Caching Strategies

Data lakes provide the flexibility to scale storage independently of compute resources. While this decoupling reduces costs and simplifies capacity planning, it introduces a significant performance bottleneck: network latency. The storage engine in a traditional database often resides on the same physical machine or a dedicated storage area network (SAN) optimized for low latency. The query engine in a data lake architecture must retrieve data over a network connection from object storage services like Amazon S3, Azure Blob Storage, or Google Cloud Storage.

Network I/O is often the slowest operation in the query lifecycle. Fetching a file header to read metadata or retrieving a column chunk for processing incurs latency measured in tens or hundreds of milliseconds. When a query requires scanning terabytes of data, this latency accumulates, resulting in sluggish performance. Caching strategies mitigate this cost by storing frequently accessed data closer to the compute resources, essentially trading the cheap, slow capacity of object storage for the expensive, fast performance of local memory or SSDs.

The Caching Hierarchy

Optimization in distributed systems occurs at multiple layers. A query typically passes through a coordinator node before tasks are assigned to worker nodes. Caching opportunities exist at each stage of this lifecycle, targeting different types of data retrieval costs.

Metadata Caching

Before a query engine reads a single row of data, it must understand the table structure. This involves communicating with the catalog (such as Hive Metastore or AWS Glue) to retrieve schema definitions and list partition locations. For tables with thousands of partitions, this metadata discovery process can take longer than the actual query execution.

Metadata caching stores the file listings and partition maps in the memory of the coordinator node. When a user executes a recurring query, the engine skips the expensive listing operation against the object store and uses the cached file map to generate the query plan immediately.

Data Caching (I/O Caching)

This is the most common form of caching in analytics. It addresses the cost of moving raw data files (Parquet or Avro) from object storage to the worker nodes. When a worker creates a task to read a file split, it first checks its local storage.

If the data is not present (a cache miss), the worker pulls the data from the object store and writes a copy to its local NVMe SSD or RAM. Subsequent requests for that same file split are served directly from the local hardware. This technique significantly increases throughput, often boosting I/O performance by an order of magnitude.

Result Set Caching

While metadata and data caching optimize the process of query execution, result set caching stores the final output. This is particularly effective for dashboarding use cases where multiple users view the same visualization. Instead of re-computing the aggregation from raw files, the engine serves the pre-calculated result from memory. This reduces the query time to near-zero but requires strict invalidation logic to ensure users do not see outdated information.

Data retrieval flow showing how worker nodes prioritize local SSD caches before falling back to remote object storage.

Effective Access Time

To quantify the impact of caching, engineers look at Effective Access Time (EAT). This metric represents the average time it takes to retrieve a unit of data, weighted by the probability of finding that data in the cache (the hit rate).

The formula for EAT is:

$EAT = H \times T_{cache} + (1 - H) \times T_{remote}$

Where:

• $H$ is the Hit Rate (percentage of requests found in cache, $0 \le H \le 1$).
• $T_{cache}$ is the latency of the local cache (e.g. 0.1 ms for RAM, 5 ms for SSD).
• $T_{remote}$ is the latency of the object store (e.g. 100 ms).

Even a modest hit rate can drastically reduce the overall latency perceived by the application. However, as the hit rate approaches 100%, the system effectively behaves as if the data were local, masking the network limitations entirely.

Relationship showing how latency decreases linearly as the cache hit rate improves.

Managing Cache Consistency

One of the primary challenges in distributed caching is maintaining consistency. In a data lake, files are immutable, but tables are not. A dataset might be updated by an ingestion job adding new partitions or a compaction job rewriting small files into larger ones.

If the cache on a worker node retains a reference to a file that has been logically deleted or overwritten, the query engine might return incorrect data or fail with a FileNotFoundException.

Modern open table formats like Apache Iceberg and Delta Lake solve this through snapshot isolation.

1. Snapshot ID: Every read operation is pinned to a specific snapshot ID of the table.
2. Immutable Files: Data files are never modified in place. An update creates a new file.
3. Cache Verification: The query engine uses the unique file path (often including a UUID) as the cache key.

Because the file path changes whenever the data changes, the cache effectively manages itself. Old file paths simply fall out of use and are eventually evicted by the replacement policy, while new file paths generate fresh cache entries.

Eviction Policies

Storage on worker nodes is finite. When the cache fills up, the system must decide which data to remove to make room for new blocks. The choice of eviction policy affects the cache hit rate and overall performance.

• Least Recently Used (LRU): The most common algorithm. It tracks when a block was last accessed. When space is needed, the system discards the block that has not been used for the longest time. This assumes that if data was read recently, it is likely to be read again soon.
• Least Frequently Used (LFU): This tracks how many times a block has been accessed. Blocks with low access counts are evicted first. This is useful for scanning tables where some partitions are "hot" (queried often) and others are rarely touched.
• Time to Live (TTL): Data is automatically removed after a set period (e.g. 24 hours). This is a brute-force method often used for metadata caching to ensure the catalog eventually reflects external changes.

Configuring Caching in Practice

When deploying engines like Trino or Apache Spark, caching is not always enabled by default due to the hardware requirements (provisioning SSDs increases infrastructure cost).

In Trino, enabling the tiered storage cache involves configuring the hive.cache.enabled property and defining the directory for the cache. Trino uses a library called Rubix to manage the interaction between the file system and the local disk.

In Apache Spark, caching is often explicit. A data engineer uses the .cache() or .persist() method on a DataFrame. This loads the data into the executor's memory. However, recent versions of Spark on commercial platforms (like Databricks) implement automatic disk caching similar to the transparent I/O caching described above, requiring no code changes from the user.

Properly tuned caching strategies transform a decoupled data lake from a cold storage repository into a high-performance analytics engine capable of serving interactive dashboards and iterative machine learning workloads.