Proxy-Wasm project's key objective is to bring the extensibility to network proxies with any programming language in a flexible way.
This Proxy-Wasm Go SDK is the SDK for extending network proxies (e.g. Envoyproxy) on top of the Proxy-Wasm ABI specification with Go programming language, and the Proxy-Wasm ABI defines the interface between network proxies and Wasm virtual machines running inside network proxies.
With this SDK, everyone can produce Proxy-Wasm spec compatible Wasm binaries easily without knowing Proxy-Wasm ABI specification which is low-level and overwhelming to anyone who doesn't have the expertise in this field. Instead, developers rely on the Go API of this SDK to perform what they want to do to extend network proxies.
This document explains the things you should know when writing programs with this SDK for your custom plugins.
Note that this document assumes that you are using Envoyproxy, and relies on its implementation detail. Therefore some statement may not apply to other network proxies such as mosn.
This SDK relies on the TinyGo, a compiler implementation of Go programming language specification. So first of all, we answer the question "Why not the official Go?".
There are several reasons why we cannot use the official Go compiler. Tl;dr is that as of this writing, the official compiler cannot produce Wasm binary which can run outside web browsers, and therefore cannot produce Proxy-Wasm compatible binaries.
For those who are insterested in the detail, please refer to the related issues in the Go repository:
Wasm virtual machine (Wasm VM) or simply VM means instances of loaded programs. In Envoy, VMs are usually created in each thread and isolated from each other. Therefore, your program will be copied up to the number of threads created by Envoy, and loaded on each of these virtual machines.
Plugin means the basic unit of your configuration for extending your network proxies. Proxy-Wasm specification allows for having multiple plugins in a single VM. In other words, a VM may be used by multiple plugins of network proxies. With this SDK, there are three types of plugins that you can configure in Envoy; Http Filter, Network(Tcp) Filter, and Wasm Service. Given that, you can write programs which can be run as, for example, Network Filter and Http Filter at the same time.
Http Filter is a type of plugins to handle Http protocol such as operating on Http request headers, body, trailers, etc. It uses a VM in worker threads which handle traffic.
Network Filter is a type of plugins to handle Tcp protocol such as operating on Tcp data frame, connection establishment, etc. It uses a VM in worker threads which handle traffic.
Wasm Service is a type of plugins running in a singleton VM (i.e. only one instance exists in the Envoy main thread). It is mainly used for doing some extra work in parallel to Network or Http Filters such as aggregating metrics, logs, etc. Sometimes, such a singleton VM itself is also called Wasm Service.
Among all the types of plugins, we share the configuration for Envoy something like
vm_config:
vm_id: "foo"
runtime: "envoy.wasm.runtime.v8"
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: '{"my-vm-env": "dev"}'
code:
local:
filename: "example.wasm"
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: '{"my-plugin-config": "bar"}'where
| Field | Description |
|---|---|
vm_config |
configures specific Wasm VM on which this plugin runs |
vm_config.vm_id |
used for semantic isolation towards Cross-VM communications. Please refer to Cross-VM communications section for detail. |
vm_config.runtime |
specifies the Wasm runtime type. Usually set to envoy.wasm.runtime.v8. |
vm_config.configuration |
arbitray configuration data used for setting up the VM. |
vm_config.code |
location of a Wasm binary |
configuration |
corresponds to each Plugin instance (which we call PluginContext explained below) inside the Wasm VM. |
The important thing is that giving exactly the same vm_config field for multiple plugins ends up sharing one Wasm VM among them.
That means you can use a single Wasm VM for multiple Http filters, or maybe Http and Tcp filters per thread (See example config for detail). This is useful in terms of memory/cpu resource efficiency, startup latency, etc.
The full API definition is here and this is what we call plugin config here and elsewhere.
Now here's some example configurations in Envoy for each plugin type. Note that how your Wasm VM is created by Envoy depends on these types.
If a plugin config is given at envoy.filter.http.wasm, you can run your program as Http Filter plugin so that it can operate on Http events.
http_filters:
- name: envoy.filters.http.wasm
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.http.wasm.v3.Wasm
config:
vm_config: { ... }
# ... plugin config follows
- name: envoy.filters.http.routerIn this case, Wasm VMs are created on each worker thread in Envoy, and each VM is responsible for operating on each Http streams on a listener handled by a corresponding worker thread. Note that the way VMs and Plugins get created is exactly the same as Network Filter, and the only difference is that Plugins only operate on Http streams rather than Tcp streams.
See example.yaml for a full example.
If a plugin config is given at envoy.filter.network.wasm, you can run your program as Network Filter plugin so that it can operate on Tcp events.
filter_chains:
- filters:
- name: envoy.filters.network.wasm
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.network.wasm.v3.Wasm
config:
vm_config: { ... }
# ... plugin config follows
- name: envoy.tcp_proxyIn this case, Wasm VMs are created on each worker thread in Envoy, and each VM is responsible for operating on each Tcp streams on a listener handled by a corresponding worker thread. Note that the way VMs and Plugins get created is exactly the same as Http Filter, and the only difference is that Plugins only operate on Tcp streams rather than Http streams.
See example.yaml for a full example.
If a plugin config is given at envoy.bootstrap.wasm, you can run your program as Wasm Service plugin.
bootstrap_extensions:
- name: envoy.bootstrap.wasm
typed_config:
"@type": type.googleapis.com/envoy.extensions.wasm.v3.WasmService
singleton: true
config:
vm_config: { ... }
# ... plugin config followsThe top singleton field is normally set true. In this way, only one VM for this configuration exists across all the threads of Envoy process, and runs on the Envoy's main thread so that it won't block any worker threads.
See example.yaml for a full example.
As we explained, we can share one VM across multiple plugins. Here's an example yaml for such configuration:
static_resources:
listeners:
- name: http-header-operation
address:
socket_address:
address: 0.0.0.0
port_value: 18000
filter_chains:
- filters:
- name: envoy.http_connection_manager
typed_config:
# ....
http_filters:
- name: envoy.filters.http.wasm
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.http.wasm.v3.Wasm
config:
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: "http-header-operation"
vm_config:
vm_id: "my-vm-id"
runtime: "envoy.wasm.runtime.v8"
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: "my-vm-configuration"
code:
local:
filename: "all-in-one.wasm"
- name: envoy.filters.http.router
- name: http-body-operation
address:
socket_address:
address: 0.0.0.0
port_value: 18001
filter_chains:
- filters:
- name: envoy.http_connection_manager
typed_config:
# ....
http_filters:
- name: envoy.filters.http.wasm
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.http.wasm.v3.Wasm
config:
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: "http-body-operation"
vm_config:
vm_id: "my-vm-id"
runtime: "envoy.wasm.runtime.v8"
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: "my-vm-configuration"
code:
local:
filename: "all-in-one.wasm"
- name: envoy.filters.http.router
- name: tcp-total-data-size-counter
address:
socket_address:
address: 0.0.0.0
port_value: 18002
filter_chains:
- filters:
- name: envoy.filters.network.wasm
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.network.wasm.v3.Wasm
config:
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: "tcp-total-data-size-counter"
vm_config:
vm_id: "my-vm-id"
runtime: "envoy.wasm.runtime.v8"
configuration:
"@type": type.googleapis.com/google.protobuf.StringValue
value: "my-vm-configuration"
code:
local:
filename: "all-in-one.wasm"
- name: envoy.tcp_proxy
typed_config: # ...You see that vm_config fields are all the same on Http filter chains on 18000 and 18001 listeners plus a Network filter chain on 18002. That means one Wasm VM is used by multiple plugins in Envoy per worker thread in this case. In other words, all of vm_config.vm_id, vm_config.runtime vm_config.configuration, and vm_config.code must be same in order to reuse the same VMs.
As a result, Three PluginContext will be created per Wasm VM and each of them corresponds to each of the above filter configurations (the top configuration fields at 18000, 18001, and 18002 respectively).
See example.yaml for a full example.
So far we have explaind the concepts and plugin configs. Now we are ready to dive into the API of this SDK.
Contexts are collection of interfaces in Proxy-Wasm Go SDK, and all of them are mapped to the concepts explained above. They are defined in types package, and developers are supposed to implement these interfaces in order to extend network proxies.
There are four types of contexts: VMContext, PluginContext, TcpContext and HttpContext. Their relationship and how they are mapped to the concepts above can be described as the following diagram:
Wasm Virtual Machine
(.vm_config.code)
┌────────────────────────────────────────────────────────────────┐
│ Your program (.vm_config.code) TcpContext │
│ │ ╱ (Tcp stream) │
│ │ 1: 1 ╱ │
│ │ 1: N ╱ 1: N │
│ VMContext ────────── PluginContext │
│ (Plugin) ╲ 1: N │
│ ╲ │
│ ╲ HttpContext │
│ (Http stream) │
└────────────────────────────────────────────────────────────────┘
To summarize,
VMContextcorresponds to each.vm_config.code, and only oneVMContextexists in each VM.VMContextis the parent of PluginContexts, and is responsible for creating arbitrary number ofPluginContexts.PluginContextcorresponds to a Plugin instance. That means, aPluginContextcorresponds to a Http Filter or Network Filter or maybe Wasm Service, configured via.configurationfield in the plugin config.PluginContextis the parent ofTcpContextandHttpContext, and is responsible for creating arbitrary number of these contexts when it is configured at Http Filter or Network Filter.TcpContextis responsible for handling each Tcp stream.HttpContextis responsible for handling each Http stream.
So all you have to do is implement VMContext and PluginContext. And if you want to plug in to Http Filter or Network Filter, then implement HttpContext or TcpContext respectively.
Let's look at some of these interfaces. First we see VMContext is defined as follows:
type VMContext interface {
// OnVMStart is called after the VM is created and main function is called.
// During this call, GetVMConfiguration hostcall is available and can be used to
// retrieve the configuration set at vm_config.configuration.
// This is mainly used for doing Wasm VM-wise initialization.
OnVMStart(vmConfigurationSize int) OnVMStartStatus
// NewPluginContext is used for creating PluginContext for each plugin configurations.
NewPluginContext(contextID uint32) PluginContext
}As you expect, VMContext is responsible for creating PluginContext via NewPluginContext method. In addition, OnVMStart is called at the startup phase of the VM, and you can retrieve the value of .vm_config.configuration via GetVMConfiguration hostcall API. This way you can do the VM-wise plugin-independent initialization and control the behavior of VMContext.
Next is PluginContext and it it defined as (here we omit some of the methods for simplicity)
type PluginContext interface {
// OnPluginStart is called on all plugin contexts (after OnVmStart if this is the VM context).
// During this call, GetPluginConfiguration is available and can be used to
// retrieve the configuration set at config.configuration in envoy.yaml
OnPluginStart(pluginConfigurationSize int) OnPluginStartStatus
// The following functions are used for creating contexts on streams,
// and developers *must* implement either of them corresponding to
// extension points. For example, if you configure this plugin context is running
// at Http filters, then NewHttpContext must be implemented. Same goes for
// Tcp filters.
//
// NewTcpContext is used for creating TcpContext for each Tcp streams.
NewTcpContext(contextID uint32) TcpContext
// NewHttpContext is used for creating HttpContext for each Http streams.
NewHttpContext(contextID uint32) HttpContext
}Just like VMContext, PluginContext has OnPluginStart method which is called on the plugin creation in network proxies. During that call, the top level .configuratin field's value in the plugin config can be retrieved via GetPluginConfiguration hostcall API. This way developers can inform a PluginContext how it should behave, for example, specifying a PluginContext should behave as a Http Filter and which custom headers it should insert as a request headers, etc.
Also note that PluginContext has NewTcpContext and NewHttpContext methods which are called when creating these contexts in response to each Http or Tcp streams in network proxies.
The definition of HttpContext and TcpContext is fairly straightforward so please refer to context.go for detail.
Hostcall API provides a variety of ways to interact with network proxies from your program, and it is defined at hostcall.go in proxywasm package. For example, GetHttpRequestHeaders API can be used for accessing Http request headers by HttpContext. The other example is that LogInfo API and it can be used for emitting strings as logs in Envoy.
Please refer to hostcall.go for all the available hostcalls, and the documentation is given at the function definitions.
When Envoy creates VMs, it calls main function of your program at startup phase before it tries to create VMContext inside VMs. Therfore you must pass your own implementation of VMContext in main function.
proxywasm package's SetVMContext function is the entrypoint used for that purpose. That being said, your main function should look like the following:
func main() {
proxywasm.SetVMContext(&myVMContext{})
}
type myVMContext struct { .... }
var _ types.VMContext = &myVMContext{}
// Implementations follow...Given that VMs are created in the thread-local way, sometimes we may want to communicate with other VMs. For example, aggregating data or stats, caching data, etc.
There are two concepts for Cross-VM communications called Shared Data and Shared Queue.
We also recommend you watch this talk for introduction.
What if you want to have global request counters across all the Wasm VMs running in multiple worker threads? Or what if you want to cache some data that should be used by all of your Wasm VMs? Then Shared Data or equivalently Shared KVS will come into play.
Shared Data is basically a key-value store that is shared across all the VMs (i.e. cross-VM or cross-threads). A shared-data KVS is created per vm_id specified in the vm_config. That means you can share a key-value store across all Wasm VMs not necessarily with the same binary (vm_config.code). The only requirement is having the same vm_id.
In the diagram above, you see that VMs with "vm_id=foo" share the same shared data storage even though they have different binary (hello.wasm and bye.wasm).
Here's the shared data related API of this Go SDK in hostcall.go:
// GetSharedData is used for retrieving the value for given "key".
// Returned "cas" is be used for SetSharedData on that key for
// thread-safe updates.
func GetSharedData(key string) (value []byte, cas uint32, err error)
// SetSharedData is used for setting key-value pairs in the shared data storage
// which is defined per "vm_config.vm_id" in the hosts.
//
// ErrorStatusCasMismatch will be returned when a given CAS value is mismatched
// with the current value. That indicates that other Wasm VMs has already succeeded
// to set a value on the same key and the current CAS for the key is incremented.
// Having retry logic in the face of this error is recommended.
//
// Setting cas = 0 will never return ErrorStatusCasMismatch and always succeed, but
// it is not thread-safe, i.e. maybe another VM has already set the value
// and the value you see is already different from the one stored by the time
// when you call this function.
func SetSharedData(key string, value []byte, cas uint32) errorThe API is straightforward, but the important part is its thread-safety and cross-VM-safety with "cas" or Compare-And-Swap value.
Please refer to an example for demonstration.
What if you want to aggregate metrics across all the Wasm VMs in parallel to request/response processing? Or what if you want to push some cross-VM aggregated information to a remote server? Now Shared Queue is here for you.
Shared Queue is a FIFO(First-In-First-Out) queue created for a pair of vm_id and the name of the queue. And a queue id is assigned uniquely to the pair (vm_id, name) which is used for enqueue/dequeue operations.
As you expect, the operations such as "enqueue" and "dequeue" have thread-safety and cross-VM-safety. Let's look at the Shared Queue related API in hostcall.go:
// DequeueSharedQueue dequeues an data from the shared queue of the given queueID.
// In order to get queue id for a target queue, use "ResolveSharedQueue" first.
func DequeueSharedQueue(queueID uint32) ([]byte, error)
// RegisterSharedQueue registers the shared queue on this plugin context.
// "Register" means that OnQueueReady is called for this plugin context whenever a new item is enqueued on that queueID.
// Only available for types.PluginContext. The returned ququeID can be used for Enqueue/DequeueSharedQueue.
// Note that "name" must be unique across all Wasm VMs which share a same "vm_id".
// That means you can use "vm_id" can be used for separating shared queue namespace.
//
// Only after RegisterSharedQueue is called, ResolveSharedQueue("this vm_id", "name") succeeds
// to retrive queueID by other VMs.
func RegisterSharedQueue(name string) (ququeID uint32, err error)
// EnqueueSharedQueue enqueues an data to the shared queue of the given queueID.
// In order to get queue id for a target queue, use "ResolveSharedQueue" first.
func EnqueueSharedQueue(queueID uint32, data []byte) error
// ResolveSharedQueue acquires the queueID for the given vm_id and queue name.
// The returned ququeID can be used for Enqueue/DequeueSharedQueue.
func ResolveSharedQueue(vmID, queueName string) (ququeID uint32, err error)Basically RegisterSharedQueue and DequeueSharedQueue are used by "consumer" of the queue
while ResolveSharedQueue and EnqueueSharedQueue are for "producer" of queue items. Note that
RegisterSharedQueueis used for creating a shared queue fornameandvm_idof the caller. That means if you want to use a queue, then this must be called by a VM beforehand. This can be called byPluginContext, and therefore we can think of "comsumers" =PluginContexts.ResolveSharedQueueis used for getting the queue id fornameandvm_id. Usually this is used by VMs that doesn't callResolveSharedQueuebut rather are supposed to enqueue items. This is for "producer".
and both of these calls return a queue id, and it is used for DequeueSharedQueue and EnqueueSharedQueue.
However, from the consumer's point of view, how can a consumer (= PluginContext) be notified when a queue is enqueued with an item? This is why we have the OnQueueReady(queueID uint32) interface in PluginContext. This method is called whenever an item is enqueued in a queue registered by that PluginContext.
Also it is highly recommended that shared queues should be created by a singleton Wasm Service, i.e. on the Envoy's main thread. Otherwise OnQueueReady is called on worker threads which blocks their processing of Http or Tcp streams.
The following diagram is an illustrative usage of shared queues:
my-singleton.wasm is loaded as a singleton VM with vm_id=foo where two Wasm Service are created which correspond to PluginContext 1 and PluginContext 2 in the VM. Each of these plugin contexts calls RegisterQueue with "http" and "tcp" names, and that results in creating two corresponding queues. On the other hand, in the worker threads, two types of Wasm VMs are created per thread. They are processing Tcp streams and Http streams, and enqueue some data into the corresponding queues respectively. As we explained above, enqueueing data into a queue ends up calling the OnQueueReady method of a PluginContext which called RegisterQueue for that queue. In this example, enqueueing data into the queue with queue id=2 invokes OnQueueReady(2) of PluginContext 2 in the singleton VM.
Please refer to an example for demonstration.
This SDK contains the testing framework for unit testing Proxy-Wasm programs without actually running network proxies and with the official Go test toolchain. proxytest package implements the Envoy proxy emulator. That is, you can run tests just like you do when writing native programs:
go test ./...
Please refer to main_test.go files under examples directory for demonstrations.
Here's what users should know when writing plugins with Proxy-Wasm Go SDK and Proxy-Wasm in general.
Some of existing libraries are not available (importable but runtime panic / non-importable). There are several reasons for this:
- TinyGo's WASI target does not support some of syscall.
- TinyGo does not implement all of reflect package.
- Proxy-Wasm C++ host has not supported some of WASI APIs yet
- Some language features are not available in TinyGo or Proxy-Wasm: examples include
recoverandgoroutine.
These issues will be mitigated as TinyGo and Proxy-Wasm evolve.
There's performance overhead of using Go/TinyGo due to GC, although, optimistically speaking, we could say that the overhead of GC is small enough compared to the other operations in the proxy.
Internally, runtime.GC is called whenever the heap runs out (see
1,
2) in TinyGo.
TinyGo allows us to disable GC, but we cannot do that since internally we need to use maps (implicitly causes allocation) for saving the Virtual Machine's state. Theoretically, we can implement our own GC algorithms tailored for proxy-wasm through alloc(uintptr) interface with -gc=none option. This is a future TODO.
recover is not implemented (tinygo-org/tinygo#891) in TinyGo, and there's no way to prevent the Wasm virtual machine from aborting. Also that means that codes rely on recover won't work as expected.
In TinyGo, Goroutine is implemented through LLVM's coroutine (see this blog post).
In Envoy, Wasm modules are run in the event driven manner, and therefore the "scheduler" is not executed once the main function exits. That means you cannot have the expected behavior of Goroutine as in ordinary host environments.
The question "How to deal with Goroutine in a thread local Wasm VM executed in the event drive manner" has yet to be answered.
We strongly recommend that you implement the OnTick function for any asynchronous task instead of using Goroutine.