Introduction to Amazon MemoryDB for Redis and its use cases

In this recipe, we will learn about Amazon MemoryDB for Redis. We will also learn about the use cases of Amazon MemoryDB for Redis.

Recipe Objective - Introduction to Amazon MemoryDB for Redis and its use cases?

The Amazon Managed Grafana is a widely used service and is defined as a fully managed service for ultra-fast performance, using a Redis-compatible, durable, in-memory database service. MemoryDB for Redis aims to combine the functions of a cache and a database into a single component that offers microsecond read latency and data durability. Build applications quickly with Redis, the "Most Loved" database on Stack Overflow for the past five years. Process over 13 trillion requests per day and over 160 million requests per second to access data at lightning speed. In-memory storage with a Multi-AZ transactional log for fast database recovery and restart ensures data durability. Scale from a few gigabytes to over a hundred terabytes of storage per cluster to meet users' application requirements. Amazon MemoryDB has a throughput of up to 1.3 GB/s read and 100 MB/s write per node and can handle up to 390K read and 100K write requests per second (based on internal Amazon testing on read-only and write-only workloads). Latency is measured in microseconds for reads and single-digit milliseconds for writes. Scaling in both vertical and horizontal directions. Data sharding and read replicas are possible. On the primary node, there is a lot of consistency, and on the replica nodes, there are a lot of consistency reads. MemoryDB's consistency model is similar to ElastiCache for Redis. MemoryDB, on the other hand, does not lose data during failovers, allowing clients to read their writes from primary nodes even if nodes fail. Only data from the multi-AZ transaction log that has been successfully persisted is visible. Replica nodes eventually become consistent.

Build a Real-Time Dashboard with Spark, Grafana and Influxdb

Features of Amazon MemoryDB for Redis

  • Lossless failover is supported by MemoryDB. When a primary node fails, MemoryDB will failover automatically and promote one of the replicas to serve as the new primary, redirecting write traffic to it. MemoryDB also employs a distributed transactional log to ensure that data on replicas remains current, even in the event of a primary node failure. For unplanned outages, failover takes less than 10 seconds, and for planned outages, it takes less than 200 milliseconds and thus it supports lossless failover. MemoryDB stores your entire data set in memory and provides data durability through a distributed multi-AZ transactional log and thus offers data durability. During the maintenance windows you specify, MemoryDB automatically patches your cluster. MemoryDB uses service updates for some updates, which you can apply immediately or schedule for a later maintenance window and thus it offers a managed service.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon MemoryDB for Redis and the Use cases of Amazon MemoryDB for Redis.

Use cases of Amazon MemoryDB for Redis

    • It Constructs web and mobile apps

Build content data stores, chat and message queues, and geospatial indexes with Redis data structures like streams, lists, and sets for demanding, data-intensive web and mobile applications that require low latency and high throughput. Service.

    • It provides user authentication and authorization making it simple to share dashboards.

With microsecond read and single-digit millisecond write latency, deliver personalised customer experiences and manage user profiles, preferences, inventory tracking, and fulfilment.

    • It provides Customer data that can be accessed quickly in the retail industry.

Users can easily grant data source access permissions and share dashboards to groups of users by creating multiple Grafana Teams. Later-added team members will inherit access permissions to shared resources, eliminating the need to grant permissions one dashboard at a time. Users can view and edit dashboards in real-time, track dashboard version changes, and share dashboards with other team members so that everyone is looking at the same data while troubleshooting operational issues. Users can also easily share dashboards with other teams or external entities by creating publicly accessible dashboard snapshots.

    • It develops online games

For gaming applications that require massive scale, low latency, and high concurrency to make real-time updates, create player data stores, session history, and leaderboards.

    • It provides Media and entertainment on demand

Run high-concurrency streaming data feeds for media and entertainment applications to ingest user activity and support millions of requests per day.

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I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

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