Wednesday, February 12, 2025

In Memory Databases: The Future of High-Speed Data Access

A purpose-built database that stores data mostly in internal memory is known as an in-memory database. By removing the requirement to access ordinary disc drives (SSDs), it allows for reduced reaction times. Applications like gaming leaderboards, session storage, and real-time data analytics that need microsecond reaction times or see significant traffic spikes are best suited for in memory databases. In-memory databases are often referred to as main memory databases (MMDB), in-memory database systems (IMDS), and real-time database systems (RTDB).

Advantages of In Memory Database

Amazon go over each of the benefits of an in-memory database in more detail below.

Low latency, providing real time responses

The delay between an application’s response and a request to access data is known as latency. Regardless of scale, in-memory databases provide consistent low latencies. They provide high throughput, single-digit millisecond write latency, and microsecond read latency.

Consequently, in-memory storage enables businesses to make data-driven decisions instantly. Applications that handle data and react to changes can be created before it’s too late. For instance, self-driving cars’ sensor data can be computed in-memory to provide the required split-second response time for emergency braking.

High volume of data

High throughput is a well-known characteristic of in memory databases. The quantity of read (read throughput) or write (write throughput) operations performed in a specific amount of time is referred to as throughput. Transactions per second or bytes/minute are two examples.

Excellent scalability

Your in-memory database can be scaled to accommodate changing application requirements. It is possible to scale both write and read without negatively affecting performance. During resizing, the database remains accessible and allows for read-and-write activities.

What are the use cases of in-memory databases

The banking, telecommunications, gaming, and mobile advertising sectors are all ideal candidates for in-memory databases. Below are a few instances of use cases for in-memory databases.

Caching

A cache is a layer of fast data storage that holds a portion of data that is usually temporary. The main goal of a cache is to improve data retrieval speed by minimising the need to contact the slower storage layer behind. This implies that subsequent requests for that data are fulfilled more quickly than if the data were accessed from its primary storage location.

You can effectively reuse previously retrieved or calculated data with caching. For quicker access to cached data, in-memory data storage performs admirably. Response time is sacrificed for durability through caching. Although caching doesn’t prevent data loss in memory, it does speed up response times by retrieving data from memory. For this reason, caching and a durable disk-based database are frequently employed together.

Real-time bidding

The purchasing and selling of internet ad impressions is known as real-time bidding. The bid must typically be placed within 100–120 milliseconds, and perhaps as little as 50 milliseconds, while the user loads the homepage. Real-time bidding apps solicit offers from all potential buyers for the ad spot during this time, choose a winning bid based on a number of factors, present the offer, and gather data after the ad is displayed. With sub-millisecond latency, in memory databases are perfect for ingesting, processing, and analysing real-time data.

Gaming leaderboards

The position of a player in relation to other players of a similar rank is displayed on a relative gaming leaderboard. When compared to the best players, these leaderboards can assist increase player engagement and prevent demotivation. For a game with millions of participants, in-memory databases can provide sorting results fast and update the leaderboard in real time.

How does an in-memory cache work

Random access memory (RAM) is used by an in-memory cache to store data. Instead of storing data tables on external devices, the technique saves them directly in the RAM. Your data records can be indexed with specialised data structures. Direct references to particular rows and columns are provided by the indexes. The real physical data, however, is in a non-relational format and is compressed. The database uses the index to find the precise data value when you submit an access request. Data that has been stored is always accessible in a format that may be used directly.

In-memory storage has become more popular due to factors including multi-core servers, 64-bit computing, and cheaper RAM prices. Furthermore, you may scale your RAM resources up or down as needed with cloud-based data stores, which increases the adaptability and accessibility of in-memory technologies.

The distinction between conventional disk-based databases and in-memory cache

All of the data in a typical database is stored on solid-state or external disc drives. Disc access is necessary for each and every read-and-write activity. On the other hand, data durability is not given priority by in-memory cache. Caches, for instance, might only sometimes save data on external storage devices. Below is a summary of the distinctions between standard databases and in-memory caches.

What distinguishes an in-memory database from an in-memory cache?

Because writes are not persisted, in-memory caches eliminate the additional time required for data persistence, leading to improved speed. Because writes are persistent in an in-memory database, data changes are long-lasting. Lower write performance is the price paid for this durability. Nonetheless, in memory databases continue to outperform disk-based databases in terms of performance. They fall somewhere between a disk-based database and an in-memory cache in terms of performance.

In Memory Database Cache

In-memory caches run the risk of losing data in the event of a process or server failure because all data is maintained and stored solely in memory. An in-memory cache may periodically persist data on disc databases to increase endurance. Below, Amazon go into further detail about a few mechanisms to increase durability.

Images of snapshots

The database state at a specific point in time is captured in snapshot files. Snapshots are created by the in-memory cache either on a regular basis or during a controlled shutdown. Data loss may still happen in between snapshots, even though snapshotting somewhat increases durability.

Logging of transactions

Transaction logging creates an external journal file that documents database modifications. Performance is unaffected by logging, which is independent of data read/write. An in-memory cache can be automatically recovered with the journal file.

Replication

Redundancy is used by certain in-memory caches to provide high availability. They keep several copies of the same information in several memory modules. When a module fails, the duplicate backup copy is automatically used. Using a cache reduces the chance of data loss.

How can AWS support your in-memory cache and database requirements

For your particular requirements, AWS offers a variety of fully managed in-memory cache and database services.

In-memory database

MemoryDB on Amazon

A robust in-memory database service with lightning-fast performance is Amazon MemoryDB. Because it is compatible with Redis OSS, users may easily create applications utilising the same adaptable and user-friendly Redis OSS data structures, commands, and APIs that they now use. In order to facilitate quick failover, database recovery, and node restarts, MemoryDB further uses a Multi-AZ transactional log to store your data durably across several Availability Zones (AZs).

In-memory caches

ElastiCache on Amazon

Amazon ElastiCache is a lightning-fast in-memory caching solution that powers real-time internet-scale applications with microsecond latency. It is compatible with Memcached and Redis OSS. ElastiCache can be used by developers for use cases that don’t require great data durability or as an in-memory cache. Customers can run workloads with up to 6.1 TB of in-memory capacity in a single cluster using the ElastiCache cluster configuration. Adding and removing shards from an active cluster is another feature that ElastiCache offers. To adjust to variations in demand, you can dynamically scale your ElastiCache cluster workloads in and out.

Thota nithya
Thota nithya
Thota Nithya has been writing Cloud Computing articles for govindhtech from APR 2023. She was a science graduate. She was an enthusiast of cloud computing.
RELATED ARTICLES

Recent Posts

Popular Post

Govindhtech.com Would you like to receive notifications on latest updates? No Yes