I have an application with a workflow that takes an object through a set of states, from S_1 -> S_N. I would like to log all of the state changes with the relevant data, like so:
event: {id, timestamp, state }
Once I have gathered the data, I would like to process it to understand how long each step of the work is taking.
An example SQL query for this data looks like this (please excuse my sql skills if this query is poorly architected):
select age(m2.event_time, m1.event_time)
from events m1 inner join events m2
on m1.m_id = m2.m_id and m1.state = 'STATE_X' and m2.state = 'STATE_Y';
This solution requires a relational database, which creates concerns for scalability and maintainability. We already have a time series database in production and I would ideally like to use that for this purpose. Is there a way to use something like ELK or Prometheus or InfluxDB to accomplish this task? Have I designed the solution incorrectly? Nothing I have found allows for queries of this nature.
Related
I use MSK and I manually build aggregate tables of my streams in my application code (e.g. TypeScript in a node.js webservice). I have lots of data (approaching 1M events per day), and I want to be able to productionise different real-time 'views' on the incoming stream. E.g. for some sales data, I might want to create these views:
sales per customer (table schema: customer, sum_of_sales)
sales per day (table schema: date, sum_of_sales)
sale per customer per day (table schema: date, customer, sum_of_sales)
Today if I wanted to achieve this I would scaffold 3 tables up (could be RDMS or something like DynamoDB), and then in my application code, I would insert/upsert into the table for every sales event that arrived. The scaffolding around that feels a little tedious, I was wondering if there is a better way without having to write a bunch of code in my webservice to actually pull from the consumer, upsert the data into a table.
All I would expect my code in my web service to do is provide APIs (e.g. REST APIs) to fetch data from these views. E.g. a client makes a REST request to get all sales in the last 7 days for customers X, Y and Z.
There seems like a lot of technologies out there, but my use case is fairly trivial and from the not-so-brief look I took nothing does this.
Thanks
If it's noteworthy, I currently keep my data indefinitely.
Trying to implement Event Sourcing and CQRS for the first time, but got stuck when it came to persisting the aggregates.
This is where I'm at now
I've setup "EventStore" an a stream, "foos"
Connected to it from node-eventstore-client
I subscribe to events with catchup
This is all working fine.
With the help of the eventAppeared event handler function I can build the aggregate, whenever events occur. This is great, but what do I do with it?
Let's say I build and aggregate that is a list of Foos
[
{
id: 'some aggregate uuidv5 made from barId and bazId',
barId: 'qwe',
bazId: 'rty',
isActive: true,
history: [
{
id: 'some event uuid',
data: {
isActive: true,
},
timestamp: 123456788,
eventType: 'IsActiveUpdated'
}
{
id: 'some event uuid',
data: {
barId: 'qwe',
bazId: 'rty',
},
timestamp: 123456789,
eventType: 'FooCreated'
}
]
}
]
To follow CQRS I will build the above aggregate within a Read Model, right? But how do I store this aggregate in a database?
I guess just a nosql database should be fine for this, but I definitely need a db since I will put a gRPC APi in front of this and other read models / aggreates.
But what do I actually go from when I have built the aggregate, to when to persist it in the db?
I once tried following this tutorial https://blog.insiderattack.net/implementing-event-sourcing-and-cqrs-pattern-with-mongodb-66991e7b72be which was super simple, since you'd use mongodb both as the event store and just create a view for the aggregate and update that one when new events are incoming. It had it's flaws and limitations (the aggregation pipeline) which is why I now turned to "EventStore" for the event store part.
But how to persist the aggregate, which is currently just built and stored in code/memory from events in "EventStore"...?
I feel this may be a silly question but do I have to loop over each item in the array and insert each item in the db table/collection or do you somehow have a way to dump the whole array/aggregate there at once?
What happens after? Do you create a materialized view per aggregate and query against that?
I'm open to picking the best db for this, whether that is postgres/other rdbms, mongodb, cassandra, redis, table storage etc.
Last question. For now I'm just using a single stream "foos", but at this level I expect new events to happen quite frequently (every couple of seconds or so) but as I understand it you'd still persist it and update it using materialized views right?
So given that barId and bazId in combination can be used for grouping events, instead of a single stream I'd think more specialized streams such as foos-barId-bazId would be the way to go, to try and reduce the frequency of incoming new events to a point where recreating materialized views will make sense.
Is there a general rule of thumb saying not to recreate/update/refresh materialized views if the update frequency gets below a certain limit? Then the only other a lternative would be querying from a normal table/collection?
Edit:
In the end I'm trying to make a gRPC api that has just 2 rpcs - one for getting a single foo by id and one for getting all foos (with optional field for filtering by status - but that is not so important). The simplified proto would look something like this:
rpc GetFoo(FooRequest) returns (Foo)
rpc GetFoos(FoosRequest) returns (FooResponse)
message FooRequest {
string id = 1; // uuid
}
// If the optional status field is not specified, return all foos
message FoosRequest {
// If this field is specified only return the Foos that has isActive true or false
FooStatus status = 1;
enum FooStatus {
UNKNOWN = 0;
ACTIVE = 1;
INACTIVE = 2;
}
}
message FoosResponse {
repeated Foo foos;
}
message Foo {
string id = 1; // uuid
string bar_id = 2 // uuid
string baz_id = 3 // uuid
boolean is_active = 4;
repeated Event history = 5;
google.protobuf.Timestamp last_updated = 6;
}
message Event {
string id = 1; // uuid
google.protobuf.Any data = 2;
google.protobuf.Timestamp timestamp = 3;
string eventType = 4;
}
The incoming events would look something like this:
{
id: 'some event uuid',
barId: 'qwe',
bazId: 'rty',
timestamp: 123456789,
eventType: 'FooCreated'
}
{
id: 'some event uuid',
isActive: true,
timestamp: 123456788,
eventType: 'IsActiveUpdated'
}
As you can see there is no uuid to make it possible to GetFoo(uuid) in the gRPC API, which is why I'll generate a uuidv5 with the barId and bazId, which will combined, be a valid uuid. I'm making that in the projection / aggregate you see above.
Also the GetFoos rpc will either return all foos (if status field is left undefined), or alternatively it'll return the foo's that has isActive that matches the status field (if specified).
Yet I can't figure out how to continue from the catchup subscription handler.
I have the events stored in "EventStore" (https://eventstore.com/), using a subscription with catchup, I have built an aggregate/projection with an array of Foo's in the form that I want them, but to be able to get a single Foo by id from a gRPC API of mine, I guess I'll need to store this entire aggregate/projection in a database of some sort, so I can connect and fetch the data from the gRPC API? And every time a new event comes in I'll need to add that event to the database also or how is this working?
I think I've read every resource I can possibly find on the internet, but still I'm missing some key pieces of information to figure this out.
The gRPC is not so important. It could be REST I guess, but my big question is how to make the aggregated/projected data available to the API service (possible more API's will need it as well)? I guess I will need to store the aggregated/projected data with the generated uuid and history fields in a database to be able to fetch it by uuid from the API service, but what database and how is this storing process done, from the catchup event handler where I build the aggregate?
I know exactly how you feel! This is basically what happened to me when I first tried to do CQRS and ES.
I think you have a couple of gaps in your knowledge which I'm sure you will rapidly plug. You hydrate an aggregate from the event stream as you are doing. That IS your aggregate persisted. The read model is something different. Let me explain...
Your read model is the thing you use to run queries against and to provide data for display to a UI for example. Your aggregates are not (directly) involved in that. In fact they should be encapsulated. Meaning that you can't 'see' their state from the outside. i.e. no getter and setters with the exception of the aggregate ID which would have a getter.
This article gives you a helpful overview of how it all fits together: CQRS + Event Sourcing – Step by Step
The idea is that when an aggregate changes state it can only do so via an event it generates. You store that event in the event store. That event is also published so that read models can be updated.
Also looking at your aggregate it looks more like a typical read model object or DTO. An aggregate is interested in functionality, not properties. So you would expect to see void public functions for issuing commands to the aggregate. But not public properties like isActive or history.
I hope that makes sense.
EDIT:
Here are some more practical suggestions.
"To follow CQRS I will build the above aggregate within a Read Model, right? "
You do not build aggregates in the read model. They are separate things on separate sides of the CQRS side of the equation. Aggregates are on the command side. Queries are done against read models which are different from aggregates.
Aggregates have public void functions and no getter or setters (with the exception of the aggregate id). They are encapsulated. They generate events when their state changes as a result of a command being issued. These events are stored in an event store and are used to recover the state of an aggregate. In other words, that is how an aggregate is stored.
The events go on to be published so the event handlers and other processes can react to them and update the read model and or trigger new cascading commands.
"Last question. For now I'm just using a single stream "foos", but at this level I expect new events to happen quite frequently (every couple of seconds or so) but as I understand it you'd still persist it and update it using materialized views right?"
Every couple of seconds is very likely to be fine. I'm more concerned at the persist and update using materialised views. I don't know what you mean by that but it doesn't sound like you have the right idea. Views should be very simple read models. No need to complex relations like you find in an RDMS. And is therefore highly optimised fast for reading.
There can be a lot of confusion on all the terminologies and jargon used in DDD and CQRS and ES. I think in this case, the confusion lies in what you think an aggregate is. You mention that you would like to persist your aggregate as a read model. As #Codescribler mentioned, at the sink end of your event stream, there isn't a concept of an aggregate. Concretely, in ES, commands are applied onto aggregates in your domain by loading previous events pertaining to that aggregate, rehydrating the aggregate by folding each previous event onto the aggregate and then applying the command, which generates more events to be persisted in the event store.
Down stream, a subscribing process receives all the events in order and builds a read model based on the events and data contained within. The confusion here is that this read model, at this end, is not an aggregate per se. It might very well look exactly like your aggregate at the domain end or it could be only creating a read model that doesn't use all the events and or the event data.
For example, you may choose to use every bit of information and build a read model that looks exactly like the aggregate hydrated up to the newest event(likely your source of confusion). You may instead have another process that builds a read model that only tallies a specific type of event. You might even subscribe to multiple streams and "join" them into a big read model.
As for how to store it, this is really up to you. It seems to me like you are taking the events and rebuilding your aggregate plus a history of events in a memory structure. This, of course, doesn't scale, which is why you want to store it at rest in a database. I wouldn't use the memory structure, since you would need to do a lot of state diffing when you flush to the database. You should be modify the database directly in response to each individual event. Ideally, you also transactionally store the stream count with said modification so you don't process the same event again in the case of a failure.
Hope this helps a bit.
Currently the site is using a relational database (MySQL) however the speed to join all the data is too long and has required caching that has lead to other issues.
The issue is how the two tables would nest into each other creating a circular reference. A simple example would be two tables, one for an ACTOR and a second for a MOVIE. The movie would have the actor and the actor would have a movie. Obviously this is easy in a relational database.
So for example, an ACTOR schema:
ACTOR1
- AGE
- BIO
- MOVIES
- FILM1 (ties to the FILM1 document)
- FILM2
Then the MOVIE schema:
FILM1
- RELEASE DATE
- ACTORS
- ACTOR1 (ties back to the ACTOR document)
- ACTOR2
Speed is the most important thing to me. I can easily add ID's to an ACTOR document in place of the full MOVIE document. However I'm back to multiple calls. Are there any features in a NoSQL database like MongoDB or DynamoDB that could solve this in a single call? Or is NoSQL just not the right choice?
While NoSQL generally recommends denormalization of data models, it is best not to have an unbounded list in a single database entry. To model this data in DynamoDB, you should use an adjacency list for modeling the many-to-many relationship. There's no cost-effective way of modeling the data, that I know of, to allow you to get all the data you want in a single call. However, you have said that speed is most important (without giving a latency requirement), so I will try to give you an idea as to how fast you can get the data if stored in DynamoDB.
Your schemas would become something like this:
Actor {
ActorId, <-- This is the application/database id, not the actor's actual ID
Name,
Age,
Bio
}
Film {
FilmId, <-- This is the application/database id for the film
Title,
Description,
ReleaseDate
}
ActedIn {
ActorId,
FilmId
}
To indicate that an actor acted in a movie, you only need to perform one write (which is consistently single-digit milliseconds using DynamoDB in my experience) to add an ActedIn item to your table.
To get all the movies for an actor, you would need to query once to get all the acted in relationships, and then a batch read to get all the movies. Typical latencies for a query (in my experience) is under 10ms, depending on the network speeds and the amount of data being sent over the network. Since the ActedIn relationship is such a small object, I think you could expect an average case of 5ms for a query, if your query is originating from something that is also running in an AWS datacenter (EC2, Lambda, etc).
Getting a single item is going to be under 5 ms, and you can do that in parallel. There's also a BatchGetItems API, but I don't have any statistics for you on that.
So, is ~10ms fast enough for you?
If not, you can use DAX, which adds a caching layer to DynamoDB and promises request latency of <1ms.
What's the unmaintainable, not-cost-effective way to do this in a single call?
For every ActedIn relationship, store your data like this:
ActedIn {
ActorId,
ActorName,
ActorAge,
ActorBio,
FilmId,
FilmTitle,
FilmDescription,
FilmReleaseDate
}
You only need to make one query for any given Actor to get all of their film details, and only one query to get all the Actor details for a given film. Don't actually do this. The duplicated data means that every time you have to update the details for an Actor, you need to update it for every Film they were in, and similarly for Film details. This will be an operational nightmare.
I'm not convinced; it seems like NoSQL is terrible for this.
You should remember that NoSQL comes in many varieties (NoSQL = Not Only SQL), and so even if one NoSQL solution doesn't work for you, you shouldn't rule it out entirely. If you absolutely need this in a single call, you should consider using a Graph database (which is another type of NoSQL database).
Would a database like Cassandra and scheme like GraphQL work well together?
Cassandra ideology is based on the idea of optimizing your queries and denormalizing data. This doesn't seem to really mesh well with a GraphQL ideology where data seems to be accessible in every level of a query.
Example:
Suppose I architect my Cassandra table like so:
User:
name
address
etc... (many properties)
Group:
id
name
user_name (denormalized user, where we generally just need the name of a user)
But with GraphQL, it's one wouldn't exactly expect a denormalized User.
query getGroup {
group(id: 1) {
name
users {
name
}
}
}
So a couple of things:
1.) This GraphQL query could end up hitting our Cassandra database multiple times (assuming no caching). Getting the group name and for each of the users we might even hit it for each user. But lets say our resolve creates multiple User objects with one cassandra call.
2.) We can't really build a cassandra idiomatic database with denormalization and graphql in mind, can we? Otherwise we should expect certain properties of a User aren't returned to us with the query.
To sum up the question, what's the graphql strategy for working with denormalized data? Is it acceptable to omit certain properties that the client thinks are accessible? E.g the client tries to access address of user but we don't have that at the moment because our data is denormalized. Or should one not even worry about denormalization and just let graphQL make calls with a caching mechanism in between the db and graphql. E.g graphql first gets the group, then gets the user data for the group id.
This is a side effect of GraphQL where a query can get quite complex in retrieving the data. But as long as the user is actually requesting the data they need if you are smart about your resolvers the end result will actually be faster.
Consider tools like dataloader to cache when resolving a query.
As far as omitting certain properties graphql validates the response and will throw an error, although it will also return the data you gave. It would probably be better to implement some sort of timeout and throw a more descriptive error if there is an issue retrieving the data.
I currently have a ReliableActor for every user in the system. This actor is appropriately named User, and for the sake of this question has a Location property. What would be the recommended approach for querying Users by Location?
My current thought is to create a ReliableService that contains a ReliableDictionary. The data in the dictionary would be a projection of the User data. If I did that, then I would need to:
Query the dictionary. After GA, this seems like the recommended approach.
Keep the dictionary in sync. Perhaps through Pub/Sub or IActorEvents.
Another alternative would be to have a persistent store outside Service Fabric, such as a database. This feels wrong, as it goes against some of the ideals of using the Service Fabric. If I did, I would assume something similar to the above but using a Stateless service?
Thank you very much.
I'm personally exploring the use of Actors as the main datastore (ie: source of truth) for my entities. As Actors are added, updated or deleted, I use MassTransit to publish events. I then have Reliable Statefull Services subscribed to these events. The services receive the events and update their internal IReliableDictionary's. The services can then be queried to find the entities required by the client. Each service only keeps the entity data that it requires to perform it's queries.
I'm also exploring the use of EventStore to publish the events as well. That way, if in the future I decide I need to query the entities in a new way, I could create a new service and replay all the events to it.
These Pub/Sub methods do mean the query services are only eventually consistent, but in a distributed system, this seems to be the norm.
While the standard recommendation is definitely as Vaclav's response, if querying is the exception then Actors could still be appropriate. For me whether they're suitable or not is defined by the normal way of accessing them, if it's by key (presumably for a user record it would be) then Actors work well.
It is possible to iterate over Actors, but it's quite a heavy task, so like I say is only appropriate if it's the exceptional case. The following code will build up a set of Actor references, you then iterate over this set to fetch the actors and then can use Linq or similar on the collection that you've built up.
ContinuationToken continuationToken = null;
var actorServiceProxy = ActorServiceProxy.Create("fabric:/MyActorApp/MyActorService", partitionKey);
var queriedActorCount = 0;
do
{
var queryResult = actorServiceProxy.GetActorsAsync(continuationToken, cancellationToken).GetAwaiter().GetResult();
queriedActorCount += queryResult.Items.Count();
continuationToken = queryResult.ContinuationToken;
} while (continuationToken != null);
TLDR: It's not always advisable to query over actors, but it can be achieved if required. Code above will get you started.
if you find yourself needing to query across a data set by some data property, like User.Location, then Reliable Collections are the right answer. Reliable Actors are not meant to be queried over this way.
In your case, a user could simply be a row in a Reliable Dictionary.