Top 20 AWS MemoryDB Questions and Answers

Redis-compatible, dependable, in-memory database service with lightning-fast performance is Amazon MemoryDB for Redis. For contemporary applications, such as those created with microservices architectures, MemoryDB enables you to achieve microsecond read latency, single-digit millisecond write latency, high throughput, and Multi-AZ durability. Redis' adaptable data structures and APIs are used in these applications' low latency and high scalability requirements as well as to facilitate rapid development. MemoryDB uses a distributed transactional log to provide in-memory speed as well as data durability, consistency, and recoverability by storing your complete dataset in memory. You may create high-performance applications without having to independently maintain a cache, durable database, or the necessary supporting infrastructure by using MemoryDB as a fully managed primary database.

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Ques. 1): Describe Redis.

Answer:

Strings, lists, sets, hashes, sorted sets, and other adaptable data structures are used by Redis, an open-source, in-memory key-value data store. Additionally, Redis has functionality for geospatial queries, pub/sub, custom Lua scripts, and other things. Redis provides lightning-fast performance and great throughput since it saves data in-memory. For the fifth year in a row, Redis is the "Most Loved Database" among Stack Overflow developers. It is also the top database chosen by the Cloud Native Computing Foundation (CNCF) End User Community. In order to promote and support cloud native computing, including containers and microservices, CNCF was established.

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Ques. 2): What is a MemoryDB cluster?

Answer:

A grouping of one or more nodes supporting a single dataset is known as a MemoryDB cluster. Each of the shards that make up a MemoryDB dataset has a primary node and a maximum of five optional replica nodes. A replica only handles read requests, whereas a primary node handles both read and write requests. When a primary node fails over to a replica node, the replica is elevated to the position of new primary node for that shard.

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Ques. 3): Does MemoryDB support high availability?

Answer:

MemoryDB does indeed provide high availability. A MemoryDB cluster with Multi-AZ availability can have up to 5 replicas spread across various AZs. MemoryDB will automatically failover and promote one of the replicas to act as the new primary and direct write traffic to it in the event of a primary node loss. MemoryDB also makes use of a distributed transactional log to guarantee that, even in the event of a primary node failure, the data on replicas is kept current. For unplanned failures, failover often occurs in less than 10 seconds, while for planned outages, it typically occurs in less than 200 milliseconds.

To reliably save data written to your database during database recovery, restart, failover, and eventual consistency between primaries and replicas, MemoryDB uses a distributed transactional log.

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Ques. 4): Can my MemoryDB cluster be backed up?

Answer:

Yes, you do make snapshots to backup your MemoryDB cluster's data and metadata. You have two options for creating snapshots: manually or automatically, using MemoryDB's automated snapshot scheduler once every day at a time you choose. You have the option to keep a snapshot in MemoryDB for up to 35 days after it is made. Amazon S3, which is built for 99.999999999% (11 9's) durability, is where snapshots are saved. Additionally, you have the option to delete your cluster after taking one final snapshot of it. Additionally, the service allows you to export MemoryDB snapshots to an Amazon S3 bucket.

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Ques. 5): Why is Amazon MemoryDB for Redis recommended to use data tiering?

Answer:

Use data tiering when you need a quicker, less expensive way to expand the data capacity for your MemoryDB clusters without sacrificing the availability of your applications. The workloads and programmes that can tolerate a bit extra latency the first time they utilise a less-frequently accessed item are the ones that benefit from data tiering the most. R6gd nodes, which have around a 5x larger total capacity (memory + SSD) while operating at peak efficiency, can cut storage expenses by more than 60% when compared to R6g nodes (memory only).

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Ques. 6): How can my MemoryDB cluster be updated?

Answer:

Your cluster's upgrades and maintenance are made simple by MemoryDB, which also offers two distinct processes for cluster upkeep. First, during the maintenance times that you set, MemoryDB automatically patches your cluster with some required upgrades. Second, MemoryDB uses service updates for some changes, which you can deploy immediately or plan for a later maintenance window. After a specific date, some service updates are automatically scheduled during a maintenance window. Your clusters' security, dependability, and operational performance are strengthened by cluster upgrades, and your cluster continues to be online and respond to read and write requests.

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Ques. 7): How is the consistency of MemoryDB different from that of free software Redis?

Answer:

On the primary node of each shard, open source Redis permits writes and strongly consistent reads, and eventually consistent readings from read replicas. If a primary node fails, these consistency qualities are not guaranteed because writes can get lost during a failover and hence go against the consistency paradigm.

MemoryDB's consistency model is comparable to Redis, an open source database. Clients can access their writes from primaries regardless of node failures with MemoryDB since data is not lost during failovers. In the multi-AZ transaction log, only information that has successfully been persisted is accessible. Lag metrics are still published to Amazon CloudWatch, and replica nodes are still eventually consistent.

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Ques. 8): How much throughput and latency can I get with MemoryDB?

Answer:

Depending on the node type, payload size, and number of client connections, MemoryDB's throughput and latency varies. On the principal node for a cluster shard, MemoryDB offers microsecond read latency, single-digit millisecond write latency, and read-after-write delay. Up to 1.3 GB/s read and 100 MB/s write throughput and 390K read and 100K write requests per second are supported by MemoryDB (based on internal testing on read-only and write-only workloads). A MemoryDB cluster divides data among one or more nodes, allowing you to expand your cluster by adding more replicas or shards to boost aggregate throughput.

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Ques. 9): When should I use MemoryDB for Redis instead of Amazon ElastiCache?

Answer:

For workloads that demand an incredibly quick, Redis-compatible primary database, MemoryDB for Redis provides a reliable, in-memory database. If your workload necessitates a robust database with lightning-fast speed, you should think about adopting MemoryDB (microsecond read and single-digit millisecond write latency). If you wish to create a Redis-based application with a primary, long-lasting database, MemoryDB might also be a good fit for your use case. Finally, you should think about adopting MemoryDB to reduce costs and simplify your application architecture by using a cache for durability and performance instead of a database.

Data from other databases and data stores that use Redis are frequently cached using the service ElastiCache for Redis. For caching workloads where you wish to speed up data access with your current primary database or data storage, you should think about ElastiCache for Redis (microsecond read and write performance). For use scenarios when you want to access data stored in a primary database or data store using the Redis data structures and APIs, you should also take ElastiCache for Redis into consideration.

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Ques. 10): How do I get started with using MemoryDB?

Answer:

Using the AWS Management Console, Command Line Interface (CLI), or Software Development Kit, you can begin by building a new MemoryDB cluster (SDK). Sign in and go to Amazon MemoryDB for Redis in the console to create a MemoryDB cluster. Choose "Get Started" and then "Create new cluster" from there.

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Ques. 11): Can I resize my MemoryDB cluster?

Answer:

Yes, you can change the horizontal and vertical sizes of your MemoryDB cluster. You can add or remove nodes to horizontally scale your cluster. Your dataset can be split among multiple shards by adding shards, and you can add more replica nodes to each shard to improve read throughput and availability. If you want to scale up your cluster, you may also delete replicas and shards. Changing the type of node you use will alter the memory and CPU resources available to each node, allowing you to expand your cluster vertically as well. Your cluster keeps running and continues to service read and write requests even when performing horizontal and vertical resizing operations.

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Ques. 12): How can I set up my MemoryDB cluster for authentication and authorization?

Answer:

Redis Access Control Lists (ACLs) are used by MemoryDB to manage cluster authorisation and authentication. With the aid of ACLs, you can provide unique permissions for various users within the same cluster. ACLs are groups of one or more users. For the purpose of authorising access to Redis commands and data, each user has a password and an access string.

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Ques. 13): How does Amazon MemoryDB for Redis' data tiering function?

Answer:

When available memory is used up, data tiering uses SSD storage in cluster nodes to continue processing data. Data tiering is automatically activated when employing cluster nodes with SSD storage, and MemoryDB controls data placement by transparently transferring things between memory and disc using a least-recently-used (LRU) strategy. MemoryDB automatically recognises the least-recently used items when memory is completely utilised and copies their values to disc to reduce cost. MemoryDB seamlessly copies an item's value to memory before completing a request when an application has to obtain it from the disc, doing so with no performance hit.

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Ques. 14): Can I migrate data from ElastiCache for Redis to my MemoryDB cluster?

Answer:

Yes, you can move data from MemoryDB to ElastiCache for Redis. Create an export to your S3 bucket of an image of your ElastiCache for Redis cluster first. Next, establish a fresh MemoryDB cluster and choose the backup to use for a restore. With the information from the snapshot and the Redis metadata, MemoryDB will establish a new cluster. See the MemoryDB documentation for more details on moving data from ElastiCache for Redis to MemoryDB.

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Ques. 15): How is the append-only file (AOF) feature of open source Redis different from MemoryDB's durability functionality?

Answer:

To store data durably, MemoryDB uses a distributed transactional log. MemoryDB offers quick database recovery and restart thanks to the distribution of data across several AZs. Additionally, MemoryDB provides consistent reads on primary nodes and eventual consistency for replica nodes.

An optional append-only file (AOF) feature in open source Redis allows for the long-term storage of data in a file on a primary node's hard drive. However, there is a chance of data loss because AOF saves data locally on primary nodes in a single availability zone. Additionally, there is a chance that replica consistency will be a problem in the event of a node loss.

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Ques. 16): Is Redis compatible with MemoryDB?

Answer:

Yes, MemoryDB supports the same set of Redis data types, parameters, and commands that you are accustomed to. MemoryDB also retains compatibility with open source Redis. This implies that you can utilise MemoryDB with the same application code, clients, and tools that you already use with Redis. All Redis data types, including strings, lists, sets, hashes, sorted sets, hyperloglogs, bitmaps, and streams, are supported by MemoryDB. Additionally, because MemoryDB administers your cluster for you, it supports the 200+ Redis commands with the exception of Redis admin commands.

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Ques. 17): My data is it encrypted by MemoryDB?

Answer:

Yes, MemoryDB supports both at-rest and in-transit data encryption. You can use a MemoryDB supplied key or customer managed keys (CMK) from AWS Key Management Service for encryption at rest. Your data is encrypted in memory using always-on 256-bit DRAM encryption when using Graviton2 instances for your MemoryDB cluster.

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Ques. 18): What does Amazon MemoryDB for Redis' data tiering mean?

Answer:

A new price-performance feature for MemoryDB called "Data Tiling for Amazon MemoryDB for Redis" automatically transfers less-frequently accessible data from memory to high-performance, locally attached solid-state drives (SSD). Data tiering boosts MemoryDB's capacity, streamlines cluster administration, and lowers total cost of ownership (TCO).

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Ques. 19): How is my data securely stored via MemoryDB?

Answer:

MemoryDB uses a distributed Multi-AZ transactional log to ensure data durability, consistency, and recoverability while storing your complete data set in memory. MemoryDB offers quick database recovery and restart thanks to the distribution of data across several AZs. MemoryDB can provide lightning-fast performance and excellent throughput by additionally keeping the data in memory.

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Ques. 20): What is the maximum size of a cluster that MemoryDB can support?

Answer:

A MemoryDB cluster of up to 500 nodes is created by you. With 250 primary nodes and one replica for high availability, this results in a maximum memory storage capacity of about 100 TB.

Top 20 AWS Neptune Interview Questions and Answers

It is simple to create and run applications that work with densely connected datasets thanks to Amazon Neptune, a quick, dependable, and fully-managed graph database service. Highly connected data requires complicated SQL queries that are challenging to optimise for performance. Instead, you may utilise open and well-known graph query languages with Amazon Neptune to run effective queries that are simple to develop and perform well on connected data.

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Ques. 1): What do you know about Amazon Neptune?

The time-consuming processes like provisioning, patching, backup, recovery, failure detection, and repair are handled by Amazon Neptune, which is fully managed. For each instance of the Amazon Neptune database you use, you just pay a monthly fee. Both up-front fees and long-term commitments are not necessary.

The heart of Neptune is a purpose-built, high-performance graph database engine designed for millisecond-latency graph queries and storing billions of relationships. Neptune can be used for graph use cases like drug discovery, knowledge graphs, fraud detection, recommendation engines, and network security.

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Ques. 2): Is a relational database the foundation of Amazon Neptune?

Contrary to popular belief, Amazon Neptune is a high-performance graph database engine. Neptune offers a scale-up, in-memory optimised architecture to provide quick query evaluation over massive graphs and effectively stores and navigates graph data.

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Ques. 3): In Amazon Neptune, what are IOs and how are they calculated?

To cut expenses and make sure resources are available for serving read/write traffic, Amazon Neptune was built to do away with pointless IO activities. Only when pushing transaction log records to the storage layer in order to make writes persistent, write IOs are used. 4KB units are used to measure write IOs. One IO operation, for illustration, would be the 1024-byte transaction log record. However, in order to reduce I/O consumption, the Amazon Neptune database engine can batch together concurrent write operations whose transaction log is less than 4KB. Amazon Neptune doesn't push updated database pages to the storage layer like traditional database engines do, which reduces IO usage even further.

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Ques. 4): How can the computational resources connected to my Amazon Neptune DB Instance be scaled?

By selecting the desired DB Instance and clicking the Modify button in the AWS Management Console, you can scale the compute resources allotted to your DB Instance. You can alter memory and CPU resources by altering the class of your DB Instance.

Your desired changes will be implemented whenever you make changes to the DB Instance class during the designated maintenance window. As an alternative, you can apply your scaling requests right away by using the "Apply Immediately" parameter. For a few minutes while the scaling procedure is being carried out, each of these alternatives will have an influence on availability. Remember that this update will be applied along with any other pending system changes.

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Ques. 5): What happens if I destroy my DB Instance to my automatic backups and database snapshots?

When you delete your DB Instance, you have the option of creating a last DB Snapshot. If so, you can later on use this DB Snapshot to recover the destroyed DB Instance. After the DB Instance is removed, Amazon Neptune keeps this last manually made DB Snapshot in addition to all other manually created DB Snapshots. After deleting the DB Instance, only DB Snapshots are maintained (i.e., automated backups created for point-in-time restore are not kept).

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Ques. 6): How is the fault tolerance of my database to disc failures improved by Amazon Neptune?

Your database volume is automatically divided into 10GB parts and distributed over several discs by Amazon Neptune. Your database volume is replicated six times, over three availability zones, for each 10GB piece. Without compromising database write availability or read availability, Amazon Neptune is built to transparently withstand the loss of up to two copies of data at once. Additionally self-healing is Amazon Neptune storage. Disks and data blocks are automatically checked for faults on a constant basis.

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Ques. 7): How can the availability of a single Amazon Neptune database be improved?

Amazon Neptune Replicas are an option. The primary instance and Amazon Neptune Replicas share the same underlying storage. To increase fault tolerance in the event of a primary DB Instance failure, any Amazon Neptune Replica can be promoted to become primary without causing any data loss. Simply add 1 to 15 replicas to your database to boost availability, and Amazon Neptune will automatically choose them for failover main selection in the event of a database failure.

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Ques. 8): How long does failover take? What happens during failover?

Amazon Neptune handles failover automatically so that your applications can resume database operations as soon as feasible without needing to call in a manual administrator.

When failing over, Amazon Neptune flips the canonical name record (CNAME) for your database's primary endpoint to a healthy replica, which is then promoted to become the new primary, if you have an Amazon Neptune Replica in the same or a different Availability Zone. Failover normally takes 30 seconds to complete from beginning to end. Additionally, during failover, the read replicas endpoint doesn't need any CNAME modifications.

Neptune will first try to establish a new DB Instance in the same Availability Zone as the original instance if you don't already have one (i.e., a single instance). Neptune will try to build a new DB Instance in a different Availability Zone if it is unable to do so. Failover normally takes less than 15 minutes to execute from beginning to end.

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Ques. 9): Is it possible to encrypt my data both in transit and at rest using Amazon Neptune?

You may use AWS Key Management Service keys to encrypt your databases with Amazon Neptune, which also supports HTTPS encrypted client connections (KMS). Data saved at rest in the underlying storage is encrypted on a database instance operating with Amazon Neptune encryption, as are its automated backups, snapshots, and replicas in the same cluster. The processes of encryption and decryption run smoothly.

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Ques. 10): Can I use RDF/SPARQL and Apache TinkerPop Gremlin on the same Neptune instance?

Yes, each Neptune instance offers both a SPARQL 1.1 Protocol REST endpoint and a Gremlin Websocket Server. Since the data is divided between the stacks, neither Gremlin nor RDF traversals are possible. This is meant to provide you the opportunity to try them out and evaluate which one works best for your application. Due to the shared nature of the resources on a single instance in production, Amazon advises clients to limit their access to an instance to either Gremlin or SPARQL.

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Ques. 11): Why is Amazon Neptune required to use Amazon RDS resources and permissions?

An efficient, high-performance graph database engine is called Amazon Neptune. Neptune makes use of operational technology that is shared with Amazon RDS for a few administration functions, including instance lifecycle management, encryption-at-rest with Amazon Key Management Service (KMS) keys, and security groups management.

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Ques. 12): What would be my recovery strategy if my database crashed?

In addition to automatically attempting to recover your database in a sound Availability Zone with no data loss, Amazon Neptune keeps 6 copies of your data across 3 Availability Zones. In the odd event that your data is not available in Amazon Neptune storage, you can perform a point-in-time restoration operation to a new instance or restore from a DB snapshot. It should be noted that a point-in-time restoration operation's latest restorable time can be up to 5 minutes in the past.

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Ques. 13): My photos can I share with another AWS account?

Yes. You can take database snapshots with Neptune that you can subsequently utilise to restore a database from. AWS accounts can be shared, and the owner of the receiving account can utilise the shared snapshot to restore a database that contains your data. Anybody can restore a database containing your (public) data if you decide to make your snapshots public. This functionality allows you to securely store backups of all your data in a second account in case your primary AWS account is ever compromised, communicate data between your multiple environments (production, dev/test, staging, etc.) that each have their own AWS accounts, and more.

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Ques. 14): How does Amazon Neptune speed up database recovery time?

Unlike other databases, Amazon Neptune does not need to replay the redo log from the most recent database checkpoint, which generally takes place after 5 minutes, to ensure that all changes have been made before rendering the database usable for operations following a database crash. In most cases, this cuts down database restart times to under 60 seconds. When the database process is restarted, Amazon Neptune removes the buffer cache and makes it immediately accessible. This saves you from having to restrict access while the cache is being refilled in order to prevent brownouts.

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Ques. 15): Is it possible to choose some replicas as failover targets before others?

Yes. Each instance in your cluster might have a different promotion priority tier assigned to it. Amazon Neptune will upgrade the replica with the highest priority to the primary instance if the primary instance fails. The replica that is the same size as the primary instance will be promoted if there is a tie between two or more replicas in the same priority tier.

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Ques. 16): What happens if a failover occurs while I have a primary database and an Amazon Neptune Replica actively handling read traffic?

If there is an issue with your primary instance, Amazon Neptune will immediately identify it and start redirecting your read/write traffic to an Amazon Neptune Replica. This failover will typically be finished in 30 seconds. Your Amazon Neptune Replicas' read traffic will also experience a brief interruption.

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Ques. 17): Which well-known query languages for graphs does Amazon Neptune support?

Both the W3C standard Resource Description Framework (RDF) SPARQL query language and the open source Apache TinkerPop Gremlin graph traversal language are supported by Amazon Neptune.

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Ques. 18): How can I switch from an Amazon Neptune triple store with a SPARQL endpoint?

An HTTP REST endpoint that uses the SPARQL 1.1 Protocol is made available by Amazon Neptune. You can set up your application to point to the SPARQL endpoint after creating a service instance. See also SPARQL Access to the Graph.

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Ques. 19): What is an Amazon Neptune database's minimum and maximum storage capacity?

Storage must be at least 10GB. Your Amazon Neptune storage will automatically increase, up to 64 TB, in 10GB increments based on your database usage, without affecting database performance. Provisioning storage in advance is not necessary.

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Ques. 20): Is it possible for me to stop some copies from becoming the primary instance?

Replicas that you don't want promoted to the primary instance might be given lower priority tiers. However, Amazon Neptune will advance the lower priority replica if the cluster's higher priority replicas are ill or unavailable for some other reason.

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