Unleash Edge is the successor to the Unleash Proxy.
Unleash Edge is compiled to a single binary. You can configure it by passing in arguments or setting environment variables.
```shell
Usage: unleash-edge [OPTIONS]
Commands: edge Run in edge mode offline Run in offline mode help Print this message or the help of the given subcommand(s)
Options:
-p, --port
Unleash Edge is distributed as a binary and as a docker image.
unleashorg/unleash-edge:<version>.ghpr.io/unleash/unleash-edge:<version>edge. This is built from HEAD on each commitIf you have the Rust toolchain installed you can build a binary for the platform you're running by cloning this repo and running cargo build --release. This will give you an unleash-edge binary in ./target/release
Edge currently supports 2 different modes:
mermaid
graph LR
A(Client) -->|Fetch toggles| B((Edge))
B-->|Fetch toggles| C((Unleash))
Edge mode is the "standard" mode for Unleash Edge and the one you should default to in most cases. It connects to an upstream node, such as your Unleash instance, and uses that as the source of truth for feature toggles.
Other than connecting Edge directly to your Unleash instance, it's also possible to connect to another Edge instance (daisy chaining). You can have as many Edge nodes as you'd like between the Edge node your clients are accessing and the Unleash server, and it's also possible for multiple nodes to connect to a single upstream one. Depending on your architecture and requirements this can be a powerful feature, offering you flexibility and scalability when planning your implementation.
mermaid
graph LR
A(Client 1) -->|Fetch toggles| C((Edge 1))
B(Client 2) -->|Fetch toggles| D((Edge 2))
C-->|Fetch toggles| E((Edge 3))
D-->|Fetch toggles| E
E-->|Fetch toggles| F((Unleash))
This means that, in order to start up, Edge mode needs to know where the upstream node is. This is done by passing the --upstream-url command line argument or setting the UPSTREAM_URL environment variable.
By default, Edge mode uses an in-memory cache to store the features it fetches from the upstream node. However, you may want to use a more persistent storage solution. For this purpose, Edge supports either Redis or a backup file, which you can configure by passing in either the --redis-url or --backup_folder command line argument, respectively. On start-up, Edge checks whether the persistent backup option is specified, in which case it uses it to populate its internal caches. This can be useful when your Unleash server is unreachable.
Edge mode also supports dynamic tokens, meaning that Edge doesn't need a token to be provided when starting up. Once we make a request to the /api/client/features endpoint using a client token Edge will validate upstream and fetch its respective features. After that, it gets added to the list of known tokens that gets periodically synced, making sure it is a valid token and its features are up-to-date.
Even though Edge supports dynamic tokens, you still have the option of providing a token through the command line argument or environment variable. This way, since Edge already knows about your token at start up, it will sync your features for that token and should be ready for your requests right away (warm up / hot start).
Front-end tokens can also be used with /api/frontend and /api/proxy endpoints, however they are not allowed to fetch features upstream. In order to use these tokens correctly and make sure they return the correct information, it's important that the features they are allowed to access are already present in that Edge node's features cache. The easiest way to ensure this is by passing in at least one client token as one of the command line arguments, ensuring it has access to the same features as the front-end token you'll be using.
Besides dynamic tokens, Edge mode also supports metrics and other advanced features.
To launch in this mode, run:
```bash $ unleash-edge edge -h Run in edge mode
Usage: unleash-edge edge [OPTIONS] --upstream-url
Options:
-u, --upstream-url
```
mermaid
graph LR
A(Client) -->|Fetch toggles| B((Edge))
B-->|Fetch toggles| C[Features dump]
Offline mode should be used when you don't have a connection to an upstream node, such as your Unleash instance itself or another Edge instance. It can also be used when you need to have full control of both the data your clients will get and which tokens can be used to access it.
Since this mode does not connect to an upstream node, it needs a downloaded JSON dump of a result from a query against an Unleash server on the /api/client/features endpoint as well as a comma-separated list of tokens that should be allowed to access the server.
If your token follows the Unleash API token format [project]:[environment].<somesecret>, Edge will filter the features dump to match the project contained in the token.
If you'd rather use a simple token like secret-123, any query against /api/client/features will receive the dump passed in on the command line.
When using offline mode, you can think of these tokens as proxy client keys.
Since offline mode does not connect to an upstream node, it does not support metrics or dynamic tokens.
To launch in this mode, run:
```bash $ ./unleash-edge offline --help Usage: unleash-edge offline [OPTIONS]
Options:
-b, --bootstrap-file
Unleash edge will scale linearly with CPU. There are k6 benchmarks in the benchmark folder and we've already got some initial numbers from hey.
Do note that the number of requests Edge can handle does depend on the total size of your toggle response. That is, Edge is faster if you only have 10 toggles with 1 strategy each, than it will be with 1000 toggles with multiple strategies on each. Benchmarks here were run with data fetched from the Unleash demo instance (roughly 100kB (350 features / 200 strategies)) as well as against a small dataset of 5 features with one strategy on each.
Edge was started using
docker run --cpus="<cpu>" --memory=128M -p 3063:3063 -e UPSTREAM_URL=<upstream> -e TOKENS="<client token>" unleashorg/unleash-edge:edge -w <number of cpus rounded up to closest integer> edge
Then we run hey against the proxy endpoint, evaluating toggles
shell
$ hey -z 10s -H "Authorization: <frontend token>" http://localhost:3063/api/frontend`
| CPU | Memory | RPS | Endpoint | p95 | Data transferred | | --- | ------ | --- | -------- | --- | ---------------- | | 0.1 | 6.7 Mi | 600 | /api/frontend | 103ms | 76Mi | | 1 | 6.7 Mi | 6900 | /api/frontend | 7.4ms | 866Mi | | 4 | 9.5 | 25300 | /api/frontend | 2.4ms | 3.2Gi | | 8 | 15 | 40921 | /api/frontend | 1.6ms | 5.26Gi |
and against our client features endpoint.
shell
$ hey -z 10s -H "Authorization: <client token>" http://localhost:3063/api/client/features
| CPU | Memory observed | RPS | Endpoint | p95 | Data transferred | | --- | ------ | --- | -------- | --- | ---------------- | | 0.1 | 11 Mi | 309 | /api/client/features | 199ms | 300 Mi | | 1 | 11 Mi | 3236 | /api/client/features | 16ms | 3 Gi | | 4 | 11 Mi | 12815 | /api/client/features | 4.5ms | 14 Gi | | 8 | 17 Mi | 23207 | /api/client/features | 2.7ms | 26 Gi |
shell
$ hey -z 10s -H "Authorization: <frontend token>" http://localhost:3063/api/frontend`
| CPU | Memory | RPS | Endpoint | p95 | Data transferred | | --- | ------ | --- | -------- | --- | ---------------- | | 0.1 | 4.3 Mi | 3673 | /api/frontend | 93ms | 9Mi | | 1 | 6.7 Mi | 39000 | /api/frontend | 1.6ms | 80Mi | | 4 | 6.9 Mi | 110000 | /api/frontend | 600μs | 252Mi | | 8 | 12.5 Mi | 141090 | /api/frontend | 600μs | 324Mi |
and against our client features endpoint.
shell
$ hey -z 10s -H "Authorization: <client token>" http://localhost:3063/api/client/features
| CPU | Memory observed | RPS | Endpoint | p95 | Data transferred | | --- | ------ | --- | -------- | --- | ---------------- | | 0.1 | 4 Mi | 3298 | /api/client/features | 92ms | 64 Mi | | 1 | 4 Mi | 32360 | /api/client/features | 2ms | 527Mi | | 4 | 11 Mi | 95838 | /api/client/features | 600μs | 2.13 Gi | | 8 | 17 Mi | 129381 | /api/client/features | 490μs | 2.87 Gi |
See our Contributors guide as well as our development-guide