We're setting up the back-end architecture for our mobile application to connect to, but we need some help. Our application is built to mimic "take a number" tickets you would see at a deli or pharmacy. Users will use our mobile application to send a request to our node controller and our node controller will respond with a spot number.
We currently have our node controller set up on Amazon Elastic Beanstalk and have enabled load balancing to handle any surges in requests. Our question is: how do we persist our spotNumber across multiple instances of our node controller? We have it built now as a local variable that starts at 1 and increments with each request, but will this persist if AWS spins up a new instance of our node controller to handle increased traffic? If not, what would be the best practice for preserving our spotNumber across all potential instances of our server?
Thank you in advance!
Use a database.
Clearly you can't store the value within the node application, not only due to scaling but to prevent data loss if the application shuts down unexpectedly.
It sounds like you don't already have a database, so DynamoDB might be a good choice, as long as your only use case is to share a counter between applications. You can find an example here.
You could also use Redis on Elasticache, but I think that it's overkill for a single counter.
Keeping accurate counters at different scales may require different implementations. At small scale, a simple session variable and locking logic in the application would be enough. However, at a larger scale session synchronization and locking is better managed with a database. In particular for your case, DynamoDB conditional writes or Redis counters seems useful. However, keep your requirements simple and clear, managing counters at scale may require algorithms and data structures with funny names, like the HyperLogLog.
Related
Recently in a system design interview I was asked a question where cities were divided into zones and data of around 100 zones was available. An api took the zoneid as input and returned all the restaurants for that zone in response. The response time for the api was 50ms so the zone data was kept in memory to avoid delays.
If the zone data is approximately 25GB, then if the service is scaled to say 5 instances, it would need 125GB ram.
Now the requirement is to run 5 instances but use only 25 GB ram with the data split between instances.
I believe to achieve this we would need a second application which would act as a config manager to manage which instance holds which zone data. The instances can get which zones to track on startup from the config manager service. But the thing I am not able to figure out is how we redirect the request for a zone to the correct instance which holds its data especially if we use kubernetes. Also if the instance holding partial data restarts then how do we track which zone data it was holding
Splitting dataset over several nodes: sounds like sharding.
In-memory: the interviewer might be asking about redis or something similar.
Maybe this: https://redis.io/topics/partitioning#different-implementations-of-partitioning
Redis cluster might fit -- keep in mind that when the docs mention "client-side partitioning": the client is some redis client library, loaded by your backends, responding to HTTP client/end-user requests
Answering your comment: then, I'm not sure what they were looking for.
Comparing Java hashmaps to a redis cluster isn't completely fair, considering one is bound to your JVM, while the other is actually distributed / sharded, implying at least inter-process communications and most likely network/non-local queries.
Then again, if the question is to scale an ever-growing JVM: at some point, we need to address the elephant in the room: how do you guarantee data consistency, proper replication/sharding, what do you do when a member goes down, ...?
Distributed hashmap, using Hazelcast, may be more relevant. Some (hazelcast) would make the argument it is safer under heavy write load. Others that migrating from Hazelcast to Redis helped them improve service reliability. I don't have enough background in Java myself, I wouldn't know.
As a general rule: when asked about Java, you could argue that speed and reliability very much rely on your developers understanding of what they're doing. Which, in Java, implies a large margin of error. While we could suppose: if they're asking such questions, they probably have some good devs on their payroll.
Whereas distributed databases (in-memory, on disk, SQL or noSQL), ... is quite a complicated topic, that you would need to master (on top of java), to get it right.
The broad approach they're describing was described by Adya in 2019 as a LInK store. Linked In-memory Key-value stores allow for application objects supporting rich operations to be sharded across a cluster of instances.
I would tend to approach this by implementing a stateful application using Akka (disclaimer: I am at this writing employed by Lightbend, which employs the majority of the developers of Akka and offers support and consulting services to clients using Akka; as my SO history indicates, I would have the same approach even multiple years before I was employed by Lightbend) along these lines.
Akka Cluster to allow a set of JVMs running an application to form a cluster in a peer-to-peer manner and manage/track changes in the membership (including detecting instances which have crashed or are isolated by a network partition)
Akka Cluster Sharding to allow stateful objects keyed by ID to be distributed approximately evenly across a cluster and rebalanced in response to membership changes
These stateful objects are implemented as actors: they can update their state in response to messages and (since they process messages one at a time) without needing elaborate synchronization.
Cluster sharding implies that the actor responsible for an ID might exist on different instances, so that implies some persistence of the state of the zone outside of the cluster. For simplicity*, when an actor responsible for a given zone starts, it initializes itself from datastore (could be S3, could be Dynamo or Cassandra or whatever): after this its state is in memory so reads can be served directly from the actor's state instead of going to an underlying datastore.
By directing all writes through cluster sharding, the in-memory representation is, by definition, kept in sync with the writes. To some extent, we can say that the application is the cache: the backing datastore only exists to allow the cache to survive operational issues (and because it's only in response to issues of that sort that the datastore needs to be read, we can optimize the data store for writes vs. reads).
Cluster sharding relies on a conflict-free replicated data type (CRDT) to broadcast changes in the shard allocation to the nodes of the cluster. This allows, for instance, any instance to handle an HTTP request for any shard: it simply forwards a representation of the important parts of the request as a message to the shard which will distribute it to the correct actor.
From Kubernetes' perspective, the instances are stateless: no StatefulSet or similar is needed. The pods can query the Kubernetes API to find the other pods and attempt to join the cluster.
*: I have a fairly strong prior that event sourcing would be a better persistence approach, but I'll set that aside for now.
I have a question about when it makes sense to use a Redis cluster versus standalone Redis.
Suppose one has a real-time gaming application that will allow multiple instances of the game and wish to implement
real time leaderboard for each instance. (Games are created by communities of users).
Suppose at any time we have say 100 simultaneous matches running.
Based on the use cases outlined here :
https://d0.awsstatic.com/whitepapers/performance-at-scale-with-amazon-elasticache.pdf
https://redislabs.com/solutions/use-cases/leaderboards/
https://aws.amazon.com/blogs/database/building-a-real-time-gaming-leaderboard-with-amazon-elasticache-for-redis/
We can implement each leaderboard using a Sorted Set dataset in memory.
Now I would like to implement some sort of persistence where leaderboard state is saved at the end of each
game as a snapshot. Thus each of these independent Sorted Sets are saved as a snapshot file.
I have a question about design choices:
Would a redis cluster make sense for this scenario ? Or would it make more sense to have standalone redis instances and create a new database for each game ?
As far as I know there is only a single database 0 for a single redis cluster.(https://redis.io/topics/cluster-spec)
In that case, how would one be able to snapshot datasets for each leaderboard at different times work ?
https://redis.io/topics/cluster-spec
From what I can see using a Redis cluster only makes sense for large-scale monolithic applications and may not be the best approach for the scenario described above. Is that the case ?
Or if one goes with AWS Elasticache for Redis Cluster mode can I configure snapshotting for individual datasets ?
You are correct, clustering is a way of scaling out to handle really high request loads and store tons of data.
It really doesn't quite sound like you need to bother with a cluster.
I'd quite be very surprised if a standalone Redis setup would be your bottleneck before having several tens of thousands of simultaneous players.
If you are unsure, you can probably mock some simulated load and see what it can handle. My guess is that you are better off focusing on other complexities of your game until you start reaching quite serious usage. Which is a good problem to have. :)
You might however want to consider having one or two replica instances, which is a different thing.
Secondly, regardless of cluster or not, why do you want to use snap-shots (SAVE or BGSAVE) to persist your scoreboard?
If you want to have individual snapshots per game, and its only a few keys per game, why don't you just have your application read and persist those keys when needed to a traditional db? You can for example use MULTI, DUMP and RESTORE to achieve something that is very similar to snapshotting, but on the specific keys you want.
It doesn't sound like multiple databases is warranted for this.
Multiple databases on clustered Redis is only supported in the Enterprise version, so not on ElastiCache. But the above mentioned approach should work just fine.
I am reading in AWS console about Redis and MemcacheD:
Redis
In-memory data structure store used as database, cache and message broker. ElastiCache for Redis offers Multi-AZ with Auto-Failover and enhanced robustness.
Memcached
High-performance, distributed memory object caching system, intended for use in speeding up dynamic web applications.
Did anyone used/compared both? What is the main difference and use cases between the two?
Thanks.
Pasting my answer from another stackoverflow question
Select Memcached if you have these requirements:
You want the simplest model possible.
You need to run large nodes with multiple cores or threads.
You need the ability to scale out/in,
Adding and removing nodes as demand on your system increases and decreases.
You want to partition your data across multiple shards.
You need to cache objects, such as a database.
Select Redis if you have these requirements:
You need complex data types, such as strings, hashes, lists, and sets.
You need to sort or rank in-memory data-sets.
You want persistence of your key store.
You want to replicate your data from the primary to one or more read replicas for read intensive applications.
You need automatic failover if your primary node fails.
You want publish and subscribe (pub/sub) capabilities—to inform clients about events on the server.
You want backup and restore capabilities.
Here is interesting article by aws https://d0.awsstatic.com/whitepapers/performance-at-scale-with-amazon-elasticache.pdf
My use case is as follow:
We have about 500 servers running in an autoscaling EC2 cluster that need to access the same configuration data (layed out in a key/value fashion) several million times per second.
The configuration data isn't very large (1 or 2 GBs) and doesn't change much (a few dozen updates/deletes/inserts per minute during peak time).
Latency is critical for us, so the data needs to be replicated and kept in memory on every single instance running our application.
Eventual consistency is fine. However we need to make sure that every update will be propagated at some point. (knowing that the servers can be shutdown at any time)
The update propagation across the servers should be reliable and easy to setup (we can't have static IPs for our servers, or we don't wanna go the route of "faking" multicast on AWS etc...)
Here are the solutions we've explored in the past:
Using regular java maps and use our custom built system to propagate updates across the cluster. (obviously, it doesn't scale that well)
Using EhCache and its replication feature. But setting it up on EC2 is very painful and somehow unreliable.
Here are the solutions we're thinking of trying out:
Apache Ignite (https://ignite.apache.org/) with a REPLICATED strategy.
Hazelcast's Replicated Map feature. (http://docs.hazelcast.org/docs/latest/manual/html-single/index.html#replicated-map)
Apache Geode on every application node. (http://geode.apache.org/)
I would like to know if each of those solutions would work for our use case. And eventually, what issues I'm likely to face with each of them.
Here is what I found so far:
Hazelcast's Replicated Map is somehow recent and still a bit unreliable (async updates can be lost in case of scaling down)
It seems like Geode became "stable" fairly recently (even though it's supposedly in development since the early 2000s)
Ignite looks like it could be a good fit, but I'm not too sure how their S3 discovery based system will work out if we keep adding / removing node regularly.
Thanks!
Geode should work for your use case. You should be able to use a Geode Replicated region on each node. You can choose to do synchronous OR asynchronous replication. In case of failures, the replicated region gets an initial copy of the data from an existing member in the system, while making sure that no in-flight operations are lost.
In terms of configuration, you will have to start a couple/few member discovery processes (Geode locators) and point each member to these locators. (We recommend that you start one locator/AZ and use 3 AZs to protect against network partitioning).
Geode/GemFire has been stable for a while; powering low latency high scalability requirements for reservation systems at Indian and Chinese railways among other users for a very long time.
Disclosure: I am a committer on Geode.
Ignite provides native AWS integration for discovery over S3 storage: https://apacheignite-mix.readme.io/docs/amazon-aws. It solves main issue - you don't need to change configuration when instances are restarted. In a nutshell, any nodes that successfully joins topology writes its coordinates to a bucket (and removes them when fails or leaves). When you start a new node, it reads this bucket and connects to one the listed addresses.
Hazelcast's Replicated Map will not work for your use-case. Note that it is a map that is replicated across all it's nodes not on the client nodes/servers. Also, as you said, it is not fully reliable yet.
Here is the Hazelcast solution:
Create a Hazelcast cluster with a set of nodes depending upon the size of data.
Create a Distributed map(IMap) and tweak the count & eviction configurations based on size/number of key/value pairs. The data gets partitioned across all the nodes.
Setup Backup count based on how critical the data is and how much time it takes to pull the data from the actual source(DB/Files). Distributed maps have 1 backup by default.
In the client side, setup a NearCache and attach it to the Distributed map. This NearCache will hold the Key/Value pair in the local/client side itself. So the get operations would end up in milliseconds.
Things to consider with NearCache solution:
The first get operation would be slower as it has to go through network to get the data from cluster.
Cache invalidation is not fully reliable as there will be a delay in synchronization with the cluster and may end reading stale data. Again, this is same case across all the cache solutions.
It is client's responsibility to setup timeout and invalidation of Nearcache entries. So that the future pulls would get fresh data from cluster. This depends on how often the data gets refreshed or value is replaced for a key.
Basically we save cached data on Redis and we want to dump it into MongoDB every X seconds.
We have a sorted set stored on Redis, saving each user's last activity as score, and we want to periodically dump user's final state after being inactive for a certain period of time, and we wish to make sure that:
We don't strain our API servers (That's why it has to run on a worker instance.
The data dump operation is very critical - We require these worker instances to be scalable and highly resilient to failure (and should handle failure gracefully).
We must ensure that if we have X machines, that the data would be spread across instances, and that every item we pull from Redis will be handled exactly once.
I was wondering what would be the best architectural approach to deploy EC2 Windows instances that periodically handle data.
I was thinking of using Elastic Beanstalk as it's easy to deploy, scale & monitor, but I was wondering if there was a better approach to this.
Thanks in advance!
Other than the application, for which Amazon Elastic Beanstalk is a fine choice, i'd recommend you take a look at Amazon Kinesis: http://aws.amazon.com/kinesis/
That is because you mentioned "scalable", "resilient", "handle failure gracefully" and "exactly once". Those attributes are quite hard to get right in a distributed system, and Kinesis Streams and Client Library can greatly help with that.