Powering up caching with Memorystore for Memcached

GCP Databases

In-memory data stores are a fundamental infrastructure for building scalable, high-performance applications. Whether it is building a highly responsive ecommerce website, creating multiplayer games with thousands of users, or doing real-time analysis on data pipelines with millions of events, an in-memory store helps provide low latency and scale for millions of transactions. Redis is a popular in-memory data store for use cases like session stores, gaming leaderboards, stream analytics, API rate limiting, threat detection, and more. Another in-memory data store, open source Memcached, continues to be a very popular choice as a caching layer for databases and is used for its speed and simplicity.

We’re announcing Memorystore for Memcached in beta, a fully managed, highly scalable service that’s compatible with the open source Memcached protocol. We launched Memorystore for Redis in 2018 to let you use the power of open source Redis easily without the burden of management. This announcement brings even more flexibility and choice for your caching layer. 

Highlights of Memorystore for Memcached

Memcached offers a simple but powerful in-memory key value store and is popular as a front-end cache for databases. Using Memcached as a front-end store not only provides an in-memory caching layer for faster query processing, but it can also help save costs by reducing the load on your back-end databases.

Using Memorystore for Memcached provides several important benefits:

  • Memorystore for Memcached is fully open source protocol compatible. If you are migrating applications using self-deployed Memcached or other cloud providers, you can simply migrate your application with zero code changes.

  • Memorystore for Memcached is fully managed. All the common tasks that you spend time on, like deployment, scaling, managing node configuration on the client, setting up monitoring, and patching, are all taken care of. You can focus on building your applications.

  • Right-sizing a cache is a common challenge with distributed caches. The scaling feature of Memorystore for Memcached, along with detailed open source Memcached monitoring metrics, allows you to scale your instance up and down easily to optimize for your cache-hit ratio and price. With Memorystore for Memcached, you can scale your cluster up to 5 TB per instance.

  • Auto-discovery protocol lets clients adapt to changes programmatically, making it easy to deal with changes to the number of nodes during scaling. This drastically reduces manageability overhead and code complexity.

  • You can monitor your Memorystore for Memcached instances with built-in dashboards in the Cloud Console and rich metrics in Cloud Monitoring.

Memorystore for Memcached can be accessed from applications running on Compute Engine, Google Kubernetes Engine (GKE), App Engine Flex, App Engine Standard, and Cloud Functions.

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The beta launch is available in major regions across the U.S., Asia, and Europe, and will be available globally soon.

Getting started with Memorystore for Memcached

To get started with Memorystore for Memcached, check out the quick start guide. Sign up for a $300 credit to try Memorystore and the rest of Google Cloud. You can start with the smallest instance and when you’re ready, you can easily scale up to serve performance-intensive applications. Enjoy your exploration of Google Cloud and Memorystore for Memcached.