28 Sep 2016, 13:15

gRPC wrong types context and Go 1.7

If you are following Go development you probably know that:
Go 1.7 moves the golang.org/x/net/context package into the standard library as context, Yeah !
Unfortunately it won’t work for everything, I’ve spent some time understanding this one.

For example if you are using gRPC you can hit this problem, here is an interface generated by gRPC:

type APRSServer interface {
    GetPastMessages(context.Context, *Point) (*ARPSMessages, error)
}

But when compiling:

/main.go:153: cannot use &s (type *Server) as type protorpc.APRSServer in argument to protorpc.RegisterAPRSServer:                                                                           
        *Server does not implement protorpc.APRSServer (wrong type for GetPastMessages method)                                                                                                
                have GetPastMessages("context".Context, *protorpc.Point) (*protorpc.ARPSMessages, error)                                                                                      
                want GetPastMessages("golang.org/x/net/context".Context, *protorpc.Point) (*protorpc.ARPSMessages, error)   

The compiler is complaining about wrong types for the context argument.
Problem is gRPC generated code is importing context as golang.org/x/net/context, that’s the only way it remains compatible between Go 1.6 & Go 1.7.

So a quick solution is to import context in your own code to use the old path:

// we have to use the old context path here for gRPC compat see https://github.com/grpc/grpc-go/issues/711
context "golang.org/x/net/context"

Also note that go tool fix is now capable of fixing the import path with -force context.

22 Sep 2016, 15:39

Using Go mobile on iOS for real

Go Mobile can generate native framework for iOS and Android using Go code, I was curious what could be achieved with it.
Most tutorials are Hello world and I wanted to test it with real code.
You can use it to generate a full app only using Go code, but I’m only interested by the bindings part (SDK applications), using a native ObjC/Swift app calling Go code.

I’m using some existing Go code regionagogo, (a geofence database), moderately complex since it uses BoltDB and Google S2 library.

Go Mobile is limited to a subset of types you can use, main reason is to be correctly transcribed to Java and ObjC.

So the first thing to do is to write some Go wrapper like you would do in ObjC to call some C++ code but first:

Functions must return either no results, one result, or two results where the type of the second is the built-in error type.
So far no major complication, but also note that you can’t return slices, that could be a major pain, there are some workaround solutions like go-mobile-collection that can generate an API to operate on slice.
You also have to respect some naming for your constructor like New...().

At the end your wrapper won’t look very Goish but it’s what it takes for it to be translated.

Knowing those constraints you can write a simple wrapper like this:

package mobile

type GeoDB struct {
	db regionagogo.GeoFenceDB
}

func NewGeoDB() *GeoDB {
	g := &GeoDB{}
	return g
}

func (g *GeoDB) OpenDB(path string) error {
	db, err := rbolt.NewGeoFenceBoltDB(path)
	if err != nil {
		return err
	}
	g.db = db
	return nil
}

...

Then use the gomobile command:

gomobile bind -target=ios github.com/akhenakh/regionagogo/mobile

It generates a native iOS Mobile.Framework, drop it into your XCode project and start using it.

  • GeoDB translates to GoMobileNewGeoDB() and returns a GoMobileGeoDB* in ObjC domain.
  • OpenDB(path string) error to - (BOOL)openDB:(NSString*)path error:(NSError**)error.

A simple example would be:

GoMobileGeoDB *db = GoMobileNewGeoDB();
NSString *resourcePath = [[NSBundle mainBundle] pathForResource:@"region" ofType:@"db"];
        
NSError *error;
[db openDB:resourcePath error:&error];
if (error != nil) {
    NSLog(@"error opening db %@", [error localizedDescription]);
    return;
}

It’s performing very well, on my iPhone 6, around 4,000 queries per second to test a position in a small fence, to 60,000 queries in a hit miss (calling Go overhead is not that big), while using a ridiculously small amount of memory.

I’ve made a demo iOS app where you can hit the map and it tells you in which fence you are.

app
Remember this is running locally on your phone without any network access (but the map), the geo computation and the Polygons are returned by Go code.

Until now I had to maintain libraries in both languages, Go mobile is a nice alternative!

Sources for the wrapper are available in regionagogo gomobile branch, and here is the iOS demo app.

20 Sep 2016, 17:40

gRPC Envoy Nghttp2 and Load Balancing

I’ve been using gRPC at work and in several personal projects for months and happy with it, but when it comes to load balancing gRPC does not come with batteries included.

For a long time the only document was the Load Balancing draft in the gRPC repo, the clients should implement a Picker interface to know about the servers, so the pooling and controling the load were handled by the clients.
HTTP/2 was new and most of the reverse proxies implementations were not capable of load balancing gRPC HTTP2 frames, the only solution was to use a TCP load balancer, generating errors, improper and weird behaviours for the clients.

At least two projects are now supporting gRPC load balancing easily.

  • The recently announced Envoy from Lyft
  • And nghttpx from nghttp2

Here are some notes to simply load balance two gRPC Helloworld server! running on ports 50050 & 50051.

  • For nghttp2, a simple configuration file will do

    frontend=*,8000;no-tls
    backend=localhost,50050;;no-tls;proto=h2;fall=2;rise=2
    backend=localhost,50051;;no-tls;proto=h2;fall=2;rise=2
    workers=8
    
  • For envoy, here is the cluster part

    "clusters": [
      {
        "name": "local_service",
        "connect_timeout_ms": 250,
        "type": "static",
        "lb_type": "least_request",
        "features": "http2",
        "hosts": [
          {
            "url": "tcp://127.0.0.1:50050"
          },
          {
            "url": "tcp://127.0.0.1:50051"
          }
        ]
      }
    ]
    

You can then tweak the greeter_client to loop for requests, so you can simulate a client doing multiple requests while killing/restarting your servers.

for {  
    r, err := c.SayHello(context.Background(), &pb.HelloRequest{Name: name}) 
    if err != nil { 
        log.Printf("could not greet: %v", err)
    }
    log.Printf("Greeting: %s", r.Message)
}

And modify the greeter_server to show on which port/server you get your response:

// SayHello implements helloworld.GreeterServer
func (s *server) SayHello(ctx context.Context, in *pb.HelloRequest) (*pb.HelloReply, error) {
    return &pb.HelloReply{Message: "Hello " + in.Name + port}, nil 
}

Those tests aren’t enabling any TLS so use grpc.WithInsecure().

Note that Envoy is also capable of bridging your HTTP/1.1 queries to gRPC, which is a killer feature (I haven’t tested it yet) , you would normally do it by code with gRPC-gateway.

Envoy is really new and I’m still digging into but already proves itself to be a complete load balancing proxy solution with or without gRPC in your stack.

22 Jul 2016, 19:11

Streaming using a Raspberry Pi Camera to Twitch in Full HD while injecting audio from rtl sdr

I have found the right setup to stream in 1080p from a Raspberry Pi using the camera to TwitchTV while injecting audio on the fly!

Create an account on Twitch and grab you stream key in the Dashboard.

This stream.sh script will create a FIFO start rtl_fm at freq 162.550M to listen to Canada weather bulletin (use your local NOAA channel) and inject and encode the audio to the existing h264 stream from the camera then stream it to twitch using rtmp.

#! /bin/bash
STREAM_URL="rtmp://live.justin.tv/app/"
KEY="live_XXXXX_XXXXXXXXXXXXXX" # put your key here

mkfifo /tmp/streamaudio.wav
rtl_fm -f 162.550M -s 22050 -g 30 | sox -r 22050 -t raw -e signed -b 16 -c 1 -V1 - -r 22050 -t wavpcm  -  > /tmp/streamaudio.wav &

/opt/vc/bin/raspivid -n -vf -t 0 -w 1920 -h 1080 -fps 25 -o - | ffmpeg -re -i - -i /tmp/streamaudio.wav -codec copy -strict experimental -acodec libmp3lame -ar 22050 -threads 8 -f flv "$STREAM_URL/$KEY"

You can of course send other audio sources or no audio at all.

This setup is taking around 10% cpu on a Raspberry Pi 2.

If you are lucky enough my channel will be up and running.

What is RTL SDR ?

Using a 20$ USB dongle you have a software radio scranner, capable of listening to radio amateurs, NOAA weather bulletin even satellites.

Note that you can also stream from your laptop using the great and free ObsProject.

Happy streaming.

03 Jul 2016, 08:15

Enabling Gometalinter with Jetbrains Editors

I’ve been using Jetbrains editor (the free Idea community edition) or Pycharm with the Go plugins and very happy with this setup, the editor is providing some realtime linting but I was missing gometalinter.

First install gometalinter

go get -u github.com/alecthomas/gometalinter
gometalinter --install --update

To add support inside Jetbrains editors use the External tools feature.
In Preferences > Tools > External Tools, add a configuration.

gometalinter setup for jetbrains

Set the Program path to your $GOPATH/bin/gometalinter
Parameters to --deadline=15s --vendor $FileDir$
Working directory to $ProjectFileDir$

For the linters messages to be clickable and jump to code add an Output filter with Regular expression to match output $FILE_PATH$:$LINE$

gometalinter setup filter for jetbrains

08 Mar 2016, 11:23

A geo database for polygons, optimization

If you read this blog, you know I’ve recently released a project called regionagogo, a geo shape lookup database, described in this blogpost.

It uses the current Go S2 implementation, which is not yet as complete as the C++ implementation, for example the region coverer of a shape does not really compute cell around the shape but around the bounding box instead.

Using the shape of the polygon makes the covered cells more precise and smaller, resulting at the end to less PIP tests which are costly.

S2 over a shape
The same coverage with Go S2 would have returned 8 big cells of the same size, covering over regions.

I’ve created regionagogogen a quick and dirty command line program for OSX that takes a GeoJSON file containing your regions and then compute the database using the S2 C++ implementation, it’s for OSX only cause I’m using ObjC as a bridge to C++ which I don’t know enough.

Also note that regionagogogen includes an S2 port that works on iOS/OSX and some ObjC geo helper classes.

The Docker image fore regionagogo is also using the optimized database.

18 Feb 2016, 17:16

A geo database for polygons, foundations

On a previous post, I’ve described how to use the S2 geo library to create a fast geo database, but it was to store locations (points) and only to perform range queries, a complete geo database would have regions/polygons queries.

Looking for a solution

I had this need: querying for the countries or subregions of hundreds of coordinates per second, without relying on an external service.

One solution, using my previous technique, could have been to store every cities in the world and then perform a proximity query around my point to get the closest cities, but it works only in populated area and it’s only an approximation.

I looked into others solutions, there is some smart ideas using UTF-grid, but it’s a client side solution and also an approximation tied to the resolution of the computed grid.

S2 to the rescue

S2 cells have some nice properties, they are segments on the Hilbert curve, expressed as range of uint64, so I had the intuition the problem to perform fast region lookup could be simplified as find all mathematical segments containing my location expressed as an uint64.

S2 over a country

Using a Segment Tree datastructure, I first tried an in memory engine, using Natural Earth Data, loading the whole world countries shapes into S2 loops (a Loop represents a simple spherical polygon), transforming then into cells using the region coverer, it returns cells of different levels, add them to the segment tree.

Segment Tree

To query, simply tranform the location into an S2 Cell (level 30) and perform a stubbing query that intersects the segments, every segments crossed are cells that covered a part of a Loop.
It will reduce the problem to test a few Loop vs thousands of them, finally perform ContainsPoint against the found loops cause the point could be inside the Cell but not inside the Loop itself.

Et voilà! It works!

The segment tree structure itself is very low on memory, the loops/polygons data could be stored on disk and loaded on requests, I’ve tested a second implementation using LevelDB using this technique.

If you have a very large tree (for example cities limits for the whole world), you can even put the segment tree on a KV storage, using this paper Interval Indexing and Querying on Key-Value Cloud Stores.

Region a gogo

As a demonstration here is a working microservice called regionagogo, simply returning the country & state for a given location.
It loads geo data for the whole world and answers to HTTP queries using small amout of memory.

GET /country?lat=19.542915&lng=-155.665857

{
    "code": "US",
    "name": "Hawaii"
}

Here is a Docker image so you can deploy it on your stack.

Note that it performs really well but can be improved a lot, for example the actual Go S2 implementation is still using Rect boxing around loops, that’s why regionagogo is using a data file so it can be generated from the C++ version.

Future

This technique seems to work well for stubbing queries, region queries, geofencing …
It can be a solid foundation to create a flexible and simple geo database.

26 Jan 2016, 14:01

A fast geo database with Google S2 take #2

Six months ago, I wrote on this blog about Geohashes and LevelDB with Go, to create a fast geo database.
This post is very similar as it works the same way but replacing GeoHashes with Google S2 library for better performances.

There is an S2 Golang implementation maintened by Google not as complete as the C++ one but close.

For the storage this post will stay agnostic to avoid any troll, but it applies to any Key Value storages: LevelDB/RocksDB, LMDB, Redis…
I personnaly use BoltDB and gtreap for my experimentations.

This post will focus on Go usage but can be applied to any languages.

Or skip to the images below for visual explanations.

Why not Geohash?

Geohash is a great solution to perform geo coordinates queries but the way it works can sometimes be an issue with your data.

  • Remember geohashes are cells of 12 different widths from 5000km to 3.7cm, when you perform a lookup around a position, if your position is close to a cell’s edge you could miss some points from the adjacent cell, that’s why you have to query for the 8 neightbour cells, it means 9 range queries into your DB to find all the closest points from your location.

  • If your lookup does not fit in level 4 39km by 19.5km, the next level is 156km by 156km!

  • The query is not performed around your coordinates, you search for the cell you are in then you query for the adjacent cells at the same level/radius, based on your needs, it means it works very approximately and you can only perform ‘circles’ lookup around the cell you are in.

  • The most precise geohash needs 12 bytes storage.

  • -90 +90 and +180 -180, -0 +0 are not sides by sides prefixes.

Why S2?

S2 cells have a level ranging from 30 ~0.7cm² to 0 ~85,000,000km².
S2 cells are encoded on an uint64, easy to store.

The main advantage is the region coverer algorithm, give it a region and the maximum number of cells you want, S2 will return some cells at different levels that cover the region you asked for, remember one cell corresponds to a range lookup you’ll have to perform in your database.

The coverage is more accurate it means less read from the DB, less objects unmarshalling…

Real world study

We want to query for objects inside Paris city limits using a rectangle:

h5
Using level 5 we can’t fit the left part of the city.
We could add 3 cells (12 total DB queries ) on the left but most algorithms will zoom out to level 4.

h4
But now we are querying for the whole region.

s2
Using s2 asking for 9 cells using a rectangle around the city limits.

s2 vs h4
The zones queried by Geohash in pink and S2 in green.

Example S2 storage

Let’s say we want to store every cities in the world and perform a lookup to find the closest cities around, first we need to compute the CellId for each cities.

// Compute the CellID for lat, lng
c := s2.CellIDFromLatLng(s2.LatLngFromDegrees(lat, lng))

// store the uint64 value of c to its bigendian binary form
key := make([]byte, 8)
binary.BigEndian.PutUint64(key, uint64(c))

Big endian is needed to order bytes lexicographically, so we can seek later from one cell to the next closest cell on the Hilbert curve.

c is a CellID to the level 30.

Now we can store key as the key and a value (a string or msgpack/protobuf) for our city, in the database.

Example S2 lookup

For the lookup we use the opposite procedure, first looking for one CellID.

// citiesInCellID looks for cities inside c
func citiesInCellID(c s2.CellID) {
  // compute min & max limits for c
  bmin := make([]byte, 8)
  bmax := make([]byte, 8)
  binary.BigEndian.PutUint64(bmin, uint64(c.RangeMin()))
  binary.BigEndian.PutUint64(bmax, uint64(c.RangeMax()))

  // perform a range lookup in the DB from bmin key to bmax key, cur is our DB cursor
  var cell s2.CellID
  for k, v := cur.Seek(bmin); k != nil && bytes.Compare(k, bmax) <= 0; k, v = cur.Next() {
    buf := bytes.NewReader(k)
    binary.Read(buf, binary.BigEndian, &cell)

    // Read back a city
    ll := cell.LatLng()
    lat := float64(ll.Lat.Degrees())
    lng := float64(ll.Lng.Degrees())
    name = string(v)
    fmt.Println(lat, lng, name)
  }
}

Then compute the CellIDs for the region we want to cover.

rect := s2.RectFromLatLng(s2.LatLngFromDegrees(48.99, 1.852))
rect = rect.AddPoint(s2.LatLngFromDegrees(48.68, 2.75))

rc := &s2.RegionCoverer{MaxLevel: 20, MaxCells: 8}
r := s2.Region(rect.CapBound())
covering := rc.Covering(r)

for _, c := range covering {
    citiesInCellID(c)
}

RegionCoverer will return at most 8 cells (in this case 7 cells: 4 8, 1 7, 1 9, 1 10) that is guaranteed to cover the given region, it means we may have to exclude cities that were NOT in our rect, with func (Rect) ContainsLatLng(LatLng).

Congrats we have a working geo db.

S2 can do more with complex shapes like Polygons and includes a lot of tools to compute distances, areas, intersections between shapes…

Here is a Github repo for the data & scripts generated for the images

11 Jan 2016, 09:33

MyAPRS APRS for iPhone

I’m happy to announce a new side project: MyAPRS, a modern iOS APRS application, for radio amateur enthusiasts.

I’ve already mentioned APRS on this blog, it will mainly be useful for radio amateurs but can be interesting to RTL-SDR listeners too.

The application is built around LevelDB and geo hashing as mentioned in this post blog, it’s a lot faster than using SQLite especially on iOS, SatSat is still using the SQLite cities lookup and you can compare it’s terribly slow.
It receives weather data & APRS data from APRS-IS connections, then decodes and stores the packets into LevelDB for indexation.

MyAPRS has some extra features over other applications, like transceiver models detection, so it can highlight all C4FM/Fusion users in your area.
It receives the latest 10mn packets for your area, helped by a small Golang API.

The first release is simple but works very well to discover repeaters and amateurs around you or a region you plan to visit, I’ll add position sharing later: due to the way APRS-IS works it’s the responsability of the developer to provide passwords & check for the amateur license of the users (even if the algorithm is known publicly…).

I plan to make it work with some bluetooth modems starting with the one I’ve worked on to be used completely off the grid, so we can have a cheap and powerful APRS application, with a real keyboard (try to answer a message with a Yaesu…).

I had to put some ads in it, a lot of people have asked for a paid SatSat version to get rid of the ads but that’s the only way I can maintain these apps, so I’m experimenting with ads for now.

It was a fun project to develop for, I hope you will find a use for it.

73 de KK6NXK

MyAPRS

10 Jan 2016, 11:32

Freebsd on Raspberry Pi 2 and Golang

FreeBSD is now fully supported on the Raspberry Pi2, makes it a fun small computer to experiment with BSD.

If you have a Raspberry Pi 1, you can simply install 10.2-RELEASE image.

For Raspberry Pi 2, you need 11.0-CURRENT which is the development branch, images can be found here.

dd the image as usual to a SD card, it will be auto resized at first boot. (See growfs_enable="YES") in rc.conf.

CPU frequency

To enable on demand cpu overclocking (ranging from 600 to 1000MHz), enable powerd by adding this to rc.conf.

powerd_enable="YES"
powerd_flags="-a hadp"

Production speed

FreeBSD CURRENT is the development version, some debugging tools may slow down your system.

As stated in UPDATING if you are running CURRENT:

ln -s 'abort:false,junk:false' /etc/malloc.conf

Wifi

Depending on your wifi dongle this may be different, for RealTek devices add an entry to /etc/wpa_supplicant.conf:

network={
    ssid="myssid"
    psk="mypass"
}

And this to /etc/rc.conf:

wlans_urtwn0="wlan0"
ifconfig_wlan0="WPA SYNCDHCP"

And this to /boot/loader.conf

legal.realtek.license_ack=1

And type service netif restart.

Installing the ports

The ports are a long list of third parties software you can install on your system, first synchronize the ports tree:

portsnap fetch
portsnap extract

It’s highly recommend you install portmaster to keep your ports updated:

cd /usr/ports/ports-mgmt/portmaster/
make install clean

To later update your ports tree and ports:

portsnap fetch update
portmaster -a

To compile and install a port simply go to its directory and run make install clean.

Keeping the sources updated (optional)

All the FreeBSD sources are available and can be used to recompile the whole system.

Subversion needs some space in /tmp to complete this task, edit /etc/fstab to grow tmpfs to at least 70M the reboot:

tmpfs /tmp tmpfs rw,mode=1777,size=70m 0 0
cd /usr/ports/security/ca_root_nss
make install clean
svnlite checkout https://svn.FreeBSD.org/base/head /usr/src

To keep it in sync later, just type:

cd /usr/src
svnlite update

Keeping your FreeBSD updated can be achieved by recompiling the system aka make world, note that this could take a long time on a Raspberry Pi but still doable (remember to use make -j 4 on RPi2).

Installing Go (optional)

If you are into Go and need a recent version, first you need to compile Go 1.4 as a bootstraper (note that you also need to install git):

cd /usr/ports/lang/go14
make install clean

Then you can compile a more recent Go, for example using /usr/local/go:

cd /usr/local
git clone https://go.googlesource.com/go
cd go/src
env TMPDIR=/var/tmp GOARM=7 GOROOT_BOOTSTRAP=/usr/local/go14 ./all.bash

Add /usr/local/go/bin to your PATH.