Google Memorystore
Author: Ronald Fung
Creation Date: 12 June 2023
Next Modified Date: 12 June 2024
A. Introduction
Many of today’s applications ranging from gaming, cybersecurity, social media require processing data at sub-millisecond latency to deliver real-time experiences. To meet demands of low latency at increased scale and reduced cost you need an in-memory datastore. Redis and Memchaced are among the most popular. Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. Like any other Google Cloud service it is fast, scalable, highly available, and secure. It automates complex tasks of provisioning, replication, failover, and patching so you can spend more time on other activities!! It comes with a 99.9% SLA and integrates seamlessly with your apps within Google Cloud.
Memorystore is used for different types of in-memory caches and transient stores; and Memorystore for Redis is also used as a highly available key-value store. This serves multiple use cases including web content caches, session stores, distributed locks, stream processing, recommendations, capacity caches, gaming leaderboards, fraud\threat detection, personalization, and ad tech.
B. How is it used at Seagen
As a biopharma research company, Seagen may benefit from using Google Memorystore for its in-memory data store needs. Here are some ways Seagen can use Google Memorystore:
Migrating from Microsoft Azure: Seagen can migrate their existing in-memory data stores from Microsoft Azure to Google Memorystore by exporting the data stores from Azure and importing them into Google Memorystore. This can be done using the Memcached migration tool or other migration tools.
Deploying new in-memory data stores: Seagen can deploy new in-memory data stores on Google Cloud Memorystore. They can choose the instance type, memory size, and network settings, and can customize the instance according to their requirements. They can also configure the access control policies to ensure the security and reliability of the data store.
Managing in-memory data stores: Seagen can manage their in-memory data stores on Google Cloud Memorystore by performing tasks such as software updates, backup and recovery, and performance optimization. They can use Google Stackdriver for monitoring and logging, and can use Google Cloud IAM for access control.
Scaling in-memory data stores: Seagen can scale their in-memory data stores on Google Cloud Memorystore vertically or horizontally, depending on their workload and performance requirements. They can also use Google Cloud Load Balancing to distribute traffic across multiple in-memory data stores for improved scalability and availability.
Integrating with other Google Cloud services: Seagen can integrate their in-memory data stores with other Google Cloud services, such as Google Cloud Functions, Google Cloud Pub/Sub, and Google Cloud Dataflow, to build more robust and scalable data pipelines and applications.
Overall, by using Google Memorystore, Seagen can benefit from a high-performance and scalable solution for their in-memory data store needs. With its support for migrating existing data stores, deploying new instances, managing instances, scaling instances, and integrating with other Google Cloud services, Google Memorystore provides a range of options that can meet the needs of a variety of workloads and use cases.
C. Features
Choice of engines
Choose from the two most popular open source caching engines to build your applications. Memorystore supports both Redis and Memcached and is fully protocol compatible. Choose the right engine that fits your cost and availability requirements.
Security
Memorystore is protected from the internet using VPC networks and private IP and comes with IAM integration—all designed to protect your data. Systems are monitored 24/7/365, ensuring your applications and data are protected.
Fully managed
Provisioning, replication, failover, and patching are all automated, which drastically reduces the time you spend doing DevOps.
D. Where Implemented
E. How it is tested
Testing Google Memorystore involves ensuring that the in-memory data store is properly configured and optimized for performance, reliability, and security. Here are some steps to test Google Memorystore:
Create a test environment: Create a test environment that mimics the production environment as closely as possible, including the data schema, data, and access control policies. Ensure that the in-memory data store is properly configured and that the security policies are in place.
Deploy the in-memory data store: Deploy the in-memory data store on Google Cloud Memorystore instances. Ensure that the instance is properly configured and that it can communicate with other resources, such as applications or APIs.
Test data store performance: Test the data store performance by sending data to the deployed instance using data store testing tools, such as Memcached or Redis benchmarking tools. Ensure that the instance is responding to requests properly and that there are no errors or timeouts.
Test data store scaling: Test the data store scaling by increasing the number of requests sent to the instance and observing how Google Cloud Memorystore automatically scales up the instance to handle the increased load. Ensure that the instance is scaling properly and that there are no performance or availability issues.
Test data store security: Test the data store security by using data store security testing tools, such as penetration testing or vulnerability scanning tools. Ensure that the instance is secure and compliant with industry and regulatory standards.
Test data store optimization: Test the data store optimization by using data store optimization testing tools, such as Redis CLI or Memcached stats. Ensure that the instance is optimized for performance, reliability, and security.
Test data store integration: Test the data store integration with other Google Cloud services or third-party tools by using data store integration testing tools, such as Google Cloud Pub/Sub or Microsoft Azure Event Grid. Ensure that the instance is integrated properly and that there are no integration issues or errors.
Overall, by thoroughly testing Google Memorystore, users can ensure that their in-memory data stores are properly configured and optimized for performance, reliability, and security. Additionally, users can reach out to Google Cloud support for help with any technical challenges they may encounter.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
While Google Memorystore is a reliable and widely used solution for in-memory data store needs, there are some known issues that users may encounter. Here are some of the known issues for Google Memorystore:
Instance health issues: Users may encounter instance health issues, such as high latency or low throughput, especially for workloads that require low latency or high throughput. This issue can often be resolved by using the appropriate machine types, adjusting the routing tables, or enabling network load balancing.
Network connectivity issues: Users may encounter network connectivity issues, such as network outages or intermittent connectivity, especially for workloads that require high availability or low latency. This issue can often be resolved by using the appropriate network settings, such as VPC peering or VPN connections, and by monitoring the network performance and logs.
Security issues: Users may encounter security issues, such as unauthorized access or data breaches, especially for workloads that require high security. This issue can often be resolved by using the appropriate security policies and access controls, such as firewall rules and IAM roles.
Memory usage issues: Users may encounter memory usage issues, such as memory leaks or excessive memory usage, especially for workloads that require high memory usage. This issue can often be resolved by optimizing the data store configuration and by using memory profiling and monitoring tools.
Integration issues: Users may encounter integration issues with other cloud services or third-party tools, such as data pipelines or analytics platforms. This issue can often be resolved by using industry-standard protocols and APIs to enable interoperability between different cloud services and tools.
Overall, while these issues may impact some users, Google Memorystore remains a powerful and reliable solution that is widely used by businesses and organizations around the world. By monitoring their performance and security alerts and logs, reviewing their data store configuration and policies, and using best practices and industry standards, users can ensure that their in-memory data stores running on Google Cloud Memorystore are optimized for performance, reliability, and security. Additionally, users can reach out to Google Cloud support for help with any known issues or other technical challenges they may encounter.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture