Based on the command, an event is created. problem. describe both the business and technical context of our project. @KaseySpeakman Using Kafka this way is not easy by any means. Yes, it's called. One of the main reasons to use a transient write model in this way is to make business logic changes cheap and easy to deploy. The ML and data streaming markets have socio-technical blockers between them, but they are finally coming together. It for sure brings a lot of great value, but not for free. No. Forever if youd like. Because it uses (mostly) standard Kafka features, Kafka Streams are also elastic, highly scalable and fault-tolerant (still sounds great, right? @KaseySpeakman Topics are not the same as partitions. The traditional way to implement event sourcing is with a simple relational database, but the more powerful and modern approaches combine data in motion with services-based architectures. Kafka is still very useful in distributed scenarios. You dont need to worry about fault-tolerance and automatic recovery. But it is still not possible to guarantee perfect occurrence order. To give you a proper background, I intend to: Before this project, none of our team members had any practical experience with DDD, Event Sourcing or Kafka. I mean while Kafka is great, it is another broker centric system. So no clear answer Im afraid, Was going to post that link :) Awesome blog post. That is valid requirement in event driven architectures. Kafka was actually "designed for this type of usage", as stated here: Using Kafka as a (CQRS) Eventstore. These are course-grained summaries of each completed transaction. As with mathematics, you can solve nearly any problem . Event-sourcing is a pattern where everything in the system is initiated by a command that triggers an event. This doesn't mean you can't use this as an event store, but it may be better to use something else. And Kafka even has one great attribute you only have one storage/source of truth for your data (events) and your messages (published events) so that you dont have to solve the problem of distributed transactions while other people might have to implement the outbox pattern or find another solution. If God is perfect, do we live in the best of all possible worlds? But it's mostly inscrutable bank jargon (domain-specific language) to you, unusable for reconciling your account. Again - not very practical. Learn what event sourcing is, how to store events in Apache Kafka, and how you can do event sourcing on top of Apache Kafka. Ive always found event sourcing to be fascinating. message is published it is available for consumption, after which it Event sourcing and CQRS are useful approaches for understanding the tradeoffs of event storage. Have a strategy for temporarily missing data. have been consumedfor a configurable period of time. Eventually you query the events, most likely aggregated by customer ID or session ID, and then you perform a chronological reduce to filter the events that are relevant for the view youd like to serve. How hard would it have been for a small band to make and sell CDs in the early 90s? Messages in Kafka are produced to topics, and one topic nowadays often contains a single message type to play nicely with the schema registry. Sure if you try hard enough and integrate deeply enough. Kafka does not support this and the suggested workaround from the experts in the field seems to be to put a "database" in front of it all to provide a consistency point. We will only share developer content and updates, including notifications when new content is added. State stores, although sourced from changelog topics on startup, are kept in memory (or on a disk if taking too much memory) and are very fast. It implements the flow we discussed earlier. The processing goes on. Doing this, I've never encountered a situation where I needed a different event-structure and/or data about an event. Once the validation is completed, we send a reply to the command_reply topic, so that the service requesting a change knows whether it was accepted or rejected. They try to develop an event-sourced, event-driven microservices architecture. Kafka's support for very large stored log data makes it an excellent backend for an application built in this style. In "Forrest Gump", why did Jenny do this thing in this scene? We do that by loading all previous events for that particular entity ID from our event store and then re-apply them on our object, fast-forwarding it into its current state. Take a look at EventStoreDB for an alternative. But keep the incomplete data in a separate area or marked unavailable until it's all filled in. This update is more about some recommendations for microservice event-driven platform. Every service can read all the events of every other service, thats no different than having a massive Oracle DB in the center of the world. Many projects that try to use Kafka as an event store build everything around Kafka. Optimistic Concurrency. A wide range of resources to get you started, Build a client app, explore use cases, and build on our demos and resources, Confluent proudly supports the global community of streaming platforms, real-time data streams, Apache Kafka, and its ecosystems, Command Query Responsibility Segregation (CQRS), Hands On: Trying Out Event Sourcing with Confluent Cloud, Incorporating Event Storage into Your System. Instead of replacing one state with another as a result of a state mutation, you persist an event that represents that mutation. Hence, it's possible to implement an event sourcing system on top of Kafka without much effort. Apache Kafka is great for delivering messages, but if you're using it for event sourcingthink again. See the original article here. Can you force Kafka to work for an app-controlled source of truth? Transformer winding voltages shouldn't add in additive polarity? What bread dough is quick to prepare and requires no kneading or much skill? Using topic-per-type is recommended instead for Kafka, but this would require loading events for every entity of that type just to get events for a single entity. One way to guarantee write consistency is by utilizing the event stores optimistic concurrency control. in the event store that cannot be removed. Hazelcast to ensure each message will be processed once and only once. One of these features is. Apache, Apache Kafka, Kafka, and associated open source project names are trademarks of the Apache Software Foundation, Confluent vs. Kafka: Why you need Confluent, Kora, The Apache Kafka Engine, Built for the Cloud, Watch demo: Kafka streaming in 10 minutes, If youd like to learn more about event sourcing, feel free to check out the, Take the Confluent Cost Savings Challenge, Event Sourcing, Stream Processing, and Serverless. If they implement some parts with stateful storage they win nothing they need to solve the problem of storing data and messages atomically if they dont want to risk losing one or the other if things go south. Once might say you never need to transform events, but that is not correct assumption, there could be situation where you do backup of original, but you upgrade them to latest versions. I think future is in broker-less messaging systems. 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 . It doesn't necessarily need to contain snapshots of each entity, you are free to choose the shape and form of the query model, as long as you can project your events to that model. For that reason, its crucial that some sort of concurrency control can be implemented. An open-minded full-stack software engineer building products, not just writing code. can be way of choice.. Who said you always have to load the state of the entity directly from the event store by replaying the events? Before I continue, please note how the title says not suitable and not completely impossible. Kafka is a great tool for delivering messages between producers and consumers and the optional topic durability allows you to store your messages permanently. Another significant aspect of CQRS is that chronological reductions are done immediately at write time, so views are updated asynchronously before they are served. But this will only work with infinitely retained events (in our case), and aside from a few brief comments, this does not seem to be a supported use case of Kafka? The tonic is CQRS (command query responsibility segregation), a form of event sourcing that separates the parts of a system that perform commands (change state) from those that query (return values). But event sourcing is actually a subset of event streaming, since it only concerns a single app or microservice with a single storage model, along with a single database featuring data at rest. Event Sourcing with Kafka and ksqlDB; Example domain. Also, HTTP is not a reliable transport, and again, if you're at that scale, you can't afford to spend time solving lost and/or duplicate message problems. The comment has been deleted, but the question was something like: what do people use for event storage then? There is no schema migration, so as the schemas of events evolve over time, lots of different ones end up inside the log. Its events use a shared data model, meaning that numerous services have access to them. It might be a good complement to your event store as a way of transporting events to downstream query services or read models. @John I think if you already have an authoritative ordering of non-conflicting events, that implies wherever they live is your actual event-store technology, and Kafka is just being used as a secondary system to distribute them. But the most important takeaway is the value of retaining events, whether as a source of truth or as part of an analytics backend. When you begin to reason about event sourcing using a stream processor like Kafka, its clear enough that the chronological events go into the log. Is it normal for spokes to poke through the rim this much? My event sourced system should probably publish course-grained events to the Kafka cluster to feed at-scale consumers rather than use Kafka as internal storage. using Kafka Streams Application Reset Tool). introduce you quickly to Kafka (and help you understand how it differs from other message brokers). : they happend, no way to change the past. View sessions and slides from Kafka Summit 2023, View sessions and slides from Current 2022, Nominate amazing use cases and view previous winners, Principal Customer Success Technical Architect (Presenter), Lead Technologist, Office of the CTO (Author). The events serve as the source of truth, and the views perform a chronological reduce that summarizes the event streams down into tables. The problem with Kafka is that it only guarantees the order within partitions, not cross-partition, which leaves you with solving the ordering problem in some other way. To keep your data consistent you must store them atomically either one or nothing (think about ACID). For example, in the image below there is one event sourceshopping cart datawhich is consumed by many other services. Kafka will work very well as a log for event sourcing. https://github.com/networknt/microservices-framework-benchmark. For us, this is the defining part of Kafka Streams State Stores which enable stateful operations. A monolithic application benefits from events, but events really become significant when data has to movewhen there are different parts of an application that need to coordinate with one another and share data across data centers, clouds, or continents. Before we start, if you only have a rough idea about what Event Sourcing is, you might have a look at this great article from Alexey Zimarev. Fortunately, a high-fidelity, event-level recording of the world can always be revisited. What is event sourcing? In the image above, writes go to a single placethe flight booking serviceand they get disseminated, via event streams, to many views that serve reads. We version both the events and the state. What really matters is that when you book a flight, you absolutely get your reservation. > Kafka only guarantses at least once deliver and there are duplicates in the event store that cannot be removed. Zalando, The New York Times, TransferWise) for various purposes, can be in-memory or fully persistent key-value stores, offer fault-tolerance and automatic recovery, allow direct read-only queries of the state stores (via Interactive Queries). Since we've come full circle back to concurrency conflicts, then perhaps you should post your own article on medium or something on how you have used Kafka for event sourcing (not stream processing) in production. Although this is POSSIBLE, its not very practical. Find centralized, trusted content and collaborate around the technologies you use most. A proper event store provides a way for the user to say "save this event only if the version of the entity is still x". When you have a database, which is capable of reading all the entity events by the entity id (read the stream), using Event Sourcing is not a hard problem. So, what attributes would then be needed for a database sued as an event store to get a decent event-sourced system working? How you accomplish it with partition by type and without entity-level concurrency control. You seem to imply that there is such a thing as exactly once delivery. What is most important is that when you book a flight, you definitely get your reservation. One-minute guides to Kafka's core concepts. Please look at eventuate.io microservices open source framework to discover more about the potential problems: http://eventuate.io/. Apache, Apache Kafka, Kafka, and associated open source project names are trademarks of the Apache Software Foundation, Event Streaming and the Future of Applications | Ben Stopford, Putting Apache Kafka To Use: A Practical Guide to Building an Event Streaming Platform (Part 1), Microservices in the Apache Kafka Ecosystem. But how do you go beyond just messing around and using it to build a real-world, production application? The issue with "classic" brokers as that messages are gone as soon as they are consumed, so you cannot build something like a query model that would be built from history. I was looking at Kafka and had another concern: I didn't notice anything about optimistic-concurrency. Kafka Streams State Stores are an enabler for all stateful operations: joins, aggregates, grouping, etc. While Kafka wasn't originally designed with event sourcing in mind, it's design as a data streaming engine with replicated topics, partitioning, state stores and streaming APIs is very flexible. The only way to work around that is to introduce another sort of locking, for example with some key-value store like Redis. roll your own event store, probably using PostgreSQL with its strong JSONB data type. However, it is ideally suited to having events fed into it from e.g. Kafka is designed to solve giant-scale data problems. Kafka topics can be partitioned. A.k.a. As numerous services in the system were created by the time we joined, a few system-wide decisions were already made. Additionally, we push the state to the state topic if its needed by other domains. But is it a good idea? Once you decide to use event sourcing for your problem, you will need to start reasoning about how to apply it conceptually within your architecture, as I discussed in a previous Kafka Summit talk. I managed yesterday (I will share the solution asap) to use avro union and deserialize the events as GenericRecord and do transformation based on the event type. To properly project all the events into the proper current state for any point in time they need to be in the right order. Update bullet 1. Kafka is not suitable for the actual event sourcing transactional side, due to needing a stream of events (topic) per domain aggregate (think millions). So, is anything missing from Kafka for it to be a good event store? Or with Alexeys words: Repeat with me: Kafka is not suitable as an Event Store! Each service has its own internal source of truth (could be events, BNF, graph, etc), then listens to Kafka to know what is happening "outside". You can use old-school triggers, or you can change-data-capture (CDC) your data out, a good option if you want to get it into Kafka later. This is paired with an event handler that subscribes to the Kafka topic, transforms the event (as required) and writes the materialized view to a read store. For messaging try to look at Chronicle Queue, Map, Engine, Network. It is possible we could make this scenario work by making our producer a consumer of its own events and effectively block until the event is committed and available in the other end of Kafka. In a CRUD model, just the end result gets stored in the database, so the fact that you ordered only one pair of trousers would be recorded, and thats it. When a new listener comes online, or an existing listener wants a full replay, it would read S3 to catch up first. The structure of data in this topic differs from the State Store structure we dont want internal implementation details to leak outside. I will go back and link it. @AndrewLarsson If you don't partition per entity, then how are you going to prevent conflicting events at the entity level? Although I've come across Kafka before, I just recently realized Kafka may perhaps be used as (the basis of) a CQRS, eventstore. It is a non-trivial problem to expose each service's key events to other services. It's "just" a framework to help you implement one (lot of work behind this..). This leads to the second advantage: replayability. If you have data that was corrupted by a bug, for example, one that may have affected systems across your application, you can rewind back to a point before the bug surfaced. How to start building lithium-ion battery charger? Only starting from Kafka Streams 2.6 theres a solution for zero-downtime deployment (we havent tested it yet). An event means a fact, something that happened, and cannot be changed or rejected. Each Kafka topic consists of one or more partitions and each partition is stored as a directory on the file system. Yet that is what event sourcing is. Event Sourcing is a way to persist state. Thats not the topic of this article, but some of those benefits are: Event Sourcing at its core is just a better way to store the state of a system! Youve already seen this in the example: the total count of particular words needs to be stored on the go but should also survive restarts/crashes of the application. Source: Event Sourcing, Stream Processing, and Serverless. When our entitys state has been recreated, its time to execute the business logic requested by the incoming command. The advantages of sourcing events are threefold. This pattern can also be applied to microservices. It may be quite undesirable to save conflicting events and resolve them after the fact. A monolithic application benefits from events, but they really become significant when data has to movewhen there are different parts of an application that need to cooperate with one another and share data, potentially on a global scale. Connect and share knowledge within a single location that is structured and easy to search. This is a significant architectural requirement/restriction. If the systems state has changed between reading the state and persisting the decision, the update must fail to guarantee that all business rules have been followed. It is fault-tolerant, scales to enormous data sizes, and has a built in partitioning model. Due to immutability, there is no way to manipulate event store when application evolves and events need to be transformed (there are of course methods like upcasting, but). Hi Geert-Jan. The stack is a standard one: As it all started a while back, we could find limited information about Kafka Streams. @Geert-Jan also take a look at "Lambda architecture", this is quite similar and the name is given from Storm author, mostly using some kind of hadoop based event log in many exemples, @Jay: Since I have renewed interest in this topic, could you please elaborate a bit on the fact that Kafka. Because it is quite similar to how it is already used in, for example, click streams. 1) at-least-once + idempotence on the consumer. Over 2 million developers have joined DZone. To scale request load, it is common to use stateless services while preventing write conflicts using conditional writes (only write if the last entity event was #x). Learn how event sourcing is used, and different ways to integrate it into your systemwith CQRS, CDC, or even the outbox pattern. What would happen if one needed to change the structure of the internal State Store and replay all the events from the beginning? Would it work? One concern with using Kafka for event sourcing is the number of required topics. Other frameworks are "just" lightweight REST or JPA or differently focused frameworks. Feel free to challenge my answer! Partitions are guaranteed to only have one consumer per group at any given moment. It also handles bank transfers and other financial information. Naming may be different but youll notice the resemblance. Software Engineer at DNA Technology (dnatechnology.io), https://www.cloudkarafka.com/blog/part1-kafka-for-beginners-what-is-apache-kafka.html. This topic has infinite retention which means the messages are stored forever. When this scale enters the picture, like with the bank statement example, the granularity of events tends to be different. The final advantage of collecting so much data is so you can feed it into analytics systems, whether for machine learning or for other types of analysis. Event store as combination of application layer interfaces (monitoring and management), SQL/NoSQL store and Kafka as broker is better choice than leaving Kafka handle both roles to create complete feature full solution. Copyright Confluent, Inc. 2014-2023. That could be a requirement, for example, to be GDPR-compliant. But perhaps a better question is whether it makes sense for my event sourced solution to operate at a giant scale. Why have God chosen to order offering Isaak as a whole-burnt offering to test Abraham? You may want to read another story about implementing Kafka consumers health check in Spring Boot Actuator or check out more DNA Technology articles. What they also miss is that such a solution is nothing but a distributed monolith with the death star Kafka to bind them all. The Event store contains all the events that happened to a certain entity. Or better yet, have idempotent actions. Single or multiple topic (stream) per Aggregate Root event in kafka, kafka as event store in event sourced system, Concurrent writes for event sourcing on top of Kafka, Thrift serialization for kafka messages - single topic per struct, Apache Kafka Streams and Event Sourcing, CQRS and validation. In this kind of system, events happen in the real world and are recorded as facts. Typically in event sourcing, there is a stream (topic) of events per entity (such as user, product, etc). Our small team (5 ppl) felt most comfortable with Java and we read that Java Kafka client libraries have the broadest support for Kafka features. A dual-write describes a situation when you need to atomically update the database and publish messages without two-phase commit (2PC). Don't use Spring - it is great (I use it myself a lot), but is heavy and slow at the same time. First, there are the evidentiary advantagesaccountants dont use erasers. Event sourcing creates a version-control-like audit of your system that allows you to determine, for example, why things went wrong at a particular point in time. For example, the consumer may receive transaction events before it receives events describing the accounts involved. The way event sourcing works with CQRS is to have part of the application that models updates as writes to an event log or Kafka topic. How to optimize the two tangents of a circle by passing through a point outside the circle and calculate the sine value of the angle? This kind of application declares its own events as a result of user requests passing through business logic. Event sourcing stores the state of a database object as a sequence of events - essentially a new event for each time the object changed state, from the beginning of the object's existence. Many projects that try to use Kafka as an event store build everything around Kafka. If you are already familiar with Kafka and Kafka Streams, you may want to skip directly to our event sourcing solution or lessons learnt. Kafka is meant to be a messaging system which has many similarities to an event store however to quote their intro: The Kafka cluster retains all published messageswhether or not they Update: I was also informed that Kafka has a way of locking a whole partition and in combination with the transactions mentioned above you can have pessimistic concurrency control.When I rolled my own event store with PostgreSQL we only had to lock one event stream (e.g. However, if you look at Greg Young's paper from 2010, it summarises the idea quite nicely, from page 32 onwards, but it doesn't contain the ultimate definition, so I dare formulate it myself. I have written the content above out of my head. Aug 11, 2021 -- 1 Photo by Leo Rivas on Unsplash The main goal of this article is to share. Event sourcing is a style of application design where state changes are logged as a time-ordered sequence of records. Talking with someone with some experience using kafka, came up with the solution explained at point 2 by nesting the events into a "carrying event". They try to develop an "event-sourced, event-driven microservices architecture". We usually call these operations "projections" as you can fold events to state in many different ways. 2) I've never encountered needing to version events. As Greg's paper mentions Event Sourcing in the context of CQRS, he explains why those two concepts play nicely with each other. You could use a globally distributed database, but that's not the most efficient solution, because reads themselves will always be eventually consistent because of browser caching and other factors. Even using Snapshots to start from a known log position, this could be a significant number of events to churn through if structural changes to the snapshot are needed to support logic changes. Therere enough articles already about Kafka and how it operates, so just a quick recap: If you need more basics before reading further, this is a good explanation with nice images: https://www.cloudkarafka.com/blog/part1-kafka-for-beginners-what-is-apache-kafka.html. Don't use Kafka at all :-)) It is half joke. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. In distributed scenarios, I've seen a couple of different implementations. Imagine you want to ask if any commodity was ever below X items. But in the event sourcing model, each action you took during the process gets stored, that is, the first action of adding three pairs of trousers, the second action of removing two pairs, and the final checking-out action of buying a single pair. The service posts public events to Kafka to inform the outside of interesting things it encountered. Moreover, resetting the stream application requires using scripts with a direct access to the cluster. 1. ) constant with respect to data size so retaining lots of data is not a Imagine one command/request/use case results in multiple events. In this course you learned about event storage, from simple monolithic event sourcing applications through to complex, multiregion applications that rely heavily on data in motion. Event streaming adds connectivity to event sourcing . What they miss, though, is that now they have to event source everything, even parts of the application where Event Sourcing is not a good match. In less siloed scenarios, when streams are arbitrarily related in a way that can cross shard boundaries, sharding is still the move (partition by stream ID). For DDD fans: we certainly used various aspects of a DDD approach (and CQRS), but its not a purely DDD-based solution in its bones. At least some nanoseconds will have passed since the data was requested from an event store (or any other storage). This usually has a much higher level of detail than would be generally useful to at-scale consumers. In this context, it is understandable why Kafka folks are advocating it as an Event Sourcing solution. Save 25% or More on Your Kafka Costs | Take the Confluent Cost Savings Challenge. Once accepted, they are handled and as a result, an event is produced. To return to the shopping cart concept, because the cart gives us a very truthful record of user behaviour, we could start to try and work out why people arent buying that much in our shop at a particular time or within a given category. : check if event already seen. Of course, to make this work, we need to reimage every change to the state of the application as events: If the business logic fails we return an error to the client but if it succeeds a new event is emitted. Kafka Streams guarantee that for any record read from the source Kafka topics, its processing results will be reflected exactly once in the output topic as well as in the state stores for stateful operations. I.e. Therefore, we use the most recent, and totally consistent state from the transactional (event) store to reconstruct the entity state when executing operations on the entity. At this point, some of you may be wondering why its called event sourcing if the command is the trigger for the whole flow. The issue is solved by separating the transactional (event-sourced) store that is used as the source of truth, and the reporting (query, read) store, which is used for reports and queries of the current system state across multiple entities. Event Sourcing and CQRS himself and heres the first problem. So while messages can potentially be retained indefinitely, the expectation is that they will be deleted. features) are built in light4j framework out of the box which uses Postgres as low storage. Published at DZone with permission of Jesper Hammarbck. Another way to get consistency is to assure serialized writes, i.e using the single-writer principle, meaning we make sure all writes concerning a particular entity ID occur on a single thread. Creating snapshots is must feature for event store from long term perspective. Bi-weekly newsletter with Apache Kafka resources, news from the community, and fun links. Learn about why Kafka isn't suitable for event . Too many uncontrollable factors. To better understand event sourcing in its traditional sense, you can start by considering it in relation to normal CRUD processes, such as might be used with an e-commerce shopping cart. pallet 123456 put on truck A, but was scheduled for truck B.) . Kafka Streams use a notion of a topology for modelling computational logic: Going from a concept to code, a simple example borrowed from Apache Kafka Streams tutorial shows an application counting the occurrence of the words split from the source text stream (streams-plaintext-input topic). Scale can still be needed for event sourcing. Moreover, as we already had lots of new stuff waiting for us, the technology choice was a quick one. However, theres no ordering guarantee when subscribing to 2 different topics (command and event in this case). All the existing answers seem to be quite comprehensive, but there's a terminology issue, which I'd like to resolve in my answer. Apache Kafka Is Not for Event Sourcing. When you receive a request from the user to delete their data, you won't be able to do it without re-shovelling events between topics. Using event sourcing to good effect requires crafting events and streams to match the business processes. Specifically I like the focus on FRP in eventstore called Projections. They are part of Kafka Streams and adhere to similar principles, but provide a few extra features: They achieve persistence and fault-tolerance by simply using custom Kafka topics underneath, called changelog topics, as storage. By subscribing, you understand we will process your personal information in accordance with our Privacy Statement. If you are serious about microservice robust design and highest possible performance in general I will provide you with few hints you might be interested. ), Provides stream processing programming API, Used by really big players (e.g. I also am interested in @Geert-Jan's question to Jay. The common reason for this is to build a transient write model for the business logic to use to process the request. The CMS, where the content originates, doesnt actually need to be immediately consistent with the serving layer. There will also be pressure from ZooKeeper as the number of znodes increases. Topics are usually centered around and partitioned by entity types like "Orders", "Payments" or "Notifications" so we would have to go through all events for all "Orders" and filter them by their ID to load up a single "Order" entity. I.e. To use Kafka and prevent conflicting events, you would need to use a stateful, serialized writer (per "shard" or whatever is Kafka's equivalent) at the application level. The first instinct is to "just use timestamps" to order received events. Update: I just published my own intro to ES. This is usually quite hard to achieve in distributed systems when things can break and migrate at any time. 2. ) An event store is typically not built for that, but it's precisely what Kafka does well. Unfortunately, lacking experience in other frameworks for event sourcing, it would be unfair to answer. My current project is a multi-tenant scenario, and I rolled my own on top of Postgres. Probably. While this suggestion might be a viable solution in some cases, choosing a tool more tailored for the specific problem is probably wiser in the long run. So this impl was also setup for Dynamo Streams to dump events to S3. There are other ways to achieve the same concept without strictly following line-by-line Greg Young's implementation. 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). Ideally I could say: "Add this event as item N+1 only if the object's most recent event is still N.", @Darien: I'm probably going with a setup where Redis feeds Kafka (using. If you wonder about performance, you can compare yourself with existing benchmark suite. It may turn out that a different internal representation of the internal state is needed at some point. If so skip. Domain-Driven Design, Hexagonal Architecture (aka. Event Sourcing using Spring Kafka This repository contains a sample application that demonstrates how to implement an Event-sourced systems using the CQRS architectural style. One of the main points that Kafka supports: Admittedly I'm not 100% versed in CQRS / Event sourcing but this seems pretty close to what an events tore should be. Projecting events to persisted state is one-way operation. So here is what some highly respected people in the ES/DDD community have to say: And another one this is a long list of articles and videos Oskar has collected I have not read/viewed them all but generally we should trust Oskar ;-), Not all of them are about Kafka, but ES antipatterns in general, https://github.com/oskardudycz/EventSourcing.NetCore#this-is-not-event-sourcing. As the query model is by definition stale (even for milliseconds), it becomes dangerous when you make decisions on stale data. As an aside, if CRUD makes sense for your application, there are ways to get some of the benefits of event sourcing with it. Is it ok to use Apache Kafka "infinite retention policy" as a base for an Event sourced system with CQRS? Such as a warehouse system to keep track of pallets of products. You can perform event sourcing with practically any database. They provide snapshots of the state out-of-the-box as the newest state is always persisted, so the warmup time is not dependent on the number of events in the topic, but rather on the number of different entities (e.g. Sometimes, you also want to remove the whole entity from the database, meaning deleting all its events. To try out event sourcing, check out Event Streams , a managed Kafka-as-a-service platform on IBM Cloud. There are frameworks like Akka Projections (, If you read my answer again, maybe it will be clearer. Kafka does not work well in this case for two primary reasons. For example, The New York Times stores every single edit, image, article, and byline from the newspaper since it started in 1851 in a single Kafka topic. We will never send you sales emails. The best way to do this is to configure the compaction process to run continuously, then add a rate limit so that it doesn't affect the rest of the system unduly: # Ensure compaction runs continuously. The service is part of a regulated environment and therefore the audit trail is utterly important. I think you don't hear much about using Kafka for event sourcing primarily because the event sourcing terminology doesn't seem to be very prevalent in the consumer web space where Kafka is most popular. But that's not what the original idea of Event Sourcing is about, it's what we today call EDA. Strategies differ depending on why. Messages are ordered only within a single partition, and you'd normally need to use a predictable partition key, so Kafka can distribute messages across the partitions. This scenario needs the ability to load the event stream for a specific entity. then your data structures were perfect from beginning :-) lucky you, haha. For non-distributed scenarios, like internal back-ends or stand-alone products, it is well-documented how to create a SQL-based event store. Good entropy from entropy test (90B) but still fail NIST800-22. Put failed events in a retry queue, processing next events, and retry failed events later. backup compare with databases. I have written a bit about this style of Kafka usage here. In a (partially) event-sourced system you often want to build some read models that aggregate events from multiple streams, or multiple event types. Kafka seems well-suited for this kind of down-stream, event processing application. If you have made a mistake, you can't revert it as the state is already persisted. So Kafka holds the state but it gets pushed into a stream processor API (generally ksqlDB), where materialized views that suit specific use cases are created. The application reconstructs an entity's current state by replaying the events. Apache Kafka and stream processing solutions are a perfect match for data-hungry models. Thinking in terms of events about everything is a challenge at the beginning but tends to solve a lot of problems in certain domains. (I.e. If you really need the data to be unavailable instead of incomplete, use the same tactic. But when you get to querying, things become a bit weird: How can you perform the necessary event queries in Kafka, given its lack of an internal query API? In the end the effort to build from Chronicle your own messaging layer will be paid by removing the burden of maintaining the Kafka cluster. Ideally you design the consumer to not require cross-stream dependencies. How to decide whether Kafka is suitable for event storing? This way, the current state of an entity can be reconstituted by re-applying all events in the stream. You are effectively collecting a time series of user activitya journal of the system itself. I believe that the reason why a lot of people claim that Kafka is a good choice to be an event store for event-sourced systems is that they confuse Event Sourcing with simple pub-sub (you can use a hype word "EDA", or Event-Driven Architecture instead). Event streaming supersets event sourcing and CQRS. The validation part as well as the internal implementation of the processor nodes are skipped. Maybe. They are validated and accepted or rejected. You don't need a topic per entity or even a partition per entity. So traditional event sourcing and its benefits are relatively straightforward, but can more sophisticated architecturesones that need to massively scale and stream using a tool like Apache Kafkaalso enjoy its benefits? The first step is validation and to do that we sometimes need to peek into the current state of an entity, so we read the state from the State Store. Finally, based on the event, we update the state held in the State Store. If a different view is needed, the log is rewound, and a new view is created by replaying it. So theres a little bit of extra complexity with CQRS, but the model works well for many different kinds of use cases. Video courses covering Apache Kafka basics, advanced concepts, setup and use cases, and everything in between. Sometimes the command doesnt contain all the information needed to build an event, and we need to look into the State Store again to retrieve extra data about the entity. However, we should always be careful when adding large and complex pieces of infrastructure to our systems - everything comes with a cost so make sure its a good enough fit for your problem at hand! Although, you have a database full of atomic state mutations for a bunch of entities, querying across the current state of multiple entities is hard work. It has clear benefits, but there are many lurking questions. Sadly they drew the wrong conclusion and dumped ES in total, instead of just moving to a suitable event store. It seems that most people roll their own event storage implementation on top of an existing database. This can all be solved by using Kafka between the client and the command processor, but yes, it comes at the cost of complexity. Thanks to that all neat tricks applied to topics apply also to State Stores. This is compelling because you want to leverage your super powerful and complicated silver bullet solution as much as you can, right? GetEventStore. And these services often drive their processes directly from the event source. I am evaluating Google Pub/Sub vs Kafka. Similarly, software has two coordinate systems: state-based and event-based. Chronicle Queue is actually even faster than Kafka. After all, the state in event sourcing is derived from the events which are the Source of Truth. Is there anything like that in Kafka/Samza? Its main purpose is to be a ledger service tracking users money flow. We were particularly tempted by one feature offered by Kafka Streams:out-of-the-box exactly-once semantics which work together with Kafka Streams state stores (lots of jargon in a single sentence, well get to it ). And I did not find the existing answers nuanced enough, so I am adding this one. Copyright Confluent, Inc. 2014-2023. Secondly, users can create race conditions due to concurrent requests against the same entity. Not the answer you're looking for? Everything has already happened, even if it was wrong. This procedure implies also some downtime because the Kafka Streams Application needs to be down to be able to perform manual work on the offsets/topics. Each microservice has an event interface to trigger actions, as well as a view that represents the shopping cart data. Consumers of integration events should be idempotent and filter duplicates and unordered events. It might be possible, there are tons of articles like this from Confluent, who are earning money by making people use Kafka as a jack-of-all-trades. I don't like anything like _V2 in service code, so either you will backup to archive and recreate old events to their new versions (you still have the original truth), or you can hide/build this functionality directly into Event Store snapshot functionality, so there is single point of upcasting -> the event store. I am one of the original authors of Kafka. The bank needs that level of detail to process transactions. I think you should look at axon framework along with their support for Kafka. Event Sourcing Event Sourcing gives us a new way of persisting application state as an ordered sequence of events. Event Sourcing and CQRS himself and here's the first problem. Basically, you create a table and append events in the order that they occur. In that case we must be able to save the new event to our event store with a guarantee that no other event has been stored for this particular entity ID in the meantime, or we would risk breaking the consistency of our domain objects. I always treat the events themselves as the source of truth and include all info I would ever need on them. Kafka does not support optimistic concurrency. Given Kafka partitions are distributed and they are hard to manage and The partition key probably should be the stream id for best data distribution (to lessen the amount of over-provisioning). Let's see how. Kafka only guarantees at least once deliver and there are duplicates Then later the facts are checked for exceptions via reporting mechanisms. For that reason, a "proper" event database would need to support what we call _real-time subscriptions that would deliver new (and historical, if we need to replay) events to the query model to project. What's the actual use -case of changing them in retrospect? Instead, the bank publishes separate events for consumers. One can say we apply the event on top of the current state. It can take hours, or even weeks. Join the DZone community and get the full member experience. Using message brokers to fan out events to other system components is a pattern known for decades. Anyway, you still need maintenance windows when you need to replay the events to change the internal state store (unless you come up with a smart migration mechanism). The event is then sent to the event topic. I have tried it, and it has impressive perf. Chances are that you have read one of those horror stories about how a big Event Sourcing / CQRS project failed. Event capturing/storing, all HA of course. As soon as this separation is clear, we, hopefully, stop seeing claims that any append-only event log is a good candidate to be an event store database for event-sourced systems. That means no data locking happens before the read and write, but the write request detects that the data has changed in-between and rejects the update. You cannot see the history of a single entity simply because all the events for, potentially, millions of entities are stored in a single topic. Look at Chronicle. Is understanding classical composition guidelines beneficial to a jazz composer? Would easy tissue grafts and organ cloning cure aging? And it doesnt just create views, it sews together data from different sources in real time. The ability to fold events to some representation of state, and store this state in another database, asynchronously, is a side feature of Event Sourcing. You might not like what I say about your favorite broker with lots of overlapping capabilities, but still, Kafka wasn't designed as event store, but more as high performance broker and buffer at the same time to handle fast producers versus slow consumers scenarios, for example. May 26, 2021 -- 1 Kafka Streams is one of the newer additions to the streaming framework market. Event Sourcing is not a top-level architecture. And the complexity goes up with the number of schemas, because different parsing code is needed for each. Sharding is another option -- a perfect fit for regional- or tenant-isolated scenarios. Each microservice individually decides the form for the view: event level, table level, a custom transformation, etc. Because we store the whole history of events, we can calculate the current state of an entity at any point in time. Using output logs from Kafka Streams and this great visualization tool we can see how the actual topology will look like: Some people ask if Kafka Streams have been a good choice. Loading events for a particular entity like this is not easy in Kafka. So, if your messages are eventsyou can use Kafka as an event store or an event log, but it really isnt a suitable tool for event sourcing. Using its production? 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