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Traditional Deployment

A Kong cluster allows you to scale the system horizontally by adding more machines to handle more incoming requests. They will all share the same configuration since they point to the same database. Kong nodes pointing to the same datastore will be part of the same Kong cluster.

You need a load balancer in front of your Kong cluster to distribute traffic across your available nodes.

 
flowchart TD

A[(Database)]
B( Kong Gateway instance)
C( Kong Gateway instance)
D( Kong Gateway instance)

A <---> B & C & D

style B stroke:none,fill:#0E44A2,color:#fff
style C stroke:none,fill:#0E44A2,color:#fff
style D stroke:none,fill:#0E44A2,color:#fff

Figure 1: In a traditional deployment, all Kong Gateway nodes connect to the database. Each node manages its own configuration.

What a Kong cluster does and doesn’t do

Having a Kong cluster does not mean that your clients traffic will be load-balanced across your Kong nodes out of the box. You still need a load-balancer in front of your Kong nodes to distribute your traffic. Instead, a Kong cluster means that those nodes will share the same configuration.

For performance reasons, Kong avoids database connections when proxying requests, and caches the contents of your database in memory. The cached entities include Services, Routes, Consumers, Plugins, Credentials, and so on. Since those values are in memory, any change made via the Admin API of one of the nodes needs to be propagated to the other nodes.

This document describes how those cached entities are being invalidated and how to configure your Kong nodes for your use case, which lies somewhere between performance and consistency.

Single node Kong clusters

A single Kong node connected to a supported database creates a Kong cluster of one node. Any changes applied via the Admin API of this node will instantly take effect. Example:

Consider a single Kong node A. If we delete a previously registered Service:

curl -X DELETE http://127.0.0.1:8001/services/test-service

Then any subsequent request to A would instantly return 404 Not Found, as the node purged it from its local cache:

curl -i http://127.0.0.1:8000/test-service

Multiple nodes Kong clusters

In a cluster of multiple Kong nodes, other nodes connected to the same database would not instantly be notified that the Service was deleted by node A. While the Service is not in the database anymore (it was deleted by node A), it is still in node B’s memory.

All nodes perform a periodic background job to synchronize with changes that may have been triggered by other nodes. The frequency of this job can be configured via:

  • [db_update_frequency][db_update_frequency] (default: 5 seconds)

Every db_update_frequency seconds, all running Kong nodes will poll the database for any update, and will purge the relevant entities from their cache if necessary.

If we delete a Service from node A, this change will not be effective in node B until node Bs next database poll, which will occur up to db_update_frequency seconds later (though it could happen sooner).

This makes Kong clusters eventually consistent.

Use read-only replicas when deploying Kong clusters with PostgresSQL

When using Postgres as the backend storage, you can optionally enable Kong to serve read queries from a separate database instance.

Enabling the read-only connection support in Kong greatly reduces the load on the main database instance since read-only queries are no longer sent to it.

To learn more about how to configure this feature, refer to the Datastore section of the Configuration reference.

What is being cached?

All of the core entities such as Services, Routes, Plugins, Consumers, Credentials are cached in memory by Kong and depend on their invalidation via the polling mechanism to be updated.

Additionally, Kong also caches database misses. This means that if you configure a Service with no plugin, Kong will cache this information. Example:

On node A, we add a Service and a Route:

# node A
curl -X POST http://127.0.0.1:8001/services \
  --data "name=example-service" \
  --data "url=http://example.com"

curl -X POST http://127.0.0.1:8001/services/example-service/routes \
  --data "paths[]=/example"

(Note that we used /services/example-service/routes as a shortcut: we could have used the /routes endpoint instead, but then we would need to pass service_id as an argument, with the UUID of the new Service.)

A request to the Proxy port of both node A and B will cache this Service, and the fact that no plugin is configured on it:

# node A
curl http://127.0.0.1:8000/example

Response:

HTTP 200 OK
...

# node B
curl http://127.0.0.2:8000/example

Response:

HTTP 200 OK
...

Now, say we add a plugin to this Service via node A’s Admin API:

# node A
curl -X POST http://127.0.0.1:8001/services/example-service/plugins \
  --data "name=example-plugin"

Because this request was issued via node A’s Admin API, node A will locally invalidate its cache and on subsequent requests, it will detect that this API has a plugin configured.

However, node B hasn’t run a database poll yet, and still caches that this API has no plugin to run. It will be so until node B runs its database polling job.

Conclusion: All CRUD operations trigger cache invalidations. Creation (POST, PUT) will invalidate cached database misses, and update/deletion (PATCH, DELETE) will invalidate cached database hits.

How to configure database caching?

You can configure three properties in the Kong configuration file, the most important one being db_update_frequency, which determine where your Kong nodes stand on the performance versus consistency trade-off.

Kong comes with default values tuned for consistency so that you can experiment with its clustering capabilities while avoiding surprises. As you prepare a production setup, you should consider tuning those values to ensure that your performance constraints are respected.

[db_update_frequency][db_update_frequency] (default: 5s)

This value determines the frequency at which your Kong nodes will be polling the database for invalidation events. A lower value means that the polling job will execute more frequently, but that your Kong nodes will keep up with changes you apply. A higher value means that your Kong nodes will spend less time running the polling jobs, and will focus on proxying your traffic.

Note: Changes propagate through the cluster in up to db_update_frequency seconds.

[db_update_propagation][db_update_propagation] (default: 0s)

If your database itself is eventually consistent (that is, Cassandra), you must configure this value.

Setting this parameter ensures that the change has time to propagate across your database nodes. When set, Kong nodes receiving invalidation events from their polling jobs will delay the purging of their cache for db_update_propagation seconds.

If a Kong node connected to an eventually consistent database was not delaying the event handling, it could purge its cache, only to cache the non-updated value again (because the change hasn’t propagated through the database yet)!

You should set this value to an estimate of the amount of time your database cluster takes to propagate changes.

Note: When this value is set, changes propagate through the cluster in up to db_update_frequency + db_update_propagation seconds.

[db_cache_ttl][db_cache_ttl] (default: 0s)

The time (in seconds) for which Kong will cache database entities (both hits and misses). This Time-To-Live value acts as a safeguard in case a Kong node misses an invalidation event, to avoid it from running on stale data for too long. When the TTL is reached, the value will be purged from its cache, and the next database result will be cached again.

By default, no data is invalidated based on this TTL (the default value is 0). This is usually fine: Kong nodes rely on invalidation events, which are handled at the db store level. If you are concerned that a Kong node might miss invalidation event for any reason, you should set a TTL. Otherwise the node might run with a stale value in its cache for an undefined amount of time until the cache is manually purged, or the node is restarted.

When using Cassandra

If you use Cassandra as your Kong database, you must set [db_update_propagation][db_update_propagation] to a non-zero value. Since Cassandra is eventually consistent by nature, this will ensure that Kong nodes do not prematurely invalidate their cache, only to fetch and catch a not up-to-date entity again. Kong will present you a warning in logs if you did not configure this value when using Cassandra.

Additionally, you might want to configure cassandra_consistency to a value like QUORUM or LOCAL_QUORUM, to ensure that values being cached by your Kong nodes are up-to-date values from your database.

Setting the cassandra_refresh_frequency option to 0 is not advised, as a Kong restart will be required to discover any changes to the Cassandra cluster topology.

Interacting with the cache via the Admin API

If for some reason, you want to investigate the cached values, or manually invalidate a value cached by Kong (a cached hit or miss), you can do so via the Admin API /cache endpoint.

Note: Retrieving the cache_key for each entity being cached by Kong is currently an undocumented process. Future versions of the Admin API will make this process easier.

Inspect a cached value

Endpoint

/cache/{cache_key}

Response

If a value with that key is cached:

HTTP 200 OK
...
{
    ...
}

Else:

HTTP 404 Not Found

Purge a cached value

Endpoint

/cache/{cache_key}

Response

HTTP 204 No Content
...

Purge a node’s cache

Endpoint

/cache

Response

HTTP 204 No Content

Note: Be wary of using this endpoint on a node running in production with warm cache. If the node is receiving a lot of traffic, purging its cache at the same time will trigger many requests to your database, and could cause a dog-pile effect.