I have an application where multiple users can send REST operations to modify the state of shared objects.
When an object is modified, then multiple actions will happen (DB, audit, logging...).
Not all the operations are valid for example you can not Modify an object after it was Deleted.
Using Kafka I was thinking about the following architecture:
Rest operations are queuing in a Kafka topic.
Operations to the same object are going to the same partition. So all the object's operations will be in sequence and processed by a consumer
Consumers are listening to a partition and validate the operation using an in-memory database
If the operation was valid then is sent to a "Valid operation topic" otherways is sent to an "Invalid operation topic"
Other consumers (db, log, audit) are listening to the "Valid operation topic"
I am not very sure about point number 3.
I don't like the idea to keep the state of all my objects. (I have billions of objects and even if an object can be of 10mb in size, what I need to store to validate its state is just few Kbytes...)
However, is this a common pattern? Otherwise how can you verify the validity of certain operations?
Also what would do you use as a in-memory database? Surely it has to be highly available, fault-tolerant and support transaction (read and write).
I believe this is a very valid pattern, and is essentially a variation to an event-sourced CQRS pattern.
For example, Lagom implements their CQRS persistence in a very similar fashion (although based on completely different toolset)
A few points:
you are right about the need for sequencial operations: since all your state mutations need to be based on the result of the previous mutation, there must be a strong order in their execution. This is very often the case for such things, so we like to be able to scale those operations horizontally as much as possible so that each of those sequences operations is happening in parallel to many other sequences. In your case we have one such sequence per shared object.
Relying on Kafka partitioning by key is a good way to achieve that (assuming you do not set max.in.flight.requests.per.connection higher than the default value 1). Here again Lagom has a similar approach by having their persistent entity distributed and single-threaded. I'm not saying Lagom is better, I'm just comforting you in the fact that is approach is used by others :)
a key aspect of your pattern is the transformation of a Command into an Event: in that jargon a command is seen as a request to impact the state and may be rejected for various reasons. An event is a description of a state update that happened in the past and is irrefutable from the point of view of those who receive it: a event always tells the truth. The process you are describing would be a controller that is at the boundary between the two: it is responsible for transforming commands into events.
In that sense the "Valid operation topic" you mention would be an event-sourced description of the state updates of your process. Since it's all backed by Kafka it would be arbitrarily partionable and thus scalable, which is awesome :)
Don't worry about the size of the sate of all your object, it must sit somewhere somehow. Since you have this controller that transforms the commands into events, this one becomes the primary source of truth related to that object, and this one is responsible for storing it: this controller handles the primary storage for your events, so you must cater space for it. You can use Kafka Streams's Key value store: those are local to each of your processing instance, though if you make them persistent they have no problem in handling data much bigger that the available RAM. Behind the scene data is spilled to disk thanks to RocksDB, and even more behind the scene it's all event-sourced to a kafka topic so your state store is replicated and will be transparently re-created on another machine if necessary
I hope this helps you finalise your design :)
Related
One of great promises of Event Sourcing is the ability to replay events. When there's no relationship between entities (e.g. blob storage, user profiles) it works great, but how to do replay quckly when there are important relationships to check?
For example: Product(id, name, quantity) and Order(id, list of productIds). If we have CreateProduct and then CreateOrder events, then it will succeed (product is available in warehouse), it's easy to implement e.g. with Kafka (one topic with n1 partitions for products, another with n2 partitions for orders).
During replay everything happens more quickly, and Kafka may reorder the events (e.g. CreateOrder and then CreateProduct), which will give us different behavior than originally (CreateOrder will now fail because product doesn't exist yet). It's because Kafka guarantees ordering only within one topic within one partition. The easy solution would be putting everything into one huge topic with one partition, but this would be completely unscalable, as single-threaded replay of bigger databases could take days at least.
Is there any existing, better solution for quick replaying of related entities? Or should we forget about event sourcing and replaying of events when we need to check relationships in our databases, and replaying is good only for unrelated data?
As a practical necessity when event sourcing, you need the ability to conjure up a stream of events for a particular entity so that you can apply your event handler to build up the state. For Kafka, outside of the case where you have so few entities that you can assign an entire topic partition to just the events for a single entity, this entails a linear scan and filter through a partition. So for this reason, while Kafka is very likely to be a critical part of any event-driven/event-based system in relaying events published by a service for consumption by other services (at which point, if we consider the event vs. command dichotomy, we're talking about commands from the perspective of the consuming service), it's not well suited to the role of an event store, which are defined by their ability to quickly give you an ordered stream of the events for a particular entity.
The most popular purpose-built event store is, probably, the imaginatively named Event Store (at least partly due to the involvement of a few prominent advocates of event sourcing in its design and implementation). Alternatively, there are libraries/frameworks like Akka Persistence (JVM with a .Net port) which use existing DBs (e.g. relational SQL DBs, Cassandra, Mongo, Azure Cosmos, etc.) in a way which facilitates their use as an event store.
Event sourcing also as a practical necessity tends to lead to CQRS (they go together very well: event sourcing is arguably the simplest possible persistence model capable of being a write model, while its nearly useless as a read model). The typical pattern seen is that the command processing component of the system enforces constraints like "product exists before being added to the cart" (how those constraints are enforced is generally a question of whatever concurrency model is in use: the actor model has a high level of mechanical sympathy with this approach, but other models are possible) before writing events to the event store and then the events read back from the event store can be assumed to have been valid as of the time they were written (it's possible to later decide a compensating event needs to be recorded). The events from within the event store can be projected to a Kafka topic for communication to another service (the command processing component is the single source of truth for events).
From the perspective of that other service, as noted, the projected events in the topic are commands (the implicit command for an event is "update your model to account for this event"). Semantically, their provenance as events means that they've been validated and are undeniable (they can be ignored, however). If there's some model validation that needs to occur, that generally entails either a conscious decision to ignore that command or to wait until another command is received which allows that command to be accepted.
Ok, you are still thinking how did we developed applications in last 20 years instead of how we should develop applications in the future. There are frameworks that actually fits the paradigms of future perfectly, one of those, which mentioned above, is Akka but more importantly a sub component of it Akka FSM Finite State Machine, which is some concept we ignored in software development for years, but future seems to be more and more event based and we can't ignore anymore.
So how these will help you, Akka is a framework based on Actor concept, every Actor is an unique entity with a message box, so lets say you have Order Actor with id: 123456789, every Event for Order Id: 123456789 will be processed with this Actor and its messages will be ordered in its message box with first in first out principle, so you don't need a synchronisation logic anymore. But you could have millions of Order Actors in your system, so they can work in parallel, when Order Actor: 123456789 processing its events, an Order Actor: 987654321 can process its own, so there is the parallelism and scalability. While your Kafka guaranteeing the order of every message for Key 123456789 and 987654321, everything is green.
Now you can ask, where Finite State Machine comes into play, as you mentioned the problem arise, when addProduct Event arrives before createOrder Event arrives (while being on different Kafka Topics), at that point, State Machine will behave differently when Order Actor is in CREATED state or INITIALISING state, in CREATED state, it will just add the Product, in INITIALISING state probably it will just stash it, until createOrder Event arrives.
These concepts are explained really good in this video and if you want to see a practical example I have a blog for it and this one for a more direct dive.
I think I found the solution for scalable (multi-partition) event sourcing:
create in Kafka (or in a similar system) topic named messages
assign users to partitions (e.g by murmurHash(login) % partitionCount)
if a piece of data is mutable (e.g. Product, Order), every partition should contain own copy of the data
if we have e.g. 256 pieces of a product in our warehouse and 64 partitions, we can initially 'give' every partition 8 pieces, so most CreateOrder events will be processed quickly without leaving user's partition
if a user (a partition) sometimes needs to mutate data in other partition, it should send a message there:
for example for Product / Order domain, partitions could work similarly to Walmart/Tesco stores around a country, and the messages sent between partitions ('stores') could be like CreateProduct, UpdateProduct, CreateOrder, SendProductToMyPartition, ProductSentToYourPartition
the message will become an 'event' as if it was generated by an user
the message shouldn't be sent during replay (already sent, no need to do it twice)
This way even when Kafka (or any other event sourcing system) chooses to reorder messages between partitions, we'll still be ok, because we don't ever read any data outside our single-threaded 'island'.
EDIT: As #LeviRamsey noted, this 'single-threaded island' is basically actor model, and frameworks like Akka can make it a bit easier.
I am trying to implement event sourcing/CQRS/DDD for the first time, mostly for learning purposes, where there is the idea of an event store and a message queue such as Apache Kafka, and you have events flowing from event store => Kafka Connect JDBC/Debezium CDC => Kafka.
I am wondering why there needs to be a separate event store when it sounds like its purpose can be fulfilled by Kafka itself with its main features and log compaction or configuring log retention for permanent storage. Should I store my events in a dedicated store like RDBMS to feed into Kafka or should I feed them straight into Kafka?
Much of the literature on event-sourcing and cqrs comes from the [domain driven design] community; in its earliest form, CQRS was called DDDD... Distributed domain driven design.
One of the common patterns in domain driven design is to have a domain model ensuring the integrity of the data in your durable storage, which is to say, ensuring that there are no internal contradictions...
I am wondering why there needs to be a separate event store when it sounds like its purpose can be fulfilled by Kafka itself with its main features and log compaction or configuring log retention for permanent storage.
So if we want an event stream with no internal contradictions, how do we achieve that? One way is to ensure that only a single process has permission to modify the stream. Unfortunately, that leaves you with a single point of failure -- the process dies, and everything comes to an end.
On the other hand, if you have multiple processes updating the same stream, then you have risk of concurrent writes, and data races, and contradictions being introduced because one writer couldn't yet see what the other one did.
With an RDBMS or an Event Store, we can solve this problem by using transactions, or compare and swap semantics; and attempt to extend the stream with new events is rejected if there has been a concurrent modification.
Furthermore, because of its DDD heritage, it is common for the durable store to be divided into many very fine grained partitions (aka "aggregates"). One single shopping cart might reasonably have four streams dedicated to it.
If Kafka lacks those capabilities, then it is going to be a lousy replacement for an event store. KAFKA-2260 has been open for more than four years now, so we seem to be lacking the first. From what I've been able to discern from the Kakfa literature, it isn't happy about fine grained streams either (although its been a while since I checked, perhaps things have changed).
See also: Jesper Hammarbäck writing about this 18 months ago, and reaching similar conclusions to those expressed here.
Kafka can be used as a DDD event store, but there are some complications if you do so due to the features it is missing.
Two key features that people use with event sourcing of aggregates are:
Load an aggregate, by reading the events for just that aggregate
When concurrently writing new events for an aggregate, ensure only one writer succeeds, to avoid corrupting the aggregate and breaking its invariants.
Kafka can't do either of these currently, since 1 fails since you generally need to have one stream per aggregate type (it doesn't scale to one stream per aggregate, and this wouldn't necessarily be desirable anyway), so there's no way to load just the events for one aggregate, and 2 fails since https://issues.apache.org/jira/browse/KAFKA-2260 has not been implemented.
So you have to write the system in such as way that capabilities 1 and 2 aren't needed. This can be done as follows:
Rather than invoking command handlers directly, write them to
streams. Have a command stream per aggregate type, sharded by
aggregate id (these don't need permanent retention). This ensures that you only ever process a single
command for a particular aggregate at a time.
Write snapshotting code for all your aggregate types
When processing a command message, do the following:
Load the aggregate snapshot
Validate the command against it
Write the new events (or return failure)
Apply the events to the aggregate
Save a new aggregate snapshot, including the current stream offset for the event stream
Return success to the client (via a reply message perhaps)
The only other problem is handling failures (such as the snapshotting failing). This can be handled during startup of a particular command processing partition - it simply needs to replay any events since the last snapshot succeeded, and update the corresponding snapshots before resuming command processing.
Kafka Streams appears to have the features to make this very simple - you have a KStream of commands that you transform into a KTable (containing snapshots, keyed by aggregate id) and a KStream of events (and possibly another stream containing responses). Kafka allows all this to work transactionally, so there is no risk of failing to update the snapshot. It will also handle migrating partitions to new servers, etc. (automatically loading the snapshot KTable into a local RocksDB when this happens).
there is the idea of an event store and a message queue such as Apache Kafka, and you have events flowing from event store => Kafka Connect JDBC/Debezium CDC => Kafka
In the essence of DDD-flavoured event sourcing, there's no place for message queues as such. One of the DDD tactical patterns is the aggregate pattern, which serves as a transactional boundary. DDD doesn't care how the aggregate state is persisted, and usually, people use state-based persistence with relational or document databases. When applying events-based persistence, we need to store new events as one transaction to the event store in a way that we can retrieve those events later in order to reconstruct the aggregate state. Thus, to support DDD-style event sourcing, the store needs to be able to index events by the aggregate id and we usually refer to the concept of the event stream, where such a stream is uniquely identified by the aggregate identifier, and where all events are stored in order, so the stream represents a single aggregate.
Because we rarely can live with a database that only allows us to retrieve a single entity by its id, we need to have some place where we can project those events into, so we can have a queryable store. That is what your diagram shows on the right side, as materialised views. More often, it is called the read side and models there are called read-models. That kind of store doesn't have to keep snapshots of aggregates. Quite the opposite, read-models serve the purpose to represent the system state in a way that can be directly consumed by the UI/API and often it doesn't match with the domain model as such.
As mentioned in one of the answers here, the typical command handler flow is:
Load one aggregate state by id, by reading all events for that aggregate. It already requires for the event store to support that kind of load, which Kafka cannot do.
Call the domain model (aggregate root method) to perform some action.
Store new events to the aggregate stream, all or none.
If you now start to write events to the store and publish them somewhere else, you get a two-phase commit issue, which is hard to solve. So, we usually prefer using products like EventStore, which has the ability to create a catch-up subscription for all written events. Kafka supports that too. It is also beneficial to have the ability to create new event indexes in the store, linking to existing events, especially if you have several systems using one store. In EventStore it can be done using internal projections, you can also do it with Kafka streams.
I would argue that indeed you don't need any messaging system between write and read sides. The write side should allow you to subscribe to the event feed, starting from any position in the event log, so you can build your read-models.
However, Kafka only works in systems that don't use the aggregate pattern, because it is essential to be able to use events, not a snapshot, as the source of truth, although it is of course discussable. I would look at the possibility to change the way how events are changing the entity state (fixing a bug, for example) and when you use events to reconstruct the entity state, you will be just fine, snapshots will stay the same and you'll need to apply correction events to fix all the snapshots.
I personally also prefer not to be tightly coupled to any infrastructure in my domain model. In fact, my domain models have zero dependencies on the infrastructure. By bringing the snapshotting logic to Kafka streams builder, I would be immediately coupled and from my point of view it is not the best solution.
Theoretically you can use Kafka for Event Store but as many people mentioned above that you will have several restrictions, biggest of those, only able to read event with the offset in the Kafka but no other criteria.
For this reason they are Frameworks there dealing with the Event Sourcing and CQRS part of the problem.
Kafka is only part of the toolchain which provides you the capability of replaying events and back pressure mechanism that are protecting you from overload.
If you want to see how all fits together, I have a blog about it
This is easy for projections that subscribe to all events from the stream, you just keep version of the last event applied on your read model. But what do you do when projection is composite of multiple streams? Do you keep version of each stream that is partaking in the projection. But then what about the gaps, if you are not subscribing to all events? At most you can assert that version is greater than the last one. How do others deal with this? Do you respond to every event and bump up version(s)?
For the EventStore, I would suggest using the $all stream as the default stream for any read-model subscription.
I have used the category stream that essentially produces the snapshot of a given entity type but I stopped doing so since read-models serve a different purpose.
It might be not desirable to use the $all stream as it might also get events, which aren't domain events. Integration events could be an example. In this case, adding some attributes either to event contracts or to the metadata might help to create an internal (JS) projection that will create a special all stream for domain events, or any event category in that regard, where you can subscribe to. You can also use a negative condition, for example, filter out all system events and those that have the original stream name starting with Integration.
As well as processing messages in the correct order, you also have the problem of resuming a projection after it is restarted - how do you ensure you start from the right place when you restart?
The simplest option is to use an event store or message broker that both guarantees order and provides some kind of global stream position field (such as a global event number or an ordered timestamp with a disambiguating component such as MongoDB's Timestamp type). Event stores where you pull the events directly from the store (such as eventstore.org or homegrown ones built on a database) tend to guarantee this. Also, some message brokers like Apache Kafka guarantee ordering (again, this is pull-based). You want at-least-once ordered delivery, ideally.
This approach limits write scalability (reads scale fine, using read replicas) - you can shard your streams across multiple event store instances in various ways, then you have to track the position on a per-shard basis, which adds some complexity.
If you don't have these ordering, delivery and position guarantees, your life is much harder, and it may be hard to make the system completely reliable. You can:
Hold onto messages for a while after receiving them, before processing them, to allow other ones to arrive
Have code to detect missing or out-of-order messages. As you mention, this only works if you receive all events with a global sequence number or if you track all stream version numbers, and even then it isn't reliable in all cases.
For each individual stream, you keep things in order by fetching them from a data store that knows the correct order. A way of thinking of this is that your query the data store, and you get a Document Message back.
It may help to review Greg Young's Polyglot Data talk.
As for synchronization of events in multiple streams; a thing that you need to recognize is that events in different streams are inherently concurrent.
You can get some loose coordination between different streams if you have happens-before data encoded into your messages. "Event B happened in response to Event A, therefore A happened-before B". That gets you a partial ordering.
If you really do need a total ordering of everything everywhere, then you'll need to be looking into patterns like Lamport Clocks.
We want to introduce a Kafka Event Bus which will contain some events like EntityCreated or EntityModified into our application so other parts of our system can consume from it. The main application uses an RDMS (i.e. postgres) under the hood to store the entities and their relationship.
Now the issue is how you make sure that you only send out EntityCreated events on Kafka if you successfully saved to the RDMS. If you don't make sure that this is the case, you end up with inconsistencies on the consumers.
I saw three solutions, of which none is convincing:
Don't care: Very dangerous, there can be something going wrong when inserting into an RDMS.
When saving the entity, also save the message which should be sent into a own table. Then have a separate process which consumes from this table and publishes to Kafka and after a success deleted from this table. This is quiet complex to implement and also looks like an anti-pattern.
Insert into the RDMS, keep the (SQL-) Transaction open until you wrote successfully to Kafka and only then commit. The problem is that you potentially keep the RDMS transaction open for some time. Don't know how big the problem is.
Do real CQRS which means that you don't save at all to the RDMS but construct the RDMS out of the Kafka queue. That seems like the ideal way but is difficult to retrofit to a service. Also there are problems with inconsistencies due to latencies.
I had difficulties finding good solutions on the internet.
Maybe this question is to broad, feel free to point me somewhere it fits better.
When saving the entity, also save the message which should be sent into a own table. Then have a separate process which consumes from this table and publishes to Kafka and after a success deleted from this table. This is quiet complex to implement and also looks like an anti-pattern.
This is, in fact, the solution described by Udi Dahan in his talk: Reliable Messaging without Distributed Transactions. It's actually pretty close to a "best practice"; so it may be worth exploring why you think it is an anti-pattern.
Do real CQRS which means that you don't save at all to the RDMS but construct the RDMS out of the Kafka queue.
Noooo! That's where the monster is hiding! (see below).
If you were doing "real CQRS", your primary use case would be that your writers make events durable in your book of record, and the consumers would periodically poll for updates. Think "Atom Feed", with the additional constraint that the entries, and the order of entries, is immutable; you can share events, and pages of events; cache invalidation isn't a concern because, since the state doesn't change, the event representations are valid "forever".
This also has the benefit that your consumers don't need to worry about message ordering; the consumers are reading documents of well ordered events with pointers to the prior and subsequent documents.
Furthermore, you've additionally gotten a solution to a versioning story: rather than broadcasting N different representations of the same event, you send out one representation, and then negotiate the content when the consumer polls you.
Now, polling does have latency issues; you can reduce the latency by broadcasting an announcement of the update, and notifying the consumers that new events are available.
If you want to reduce the rate of false polling (waking up a consumer for an event that they don't care about), then you can start adding more information into the notification, so that the consumer can judge whether to pull an update.
Notice that "wake up and maybe poll" is a process that is triggered by a single event in isolation. "Wake up and poll just this message" is another variation on the same idea. We broadcast a thin version of EmailDeliveryScheduled; and the service responsible for that calls back to ask for the email/an enhanced version of the event with the details needed to construct the email.
These are specializations of "wake up and consume the notification". If you have a use case where you can't afford the additional latency required to poll, you can use the state in the representation of the isolated event.
But trying to reproduce an ordered sequence of events when that information is already exposed as a sharable, cacheable document... That's a pretty unusual use case right there. I wouldn't worry about it as a general problem to solve -- my guess is that these cases are rare, and not easily generalized.
Note that all of the above is about messaging, not about Kafka. Notice that messaging and event sourcing are documented as different use cases. Jay Kreps wrote (2013)
I use the term "log" here instead of "messaging system" or "pub sub" because it is a lot more specific about semantics and a much closer description of what you need in a practical implementation to support data replication.
You can think of the log as acting as a kind of messaging system with durability guarantees and strong ordering semantics
The book of record should be the sole authority for the order of event messages. Any consumer that cares about order should be reading ordered documents from the book of record, rather than reading unordered documents and reconstructing the order.
In your current design....
Now the issue is how you make sure that you only send out EntityCreated events on Kafka if you successfully saved to the RDMS.
If the RDBMS is the book of record (the source of "truth"), then the Kafka log isn't (yet).
You can get there from here, over a number of gentle steps; roughly, you add events into the existing database, you read from the existing database to write into kafka's log; you use kafka's log as a (time delayed) source of truth to build a replica of the existing RDBMS, you migrate your read use cases to the replica, you migrate your write use cases to kafka, and you decommission the legacy database.
Kafka's log may or may not be the book of record you want. Greg Young has been developing Get Event Store for quite some time, and has enumerated some of the tradeoffs (2016). Horses for courses - I wouldn't expect it to be too difficult to switch the log from one of these to the other with a well written code base, but I can't speak at all to the additional coupling that might occur.
There is no perfect way to do this if your requirement is look SQL & kafka as a single node. So the question should be: "What bad things(power failure, hardware failure) I can afford if it happen? What the changes(programming, architecture) I can take if it must apply to my applications?"
For those points you mentioned:
What if the node fail after insert to kafka before delete from sql?
What if the node fail after insert to kafka before commit the sql transaction?
What if the node fail after insert to sql before commit the kafka offset?
All of them will facing the risk of data inconsistency(4 is slightly better if the data insert to sql can not success more than once such as they has a non database generated pk).
From the viewpoint of changes, 3 is smallest, however, it will decrease sql throughput. 4 is biggest due to your business logic model will facing two kinds of database when you coding(write to kafka by a data encoder, read from sql by sql sentence), it has more coupling than others.
So the choice is depend on what your business is. There is no generic way.
I am working on my bc thesis project which should be a Minecraft server written in scala and Akka. The server should be easily deployable in the cloud or onto a cluster (not sure whether i use proper terminology...it should run on multiple nodes). I am, however, newbie in akka and i have been wondering how to implement such a thing. The problem i'm trying to figure out right now, is how to share state among actors on different nodes. My first idea was to have an Camel actor that would read tcp stream from minecraft clients and then send it to load balancer which would select a node that would process the request and then send some response to the client via tcp. Lets say i have an AuthenticationService implementing actor that checks whether the credentials provided by user are valid. Every node would have such actor(or perhaps more of them) and all the actors should have exactly same database (or state) of users all the time. My question is, what is the best approach to keep this state? I have came up with some solutions i could think of, but i haven't done anything like this so please point out the faults:
Solution #1: Keep state in a database. This would probably work very well for this authentication example where state is only represented by something like list of username and passwords but it probably wouldn't work in cases where state contains objects that can't be easily broken into integers and strings.
Solution #2: Every time there would be a request to a certain actor that would change it's state, the actor will, after processing the request, broadcast information about the change to all other actors of the same type whom would change their state according to the info send by the original actor. This seems very inefficient and rather clumsy.
Solution #3: Having a certain node serve as sort of a state node, in which there would be actors that represent the state of the entire server. Any other actor, except the actors in such node would have no state and would ask actors in the "state node" everytime they would need some data. This seems also inefficient and kinda fault-nonproof.
So there you have it. Only solution i actually like is the first one, but like i said, it probably works in only very limited subset of problems (when state can be broken into redis structures). Any response from more experienced gurus would be very appriciated.
Regards, Tomas Herman
Solution #1 could possibly be slow. Also, it is a bottleneck and a single point of failure (meaning the application stops working if the node with the database fails). Solution #3 has similar problems.
Solution #2 is less trivial than it seems. First, it is a single point of failure. Second, there are no atomicity or other ordering guarantees (such as regularity) for reads or writes, unless you do a total order broadcast (which is more expensive than a regular broadcast). In fact, most distributed register algorithms will do broadcasts under-the-hood, so, while inefficient, it may be necessary.
From what you've described, you need atomicity for your distributed register. What do I mean by atomicity? Atomicity means that any read or write in a sequence of concurrent reads and writes appears as if it occurs in single point in time.
Informally, in the Solution #2 with a single actor holding a register, this guarantees that if 2 subsequent writes W1 and then W2 to the register occur (meaning 2 broadcasts), then no other actor reading the values from the register will read them in the order different than first W1 and then W2 (it's actually more involved than that). If you go through a couple of examples of subsequent broadcasts where messages arrive to destination at different points in time, you will see that such an ordering property isn't guaranteed at all.
If ordering guarantees or atomicity aren't an issue, some sort of a gossip-based algorithm might do the trick to slowly propagate changes to all the nodes. This probably wouldn't be very helpful in your example.
If you want fully fault-tolerant and atomic, I recommend you to read this book on reliable distributed programming by Rachid Guerraoui and Luís Rodrigues, or the parts related to distributed register abstractions. These algorithms are built on top of a message passing communication layer and maintain a distributed register supporting read and write operations. You can use such an algorithm to store distributed state information. However, they aren't applicable to thousands of nodes or large clusters because they do not scale, typically having complexity polynomial in the number of nodes.
On the other hand, you may not need to have the state of the distributed register replicated across all of the nodes - replicating it across a subset of your nodes (instead of just one node) and accessing those to read or write from it, providing a certain level of fault-tolerance (only if the entire subset of nodes fails, will the register information be lost). You can possibly adapt the algorithms in the book to serve this purpose.