chill, contemporary art. Yum, yum !
Yum for eyes and tongue.
Apart from downtown of Yokosuka city, along the coast, it is hidden in abandoned villeage. In a scourching sunshine like a daydream, it exists. Inside the cafe, there is definitely different air. a few talks but not loud and not too many. Small sound of ripples occasionaly happens. Cafe is not air-conditioned, so you may feel moist, but cool breeze always run through and feels comfortable. Never ending chill surf and bossa-nova music. It looks like some world of abandoned daydream.
Comtemporary foods made from fresh fish, crams, and seaweeds. All of ingredients are taken by cafe’s master on every morning. Looks good and tastes great!
check out more beautiful photos and stories. https://art-onthebeach.com/
How to get there ? https://art-onthebeach.com/access
Enjoy your dazzle.
Serverless + CDN
This is my another reinventing the wheel. You can view TV program in realtime and record them.
I’ve compiled my personal project that show TV program in realtime and record them using RECPT1. This is snap-in replacement of epgrec.
I had been long time user of epgrec. epgrec works well in beginning a few years, but I had to tweak some per several year because of configuration change held by broadcast station. Sometimes channel number and sid moved among satellite transponder and epgrec could not follow them. Another time some channel disappear and new channel come into list because of lineup renewal, but epgrec could not follow them. I had to tweak by hand to follow these changes.
epgrec consists of mysql(mariadb) database and some configuration file. These several configuration made me confuse which change should be applied and some try takes corruption of channel list and other thing. original EPG is divided and reformatted in order to fit among some mysql table with its own schema, but cannot reconstruct original. It results that I had to come some fresh install and re-config entire things. It behavior is confusing and hard to trace in case of wrong function. It frustrates me so much.
epgrec is made on the top of mysql, php, and some shell script. It looks stable, but also looks some old-fashioned. Today, there is many reactive web framework among there, but epgrec still stays old-fashioned CGI style web interface. It’s not cool. Some people have forked and tried to improve epgrec, but their effort goes to feature improvement such as support for 1seg-broadcast(more minor format for mobile phone) and I don’t need them.
So, I decide to walk away from epgrec and re-create another wheel for me.
Subject to resolve
These are my initial goal of this project.
- Basic function
- compile program guide without tweaking original EPG. In other words, when EPG changes some (eg. ch, sid changes, name of broadcaster changes, new channel comes, some channel leave), program guide will silently follow these changes. It makes me friction-less and stress-free so much because only thing I have to do is capture EPG and send it to database. the program automatically compiles the guide from original EPG in any time!
- shows real-time video stream on web browser. This results that I can select any operating system such as Linux, Windows, Macintosh, and many minor operating system like ChromeOS as playback client. It also let me possible to send video stream to mobile device such as Android, iOS smartphone.
- program search with keywords
- full reactive behavior on the top of modern web browser and reactive framework. I don’t want to see loading animation and full change of browser document.
- on demand program compilation
- follow or on-demand move to current time
- customizable channel list (ground terrestrial/bs/cs/each channel)
- basic client identification (NOT authorization). I want for this program to send programs and video stream to multiple web browser in real time. It takes me comfortable that I can view TV program in the living room and my room simultaneously.
- Advanced function
- minimal configuration by hand. I want to kill configuration file and each config should be stored into browser itself instead of central database or some easy-to-crap config file.
- keyword sensitive auto program capture
- support for multiple bitrate video stream using MPEG Dash. It works good for rich client in my home and relatively poor device like smart phone in outside.
- Side (and helps me so much) effect
- my learn about modern web and api framework
- takes back product control to myself. I won’t depend on other’s unstable product in case I use it for daily use.
I’ve used Vue.js, Vuex, Go, echo, and MongoDB. In short, MEAN-like framework. Number of line of codes is 3.3k. epgrec has roughly 6k lines of codes. I’ve be able to archive roughly 50% efficiency. It has some subject to improve performance and lack of function for now, but I suspect it can be improved. It efficiency does not comes from my skill but from framework improvement.
Frontend frameworks are rapidly improving now, so I may recompile some using other framework but basic reactive design will stay live for many years and document database can be migrated to another product with minimal work.
stays in private
The code is in private repository in github. I won’t publish them to public for now. This project is definitely my personal project and anyone doesn’t think to use them or improve code and contribute them. So I won’t publish temporally. This is not final decision and current thought, so I may change my thought in the future.
See you in other day !
Background: How do you keep your recordings ?
Recording TV into HDD is common use today. In addition to tech-savvies, many non-professional people always uses hdd recording. In Japan, almost all TV programs provided with MPEG2-TS. With Full HD qualiy, its bandwidth is going to be 12-20 Mbps. It seats roughly 20GB disk space when you are going to save 2 hours movie or sports program. As a result, many people suffer from a lot of occupied space in home’s hdd space. Including me. Some broadcast company may deny, but it is definitely true that stored movie is the part of his memory. He wants to keep them as long as possible. Saving them to HDD or cloud: seems straight forward, but not works.
Most easy and straight forward solution is move your recordings into another bulk HDD or cloud storage. But as a result of a few years activity, data size of your recordings may grow 10-50 TB. Many people are going to offload their recordings into monthly fixed rate cloud storage, so customer who do not pay much consumes a large part of storage capacity. It greatly exceeds cloud vendor’s forecast. I’ve heard that some user ware going to save 70TB data into cloud storage. Today, almost all cloud vendor declines monthly fixed rate cloud backup solutions and offers only “pay as you go” plans. Storing your recordings into bulk hdd is more reasonable from viewpoint of cost. But your huge recordings wastes your time so much. When you are going to move some “old” recordings into bulk hdd, it consomes a lot of time (It sometimes takes several days). When you’ve completed to move your old recordings, you may start to think about how to keep your backup healthy and can be read everytime. You may take a few hdd up into RAID in order to avoid data lost in case of HDD failure. As a result, you have to run several hdd 24⁄365 in addition to main disks. These disks looks ugly, makes some noise in your home, and make your home some geeky(Your friends may put off from you !).
Motivation: Heuristic Compression
Movie compression is one of hot topic among tech domain. Recentry, they have released cutting-edge H.265 compression codec it archivess rouchly 90% compression rate compaired with MPEG2-TS. If you have 20TB movie archive, it gonna be 2TB ! Disadvantage about H.265 is extraordinary long compression time. When you’re going to compress 2h movie, it will take about 50-80 hours. Fortunately, almost all movie compression task can be separated and run in parallel and can takes your compression time shorter. So here comes a need for multicore and multinode processing environment. As you know, heuristic compression is quite efficient, but it requires a lot computing resources. Its hard to estimate how much computing resources are required to do it done, but I think that you can make sense about you need for elastic computing cluster to do that. (My estimation is written below of this article.)
Torque and AWS EC2 Spot Fleet
Adaptive computing is long providing TORQUE batch scheduler which make each single computer up as cluster computer, and provides user aggregated computing resources. You can find TORQUE can do for at http://www.adaptivecomputing.com/products/open-source/torque/ . AWS EC2 Spot Fleet is capable for taking several EC2 Spot Instance into elastic cluster. You may know that EC2 Spot Instance is provided as “spare” AWS’s computing capacity, so it is provided at discounted pricing. You can use relatively higher computing resource with reasonable cost. You can see about AWS EC2 Spot Fleet here http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-fleet.html . I gonna mix them up.
Here is a picture I’ve compiled. Upper side show AWS. It consists from “API server” and “Encoding instances”. API Server acts as administrator of encoding servers and delivers certificate for each encoding instance in order to make VPN connection into my home. Lower side shows my home. TORQUE server and the data source is here. Nas provides source file to each encoding instance via VPN and receives compressed movie.
REST is modern RPC
When you’re going to take several instances properly configured, you have to communicate with each servers with proper manner, but your time is limited to develop actual code. You cannot choose complicated framework to do so. I believe that you want to make it easy about framework. Today, I think that REST API is most easy solution to communicate among each servers. REST uses http to communicate and you can call API with curl command. It’s super easy. On the server side, I use Flask( http://flask.pocoo.org/ ) to build API server. Flask is python library which can make api server so easy. You can write just 5 lines code and can work as http api server. I write two types of api server. 2. VPN certificate administrator I uses OpenVPN as VPS software. OpenVPN requires that each client should have unique certificate to make unique connection between server. So I have to manage which certificate is paid to instance, and which certificate has returned along with instance termination. 4. TORQUE computing node registrer TORQUE only has CLI interface to register/unregister incoming computing node for now. I’ve tweaked some REST api which receives http message from encoding instance and translate into CLI command to TORQUE server. I’ve successfully glued them up, and finally EC2 Spot instances starts working as a part of existing my home’s TORQUE cluster ! I’ve taken about 50 man hours at this point.
First run: take base info for estimation
I select AWS Ohio region as a place to expand spot instances. The reason is that Ohio region offers most cheap price about 4-core instance. AWS almost alway offers more cheap price against “previous generation” instance. At the first, I’ve played at North Virginia region, but it doesn’t offers previous generation 4-core instances and the price has frequentry changed. So I understand that N.Virginia region is so crowded and I cannot run computing jobs over 24 hours at there. Ohio region offers stable previous generation 4-core instances with cheap price and bidding price is more stable than N.Virginia. So I decide to play around at Ohio region. I decided to pay $0.03/hour per instance. Then, I’ve configured Spot Fleet and launched just one instance. I want to measure how long my computing task takes in order to estimate my budget to complete mission. Here is my first job execution time detail. This is the result of encoding roughly 2 hours movie.
- Input Data Transfer: 18GB, 9 hours
- Compression: roughly 30 hours
- Output Data Transfer: up to 2.5GB, 1 hour
And Here is billing snapshot from AWS. * Data Transfer: $0.46 + $0.000 per GB - data transfer in per month: $0.00(45.294GB) + $0.000 per GB - first 1 GB of data transferred out per month: $0.00(1GB) + $0.090 per GB - first 10 TB / month data transfer out beyond the global free tier: $0.45(5.048GB)
Elastic Computing Cloud: $2.19
- $0.0116 per On Demand Linux t2.micro Instance Hour: $0.58(50.254hrs)
- c4.xlarge Linux/UNIX Spot Instance-hour in US East (Ohio): $1.19(45hrs)
- EBS: $0.05 per 1 million I/O requests: $0.04(860,000IOs)
- EBS: $0.05 per GB-month of Magnetic provisioned storage: $0.33(6.5GB-Mo)
Cost Estimation #1
So I can say that AWS costs about $3.00 per 2 hours encodings. Next, I gonna examine how many movies I have to compress. Along with my rough examination, I realised that I have roughly 1500 movies which have about 840 hours. So I can calcurate like this.
- Total encoding time: 840 x (40(hrs) /2) = 16800 (hrs)
When I going to encode all of movies in reasonable time window, I should prepare like this.
- Case1) 10 servers: 16800(hrs) / 10(svrs) = 1680(hrs) = 70 days
- Case2) 20 servers: 16800(hrs) / 20(svrs) = 840(hrs) = 35 days
Hmm… 35 days encoding time(Case2) looks nice from my viewpoint. How much does it costs ? like this.
- Case1): (70(days) * 24(hrs)) * ($3.00 / 2(hrs)) * 10(svr) = $25200.0
- Case2): (35(days) * 24(hrs)) * ($3.00 / 2(hrs)) * 20(svr) = $25200.0
Damn. This cost is too huge to take for me.
Cost Estimations #2
Previous estimation is based on that encoding server have 4 vCPU(core). When I increased the number of vCPUs, I’ll not need a lot of servers. Ohaio Region also offers i2.8xlarge instance which have 32 vCPU and it can take 8 server’s task in 1 server. The price of i2.8xlarge is $0.83 per hour. Then, how much does it change costs ?
- Encoding cost: 0.83 x 45(hrs) = $37.35 / 8(parallel) = $4.66 /cost per 2hours movie
Unfortunatelly, large size instance seems not help me so much.
This trial shows possibilities for distributed computing cluster will help your piled-up recorded movies which sits in your storage. Unfortunately, heuristic compression on public cloud environment costs so much and your budget will not meet. I’ll try different approach and write about later. Stay tuned !
Hi, there. Long time no see. I’ve spending super-hard time to work, unfortunatelly. Recently, I’m so lucky to have some private time, so decide to write some code to transmit TV to Kodi.
history #1(Gen.1 transmitter,around 2000-2005)
Its so long time from I had disconnected terrestrial tv cable from my display and connect into IPTV box. My first experience is Sony’s “Location Free TV( https://en.wikipedia.org/wiki/LocationFree_Player )“. It’s proprietary product and limited in order to view programs via special software (and, it worked only on windows of cource !). I remember that my father had worked in Korea and I had told him to use this and he pleased to view Japanese TV in Korea. It’s one of my happy memory. At that time(and even now !), Japanese TV company was so much nervous about people transcode TV program into IP datagram and send to other location outside of his home. There was some reason to do such thing. There was a lot of tech-native and rogue young guys rip a lot of TV programs and movies and shared through P2P softwares(such as Winny, Share, and so on). That was difinitely a challenge to existing authorities, and people were afraid of their bothered behavior. Authorities tried to limit their challenge using every means, and finally nuked and wiped them practically. IPTV was also disappeared from the market in a form that involved collateral.
history #2(Gen.2, 2010-2014)
I had so tired about japanese closed product so much and switched almost all equipment to linux box including my home TV. Japanese homebrew hardware vendor Earthsoft( https://earthsoft.jp/ ) released PT1 which can receive and decode terrestrial and can be save as raw binary file into computer. It was landsliding phenomenon among tech savvies in Japan and tried to hack aboud. Currently japanese terrestrial broadcast is encrypted with MULTI2 protocol( https://ja.wikipedia.org/wiki/MULTI2 ) and cannot be decryted in simple way, but there is some decrypting software made by volunteer hacker with legal decrypting key(B-CAS card. This is sold with legal device.). (Note: There is a lot of discussion to decript terrestrial with legal key and homebrewed software) I won’t write detail, but purchased PT3(3rd gen Earthsoft’s terrestrial receiver) and connect them up into a linux box. I’ve sent decoded stream to gstreamer and forward to other linux box. I’ve finally succeed to view live TV at outside in adition inside of my home ! I’ve written transmitter and receiver using perl and it worked so good. Disclaimer: All of my terrestrial stream is decrypted using legal key and legal equipment.
history #3(Gen.3, 2014-2017)
Gstreamer is complete suite about media handling, but have some nervousness to treat transport stream. So gstreamer missed to catch media stream from trancoded terrestrial stream which includes some other information other than media stream itself. So, I’ve switched gstreamer into ffmpeg. I’ve modified my transmitter and receiver in order to fit ffmpeg. Ffmpeg did its job nicely. I’ve added additonal feature ffmpeg can transcode transpote stream into H.264 in case of narrow bandwith connection. This feature was so nice when I went business trip and want to view my subscribed TV channel.
I’ve switched my linux box into Raspberry Pi 3 and Kodi. Kodi is sweat software to handle personal media content. Kodi also has smart controller works on android smartphone. Kodi also has feature to handle realtime TV stream and network video stream service such as YouTube in addition to saved media content. I’ve rewritten again my transmitter Kodi can receive stream in proper way. Kodi can receive realtime TV as HTTP live streaming(HLS) and I’ve hacked my transmitter to work as HLS server. My transmitter can reply my subscribed channels and realtime media stream along with HLS manner. Source code is here( https://github.com/mkiuchi/epgrec-kodi-backend ). Now, I can see my tv in my home and outside, I can have my smart remote controller, and low-powered receiver !
IPTV for terrestrial is niche. Almost all people view authorized network tv such as Hulu, NetFlix, dTV, and so on. Additinaly, people spent their times in SNSs and tons of CGMs. So demand for transport terrestrial into IPTV gonna be still niche, I think. But I’m happy, for now.
There is a lot of japanese animation fun among Japan. Especially some famous animation made by Hayao Miyazaki still stays gold among mid 30’s and 40’s. “Castle in the sky(天空の城ラピュタ)” is shown so many times on TV in every year. Japanese people have some habit to act same move on the same time. On the Twitt-sphere, they moves same. They tweets same word “バルス(barusu)” along with some movie scene. This behavior has generated massive traffic on the twitt-sphere and they feels some collective feeling. This is really weired but draws some insanity about Japan. On early days, These tweets let Twitter service goes down, but it works well by now. Twitter company sometimes uses this habit to promote there power of influence and their stable infrastructure. From “Arab Spring”, SNS giants starts appealing that their infrastructure is already social infrastructure instead of the fun circle. https://2012.twitter.com/ https://blog.twitter.com/ja/search/node/%E3%83%90%E3%83%AB%E3%82%B9%20term%3A139%20type%3Ablog%20language%3Aja Yesterday, japanese biggest tech company NTT-Data tried to capture full of tweets and to illustrate how many times people tweets “barusu(バルス)” in nearly-real-time. They already contracted with Twitter to stream all of their tweets. It is called “firehose(https://dev.twitter.com/streaming/firehose)“. I suspect that they try to capture these tweets from firehose-tweet-stream. Their message is clear. “Yes, we can see your move.”. Here is the result(01/15/2016 23:41 JST). I found the number of tweets including “バルス” in 01/16/2016 00:13am. Here is screenshot. You can find that there is about 0.3m missing tweets between two observed summary. NTT-Data doesn’t disclosure current summary yet, but along with the trend after the peak time, I suspect that these difference doesn’t come from the gap of observed time. You can suspect something. 2. The Firehose tweet bulk stream doesn’t include true bulk stream. 4. On the SNS world, we may experience some gap from the point of observe. It will be about 20% of truth. Its something worth for me to observe this gap. Interesting. I can advice to you. “Stop this fxxking behavior”.
OpenIndiana is my good friend for the past several years. It works nicely, but I’ve decided to move on it to NAS4Free.
To catch it up current hardware especially 4k block device such as recent 4TB HDD, SSD, and so on. Yes, I know that OI supports 4k devices to set ashift=12, but I cannot make good result. In addition, I seem that the development of opened solaris eco-system goes more shrink and slow than before. I believe that OpenIndiana and Illumos is fabulous and well sophisticated operating system even now, but I also have to take it suit for current hardware in order for me to choose most cost effective hardware. I need actively working development community in addition to well stabilized operating system. So, I decided to choose another way.
Fortunately, ZFS of OpenIndiana can be moved to FreeBSD with zpool export and import. So, I’m going to seek some nice FreeBSD distribution for some file server work and found that NAS4Free is good choise for me.
NAS4Free on HP Microserver
I’ve booted NAS4Free 220.127.116.11 from cdrom and installed into 2gb usb stick. It works super easily and perfect. After installation, I’ve imported exported zpool from OpenIndiana and NAS4Free detects and synchronizes it into its repository. I’ve add CIFS and NFS services on the gui and it can be configured super easy and just works !
NAS4Free works so far, so good. I’ll watch it and do additional works(some resource monitoring) for the future. Enjoy !