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| <h1> |
| <a href="/2020/10/13/stateful-functions-internals-behind-the-scenes-of-stateful-serverless/">Stateful Functions Internals: Behind the scenes of Stateful Serverless</a> |
| </h1> |
| |
| |
| |
| October 13, 2020 - |
| |
| |
| |
| Tzu-Li (Gordon) Tai |
| |
| <a href="https://twitter.com/tzulitai">(@tzulitai)</a> |
| |
| |
| |
| |
| <p><p>Stateful Functions (StateFun) simplifies the building of distributed stateful applications by combining the best of two worlds: |
| the strong messaging and state consistency guarantees of stateful stream processing, and the elasticity and serverless experience of today’s cloud-native architectures and |
| popular event-driven FaaS platforms. Typical StateFun applications consist of functions deployed behind simple services |
| using these modern platforms, with a separate StateFun cluster playing the role of an “<a href="https://flink.apache.org/news/2020/04/07/release-statefun-2.0.0.html">event-driven database</a>” |
| that provides consistency and fault-tolerance for the functions’ state and messaging.</p> |
| <p>But how exactly does StateFun achieve that? How does the StateFun cluster communicate with the functions?</p> |
| <p>This blog dives deep into the internals of the StateFun runtime. The entire walkthrough is complemented by a |
| <a href="https://github.com/tzulitai/statefun-aws-demo/">demo application</a> which can be completely deployed on AWS services. |
| Most significantly, in the demo, the stateful functions are deployed and serviced using <a href="https://aws.amazon.com/lambda/">AWS Lambda</a>, |
| a popular FaaS platform among many others. The goal here is to allow readers to have a good grasp of the interaction between |
| the StateFun runtime and the functions, how that works cohesively to provide a Stateful Serverless experience, and how they can apply |
| what they’ve learnt to deploy their StateFun applications on other public cloud offerings such as GCP or Microsoft Azure.</p> |
| <h2 id="introducing-the-example-shopping-cart"> |
| Introducing the example: Shopping Cart |
| <a class="anchor" href="#introducing-the-example-shopping-cart">#</a> |
| </h2> |
| <div class="alert alert-info" markdown="1"> |
| <span class="label label-info" style="display: inline-block"><span class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span> |
| You can find the full code [here](https://github.com/tzulitai/statefun-aws-demo/blob/master/app/shopping_cart.py), which |
| uses StateFun's [Python SDK](//nightlies.apache.org/flink/flink-statefun-docs-master/sdk/python.html). Alternatively, if you are |
| unfamiliar with StateFun's API, you can check out this [earlier blog](https://flink.apache.org/2020/08/19/statefun.html) |
| on modeling applications and stateful entities using [StateFun's programming constructs](//nightlies.apache.org/flink/flink-statefun-docs-master/concepts/application-building-blocks.html). |
| </div> |
| <p>Let’s first take a look at a high-level overview of the motivating demo for this blog post: a shopping cart application. |
| The diagram below covers the functions that build up the application, the state that the functions would keep, and the messages |
| that flow between them. We’ll be referencing this example throughout the blog post.</p> |
| <center> |
| <figure> |
| <img src="/img/blog/2020-10-13-stateful-serverless-internals/shopping-cart-overview.png" width="600px" alt="Shopping cart application"/> |
| <figcaption><i><b>Fig.1:</b> An overly simplified shopping cart application.</i></figcaption> |
| </figure> |
| </center> |
| <br> |
| <p>The application consists of two function types: a <code>cart</code> function and an <code>inventory</code> function. |
| Each instance of the <code>cart</code> function is associated with a single user entity, with its state being the items in the cart |
| for that user (<code>ItemsInCart</code>). In the same way, each instance of the <code>inventory</code> function represents a single inventory, |
| maintaining as state the number of items in stock (<code>NumInStock</code>) as well as the number of items reserved across all |
| user carts (<code>NumReserved</code>). Messages can be sent to function instances using their logical addresses, which consists |
| of the function type and the instance’s entity ID, e.g. <code>(cart:Kim)</code> or <code>(inventory:socks)</code>.</p> |
| <p>There are two external messages being sent to and from the shopping cart application via ingresses and egresses: |
| <code>AddToCart</code>, which is sent to the ingress when an item is added to a user’s cart (e.g. sent by a front-end web application), |
| and <code>AddToCartResult</code>, which is sent back from our application to acknowledge the action.</p> |
| <p><code>AddToCart</code> messages are handled by the <code>cart</code> function, which in turn invokes other functions to form the main logic of the application. |
| To keep things simple, only two messages between functions are demonstrated: <code>RequestItem</code>, sent from the <code>cart</code> function to the <code>inventory</code> |
| function, representing a request to reserve an item, and <code>ItemReserved</code>, which is a response from the inventory function to acknowledge the request.</p> |
| <h2 id="what-happens-in-the-stateful-functions-runtime"> |
| What happens in the Stateful Functions runtime? |
| <a class="anchor" href="#what-happens-in-the-stateful-functions-runtime">#</a> |
| </h2> |
| <p>Now that we understand the business logic of the shopping cart application, let’s take a closer look at what keeps the state |
| of the functions and messages sent between them consistent and fault-tolerant: the StateFun cluster.</p> |
| <figure style="float:right;padding-left:1px;padding-top: 0px"> |
| <img src="/img/blog/2020-10-13-stateful-serverless-internals/abstract-deployment.png" width="400px"> |
| <figcaption style="padding-top: 10px;text-align:center"><i><b>Fig.2:</b> Simplified view of a StateFun app deployment.</i></figcaption> |
| </figure> |
| <p>The StateFun runtime is built on-top of Apache Flink, and applies the same battle-tested technique that Flink uses as the |
| basis for strongly consistent stateful streaming applications - <i><b>co-location of state and messaging</b></i>. |
| In a StateFun application, all messages are routed through the StateFun cluster, including messages sent from ingresses, |
| messages sent between functions, and messages sent from functions to egresses. Moreover, the state of all functions is |
| maintained in the StateFun cluster as well. Like Flink, the message streams flowing through the StateFun cluster and |
| function state are co-partitioned so that compute has local state access, and any updates to the state can be handled |
| atomically with computed side-effects, i.e. messages to send to other functions.</p> |
| <p>In more solid terms, take for example a message that is targeted for the logical address <code>(cart, "Kim")</code> being routed |
| through StateFun. Logical addresses are used in StateFun as the partitioning key for both message streams and state, so |
| that the resulting StateFun cluster partition that this message ends up in will have the state for <code>(cart, "Kim")</code> |
| locally available.</p> |
| <p>The only difference here for StateFun, compared to Flink, is that the actual compute doesn’t happen within the StateFun |
| cluster partitions - <i><b>computation happens remotely in the function services</b></i>. So how does StateFun |
| route input messages to the remote function services and provide them with state access, all the while |
| preserving the same consistency guarantees as if state and compute were co-located?</p> |
| <h3 id="remote-invocation-request-reply-protocol"> |
| Remote Invocation Request-Reply Protocol |
| <a class="anchor" href="#remote-invocation-request-reply-protocol">#</a> |
| </h3> |
| <p>A StateFun cluster partition communicates with the functions using a slim and well-defined request-reply protocol, as |
| illustrated in <strong>Fig. 3</strong>. Upon receiving an input message, the cluster partition invokes the target functions via their |
| HTTP service endpoint. The service request body carries input events and current state for the functions, retrieved from |
| local state. Any outgoing messages and state mutations as a result of invocations are sent back through StateFun as part |
| of the service response. When the StateFun cluster partition receives the response, all state mutations are written back |
| to local state and outgoing messages are routed to other cluster partitions, which in turn invokes other function |
| services.</p> |
| <center> |
| <figure> |
| <img src="/img/blog/2020-10-13-stateful-serverless-internals/request-reply-protocol.png" width="750px"/> |
| <figcaption><i><b>Fig.3:</b> The remote invocation request/reply protocol.</i></figcaption> |
| </figure> |
| </center> |
| <br> |
| <p>Under the hood, StateFun SDKs like the Python SDK and other <a href="//nightlies.apache.org/flink/flink-statefun-docs-master/sdk/external.html">3rd party SDKs for other languages</a> |
| all implement this protocol. From the user’s perspective, they are programming with state local to their function deployment, |
| whereas in reality, state is maintained in StateFun and supplied through this protocol. It is easy to add more language SDKs, |
| as long as the language can handle HTTP requests and responses.</p> |
| <h3 id="function-state-consistency-and-fault-tolerance"> |
| Function state consistency and fault-tolerance |
| <a class="anchor" href="#function-state-consistency-and-fault-tolerance">#</a> |
| </h3> |
| <p>The runtime makes sure that only one invocation per function instance (e.g. <code>(cart, "Kim")</code>) is ongoing at any point in |
| time (i.e. invocations per entity are serial). If an invocation is ongoing while new messages for the same function |
| instance arrives, the messages are buffered in state and sent as a single batch as soon as the ongoing invocation completes.</p> |
| <p>In addition, since each request happens in complete isolation and all relevant information is encapsulated in each |
| request and response, <i><b>function invocations are effectively idempotent</b></i> |
| (i.e. results depend purely on the provided context of the invocation) and can be retried. This naturally avoids |
| violating consistency in case any function service hiccups occur.</p> |
| <p>For fault tolerance, all function state managed in the StateFun cluster is periodically and asynchronously checkpointed |
| to a blob storage (e.g. HDFS, S3, GCS) using Flink’s <a href="//nightlies.apache.org/flink/flink-docs-master/concepts/stateful-stream-processing.html#checkpointing">original distributed snapshot mechanism</a>. |
| These checkpoints contain <i><b>a globally consistent view of state across all functions of the application</b></i>, |
| including the offset positions in ingresses and the ongoing transaction state in egresses. In the case of an abrupt failure, |
| the system may restore from the latest available checkpoint: all function states will be restored and all events between |
| the checkpoint and the crash will be re-processed (and the functions re-invoked) with identical routing, all as if the failure |
| never happened.</p> |
| <h3 id="step-by-step-walkthrough-of-function-invocations"> |
| Step-by-step walkthrough of function invocations |
| <a class="anchor" href="#step-by-step-walkthrough-of-function-invocations">#</a> |
| </h3> |
| <p>Let’s conclude this section by going through the actual messages that flow between StateFun and the functions in our shopping |
| cart demo application!</p> |
| <p><strong>Customer “Kim” puts 2 socks into his shopping cart (Fig. 4):</strong></p> |
| <center> |
| <figure> |
| <img src="/img/blog/2020-10-13-stateful-serverless-internals/protocol-walkthrough-1.png" width="750px"/> |
| <figcaption><i><b>Fig.4:</b> Message flow walkthrough.</i></figcaption> |
| </figure> |
| </center> |
| <br> |
| <ul> |
| <li> |
| <p>An event <code>AddToCart("Kim", "socks", 2)</code> comes through one of the ingress partitions <b><code>(1)</code></b>. The ingress event router is |
| configured to route <code>AddToCart</code> events to the function type <code>cart</code>, taking the user ID (<code>"Kim"</code>) as the instance ID. The |
| function type and instance ID together define the logical address of the target function instance for the event <code>(cart:Kim)</code>.</p> |
| </li> |
| <li> |
| <p>Let’s assume the event is read by StateFun partition B, but the <code>(cart:Kim)</code> address is owned by partition A. |
| The event is thus routed to partition A <b><code>(2)</code></b>.</p> |
| </li> |
| <li> |
| <p>StateFun Partition A receives the event and processes it:</p> |
| <ul> |
| <li>First, the runtime fetches the state for <code>(cart:Kim)</code> from the local state store, i.e. the existing items in Kim’s cart <b><code>(3)</code></b>.</li> |
| <li>Next, it marks <code>(cart:Kim)</code> as <i>“busy”</i>, meaning an invocation is happening. This buffers other messages targeted for |
| <code>(cart:Kim)</code> in state until this invocation is completed.</li> |
| <li>The runtime grabs a free HTTP client connection and sends a request to the <code>cart</code> function type’s service endpoint. |
| The request contains the <code>AddToCart("Kim", "socks", 2)</code> message and the current state for <code>(cart:Kim)</code> <b><code>(4)</code></b>.</li> |
| <li>The remote <code>cart</code> function service receives the event and attempts to reserve socks with the <code>inventory</code> function. |
| Therefore, it replies to the invocation with a new message <code>RequestItem("socks", 2)</code> targeted at the address <code>(inventory:socks)</code>. |
| Any state modifications will be included in the response as well, but in this case there aren’t any state modifications yet |
| (i.e. Kim’s cart is still empty until a reservation acknowledgement is received from the inventory service) <b><code>(5)</code></b>.</li> |
| <li>The StateFun runtime receives the response, routes the <code>RequestItem</code> message to other partitions, |
| and marks <code>(cart:Kim)</code> as <i>“available”</i> again for invocation.</li> |
| </ul> |
| </li> |
| <li> |
| <p>Assuming that the <code>(inventory:socks)</code> address is owned by partition B, the message is routed to partition B <b><code>(6)</code></b>.</p> |
| </li> |
| <li> |
| <p>Once partition B receives the <code>RequestItem</code> message, the runtime invokes the function <code>(inventory:socks)</code> in the same |
| way as described above, and receives a reply with a modification of the state of the inventory (the number of reserved socks is now increased by 2). |
| <code>(inventory:socks)</code> now also wants to reply reservation of 2 socks for Kim, so an <code>ItemReserved("socks", 2)</code> |
| message targeted for <code>(cart:Kim)</code> is also included in the response <b><code>(7)</code></b>, which will again be routed by the StateFun runtime.</p> |
| </li> |
| </ul> |
| <h2 id="stateful-serverless-in-the-cloud-with-faas-and-statefun"> |
| Stateful Serverless in the Cloud with FaaS and StateFun |
| <a class="anchor" href="#stateful-serverless-in-the-cloud-with-faas-and-statefun">#</a> |
| </h2> |
| <p>We’d like to wrap up this blog by re-emphasizing how the StateFun runtime works well with cloud-native |
| architectures, and provide an overview of what your complete StateFun application deployment would look like |
| using public cloud services.</p> |
| <p>As you’ve already learnt in previous sections, invocation requests themselves are stateless, with all necessary information |
| for an invocation included in the HTTP request (i.e. input events and state access), and all side-effects of the invocation |
| included in the HTTP response (i.e. outgoing messages and state modifications).</p> |
| <figure style="float:right;padding-left:1px;padding-top: 0px"> |
| <img src="/img/blog/2020-10-13-stateful-serverless-internals/aws-deployment.png" width="450px"> |
| <figcaption style="padding-top: 10px;text-align:center"><i><b>Fig.5:</b> Complete deployment example on AWS.</i></figcaption> |
| </figure> |
| <p>A natural consequence of this characteristic is that there is no session-related dependency between individual HTTP |
| requests, making it very easy to horizontally scale the function deployments. This makes it very easy to deploy your |
| stateful functions using FaaS platforms solutions, allowing them to rapidly scale out, scale to zero, or be upgraded |
| with zero-downtime.</p> |
| <p>In our complementary demo code, you can find <a href="https://github.com/tzulitai/statefun-aws-demo/blob/master/app/shopping_cart.py#L49">here</a> |
| the exact code on how to expose and service StateFun functions through AWS Lambda. Likewise, this is possible for any other |
| FaaS platform that supports triggering the functions using HTTP endpoints (and other transports as well in the future).</p> |
| <p><strong>Fig. 5</strong> on the right illustrates what a complete AWS deployment of a StateFun application would look like, with functions |
| serviced via AWS Lambda, AWS Kinesis streams as ingresses and egresses, AWS EKS managed Kubernetes cluster to run the |
| StateFun cluster, and an AWS S3 bucket to store the periodic checkpoints. You can also follow the |
| <a href="https://github.com/tzulitai/statefun-aws-demo#running-the-demo">instructions</a> in the demo code to try it out and deploy this yourself right away!</p> |
| <hr> |
| <p>If you’d like to learn more about Stateful Functions, head over to the <a href="//nightlies.apache.org/flink/flink-statefun-docs-master/">official documentation</a>, where you can also find more hands-on tutorials to try out yourself!</p> |
| </p> |
| </article> |
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