Video upload

Normal

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Erikka Innes

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Tutorials

Python

Uploading video with api.video's Python Client is easy. You can use the method described here to upload big files over 199 MiB or small files. The client will handle chunking the upload for you. 

NOTE: If you're wondering how this differs from the previous Python tutorials:
* [Upload a Video From Your Computer wiht the api.video API (Python)](https://api.video/blog/tutorials/upload-a-video-from-your-computer-with-the-api-video-api-python/) 
* [Upload a Big Video File Using Python](https://api.video/blog/tutorials/upload-a-big-video-file-using-python/) 

It differs in that it uses our new [Python Client](https://github.com/apivideo/python-api-client).  

If you'd prefer to watch a video about how to upload video from your computer with the new client, you can check that out here: 

<iframe width="560" height="315" src="https://www.youtube.com/embed/4kWwSvrLtCA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

The API reference documentation for the endpoints used in this tutorial can be reviewed here: 
* [Create a video](https://docs.api.video/reference/api/Videos#create-a-video-object)
* [Upload a video](https://docs.api.video/reference/api/Videos#upload-a-video)

If you prefer written walkthroughs, read on. I'll cover all the same information here. 

## Install the Python Client 
Installing api.video's Python Client is easy: 

```pip install api.video``` 

I recommend using a virtual environment to install, but it's not required. 

## How to Upload a Video from Your Computer 

What you'll be doing, is sending a request to the api.video server to create a video container. You'll get back a video ID you can use to reference your container. Then you'll prepare your video for upload and send a request to upload the video to the video ID you specify. 

Be aware that you can only upload to a video ID once. You can change settings and details about your video later using the update feature of the api.video API. 

You can get the code sample I'm using here: 
[https://github.com/apivideo/python-api-client/blob/master/examples/upload_video.py](https://github.com/apivideo/python-api-client/blob/master/examples/upload_video.py)

When you are creating your video, you may want to include more settings and metadata than I show in the walkthrough. A complete list of choices are shown here: 
[https://github.com/apivideo/python-api-client/blob/master/docs/VideoCreationPayload.md](https://github.com/apivideo/python-api-client/blob/master/docs/VideoCreationPayload.md)

And here's the code sample (tweaked a bit from the repository sample):

## Code Sample Walkthrough
To use the api.video Python Client, you'll want to start by making your imports. You need three. The first one you'll need to import is **apivideo**. This will let you use authentication and the client. Next, you want to import **VideosApi** from apivideo.apis. It is best practice to import just the part of the client you'll use, because it improves performance. VideosApi will let us use the create and upload features. Finally, you'll import **ApiAuthException** for error handling. 

Next, we'll add our API key and set up the Client. In the Python Client, authentication is handled for us, including refreshing the access token when needed. All we need to do is add our API key to the Client, then connect. After connecting, we can pass the Client to VideosApi, and we're ready to upload our video! 

To upload our video, we first want to create our video container. The video container contains details about the video as well as any configuration settings like whether you want your video to be public or downloadable. As part of our request to create our container, we'll want to send all those details and settings in a payload. For this walkthrough, I'm adding a title because that's the only required setting for container creation. Then I'm including a description of what my video is, setting my video to be private (make public = false) and then I'm adding tags to search with - bunny and tree. 

If you want to change the settings further, refer to the choices here: [https://github.com/apivideo/python-api-client/blob/master/docs/VideoCreationPayload.md](https://github.com/apivideo/python-api-client/blob/master/docs/VideoCreationPayload.md)

Let's create a request to make our video container and retrieve the video ID:

We grabbed the video ID, so next we prepare our file. To upload files with api.video, we open them in read-only binary format. Then we'll add our file and video ID to our upload request and check out the response! 

If everything works out, you'll get a response showing you all the metadata and configuration details for your upload. You can navigate to [your api.video dashboard](https://dashboard.api.video/overview) to see the video you uploaded there. If you're uploading a big video, give it a few minutes to be ready. 

If you get stuck or have questions, you can write us on our [community forum](https://community.api.video).

Upload a video with the api.video Python Client

We recently released a new Python client. To make it easier to work with, I set up basic code samples for every endpoint showing you how your requests should be created. The basic samples are all in the [Examples](https://github.com/apivideo/python-api-client/tree/master/examples) folder in the [Python client repository](https://github.com/apivideo/python-api-client).

## Tips for working with the Python client
These are some tips for working with the Python client. 

* Don't mix n' match the Python client's documentation with the docs at [docs.api.video](https://docs.api.video). Parameters are sometimes mapped to more Pythonic parameters in the Python client. For example enablesApi becomes enables_api. This isn't the case everywhere, check the code sample and the documentation in the repository. 
* When you're using the API, typically you'll import the endpoint you want to work with. So you'll import the API with 'import apivideo' and then you'll also have an import statement like 'from apivideo.apis import VideosApi' if you want to use the video endpoint. If you also want to use exceptions, you'd add 'from apivideo.exceptions import ApiAuthException.'
* Authentication is handled for you. It's arranged so you never need to worry about it. You may wonder then, why we provide a way to manually retrieve a refresh token. This is in case your token is exposed. Retrieving a new refresh token will cancel the old one. 
* When you create requests without the client, you'll notice that you open your file in binary, then attach it to a dictionary and send it. In the client, you just open your file in binary, assign that to a variable and send the open file. This is demonstrated in the examples, if the explanation is unclear. For an example, see the [upload_video.py](https://github.com/apivideo/python-api-client/blob/master/examples/upload_video.py) sample. 
* Everything for making requests is built using urllib3. 

## Documentation Examples
If you don't want to use the simplified examples in the code sample folder, you can also use the samples provided by the Python client documentation. Just look up the endpoint you want details about and there are samples provided there.


New code samples for the Python client

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Video trends

Java

We just released our new Python and Java clients for the api.video API, so today's post seems appropriate. Let's take a look at why these languages are popular for video processing. Netflix uses them both and they have one of the most popular video content platforms in the world. Much has been written about Netflix when it comes to what programming language it's built with. Dig around online, and you'll find iconic quotes like these: 

"Java is the bedrock of our software architecture." -Andy Glover, Director of Productivity Engineering at Netflix

"We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members." -Pythonistas at Netflix, coordinated by Amjith Ramanujam and edited by Ellen Livengood 

Netflix has used Java and Python to deliver an exceptional customer experience at least as far back as 2013. A Netflix blog post titled ‘Python at Netflix,’ dated Mar 11, 2013, states that: 

> "Developers at Netflix have the freedom to choose the technologies best suited for the job. More and more, developers turn to Python due to its rich batteries-included standard library, succinct and clean yet expressive syntax, large developer community, and the wealth of third party libraries one can tap into to solve a given problem." 

It’s interesting to note that when permitted to choose anything, developers started in Java, then Python grew in popularity. Today they continue using both, though a number of articles suggest they are shifting more and more to use Python. 

Netflix initially chose Java because of its scalability and ability to easily run anywhere. They use open source Java tools to monitor, upgrade, and scale their services. Their engineers continuously deliver new features, and Java processes are constantly running during a customer's use of Netflix. Netflix's Director of Engineering, Andy Glover, says "The bread and butter of Netflix involves using data to recommend shows that customers might like. Everyone at Netflix plays a role in either capturing that data or making use of that data. Our business units can make changes quickly, partly because the entire infrastructure is built on Java." Their engineers also encode videos using Java on Linux computers. 

Over time, Netflix added Python to the mix. They rewrote some Java processes using Python, and they also use Python for security. It's used for risk classification, security automation and vulnerability identification. Like Java, Python is often used in Netflix's recommendation algorithms as well as to serve content to viewers. Amazingly, Python is also used to crunch the data that helps Netflix decide what content to fund or buy.

Netflix chose Python because it's one of the fastest growing languages in terms of popularity, it offers numerous modules that are great for video processing and data science, and its an industry standard for a lot of animated and VFX content. Will Python replace Java at Netflix? That remains to be seen. 

For now though, both these languages are go to choices for Netflix. And [api.video](https://api.video/) is proud to offer you the choice of a [Python](https://docs.api.video/sdks/api-clients/apivideo-python-client) or [Java client](https://docs.api.video/sdks/api-clients/apivideo-java-client) for whatever you want to develop with our video API! 

Nextflix’s preferred programming languages, Python and Java

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Python and Java, the preferred programming languages of Netflix

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Product updates

For all our Python programmers out there, it's finally happened! api.video is releasing a Python client! The repository is here: [https://github.com/apivideo/python-api-client](https://github.com/apivideo/python-api-client). And we invite you to submit code sample showing you use the client to the examples folder here: [https://github.com/apivideo/python-api-client/tree/master/examples](https://github.com/apivideo/python-api-client/tree/master/examples).

It's our first time releasing a Python client, so let us know what you think of it. What do you like about it? What could be improved? We're all ears! 

Our client allows you to make requests with or without threading, depending on your project. It also handles chunked uploads for large files, pagination, and authentication. You'll be able to use everything in api.video's API through the Python client.

Python is an incredible choice when it comes to working with video. Enthusiastic fans of FFMPEG have created many Python bindings you can use to work with FFMPEG directly in your code instead of just at the command line. And you're able to access the powerful OpenCV libraries via Python bindings if your project involves the use of real-time computer vision. 

Python is also a great choice because it's popular, robust, easy-to-learn and well documented. There are many excellent frameworks and libraries that can help you complete all kinds of tasks, and Python is regularly used for big data and machine learning projects. These are often complimentary to video projects, especially if you want to build something like a ratings or recommendation system, or collect analytics about your video content. 

It's a very flexible language, that compliments other languages well. For web applications, you'll find that many use Python for the back end and JavaScript for the front end. These combinations are also well documented, so if you're a newcomer, it's easy to get started. If you're a seasoned developer, there's also lots of details for advanced implementation.

We hope you'll enjoy using the new Python SDK and share your projects with us. Happy coding!

Try api.video's new Python client!

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Try api.video's new Python client! 

You can extract a list of frames at evenly spaced intervals through your video using Python and FFMPEG. This information could be used to do a quick skim of a video or create a trailer for the video programmatically - you could stitch the frames back together afterwards into a short video. 

If you need a place to upload and host your video from, api.video has you covered! Sign up here: [api.video sign up](https://dashboard.api.video/register)

And check out the reference documentation for our API here: [API reference documentation](https://docs.api.video/reference)

## Prerequisites
You need to have Python and FFMPEG installed. 

## Extract Images From Your Video 
The code sample for how to extract images for your video uses the [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) module. 

1. You should have the video you want to extract images from in the same folder as the code sample if you want to use the sample as-is. Add the complete name of your video file in the spot that says **'sample-mov-file.mov'**. 

2. Now we use ffmpeg.probe to get information about your video. You're going to want the length of the video so we can figure out how far apart the images we extract should be. 

3. Set **parts** equal to the number of spots you want to extract a video from. 

4.  Now I use list comprehension to grab a list of intervals in seconds in your video. 

5.  I want to start getting images from the end of each interval, so I use **item[1]** in the for loop to get the end of each interval for my images. The loop will run through and extract one frame each time, label it **ImageZ** where Z starts at 0 and goes up to the total number of images you requested. 

6. You can stitch these images together into a video using this code sample: [Assemble video from sequence of frames](https://github.com/kkroening/ffmpeg-python/tree/master/examples#assemble-video-from-sequence-of-frames) 


If you have any questions or suggestions, please share them on [our community forum](https://community.api.video/).  

[Start building](https://dashboard.api.video/register) right now with api.video! 


Extract a set of frames from a video with FFMPEG and Python

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Authenticate

If you'd prefer to watch this tutorial, here's a video version (without the Python client): 
                        <iframe src="https://embed.api.video/vod/vi1cigI8kYmZL5vr4cTJGch6" width="100%" height="100%" frameborder="0" scrolling="no" allowfullscreen="true"></iframe>   

api.video issues a refresh token alongside an access token. You may have seen this if you've examined the json that comes back when you authenticate with your api key. It looks like this (I edited the access_token and refresh_token a lot, they're very long strings in an actual response): 

Each access token is good for one hour, then it expires. You have three ways you can deal with this ongoing expiration: 

* Delegated tokens - If you want, you can use delegated tokens for some of your work. Delegated tokens can have any expiration time you want, so you can avoid dealing with the regular expiration. Delegated tokens are only for use with uploads though. 
* Authenticate again - You can use your API key every time your access token expires, and get a new token. Or, you can use it before your access token expires. 
* Use a refresh token - A refresh token lets you get a new access token without exposing your API credentials. 

In many situations, using a refresh token will be the best option. When you authenticate, store the refresh token somewhere secure so you can use it later. Today's code sample shows you how to retrieve the refresh token, then use it to get a new token. 

And that's it! You can continue developing, or continue services in your system that need a new token to complete tasks. 

If your token expires, hit refresh and protect your API key

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When your token expires, hit refresh and protect your API key

Analytics

Video analytics can tell you a lot about how people are watching your video, especially if you decide to dig into player sessions. With api.video's [analytics endpoint](https://docs.api.video/reference/api/Analytics) you can figure out details like where people fast forward, or rewind your video. This information can be used to help you determine things like:
* Whether something is confusing or boring
* Whether something is interesting or entertaining

...and more! 

For this article, I decided to pull analytics for a popular api.video demo called [Upload a video to Discord](https://a.video/works/upload-a-video-to-discord). 

## Pull Analytics Data for a Video
If you know what video you want to pull analytics for, it's pretty easy to grab this information: 

1. Open your dashboard by clicking **Login** on the [api.video](https://api.video) homepage and logging in if you are not logged in to api.video. 
2. Navigate to [your dashboard](https://dashboard.api.video/).
3. Click on the video you want analytics data for. 
4. On the video's page, there's a copy link section that will contain a video link. It will look something like: `https://embed.api.video/vod/vi5PBib80QrC8q73AekJqNDH`. Get the videoId, which is the part that comes after **vod/**. 

NOTE: You could also collect this information programmatically. To do that, you'd send a request to the [videos](https://docs.api.video/reference/api/Videos#list-all-video-objects) endpoint. You'd look through the list returned, pull the videoId for the one you wanted, and continue on. 

5. After you have your videoId, you're going to want to retrieve an exhaustive list of player sessions for your video. The code for that is: 

For our data file, for each row we list the sessionID, what type of action was taken, when the session with the video first started (emitted at), and then three columns - at, from, and to. Some of the action types don't use some of these last three columns.

## Get Familiar with Your Data
Now that you have your csv file containing all the sessions for the video you selected, it's time to start thinking about how to analyze it. You can see a lot of interesting information: 
* pause - when someone pauses a video 
* resume - when someone resumes a video
* ready - when the video is at the start position
* play - when someone starts the video
* end - when someone watches to the end of the video
* seek.backward - when the video winds backward
* seek.forward - when the video winds forward

In our table, a sample row for each will look like: 

pause
`-sessionID-, pause, 2021-02-25T09:57:14+00:00, 45, not, not` 

What we can see from this sample row, is that someone paused their video 45 seconds in. If the row right after pause isn't resume, we can check if the sessionID is different. If it is, we can be sure that whoever was watching the video, stopped watching at 45 seconds. Who would do such a thing? There's an extra 5 seconds of video after that! As it turns out, if you watch the video here: [https://a.video/works/upload-a-video-to-discord](https://a.video/works/upload-a-video-to-discord) you can see that by 45 seconds, Doug successfully completed the task he's teaching with the demo. The video runs on Discord. If you were satisfied with the demo, you might pause the video and then leave, or close the tab (which would count as a pause to api.video's analytics). 

resume
`-sessionID-, resume, 2021-02-25T18:10:28+00:00, 25, not, not` 

The resume action will usually occur right after pause, or after a seek.forward or seek.backward action. This action is anytime the video is stopped, and you press play. If your video is at the very start, clicking play doesn't register as a resume. 

ready
`-sessionID-, ready, 2021-02-25T14:48:49+00:00, 0, not, not` 

Ready appears when you open the video and you haven't started playing it yet. If you see ready and then you see a different sessionID, you know the user reached the video, then decided for some reason not to play it. You will need other contextual data to guess why that might be.

play
`-sessionID-, play, 2021-02-25T18:10:20+00:00, 0, not, not`

The play action occurs at the start of the video, when the user presses play. This can be a useful event to check for since if you don't find it in a session, you can be sure the user didn't start the video. 

end 
`-sessionID-, end, 2021-02-27T17:30:18+00:00, 50, not, not`

The end action indicates that the user watched the video to the very end. This can be useful if you want to filter through sessions to see what percentage of sessions reach the very end of the video. 

seek.backward
`-sessionID-, seek.backward, 2021-02-27T14:53:02+00:00, not, 35, 29`

Something to watch for is where users wind back. If you're teaching them something, they might be winding back because they don't understand a concept, or they can't clearly see something in the video. They could also wind something back because it's interesting, or they weren't paying attention and need to rewatch it. 

seek.forward
`-sessionID-, seek.forward, 2021-02-26T09:52:56+00:00, not, 29, 38`

Seeking forward can mean a lot of things. If you've made an instructional video, a user could seek forward to surf through the video and see if they can find the information they need more quickly. They could seek forward because a concept was well explained, and they're ready for the next concept or step. It could also be because they feel bored. It will depend on the video and your analysis of the content vs. the behavior. 

## Grooming the Data
To do find patterns in your data, you can start trying to filter it to see if you find something. Here's a few ideas I started with: 

* See if there's anything repetitive that stands out when I scroll through the csv that gives me ideas. 
* See if there's anything weird that stands out. 

NOTE: Obviously looking at a table of data when there's tons of entries isn't the best way to review your information. It's a starting point that acquaints you with what it looks like and what you have. You should always make sure to come up with questions to investigate where you can filter content to answer them or visualize your content. 

* Come up with ways to filter things that might display a pattern. This would be where you ask questions like, how often do people seek backwards or forwards? What do they seek backwards and forwards across the most? 
* Come up with ways to count things that might yield interesting information. For example, how many sessions are there where people watch the video all the way to the end? 

### Browsing the Data
As luck would have it, when I started browsing the data I did notice something I thought was odd. There were a lot of instances where it looked like a user wound the video back from the very end to the very beginning. The lines of data looked something like this: 

`-sessionID-, seek.backward, 2021-02-28T15:49:44+00:00, not, 50, 0`

Why, I wondered, would a user want to rewind all the way to the beginning? Examining the data, I noticed something else- the seek.backward like this happened after the end event in the same session.

`-sessionID-, end, 2021-02-28T15:48:42+00:00, 50, not, not`

I checked and discovered the player for this project is set on loop. I didn't know before, but now know that if your player is looping, the loop is shown as two events - first, end. Second, seek.backward to 0. Instances where I found this in the data were spots where someone left the video running and it just looped around again. 

### Filtering the Data
To filter the data, I decided to start with an easy question. Out of the total number of sessions I pulled, how many sessions did a user watch the video all the way to the end? 

NOTE: You need to have pandas installed in your Python environment.

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From this data, we get answers to questions I have not asked yet! Starting with the first question, what percentage of users watch all the way to the end? To answer this, we must figure out how many sessions we have total. That's easy - we know that 'ready' only occurs once per session, and indicates that a video is ready to be played. From our chart, we can see we have 725 instances of ready. So that means 

Answer: 25.5% of users! That's great. Especially because in an instructional video, people often learn the concept, and leave immediately. The end of this video is an interesting shot of bells chiming in a church, which 25% of viewers liked enough to stay for, even though it could be argued that you learn everything you need about this demo by about the 42 second mark - where you see the video successfully play on Discord. I would interpret this statistic as an indication that the content is educational and entertaining. 

We also learn something that might seem a little strange, we have 725 sessions, but of those 725 sessions, people only played the video 582 times. That means about 80.28% of users watch the video, but about 19.72% don't. That might seem weird - who goes to a page to see a demo then says nope nevermind? A possible answer lies in the page the video is shown on. Here's an image for you: 


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So we can see the video here, but check out the choices we offer below the video - Blog Post and Github. As you may know, some developers don't want to read blog posts or watch videos. They just want a code sample. So a possibility is they come here for the demo, then they see we offer the code, decide that's what they prefer, and away they go! 

What I could maybe be looking at here is the answer to the question 'What percentage of developers prefer a code sample to a written tutorial or demo?' (specifically for our company, don't quote me on this one). I could pull more of api.video's demo analytics and see if the percentage stays consistent across the demos. With a tool like HotJar, I could figure out if we have a lot of users that immediately click on that GitHub link or not, and start building a case for my hypothesis, or disproving it.

Because we're creating educational content, I could maybe use the ~20% statistic to make a case for always including code samples for our demos on github. You could use this information other ways too. If your video was a movie or a short film for example, you might want more people to watch the video than 80%. Maybe it would lead you to test other page designs, or changing the thumbnail for your video to make it more interesting to everyone. 

Okay well this was fun. Let's try some more filtering and a little bit of visualizing. 

### Visualizing the Data
There are lots of ways to visualize your data. For this post, I decided to keep it simple, and use a graph you can create with plotly. But before we get to the tools, how about the question? I decided to plot all instances where somebody is seeking forward or backward. 

For my first try, I thought, why not use the index to plot each instance of winding forward and backward, and I'll use colors. I narrowed my data down to just rows that contained seek.forward or seek.backward. Here was my code:

In the sample, I take all the master file with all my sessions for this video, and then I choose only rows that contain seek in the type. I output this into a new file 'discord_seek_sessions.csv'. 

Now it's time to try making some kind of chart. I went with something simple. I thought maybe I can use the index to represent separate events. Each event gets its own index marker. It came out like this: 

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There's a lot going on. There's so many entries, they scroll off the top and bottom right side of the page. If it's not clear, the legend on the right represents the time interval in seconds someone used seek.forward or seek.backward. The line represents the color that will be displayed in the graph. On the graph, across the bottom you can see the time in seconds. Up the left side, is the index that went with the event displayed. 

Let's make this better. The first thing I tried, was regrouping everything. I gave a unique index to anything with the same session ID and seek direction. That way each session would have two bands where you could see the lines. It didn't improve the chart's clarity, but the code for it is neat and will help us later, so I'm going to share that for you here: 

If you ever need to assign an index value based on a group, this code will help you! But it didn't help with our situation. That led me to the next idea, what if I split the sessions into separate dataframes? or csv files, depending on how you prefer to do things? 

This yielded a bit more clarity. Also I chose to put the data into separate csv files, this is the code for that:

Checking out the different files in my chart, I used the groupby trick I just told you about. Because there is only one type of action per chart, it places every instance of a particular seeking action on one line. A little less busy, but not a big improvement. 

The next thing I thought of was going back and counting how popular it was to wind either forward or backward at any point in the video. The code for that is pretty simple, and I decided to use it for winding forward: 

Now it's getting interesting. What if I make a chart of the most popular place to forward.seek from? It looks like it's about 33 seconds into the video. (See?) 

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Let's make the chart now! We can reuse some code from earlier, and filter for instances when from = 33. 

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Why is seeking forward so popular here? If you watch the video, you'll note that this is the part where Doug walks through using Discord. If you are using the tutorial because you like Discord, it's possible some users are really familiar with Discord already. So during this section, they seek forward to find out what comes next. When it gets to where the video plays, 25% watch the video, everyone else says aha! I've learned how to do this now I will go implement what I've learned etc. 

You can repeat this process with other areas in your video. For example, in this video, there's high seek forward rates from around 15 seconds through to 22 seconds. In the video, this is the part where you wait for an upload to complete. If you were trying to optimize the video or edit it, you might make a case that part of this section could be edited out. 

While we're applying this to an educational demo, you could easily apply this kind of simple research to short films and movies. Find out where people fast forward to learn if a section of the film isn't working the way you planned. If someone keeps winding a section back to rewatch it, maybe it's because it's an incredible scene, and you can make sure to do more things a similar way in the future. 

Hope this was a fun start to analyzing your content! See you next time.

Using analytics to analyze where to edit a video

Videos - Upload a Thumbnail

Video thumbnails have the power to make a video more watchable. Thumbnails give people an initial impression of what your video is about. 
If you have time, the best thumbnail for a video is going to be one where you choose something representative of the video's content, and then add it. But sometimes, you might not have the time to manually choose the perfect thumbnail for every video you create. Sometimes, you might just want to quickly add a thumbnail that represents a frame from the video, and move on. 
You can easily accomplish this with FFMPEG for Python. 

## Pre-requisites

* Mac that's OSX10.7 or higher (as always, you can tweak the instructions for Linux or PC if you want to try that but it's a Mac focused tutorial)
* Homebrew - if you don't have it we'll include a little about installing it
* Xcode - you can add this as part of a homebrew installation
* api.video account - you can get one for FREE right [here](https://dashboard.api.video/register)
* Python
* requests module for Python
* ffmpeg-python
* Homebrew - installation of FFMPEG is much easier with homebrew

## Install FFMPEG
Sometimes an FFMPEG install can get complicated. You'll see a lot of people online asking why their installation doesn't work. If you have a Mac, there is an easy answer to this problem...hopefully. Let's go over one simple way you can install FFMPEG so it starts working at the command line for you right away. We're going to use the homebrew method, because it makes installation simple if you already have homebrew installed.

## For Users Who Already Have Homebrew
If you've got homebrew installed, all you need to do is this:

`brew install ffmpeg`

That's it. It should install without issue. (Famous last words right?) Test that it works with this:

`ffmpeg -version`

If it's installed, you will get back a bunch of information about your configuration. If not you will get an error of some type. Troubleshooting installation is beyond the scope of this tutorial, but you can post questions in the [community forum](https://community.api.video) and we'll help you figure it out.

## For Users Who Don't Have Homebrew
If you fall into this category, you may be wondering what homebrew is. It's a free, open-source software package manager. It simplifies installation of software on macOS and Linux.

To install homebrew, you'll need to run this command:

`/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"`

If you don't have Xcode, or the appropriate Xcode components installed, you'll have to install those. Fortunately, you'll be told what's missing so you can install it. Follow the instructions to install and if you run into problems, there is a lot of documentation online to help you through. We can't be more specific for Xcode, because there's a lot of ways your installation can differ from another one. However, if you need help, feel free to contact us in the 
[community forum](https://community.api.video/).

## Thumbnail Creation Method - Using FFMPEG 

You can use ffmpeg-python to create a thumbnail and upload it for use with your video. If you don't want to get into learning FFMPEG, you can also use api.video's endpoint that allows you to pick a time from the video, and have it set as the video thumbnail for you automatically. 

NOTE: The thumbnail must be .jpg or .jpeg and 8MB or smaller.


If you're interested in trying it with ffmpeg-python, then here's the code: 

Let's dig in a bit to what's going on here. First, to make the sample easier to work with, we have the file we want to take a thumbnail from in the folder. Our thumbnail will output to the same folder. 

Next in our code, we create a function that will create a thumbnail for us. It takes the path to the file we want to grab a thumbnail from. Then we retrieve the length in seconds of our movie. Right now this function takes a thumbnail from about halfway through the video it receives. It then outputs the file, named 'THUMBNAIL.jpg' to the same folder where we can check it out if we want. 

Now we're ready to upload our video and thumbnail. 

## Upload the Video 

The code for this is straightforward. You can upload a video using one of these options: 

* [Delegated Uploads for Videos Large and Small](https://api.video/blog/tutorials/delegated-uploads-for-videos-large-and-small-python/)
* [Upload a Big Video File Using Python](https://api.video/blog/tutorials/upload-a-big-video-file-using-python/)
* [Upload a Video From Your Computer with the api.video API (Python)](https://api.video/blog/tutorials/upload-a-video-from-your-computer-with-the-api-video-api-python/)

Make sure you grab the videoId for your video. 

## Add Your Thumbnail 

The code to add a thumbnail looks like this:

In this code, you retrieve your access token, then you set up your headers and URL. The URL must contain the videoId for the video you want to add a thumbnail to. Add your thumbnail as a binary file. You can check the json response to make sure everything was a success, and double check by looking in your dashboard at your video. Sometimes the new thumbnail populates to the video faster on the individual page for viewing that video, so give it a few minutes and then hit refresh to be sure your thumbnail was added. 

You could potentially use this method with some tweaks to create a short trailer of thumbnail images. We'll go over how to do that next! 

Don't hesitate to ask us questions on [our community forum](https://community.api.video/)!

Create a thumbnail for your video with Python and FFMPEG

Create a Thumbnail For Your Video with Python and FFMPEG

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Video - Pick a Thumbnail

There are several ways you can add a thumbnail to your video content with api.video. If you're doing a live stream, you can add one with the Upload a Thumbnail endpoint for live streams (I go over how to do that with Python here - [How to Add a Thumbnail to Your Live Stream](https://api.video/blog/tutorials/how-to-add-a-thumbnail-to-your-live-stream/)). If you want to choose and add a thumbnail for a video yourself, you can do that with Python and FFMPEG ([tutorial available here](https://api.video/blog/tutorials/automatically-add-a-thumbnail-to-your-video-with-python-and-ffmpeg/)). And you can also add a thumbnail by telling the api.video API what frame you want to use. api.video will then set that frame for you. If you choose a frame that's not within the range of your video, the default will be selected, which is the frame at 00:00:00:000. 

## Prerequisites

* api.video account - [Sign up here](https://api.video).
* A video to add a thumbnail to - [Free sample videos](https://www.learningcontainer.com/mp4-sample-video-files-download/) 
* Python installed with the requests library 

You can also check out the API reference documentation for video thumbnails here: [Upload a thumbnail](https://docs.api.video/reference/api/Videos#upload-a-thumbnail)

## Upload Your Video and Retrieve the videoId

To add a thumbnail, you first need to choose a video you want to add a thumbnail to. Do the following:

1. Upload a video to your api.video dashboard if you haven't already. You have a few choices for how to accomplish this: 
	* Use the GUI to add a video to your dashboard 
	* If your video is under 128 MB, [use these instructions for upload](https://api.video/blog/tutorials/upload-a-video-from-your-computer-with-the-api-video-api-python/).
	* If your video is over 128 MB, you can use the [Upload a Big File Using Python](https://api.video/blog/tutorials/upload-a-big-video-file-using-python/) tutorial.

2. If your video is already uploaded, you can retrieve the videoId from your dashboard, or you can use the [list all videos](https://docs.api.video/reference/api/Videos#list-all-video-objects) endpoint to retrieve and browse through your videoIds for the one you need. I've included the code for retrieving videos here, if you need it. 

## Add a Thumbnail 
To add a thumbnail, you have a few ways you can do things. If you wanted something you could repeat in bulk for each video, then you'd need to choose a spot that you always choose the thumbnail from. You could plan to always choose a thumbnail from five seconds into each video, but what if the video were shorter? While that's unlikely, if this were to occur, api.video automatically adds a thumbnail from 00:00:00:00. 

The code for adding your thumbnail looks like this:

## Add a Thumbnail from Halfway Through the Video
We just saw how to add a thumbnail to a video, and learned how the thumbnail defaults. But what if we wanted to create a thumbnail that's always about halfway through our video? We can use the [Show video status](https://docs.api.video/reference/api/Videos#retrieve-video-status-and-details) endpoint to retrieve the length of the video in seconds. 

Here's the code for retrieving the length of the video:

The duration will be returned in seconds. You can divide this in half using '//' in python to get an integer, and then create the timecode using the datetime library. 

The code to set up your request with everything, including a timecode, would look like this:

You can add thumbnails this way for any video. Happy coding! 

Choose a thumbnail with api.video's pick a thumbnail endpoint 

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JavaScript

You can use api.video's analytics with Observable to create a lot of different charts. We've already made a [choropleth](https://api.video/blog/tutorials/use-observable-to-create-a-choropleth-map-of-your-viewers/), [a map that shows viewer density by marker size](https://api.video/blog/tutorials/api-video-analytics-add-markers-representing-viewer-density-per-city-to-a-map-wit/), and now today we'll create a pie chart.

## Prerequisites
For this tutorial, you'll need: 

* api.video account - sign up here
* Observable account - sign up here
* Python installed with requests, pandas, csv



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## Retrieve Your Data
To retrieve your data, you're going to authenticate with your api.video API key. Then when you have the token, you retrieve all live streams, VODs and the sessions associated with them. Before going into the code sample, if you want you can check out the API reference documentation for live streams here: [Live streams](https://docs.api.video/reference/api/Live-Streams#list-all-live-streams)

## Create a DataFrame with Your CSV File
With this code snippet, we'll create a dataframe, and drop information down to only what we'll need for our pie chart. The other information was included for reference, and in case you want to do something else with this subset of data.


The code sample assumes your csv file is in the same folder as your code. 

Now we're ready to build our map in Observable.

## Configure Observable

In this section, we walk through how to get your pie chart set up on Observable. If you haven't done so, sign up for an Observable account. Then do the following:

1. Navigate to our Observable notebook [Pie Chart for api.video Analytics Data](https://observablehq.com/@erikkai/pie-chart-for-api-video-analytics-data).
2. Click the three dots in the upper right corner. A menu opens.
3. From the menu, choose **Fork**. Now you have your own copy of the map to edit and play around with.
4. Click the three dots in the upper right corner again and choose **File attachments**.
5. Add the csv file you made using the pandas code. 

Everything should work. You might find that the count for each operating system doesn't show, or the operating system name doesn't show if the pie slice would be too small. You can tweak the titles in the CSV to make them shorter, leave it, or you can try a [Donut Chart](https://observablehq.com/@erikkai/donut-chart) to see if that looks better. 

There's lots of things you can learn from your data. 

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## Analyzing Your Data
You can analyze your data many ways. For our example, because this is an api.video account, we might use this data a few different ways. We might assume that creating content suited to Mac and Linux is ideal, because half of our viewers are looking at demos from platforms that are Mac or Linux related. But, we might also make a choice to create more Windows focused content, because we have a lot of viewers from Windows operating systems. 

If we were building demos for mobile, this pie chart might indicate that Android demos would be more popular than iOS. 

Another way to interpret the data would be that we have less viewers from certain platforms because we don't target their use cases in our demos as much. With trial and error, we could use this data to make some educated guesses, tweak our content strategy and then come back and check the data. 

You could also combine this information with details about what country a viewer is from, and then figure out if certain regions have platform preferences. 

Have fun playing around with your data! 

Which operating system is most popular with api.video Analytics

api.video Analytics: Create a pie chart showing what operating system is most popular to view

codec

video create

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Cédric Montet

At api.video, our goal is for video to be easy and affordable for anyone to build, be it individuals or organizations. We understand that achieving this goal means reducing costs at some stage of the video streaming process. That’s why, we now offer adaptive bitrate encoding for your videos at zero cost. 

&nbsp;

Yes, you heard me right. 

&nbsp;

**Encoding videos will be absolutely free for all video qualities (1080p or 4K) at [api.video](https://api.video/) - no hidden calculations, footer notes or tricks.**

(Check out this [video](https://www.youtube.com/watch?v=ouosgLbW7Ts) for more details) 


## A step towards making video streaming affordable

Video encoding is crucial as it makes it possible for videos to be shared and played smoothly on the internet. It is why we are easily able to consume various types of video media, whether it’s a YouTube video, our favorite shows on Netflix, or even catching up with friends across the globe on Zoom. Video encoding basically makes the world go around in regards to anything related to video and video content.

&nbsp;

We at api.video recognize that. From [increasing the transcoding and playback speed ](https://api.video/blog/product-updates/2021-in-review/)to building [specialized hardware like ASICs](https://api.video/blog/video-trends/the-comeback-of-specialized-hardware/) cards to increase our transcoding capacities, we've been working to make video streaming easier for you every day. And after dedicating ample time to engaging with our customers and learning about their concerns, free encoding seemed to be the next step in this direction. 

&nbsp;

So, we worked tirelessly behind the scenes to give you what no one else will - free encoding for all video qualities, be it 360p or 4K. 

## How does free video encoding benefit you as a customer

The benefit is straightforward: you save a LOT of money!

&nbsp;

- With free video encoding, you will have one less thing to worry about. As you know, you pay for three things for streaming videos — encoding, hosting, and delivery – out of which encoding is the most expensive, especially if you have a lot of videos. Free video encoding will allow you to focus your attention and resources to what is most important.

&nbsp;

- As the video needs of your app users grow, you might need to scale up your encoding capacity. Free video encoding services will give you the power to scale without any additional costs, meaning your users can handle more videos without increasing your expenses.

&nbsp;

- You can [migrate from your previous provider to api.video](https://api.video/compare/azure-media-services/) without paying an ‘entry tax’ to us. With free video encoding, you no longer need to spend money on encoding videos with a new provider when you have already paid for encoding with an older one.

&nbsp;

Now with all the saved cost, you can allocate your resources and money to a place where it is actually important – building your product and delighting your users. 

## How to get started

To use the free video encoding benefit, getting started is easy. 

&nbsp;

- If you’re a new user, [sign-up](https://dashboard.api.video/register) for a free sandbox account and start uploading your videos
- If you are looking to migrate from another vendor, [import](https://import.api.video/) your videos from your current vendor
- If you have any questions, [talk to us](https://meetings.hubspot.com/apivideo/talk-to-us) by booking a slot in our calendar

&nbsp;

To know more about our[ features](https://api.video/features/), [pricing](https://api.video/pricing/) or ask any other questions, [talk to our team ](https://meetings.hubspot.com/apivideo/talk-to-us)and we will help you out. 

&nbsp;

Or, you can straightaway [sign-up](https://dashboard.api.video/register) for a free sandbox account and check our product out for yourself. 


Huge pricing update: Introducing video encoding at no cost

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api.video

Modern technology has been helpful for an increasingly fast and simple method of consuming various types of video media, whether it’s a YouTube video, our favorite shows on Netflix, or even catching up with friends across the globe on Zoom. Despite the relative ease of doing anything related to video, a few complex processes take place behind the scenes, away from public view. One such process is video encoding.

Video encoding basically makes the world go around in regards to anything related to video and video content. Thanks to the technologies behind the video encoding process, it has become much easier for people and companies to share video content on the internet. But what exactly is video encoding, and what happens during the process? We’ll take a closer look at this and much more in this handy guide to video encoding.

## What is video encoding?

In a nutshell, video encoding occurs when you take raw video footage, either from a digital camera or an analog film camera, and digitally compress it to make it a more manageable file size. Video encoding happens daily in both professional and personal capacities, and many people don’t realize it.

But, without encoding, services like Netflix and YouTube would not exist – or, at least, not in the same way that it does today. Without it, we couldn’t send videos of our beloved pets to our friends and family as easily as we can today.

## Why is video encoding used?

There are a variety of reasons why video encoding is used, but all factors point to the single function of video encoding – **compression**.

This is done to ensure optimal performance and compatibility with a selected suite of software and hardware, be it for DVD/Blu-ray playback, mobile viewing, online video streaming, or general video editing. A file's video and audio information undergo transformation and compression during the encoding process, all in accordance with the requirements of the selected encoding standard.

On a professional level, the ability to compress raw video into more manageable sizes makes it easier to transmit video content such as training, promotion, presentation, and so on over the internet. 

On a personal level, video encoding makes it much easier to send videos taken from your phone directly to friends and family. Most modern-day personal devices have built-in video encoding capabilities to allow for this to happen without a second thought.

To put things in perspective, uncompressed or unencoded video can have a data rate that’s as low as 3,000 Mbps and as high as 12,000 Mbps, depending on the length and quality of the video recorded. 

**Without encoding, it would take up to days for anyone to not just transmit video, but to be able to consume it, too.**

## Differences between video encoding and video transcoding

You need to be familiar with two more [essential terms related to video encoding](https://api.video/what-is/) – transcoding and codecs.

Transcoding relates to the conversion from one digital encoding format to another. It takes the already encoded video file, decompresses it, and then alters it before re-encoding or compressing it into a different codec. Codecs are what allow encoding to occur in the first place. They are software or hardware that gives us the ability to compress video to make them more easily stored and delivered tightly.

While you may hear “encoding” and “transcoding” used interchangeably, the two terms actually mean different things. Putting it succinctly, encoding is the initial process of compressing a video from a source – such as a camera – into a codec, while transcoding takes an already encoded video file to convert it into a different codec.

## Examples of video codecs

In order to understand video encoding, you must first be familiar with two interrelated terms: [video codecs and video formats](https://api.video/blog/product-updates/every-video-format-codec-and-container-explained/). These also tend to be used interchangeably, but much like the terms encoding and transcoding, they refer to two different things.

Video formats are the type of files in which the compressed video/audio is stored. Also known as a “container”, you can tell what a video format is by looking at the file’s extension — the three to four characters that sit after a period in a file name. Here are some of the most common video formats used today:

* MP4
* FLV
* MOV
* MKV
* LXF
* MXF
* AVI
* WebM
* QT

On the other hand, **the codec is short for “Coder / Decoder” (compression / decompression)** and relates to the video compression algorithms that allow encoding to happen. Over the years, codecs have gone through multiple iterations as it coincides with the increasing sophistication and rise in the quality of filmmaking. 

If you’re a content creator, you might be aware of some of the video codecs that exist today:

- **H.264 – also known as AVC, this is the most widely-used video codec available on every platform and mobile device.**
- H.265 – also known as HEVC, stands for High-Efficiency Video Coding, and it’s slowly overtaking H.264 as the standard. This codec can compress video much more efficiently than its predecessor.
- **VP9 – is an open-source, royalty-free codec developed by Google and is considered the direct competitor of H.265. The video compression codec VP9 was created to reduce file sizes, making streaming videos simpler.**
- **AV1 – is another royalty-free, open-source codec released in 2019 by the non-profit Alliance for Open Media. It was developed to become the successor to Google’s VP9 and contend with HEVC more directly.**

## How do different technologies handle video encoding

As you may have suspected, there are a majority of ways that different technologies handle the video encoding process. Let’s take a look at the most common technologies that use video and how video encoding works on each of them:

### Video editing software

When it comes to video content creation, a majority of people will use one of two video editing software — Adobe Premiere Pro or Apple’s Final Cut Pro. Both of these video editing software handle video encoding in very similar ways: when exporting the finished video.

Depending on which device the video content is recorded on, it will already be encoded and formatted according to the settings of the device. For those using iPhones recording 1080p video at 25 frames per second (FPS), for example, the iPhone will automatically encode the video using H.264 and contain it in a .mov file format. The video editing software will “open up” the container to get to the encoded, compressed video file within so the editing process can begin.

After editing the footage is complete, it will need to be exported, which typically concerns video encoding. The edited raw media taken from a recording device such as a camera will be encoded via the codec of the person’s choosing before being put into a .mp4 container once they’ve hit “export”. As we’ve mentioned before, the most common codec used in video content creation is H.264 for HD videos, with 4K videos using H.265. 

### Websites

Different websites utilize different techniques to handle video encoding – with a majority of the major sites like YouTube, Netflix, and so on using their own technologies to handle the process. A majority of social media websites allow you to upload video files directly onto the site to share, although most of them require the files to be saved in specific containers. 

These websites will take your video file and transcode it before using their own systems and serves to re-encode it to match multiple display resolutions. This process will happen regardless of your video file's original codec and settings. Different websites have different processes, but companies that don’t have the capacity to build their own video encoding systems can use a third-party application programming interface (or API) to handle this process. These APIs act as [dedicated video encoder](https://api.video/what-is/video-encoding/) that allows companies to add on-demand videos to their websites more efficiently.

Using [api.video](https://api.video) as an example, all of the videos submitted to the platform will be transformed into H.264 codec in an MP4 container. This may be done using transmuxing and/or transcoding.

We expect the video bitrate to be lower than 16 Mbps for up to 1080p resolution and 50 Mbps for up to 4K resolution. We accept any bitrates, however, we will scale them down to the limits mentioned because anything above is usually challenging for most viewers’ connection to watch.

### API

The ability to generate, store, and distribute video is made possible via a video API. [api.video](https://api.video)’s solution can be tailored to suit any specific need. 

The backend process of video on demand (VOD) is exceptionally complex, even if you’re a software programmer specializing in video. It is necessary to encode, package, and typically transcode the data before sending it to any of several possible destinations. All of these things, and more, may be done with minimal fuss using api.video.

Our API basically keeps the complexities of streaming media hidden from view, so you can rapidly develop apps with as much control as you need. This saves time and effort so developers can concentrate on making the product.

## Conclusion

As you can see through this video encoding guide, a lot of work goes into making the video you watch and the video you share possible. Without video encoding compressing raw video footage, it could take days to upload and transmit video content over the internet and perhaps even longer to be able to download and enjoy them. 

The best part about encoding at api.video is that is offered at ZERO cost. You will only be charged for delivery and hosting of your videos, and the encoding will be free. Now integrate your videos in minutes with api.video or [contact us](https://api.video/contact/) for more information.

A Guide to Video Encoding & Video Codecs

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Encoding and transcoding are often used interchangeably but they have slightly different definitions. **Encoding is the initial process of converting raw video files into a specific digital format or codec.** It involves compressing and packaging the video data, often to reduce file size while maintaining acceptable quality. Encoding prepares the video for storage, transmission, or playback.

&nbsp;

**Transcoding, on the other hand, involves converting a video from one digital format or codec to another.** This is typically done to optimize the video for different devices or platforms, adapt to varying network conditions, or ensure compatibility with specific playback requirements. Transcoding occurs after encoding and often involves changing resolution, bitrates, or formats without altering the content itself.

## What goes on behind encoding a video?

It's easy to say that encoding is when you go from an _uncompressed source_ to a _compressed source_, but you may ask, what exactly do compressed and uncompressed sources mean?

&nbsp;

Uncompressed sources are video files that contain data that comes directly from a camera sensor vs. encoded video. In the digital space, it's SDI (Serial Digital Interface) or HDMI (High Definition Multimedia Interface). Both of them are popular standards. You'll find HDMI in places like your GoPro camera, or a mini camcorder. These days, you might also find HDMI in professional settings, but SDI is more common due to its reliability. Another example of an uncompressed source would be something analog, for instance, VHS tapes.

&nbsp;

A compressed sources, on the other hand, are video files that have undergone compression during the encoding process. Compression involves reducing the file size by removing unnecessary information or encoding the data in a more efficient format. This process results in a smaller file size, making it more manageable for storage, transmission, and playback.


## What are the types of transcoding methods?

There are three types of transcoding, depending on what you are trying to do to get a video to suit a particular browser, player, or platform:

* **Lossless-to-lossless** - This type of transcoding maintains the quality of your video across formats but allows you to take advantage of better hardware or compression algorithms. 
* **Lossless-to-lossy** - This type of transcoding will decrease your video in quality, but it will benefit you in ways such as – you will get a smaller and/or faster playing file, or a file that is compatible with the requirements of a particular platform, player, or browser.
* **Lossy-to-lossless** - Sometimes people think this means they are regaining quality. This isn't the case in this type of transcoding. If you transcode from a lossy format to a lossless format, all that it means is that you will not lose additional quality in the conversion process. You will not regain data you already lost through compression.

## Codecs for encoding and transcoding

For both encoding and transcoding, you will need to make use of codecs. A codec's job is to compress and decompress video files. It's right there in the name -  **co**mpress **dec**ompress or encode decode. So basically, codec is an algorithm that takes the video and shrinks it or changes it into a format that makes it compatible with the requirements of wherever you want the video to play. There are many codecs because there are many ways to compress a video or livestream for playback.



## What is transmuxing?

Now that you know the difference between encoding and transcoding, let's throw one more term in there - transmuxing. Transmuxing is when you change the container for a video instead of the format. It's a way of repackaging a video to make it compatible somewhere without changing what's in the file. 

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This process is faster than encoding or transcoding and takes less computing power because you only change the container, not the file. It's great when you need to quickly make something available, but the downside is that it doesn't handle things like making multiple video resolutions available. If you choose a container type that's incompatible with your video file, you may also cause compatibility issues, resulting in not being able to play the video.

## In closing

* **Encoding** - Encoding happens when you compress a raw data file from a video camera sensor or an analog source like a VHS tape. 
* **Transcoding** - Transcoding happens when you take an already compressed video file and change it to another format.
* **Transmuxing** - Transmuxing happens when you change the container of a video file but leave the video file untouched. 

We hope that explains the difference between encoding and transcoding clearly. 

_If you are looking for an affordable video API solution that does not burn a hole in your pocket for transcoding and encoding videos, check out our offerings on our [homepage](https://api.video/) or [sign-up for a free account here](https://dashboard.api.video/register)!_

Encoding and transcoding: what's the difference? 

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Doug Sillars

Once I upload my video, how quickly will it be ready to be viewed?

Our reply to this question has always been “it's really fast.”  And it is really fast. But, the dev team gave me access to the backend database, and pointed me to a few tables that might be helpful (thanks to Thomas Persohn for your patient help!)

Thus armed, I waded into a sea of data to better understand how fast our videos are encoded at api.video

## Encoding

When a video is uploaded to api.video, it is re-encoded to a HLS stream, with several different video dimensions and bitrates.  This allows api.video to deliver properly sized video to all users - no matter the device or network connection. (Read our post on [Responsive video delivery](https://api.video/blog/video-trends/responsive-video-delivery/) to learn more.)

> Summary:  All videos are encoded at 240p, 360p, 480p, 720p, 1080p and 2160p. However, the videos are only encoded up to the initial size of the video (so if the original video is 720p, there will not be 1080p or 2160p versions available). 

**FYI:** For our customers, encoding is absolutely free – we offer adaptive bitrate encoding at zero cost. 

How long does this step take? If a customer uploads a video to our service - they want to know how efficient api.video creates the content for viewing.

### Data in the study

We are constantly making adjustments to our infrastructure to speed the encoding step of our video (and that is one reason this post is dated 2H 2020 - we expect to be faster in early 2021). The last major adjustment was made in mid October, so the data presented in this study from every video uploaded to the api.video service between 15 October and 2 December 2020.

### Parameters that affect encoding

There are 2 major parameters that affect the speed of encoding a new video stream: the output dimensions and the length (duration) of the video.  

#### Dimensions

The output video dimensions have a huge impact on video creation - a 3840 × 2160 (or 4K or 2160p) video has millions more pixels than a 320x240 (240p) video, so it takes a lot longer to create.  In general, the videos exit the encoding process from smallest to largest.

#### Duration

If a video is 30 frames per second, and is 60 seconds long, there are 1800 frames to be created.  If a 30 frame per second video is an hour long 3600s), there are 108,000 frames to be created, so the longer the video - the more time for creation.

## Analysis

To better understand the encoding time, I divided the data by the size of the output video size, and looked at 2 lengths of video  - those under 5 minutes, and videos that are 45-75 minutes long (we are often asked how long a 1 hour video takes to encode).

I calculated encoding time for each bucket of video using percentiles.  

> Quick review - percentiles break the data into 100 evenly sized segments, so the 25th percentile is when 25% of the videos have been encoded. The median is the 50th percentile, and is when 50% of videos have been encoded.  We report here to the 95th percentile for video encoding.

### Short videos - under 5 minutes long

Many of the videos uploaded to api.video are short - under 5 minutes long.  These videos are generally ready to play in a few seconds:



The above chart has a lot of data, the 25, 50, 75, 90 and 95th percentile encoding times for all 6 variants of the video that api.video creates.

The median video is ready to play (at 240p) in 5.6 seconds, but a 720p version will be ready in 15s after encoding begins, and the 4K version will be ready in 37 seconds.

95% of all videos (under 5 minutes) will be playable in 32.6s, and will have 1080p versions ready in just over 2 minutes (140s).

### Long videos - 45 - 75 minutes long

Our most frequently asked question on encoding is "how long for a 1 hour video to be encoded and ready?"  Here are the numbers:


Observant viewers will see there are no 4K videos in this report. At this time, there are very few 4K streams that reach the 1 hour timeframe, and the data was quite noisy.  Hopefully, more of our 'long uploads' will add 4K support, so we can add this metric in our next update.

The median hour long video (red line) will be ready to play (at 240p) in just over 9 minutes (555s), and a 720p version will be ready about 18 minutes after encoding begins (1099s).

95% of hour long videos will have a 240p version ready in 33 minutes, and 720p in 54 (3230s).  the 1080p version will be available for 95% of videos in 108 minutes,

## Conclusion

Short videos will be ready to play - even at high resolution - in under a minute: the median 4K encoding for a short video is 37s. 

Hour long videos take longer to encode, and the median 720p video will be ready in 18 minutes.  95% of 720p videos will be ready to play in 54 minutes.

This is really fast! You can have responsive streaming video ready for your customers in just a few minutes with a simple upload!

That said, our team is working hard in the background to improve our infrastructure to make our encoding speeds even faster! 

We're really excited to have hard numbers to share with you on our encoding speeds, and we cannot wait to re-benchmark this data in 2021 to see how much faster your videos will be encoded as we improve our service.

Do you have questions about api.video or video encoding? Reach out on our [community page](https://community.api.video).


How fast does api.video encode video? 2H 2020 data

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