delta cache

The cache disk usage is automatically set on the instance types with local SSDs. Databricks recommends that you do not explicitly set the cache disk usage.

Configure disk usage

To configure how the Delta cache uses the worker nodes’ local storage, specify the following Spark configuration settings during cluster creation:

  • spark.databricks.io.cache.maxDiskUsage: disk space per node reserved for cached data in bytes
  • spark.databricks.io.cache.maxMetaDataCache: disk space per node reserved for cached metadata in bytes
  • spark.databricks.io.cache.compression.enabled: should the cached data be stored in compressed format

Example configuration:

spark.databricks.io.cache.maxDiskUsage 50g
spark.databricks.io.cache.maxMetaDataCache 1g
spark.databricks.io.cache.compression.enabled false

Enable or disable the Delta cache

Delta caching works by default with all instance types that have local SSDs.

To enable and disable the Delta cache, run:

spark.conf.set("spark.databricks.io.cache.enabled", "[true | false]")

Disabling the cache does not result in dropping the data that is already in the local storage. Instead, it prevents queries from adding new data to the cache and reading data from the cache.