GCP – Supercharge data access performance with GKE Data Cache

Today, we’re excited to announce the general availability (GA) of GKE Data Cache, a powerful new solution for Google Kubernetes Engine to accelerate the performance of read-heavy stateful or stateless applications that rely on persistent storage via network attached disks. By intelligently utilizing high-speed local SSDs as a cache layer for persistent disks, GKE Data Cache helps you achieve lower read latency and higher queries per second (QPS), without complex manual configuration.

Using GKE Data Cache with Postgres we have seen:

• Up to a 480% increase in transactions per second for PostgreSQL on GKE
• Up to a 80% latency reduction for PostgreSQL on GKE

“The launch of GKE Persistent Disk with Data Cache enables significant improvements in vector search performance. Specifically, we’ve observed that Qdrant search response times are a remarkable 10x faster compared to balanced disks, and 2.5x faster compared to premium SSDs, particularly when operating directly from disk without caching all data and indexes in RAM. Qdrant Hybrid Cloud users on Google Cloud can leverage this advancement to efficiently handle massive datasets, delivering unmatched scalability and speed without relying on full in-memory caching.” – Bastian Hofmann, Director of Engineering, Qdrant 

Stateful applications like databases, analytics platforms, and content management systems are critical to many businesses. However, their performance can often be limited by the I/O speed of the underlying storage. While persistent disks provide durability and flexibility, read-intensive workloads can experience bottlenecks, impacting application responsiveness and scalability. 

GKE Data Cache addresses this challenge head-on, providing a managed block storage solution that integrates with your existing Persistent Disk or Hyperdisk volumes. When you enable GKE Data Cache on your node pools and configure your workloads to use it, frequently accessed data is automatically cached on the low-latency local SSDs attached to your GKE nodes.

This caching layer serves read requests directly from the local SSDs whenever the data is available, significantly reducing the need to access the underlying persistent disk and potentially allowing for the use of less system memory cache (RAM) to service requests in a timely manner.

The result is a substantial improvement in read performance, leading to:

• Lower read latency: Applications experience faster data retrieval, improving the user experience and application responsiveness.

• Higher throughput and QPS: The ability to serve more read requests in parallel allows your applications to handle increased load and perform more intensive data operations.

• Potential cost optimization: By accelerating reads, you may be able to utilize smaller or lower-IOPS persistent disks for your primary storage while still achieving high performance through the cache. Additionally you may be able to reduce the required memory of the machine’s paging cache by pushing the read latency to the local SSD. Memory capacity is more expensive than capacity on a local SSD.

• Simplified management: As a managed feature, GKE Data Cache simplifies the process of implementing and managing a high-performance caching solution for your stateful workloads.

“Nothing elevates developer experience like an instant feedback loop. Thanks to GKE Data Cache, developers can spin up pre-warmed Coder Workspaces on demand, blending local-speed coding with the consistency of ephemeral Kubernetes infrastructure.” – Ben Potter, VP of Product, Coder

GKE Data Cache supports all read/write Persistent Disk and Hyperdisk types as backing storage, so you can choose the right persistent storage for your needs while leveraging the performance benefits of local SSDs for reads. You can configure your node pools to dedicate a specific amount of local SSD space for data caching.

For data consistency, GKE Data Cache offers two write modes: writethrough (recommended for most production workloads to ensure data is written to both the cache and the persistent disk synchronously) and writeback (for workloads prioritizing write speed, with data written to the persistent disk asynchronously).

Getting started

Getting started with GKE Data Cache is straightforward. You’ll need a GKE Standard cluster running a compatible version (1.32.3-gke.1440000 or later), node pools configured with local SSDs, the data cache feature enabled, and a StorageClass that specifies the use of data cache acceleration. Your stateful workloads can then request storage with caching using PersistentVolumeClaims that reference this StorageClass. The amount of data to store in a cache for each disk is defined in the StorageClass. 

Here’s how to create a data cache-enabled node pool in an existing cluster:

When you create a node pool with the `data-cache-count` flag, local SSDs (LSSDs) are reserved for the data cache feature. This feature uses those LSSDs to cache data for all pods that have caching enabled and are scheduled onto that node pool.

The LSSDs not reserved for caching can be used as ephemeral storage. Note that we do not currently support using the remaining LSSDs as raw block storage.

Once you reserve the required local SSDs for caching, you set up the cache configuration in the StorageClass with `data-cache-mode` and `data-cache-size` then reference that StorageClass in a PersistentVolumeClaim.

Setting up data cache with StorageClass

To learn more about GKE Data Cache and how to implement it for your stateful workloads, please refer to the official documentation.