Use native code on iOS with Unity Native Code in the Unity

First, write native code you used for iOS. Read this document

I needed to write native code for the In-App Purchase on iOS.

Then, just put your native code in the Assets/Plugins/iOS directory. The hierarchy like this;

For example, using Objective-C, Assets/Plugins/iOS/sample.m and Assets/Plugins/iOS/sample.h.

Build on Unity

In File > Build Settings, switch iOS platform and then click Build and Run. Next, when the build complete, launch Xcode and build the project created by Unity on it automatically.

Build on Xcode

Initial build might occur a signing problem that is Team is None. To solve this problem, just select a team in the pull-down menu of the team. Automatically sign to your app as the team you selected.

Click the build button top left of the Xcode window!

Receive a callback as JSON from an iOS code

Set Game Object on a Unity script before call the native code

var gameObject = GameObject.Find("PurchaseHandler");
            if (gameObject != null) return;

            gameObject = new GameObject("PurchaseHandler");
            if (UnityEngine.Application.isPlaying)


Call UnitySendMessage on iOS


void GetProductList(const char* json)
    NSString *jsonString = @"{list: [ ... ]}"
    UnitySendMessage([@"PurchaseHandler" UTF8String], [@"onSuccess" UTF8String], [jsonString UTF8String]);

Receive a message of UnitySendMessage

public class PurchaseHandler : MonoBehaviour
         private IEnumerator onSuccess(string message)
            yield return doSomething(message);

How to find out bottlenecks in HTTP server and MySQL

I participated in ISUCON for the first time.

I acquired some knowledge to find out bottleneck in HTTP server and MySQL. Our team used alp for HTTP response analysis and pt-query-digest for MySQL slow query analysis.

I try to introduce these tools that were useful in our situation or might also other teams(or situations).

How to find the bottleneck of web APIs on web application

We need to analyze access log of API generated with Nginx to decrease API’s response time.

So, I used alp to analyze access log.

Download alp binary proper your architecture, usually amd64 if you use Linux, from here

$ wget
$ unzip

Change Nginx log formart to LTSV format

$ sudo vim /etc/nginx/nginx.conf
log_format ltsv "time:$time_local"

access.log ltsv;

Analyze Nginx log

$ ./alp -f /var/log/nginx/access.log

The list analyzed is like this;

| COUNT |  MIN  |  MAX  |   SUM   |  AVG  |  P1   |  P50  |  P99  | STDDEV | MIN(BODY)  | MAX(BODY)  |  SUM(BODY)  | AVG(BODY)  | METHOD |                         URI                         |
|    12 | 0.000 | 0.000 |   0.000 | 0.000 | 0.000 | 0.000 | 0.000 |  0.000 |    318.000 |    318.000 |    3816.000 |    318.000 | GET    | /favicon.ico                                        |
|     1 | 0.000 | 0.000 |   0.000 | 0.000 | 0.000 | 0.000 | 0.000 |  0.000 |   4162.000 |   4162.000 |    4162.000 |   4162.000 | GET    | /js/chat.js                                         |
|     6 | 0.770 | 1.932 |   7.167 | 1.194 | 0.770 | 0.988 | 1.628 |  0.432 | 327999.000 | 327999.000 | 1967994.000 | 327999.000 | GET    | /icons/b1c8c5bc9b026507ae62e6bf62cf55f70f4ac3d8.png |
|     7 | 0.212 | 1.967 |   7.447 | 1.064 | 0.212 | 0.998 | 1.501 |  0.536 | 105173.000 | 105173.000 |  736211.000 | 105173.000 | GET    | /icons/e58ed7194ed4a99d9d6ada9e3383983edcbd1edc.png |
|   301 | 0.099 | 3.752 | 473.578 | 1.573 | 0.549 | 1.507 | 2.921 |  0.407 |      0.000 |   3005.000 |  885578.000 |   2942.120 | GET    | /fetch                                              |

This table shows the API response times that are listed in ascending order of time.

We started to improve the response time on worst. This case, we should start at /fetch and /icon.

How to find bottleneck queries on MySQL

To find bottleneck of SQL queries, we used pt-query-digest.

Install percona toolkit

See this document.

$ wget$(lsb_release -sc)_all.deb

$ sudo dpkg -i percona-release_0.1-4.$(lsb_release -sc)_all.deb

$ sudo apt-get update

$ sudo apt-cache search percona

$ sudo apt-get install percona-toolkit
$ sudo vim /etc/mysql/my.cnf
# Enable slow query log
slow_query_log = 1
slow_query_log_file = /var/log/mysql/slow_query.log

Analyze slow query log

$ pt-query-digest /var/log/mysql/slow_query.log

The list analyzed is like this;

# 660ms user time, 30ms system time, 39.37M rss, 121.42M vsz
# Current date: Sat Oct 21 14:54:35 2017
# Hostname: app1053
# Files: /var/log/mysql/slow-query.log
# Overall: 1.50k total, 23 unique, 2.73 QPS, 0.01x concurrency ___________
# Time range: 2017-10-21T05:45:19 to 2017-10-21T05:54:29
# Attribute          total     min     max     avg     95%  stddev  median
# ============     ======= ======= ======= ======= ======= ======= =======
# Exec time             3s     1us   608ms     2ms     5ms    20ms   103us
# Lock time           78ms       0     7ms    51us   131us   174us    36us
# Rows sent          1.96k       0     100    1.33    0.99    8.72       0
# Rows examine       2.91M       0   9.77k   1.98k   9.33k   3.74k       0
# Query size        90.88k      20     202   61.88  136.99   25.68   62.76

# Profile
# Rank Query ID           Response time Calls R/Call V/M   Item
# ==== ================== ============= ===== ====== ===== ==============
#    1 0x16A13978F3B7FCFA  1.7328 55.1%     6 0.2888  0.06 SELECT image
#    2 0x4866C96BA295C56E  0.7782 24.7%   301 0.0026  0.00 SELECT message
#    3 0xEBDB62606B914A34  0.4282 13.6%    90 0.0048  0.00 INSERT channel
#    4 0x91283C307570F439  0.0334  1.1%     4 0.0083  0.00 SELECT message
#    5 0x41883793B68D128E  0.0302  1.0%   100 0.0003  0.00 SELECT user
# MISC 0xMISC              0.1427  4.5%  1003 0.0001   0.0 <18 ITEMS>

# Query 1: 6 QPS, 1.73x concurrency, ID 0x16A13978F3B7FCFA at byte 190178
# Scores: V/M = 0.06
# Time range: 2017-10-21T05:46:17 to 2017-10-21T05:46:18
# Attribute    pct   total     min     max     avg     95%  stddev  median
# ============ === ======= ======= ======= ======= ======= ======= =======
# Count          0       6
# Exec time     55      2s   203ms   608ms   289ms   580ms   136ms   242ms
# Lock time      1   840us   109us   193us   140us   185us    29us   152us
# Rows sent      5     103      10      23   17.17   22.53    4.06   17.65
# Rows examine   0   5.87k    1001    1001    1001    1001       0    1001
# Query size     0     474      79      79      79      79       0      79
# String:
# Databases    isubata
# Hosts        app1051
# Users        isucon
# Query_time distribution
#   1us
#  10us
# 100us
#   1ms
#  10ms
# 100ms  ################################################################
#    1s
#  10s+
# Tables
#    SHOW TABLE STATUS FROM `isubata` LIKE 'image'\G
#    SHOW CREATE TABLE `isubata`.`image`\G
SELECT * FROM image WHERE name = '78a9228a393eb2621f346fc6a5e099d5bc373f76.png'\G

# Query 2: 1.08 QPS, 0.00x concurrency, ID 0x4866C96BA295C56E at byte 177211
# Scores: V/M = 0.00
# Time range: 2017-10-21T05:46:16 to 2017-10-21T05:50:54
# Attribute    pct   total     min     max     avg     95%  stddev  median
# ============ === ======= ======= ======= ======= ======= ======= =======
# Count         20     301
# Exec time     24   778ms     2ms     9ms     3ms     5ms   966us     2ms
# Lock time     15    12ms    30us   208us    39us    66us    15us    33us
# Rows sent     15     301       1       1       1       1       0       1
# Rows examine  98   2.87M   9.77k   9.77k   9.77k   9.77k       0   9.77k
# Query size    19  17.58k      58      60   59.81   59.77    0.85   59.77
# String:
# Databases    isubata
# Hosts        app1052 (298/99%), app1051 (3/0%)
# Users        isucon
# Query_time distribution
#   1us
#  10us
# 100us
#   1ms  ################################################################
#  10ms
# 100ms
#    1s
#  10s+
# Tables
#    SHOW TABLE STATUS FROM `isubata` LIKE 'message'\G
#    SHOW CREATE TABLE `isubata`.`message`\G
SELECT COUNT(*) as cnt FROM message WHERE channel_id = '733'\G

This table shows the queries time are listed in desacending order of time.

In this case, we should start to improve the queryt time that is SELECT * FROM image WHERE name = '78a9228a393eb2621f346fc6a5e099d5bc373f76.png'\G. For example to improve it, use caching on Nginx.

I implemented GANs

I implemented GANs(Generative Adversarial Networks) because I want to learn about GANs with TensorFlow.

I referred to here.

And this is my code implemented GANs. This code almost same what I referred.

My GANs repeatedly learn with MNIST handwritten image 100k times.

These are the learned images 0, 1k, 50k, 100k steps.

0 step

The 0 step image is just noise.

1k steps

The 1k steps image is just noise yet.

5k steps

The 5k steps image look like a handwritten image, but it is a little bit noisy.

50k and 100k steps

50k steps

100k steps

The 50k and 100k steps image pretty look like a handwritten image.

Above implementation is the vanilla GAN. So by using extended GAN like DCGAN would be able to increase the precision of generated handwritten image. And I’ll try to use some other training data.

However, I am going to implement GAN on iOS with CoreML before I use DCGAN and other training images.

I went to iOSDC 2017

I went to iOSDC 2017 on September 17th.
This conference held for iOS developers on September from 16th to 17th at Waseda University.

I heard these sessions:

This session told us how to use Metal to render an image on display. What most interesting is that to draw a picture without Metal is faster than with it.

Apple TV – tvOS入門 –

React Native vs. iOSエンジニ

I had no time to listen to all the sessions this time. And I haven’t had knowledge of developing iOS. So There were some sessions I couldn’t make sense. Now, I’ve been developing an iOS app with Swift!

I participated in CEDEC 2017

I participated CEDEC 2017 from August 30th to September 1st in Yokohama. The conference is the first time I went to DECEC that is held every year as far as I know. All participants engage in game developing.

I look at below sessions.

Day 1


They have tried to analyze card deck’s trends that user in the game use. They use a technique like “edit distance” among different decks.

アイドルマスター シンデレラガールズ ビューイングレボリューション制作事例 ~最高のVRライブ体験に必要となる要素とは~

They created the great experience of a live concert in VR. It is important to render mob’s detail whom body, showing the light on a body it reflected by lighting sticks in VR and mob’s cheers.

Day 2

- 人工知能(メタAI)を用いたゲームデザインの変革
[人工知能学会×CEDECコラボセッション] 人工知能と対話システム ~キャラクターとの自然な会話~
データ分析の固定観念を覆す -ユーザー体験を向上し続けるモンスターハンター エクスプロアのデータサイエンス-

Day 3


They have been creating the player AI that emulates a game player. The reason to create it that reduce quality assurance costs because a lot of contents in a game are created continuously, and they need to check contents also created continuously. It is easy we could imagine that the contents created which increase the cost of quality check.

キャラクターらしさ学習AI: 多数のキャラクターの個性や違いの可視化によるシナリオライティング支援システム事例

This support a writer in writing scenario for games with a script written by a writer.
The AI learns characteristics of a character in a game and classifies a script by it into several characters. A writer can fix the script with the scores calculated by the AI. The score indicates a likelihood of characters in a game is like this:

Character Name Score
A 10
B 5
C 2

This example indicates that a script might be the A because its core is higher than the others.


They have built the system that bans the user request illegal accesses to game API servers in real time.

Learning how to learn

I’ve been reading SOFT SKILLS ソフトウェア開発者の人生マニュアル(Japanese version) of “Soft Skills”. I’ve made sense implicitly this method before I read the book.

I found the chapter “Learning how to learn” that is helpful. So I try to introduce the summary of it.

There are ten steps to learn something.

Among step 1 and step 6 are only once steps. Among step 7 and step 10 are repeatedly steps.

Step 1 Get the general picture

You need to know what you do for learning it and find some information for getting the general picture. For example, you want to learn C#; you might find information from the internet, books, blog post, etc.

Step 2 Decide scope

You decide a learning scope in detail in this step. You might have known what you are learning after the previous step. For example, You learn a basic knowledge of C# to create a simple console application.

Step 3 Define goal of achieving learn

It’s important to define a goal before you begin learning.

For example, You have learned how to create a simple console application with C#.

Step 4 Find references

You find various references to learn it from books, blog posts, experts, source code, online documents, etc. You should find many referrences rather than only one reference.

Step 5 Make the learning plan

You should find the shortest path to achieve the goal. Some references explain it too much that you want to know.
You look at the table of contents of references you found, and you find common entries from them. The entries you found might be the path you have to learn.

Step 6 Filter references

To filter references is necessary to step because you cannot read the all books and blog posts.

For example, you check Amazon review and choose high-rated books from there.

Step 7 Learn how to use a minimum of one

You might create a tiny program like a “Hello, world!” and set up a development environment. One of the important things is not to learn too much about it.

Step 8 Play around

Now, you should know a basic knowledge. Just give it a try to create a program! It is important to learn through practice.

Step 9 Learn deep

This step, you might have known a part of them. So go back to references and read the chapter you are learning.

Step 10 Teach someone things you learned

You show other people you learned with a blog post or YouTube, etc.

What is the difference between tf.placeholder and tf.Variable

I read this tutorial. There were the confusing terms they are tf.placeholder and tf.Variable. So I checked the difference point.

tf.placeholder is called when a session runs a calculation.
If once you set a value with tf.placeholder, it can not change an own variable.

tf.Variable can change an own variable by assign method. So tf.Variable is literally “variable.”

Finally, I found this question was beneficial for me.

The top rated answer said:

You use tf.Variable for trainable variables such as weights and biases for your model.

weights = tf.Variable(
    tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                    stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name='weights')

biases = tf.Variable(tf.zeros([hidden1_units]), name='biases')

tf.placeholder is used to feed actual training examples.

images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IMAGE_PIXELS))
labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))

for step in xrange(FLAGS.max_steps):
    feed_dict = {
       images_placeholder: images_feed,
       labels_placeholder: labels_feed,
    _, loss_value =[train_op, loss], feed_dict=feed_dict)

And the another answer said:

The more important difference is their role within TensorFlow. Variables are trained over time, placeholders are are input data that doesn’t change as your model trains (like input images, and class labels for those images).

I appreciate roughly my question thanks to the StackOverflow’s answer.

Index Key Prefix Length Limitation on InnoDB

Index Key Prefix Length Limitation on InnoDB

I encountered the problem index key prefix length limitation on InnoDB when I migrate a database. The problem was like this:

Specified key was too long; max key length is 767 bytes

The index key prefix length limit is 767 bytes if you use the version of MySQL either 5.5 or 5.6. Or you use 5.7 the default is 3076 bytes.

innodb_large_prefix is deprecated and will be removed in a future release. Therefore, We need to consider whether we should enable innodb_large_prefix.

And Index key prefixes larger than 767 bytes are silently truncated if you change innodb_large_prefix to OFF from ON.

I went to builderscon tokyo 2017

I went to builderscon tokyo 2017 from August 3rd to 5th.

The eve of the conference

I heard some fascinating talks, but I was told not to tell anyone about them, so unfortunately I can’t write about them.

I think the organizers should keep this event next time.

Day 1

Recognizing Japanese idols with Deep Learning

@sugyan spoke about recognizing Japanese idols with Deep Learning.It helped me to learn about Deep Learning recognizes images.

He gave me some hints on image recognition. Thank you @sugyan.

Building high performance push notification server in Go

@cubicdaiya[] spoke abount a push notification server.

Gaurun might be able to use my project. I’ll look up detailed information about it.

After party

I went to the after party, which was held after the end the day 1. Almost all of the dishes were delicious.

Day 2

Factory Class

Jesse Vincent told me the exciting episode that’s about developing keyboards from scratch.

He said it’s important to meet up with a supplier and keep a good relationship with them if you want to make a good product in China.


The name card the organizers made was useful for me, and I think the voting system (It uses a QR code) to choose best speakers was convenient for us.

There was only one thing I was dissatisfaction at the lunch session. I think that I and the other audience members were crammed into there, so I think the room was a little small.

But I had a totally enjoyable time at the conference, and I would love to go again!

How to make development environment of Go with Mono and Protocol Buffers

I joined a project, and I begin to develop an application in it. This project has used golang. So I studied it and prepared its developing environment.

This article is the what I did to make development environment of GO.


What our golang project needs.


Glide manage a project dependency.

What is the difference between “go get” and “Glide”

As far as I searched, “go get” just installs a package into your local directory, and solves library dependencies. By contrast, “Glide” is package manager sort of npm. It uses a file defined depending packages.


Fresh is an auto-reloader for a web application. When you change a file of golang or template, Fresh reload (recompile) the file automatically, and apply the web application.


Goose is a migration tool.


Add provisioning config

We’ll provision from a shell script file.


Vagrant.configure("2") do |config|
  config.vm.provision :shell, path: ""

Environment variable golang uses

Golang uses GOROOT and GOPATH environment variables.

To installing a custom location use the GOROOT. If you don’t want change one, you don’t need to set this environment variable.

The GOPATH is used to specify directories for a golang project.

Install Go

Install Go and set GOROOT, GOPATH and PATH.

Install go and set environment variables for go

apt-get update
apt-get -y install curl git
tar -xvf go1.8.3.linux-amd64.tar.gz
mv go /usr/local

export GOROOT=/usr/local/go
export GOPATH=/vagrant/go
mkdir -p $GOPATH/bin
export PATH=$PATH:$GOPATH/bin:$GOROOT/bin
source ~/.bashrc

And You should add commands because GOROOT, GOPATH and PATH I set in doesn’t influence the vagrant environment.

The environment variables for golang in the vagrant environment

echo 'export GOROOT=/usr/local/go' >> /home/vagrant/.bashrc
echo 'export GOPATH=/vagrant/go' >> /home/vagrant/.bashrc
echo 'export PATH=$PATH:$GOPATH/bin:$GOROOT/bin' >> /home/vagrant/.bashrc

I install Glide.

Install Glide

curl | sh

In the vagrant environment

Solve library dependency with Glide

It’s simple; you just execute above command.

Install glide

$ glide install

And install Fresh and Goose by go get.

Install Fresh and Goose

$ go get
$ go get

Install protobuf

I referred to this article.

Install protobuf

$ curl -OL
$ unzip -d protoc3
$ sudo mv protoc3/bin/* /usr/local/bin/
$ sudo mv protoc3/include/* /usr/local/include/

Install Mono

I had to install an old version of Mono for the project reason. So I install it from tar ball.

This article is useful for me.

Install Mono

$ sudo apt-get install g++ gettext make
$ tar xvjf mono-2.11.4.tar.bz2
$ cd mono-2.11.4
./configure --prefix=/opt/mono-2.11

$ make
$ make install

ln -s /opt/mono-2.11/bin/mono /usr/bin/mono
ln -s /opt/mono-2.11/bin/gmcs /usr/bin/gmcs