-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
e064bf0
commit 46f2034
Showing
32 changed files
with
472 additions
and
269 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
14 changes: 14 additions & 0 deletions
14
docs/computer-science/interview-question/system-design-youtube.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,14 @@ | ||
| # System Design of YouTube | ||
|
|
||
|  | ||
|
|
||
| 1. The user creates a video upload request and provides the video files along with the details about the video. | ||
| 2. The raw video files are uploaded to an Object Storage (such as S3). | ||
| 3. Also, the metadata is saved in a database as well as a cache for faster retrieval when needed. | ||
| 4. The raw video files are sent for transcoding to a special transcoding server. Transcoding is the process of encoding the videos into compatible bitrates and formats for streaming. | ||
| 5. The transcoded video is uploaded to another object storage. | ||
| 6. The notification for transcoding completion is sent to a special service via a message queue. | ||
| 7. The Transcoding Status Handler updates the metadata DB and cache with the latest details of the video. | ||
| 8. The user raises a video streaming request that goes to a Content Delivery Network (CDN). | ||
| 9. The CDN fetches the video from the object storage for streaming. It also caches the video locally for subsequent streaming requests. | ||
|
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,25 @@ | ||
| # Fraud Types | ||
|
|
||
| ## Financial Frauds | ||
|
|
||
| | Type | Description | | ||
| | -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ||
| | **Document Fraud (First party)** | This classifies under first party fraud where applicant commits fraud in his own name. Applicant provides an altered / fabricated document which does not match the real document at the time of verification with data source. Usually done for National IDs like PAN, Voter ID, etc. However it can be applicable to other types of documents as well which are submitted at the time of loan application. | | ||
| | **Identity Fraud (Third party)** | This classifies under third party fraud where applicant commits it in the name of another person (victim). Applicant deliberately uses personal information of another person without any approval, for the sole purpose of performing a fraud. Hence, the 3rd party here becomes the victim. | | ||
| | **Synthetic Identity Fraud** | It is created by using a combination of real and fabricated credentials leading to a fictitious entity that is not associated with an actual / real person. E.g., using A's PAN with B's address or a fictitious address altogether. | | ||
| | **Financial Fraud** | Cases where financial crimes have been committed like Cheque fraud, manipulation of bank statements, false financial statements, chargeback fraud, etc. | | ||
| | **Employment Related Fraud** | Instances of misrepresentation of information / documents on account of education, personal background or professional history. E.g., salary slip / employment letter tampering. This can be clubbed under Document Fraud if not a significant category. | | ||
| | **Non-starter fraud** | Cases where post loan disbursal the borrower does not repay starting from the first installment itself. It then clearly shows his fraudulent intention. Also known as never payment fraud, lenders can start tagging borrowers under this category once DPD has reached 90 days - as a uniform definition to classify. This is different from delinquency since it is linked to non-payment from inception of the loan and not midway in the lifecycle. | | ||
| | **First-party application fraud** | A consumer refuses to repay legitimate debts and/or falsely claims to be a victim of identity fraud to evade the obligation | | ||
| | **Account takeover** | Someone other than the owner uses an account without permission | | ||
|
|
||
| ## Other Frauds | ||
|
|
||
| | Type | Description | | ||
| | -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ||
| | **New account** | Accurately distinguish between legitimate and high-risk customer account registrations so you can selectively introduce additional steps or checks based on risk. For example, you can setup your customer account registration workflow to require additional email and phone verification steps only for account registrations that exhibit high risk characteristics. | | ||
| | **Online payment** | Reduce online payment fraud by flagging suspicious online payment transactions before processing payments and fulfilling orders. With Amazon Fraud Detector, you can setup your checkout flow to evaluate new orders and flag suspicious ones for review prior to processing payments. | | ||
| | **Guest checkout** | Spot potential fraudsters among customers without transaction histories. With Amazon Fraud Detector, you can send as little as two pieces of data from a guest checkout order (e.g., email, IP address) to assess its potential fraud risk, so you can decide whether to accept it, review it, or collect more customer details. | | ||
| | **Online service and loyalty program abuse** | Identify accounts that are more likely to abuse online services such as loyalty or ‘Try Before You Buy’ programs that ship goods for customers to try before sending payment. With Amazon Fraud Detector, online businesses can assess the risk of customers abusing programs, for example by stealing merchandise or engaging in returns abuse, so businesses can limit their risks by applying appropriate limits on the value of goods or services provided. | | ||
| | **Account takeover** | Identify fraudulent online activity by detecting accounts that were compromised through malicious takeovers, phishing, or from credentials being stolen. With Amazon Fraud Detector, you can improve account takeover detection by observing behavior patterns and treating anomalous user login activity as more risky, while minimizing the level of friction for legitimate users. Based on outcomes you receive, you can take appropriate actions on each login, for example challenge only the highest-risk authentication attempts. | | ||
| | **Product review** | Safely automate whether to approve or block review submissions. With Amazon Fraud Detector, you can reduce the number of review submissions sent to moderation queues, while reducing fraud and spam and adding friction for risky review submissions. | |
Oops, something went wrong.