You are viewing the technical documentation for Sharetribe. If you are looking for our no-code documentation, see our new help center.

Last updated

Reacting to events

This guide demonstrates how to build an integration that reacts to events that happen in your Sharetribe marketplace, using the Sharetribe Integration API.

Table of Contents

One of the main purposes of events in Sharetribe is to allow building integrations that programatically react to changes and actions in a marketplace. In this guide, we will show how to use the Sharetribe Integration API to continuously query events efficiently. We will also demonstrate how to interpret the event data to detect actions, using publishing a listing as an example.

This guide assumes that you have already set up a Sharetribe Integration API application and have a Node.js app with the Integration API SDK ready. If you have not yet used the Sharetribe Integration API, follow the Getting started with the Integration API guide first and you will be ready to proceed with this guide.

In this guide we will cover the following main topics:

  • querying events via the Integration API, using filters to receive only relevant events
  • using event data to determine the change that happened
  • understanding event sequence IDs and using them to correctly query for new events
  • understanding at-least-once and at-most-once types of event processing

Querying events

The Integration API /events/query endpoint will be used at the core of this guide. In the most basic form, a query for all available events using the SDK looks like this:


In this guide, we are interested in detecting when a new listing is published and therefore do not need to process all events that happen in the marketplace. Instead, we can filter out only the relevant listing-related events. Recall that the listing state attribute indicates whether a listing is draft or published (among other possible states) and that a new listing may become published in one of three ways:

Therefore, in order to cover all cases, we need to process both listing/created and listing/updated events. A query for only these types of events looks like this:

  eventTypes: 'listing/created,listing/updated',

This would still give us all events with the given types that Sharetribe keeps in history. In practice, often we are interested in new events only. In the absense of another point of reference, it is possible to query events starting from a given timestamp:

const now = new Date();
const fiveMinutesAgo = new Date(now - 300000);
  createdAtStart: fiveMinutesAgo,
  eventTypes: 'listing/created,listing/updated',

Later in the guide, we will see how to use the data of events that the application has already processed to query strictly for subsequent events.

Using event data to detect change

Even with the event type filtering in place, not all listing/created or listing/updated events represent the logical change we may be interested in (such as a listing being published for the first time). We need to use the event data and in particular the resource and previousValues to detect which events correspond to the change we want to react to.

A new listing is first published when one of these happen:

  • a listing/created event shows that the current listing state is published
  • a listing/updated event shows that the current listing state is published and the previous state was draft
  • a listing/updated event shows that the current listing state is published and the previous state was pendingApproval

In code, analyzing the event data can be done like this:

const now = new Date();
const fiveMinutesAgo = new Date(now - 300000);

const handleListingPublished = event => {
  const { resourceId, resource: listing } = event.attributes;
  const listingId = resourceId.uuid;
  const authorId = listing.relationships.author.data.id.uuid;

  // Do something about the new published listing, such as send notification,
  // synchronize data to external system, etc.
    `A new listing has been published: listingId ${listingId}, author ID: ${authorId}`

const analyzeEvent = event => {
  const {
    resource: listing,
  } = event.attributes;
  const listingState = listing.attributes.state;
  const { state: previousState } = previousValues.attributes || {};

  const isPublished = listingState === 'published';
  const isPendingApproval = listingState === 'pendingApproval';
  const wasDraft = previousState === 'draft';
  const wasPendingApproval = previousState === 'pendingApproval';

  switch (eventType) {
    case 'listing/created':
      if (isPublished) {
    case 'listing/updated':
      if (isPublished && (wasPendingApproval || wasDraft)) {

    createdAtStart: fiveMinutesAgo,
    eventTypes: 'listing/created,listing/updated',
  .then(res => {
    const events = res.data.data;


Naturally, instead of simply logging the event, the application can be doing something else, such as sending an email notification to a marketplace operator, synchronising data with an external system and so on.

Polling events continuously using sequence IDs

In the guide so far we saw how to process events from a single query. In practice, an application that reacts to events needs to poll for new events continuously. In this section we will show how this is best achieved.

In Sharetribe, each event has a unique sequenceId and the sequence IDs are strictly increasing. Moreover, the /events/query Integration API endpoint always returns events sorted by their sequence IDs in ascending order. This means that once a batch of events is processed, the sequence ID of the last event can be used to query for strictly newer events, using the startAfterSequenceId query parameter. For example:

// Given the sequence ID of last processed event, query only for newer events
const lastEventSequenceId = 1234;
  startAfterSequenceId: lastEventSequenceId,
  eventTypes: 'listing/created,listing/updated',

Using that knowledge, a continuous polling loop works like this:

  1. start querying events from some point in time, such as the current timestamp
  2. if there are some new events, process them and note the sequence ID of the last one
  3. after processing the batch, wait for a time and poll again using the sequence ID of the last event (if there was one) or the same timestamp as in the original query and go to step 2.

Persisting the last seen sequence ID

The polling loop described above works for a single execution of the application. However, if the application is stopped and then restarted, some events may be missed. To remedy that, the application needs to persist somehow the sequence ID of the last processed event. How that is achieved, depends on the particular application and available infrastructure, but some options include:

  • store the sequence ID in a local file
  • store the sequence ID in some cloud storage, such as AWS S3, Azure Blob Storage or GCP Cloud Storage
  • store the sequence ID in some database

With the sequence ID persisted, subsequent executions of the application can resume polling events from the exactly correct point of reference and will therefore not miss any events that may have happend in the meantime.

A suitable polling interval that applications should use depend on a few factors, including:

  • how much activity there is in the marketplace
  • how important it is that the events are processed without delay
  • maintaining good practice when accessing the Sharetribe APIs

In most cases, a polling interval of 1-10 minutes may be completely sufficient. In cases were more rapid reaction is required, we highly recommend that the polling interval should not be smaller than 10-30 seconds.

Note that a single query returns up to a 100 events. If a query returns a full page of events, there may be more events already available for querying. In that case, a smaller timeout is acceptable, so that the application can process all available events in a timely manner (e.g. 250-1000 ms).

Poll loop example using local file to store state

Below is a full polling loop example, using a local file to store the last processed event's sequence ID:

const fs = require('fs');

// Start polloing from current time on, when there's no stored state
const startTime = new Date();

// Polling interval (in ms) when all events have been fetched.
const pollIdleWait = 300000; // 5 minutes
// Polling interval (in ms) when a full page of events is received and there may be more
const pollWait = 1000; // 1s

// File to keep state across restarts. Stores the last seen event sequence ID,
// which allows continuing polling from the correct place
const stateFile = './last-sequence-id.state';

const queryEvents = args => {
  var filter = { eventTypes: 'listing/created,listing/updated' };
  return integrationSdk.events.query({ ...args, ...filter });

const saveLastEventSequenceId = sequenceId => {
  // Save state to local file
  try {
    fs.writeFileSync(stateFile, sequenceId);
  } catch (err) {
    throw err;

const loadLastEventSequenceId = () => {
  // Load state from local file, if any
  try {
    const data = fs.readFileSync(stateFile);
    return parseInt(data, 10);
  } catch (err) {
    return null;

const handleEvent = event => {
  // detect change and handle event
  // ...

  // Then store the event's sequence ID

const pollLoop = sequenceId => {
  var params = sequenceId
    ? { startAfterSequenceId: sequenceId }
    : { createdAtStart: startTime };
  queryEvents(params).then(res => {
    const events = res.data.data;
    const fullPage = events.length === res.data.meta.perPage;
    const delay = fullPage ? pollWait : pollIdleWait;
    const lastEvent = events[events.length - 1];
    const lastSequenceId = lastEvent
      ? lastEvent.attributes.sequenceId
      : sequenceId;

    events.forEach(e => {

    setTimeout(() => {
    }, delay);

// Load state from local file, if any
const lastSequenceId = loadLastEventSequenceId();

// kick off the polling loop

At-least-once and at-most-once event processing

No system ever operates without failures and therefore applications should account for possible failure conditions during event processing. For instance, an API call to another system during event handling may fail (sending notification, synchronizing state, executing other actions, etc), or the application itself may crash due to a bug or an issue on the host system.

An important aspect of this is what is often referred to as at-least-once and at-most-once processing in message processing systems.

Consider the following scenarios:

  1. the application receives an event, records persistently its sequence ID and then proceeds to handle the event
  2. the application receives an event, handles it fully and then records its sequence ID persistently

Suppose that a failure occurs during the event handling and the application crashes. In scenario 1, when the application is restarted, it will query for new events, skipping over the event that failed to be handled, as its sequence ID is already stored. Depending on when exactly the failure occurred, the event may not have been fully handled and therefore will be left unprocessed (this is at-most-once processing). On the other hand, in scenario 2, when the application is restarted, it will retry handling the failed event (since the last recorded sequence ID is that of some previous event) and potentially may process the event a second time (that is, at-least-once processing).

The preferable strategy for an application depends on the exact event handling logic. For example, at-least-once processing may be preferable in any of the following situations:

  • when the event processing is idempotent, meaning that processing and event more than once does not produce undesired side-effects
  • when it is tolerrable that some events may be processed multiple times (e.g. when a user may receive an occasional duplicate notification)

On the other hand, at-most-once processing is preferable if processing an event produces a side-effect that must not be repeated (such as making a money transfer) and can not be performed safely in an idempotent manner. In those cases, it may be desirable to handle the failed events somehow manually and resume automatic event processing only for subsequent events.

Note that, if events are processed in batches and only the sequence ID of the last event in the batch is recorded, the effect of potential failure may be amplified to affect all events in the failed batch. Again, whether that is acceptable depends on the concrete event handling mechanics.

Finally, exactly-once processing may not be achievable in most scenarios when idempotent operations are impossible. That is especially true when event handling requires working across multiple systems (such as API calls to 3rd party services). In practice, at-most-once processing can be considered the safer choice in those cases.

Additional resources