System Design - General

Question 1

Latency versus Throughput

Answer 1

Latency is the time needed to perform some action.
Throughput is the number of actions per unit time.
Generally, you want to maximize throughput for acceptable latency.

Question 2

Performance Requirements

Answer 2

• Scale
• Latency
• Consistency
• Uptime
• Reliability (data loss scenarios)
• Fail open / Fail closed

Question 3

Approach to System Design Interviews

Answer 3

1. Clarify requirements
2. Functional Requirements
3. Performance Requirements
4. APIs
5. Back of the envelope estimations
6. Data Model
7. High level design
8. Individual component design
9. Monitoring & Observability & Alerting

Question 4

CPU vs Core

Answer 4

The core is the processor part of the CPU. In the past, each CPU had a single processor (a single core). Now a single CPU can have multiple processors.

Question 5

CAP Theorem

Answer 5

Consistency, Availability, Partition Tolerance

You can only have two

Question 6

Strong Consistency

Answer 6

Every read receives the most recent write or an error

Data is the same across the cluster so you can read and write to/from any node and get the same data

Question 7

Availability

Answer 7

Ability to access the cluster even if a node in the cluster goes down

Every request receives a response but the response may not reflect the most recent data

Question 8

Partition Tolerance

Answer 8

A partition means two nodes in the cluster are unable to communicate with one another

Partition tolerance means the system continues to operate even if there is a communication failure (a partition) between nodes due to network failures

Since networks aren’t reliable, you need to support partition tolerance. That means the tradeoff becomes between consistency and availability.

Question 9

Consistency and partition tolerance

Answer 9

CP system

Waiting for a response from a partitioned node might result in a timeout or an error. Good choice if your business requires atomic reads and writes.

Question 10

Consistency and Availability

Answer 10

CA system

Not possible unless you’re ok with losing data once the network failure (the partition) is resolved

Since network failures are inevitable, the real tradeoff is between consistency and availability

Question 11

Availability and partition tolerance

Answer 11

The response returns the most readily available version of the data available on any node (which might not reflect the most recent write). Good choice is you need eventual consistency or when the system needs to continue working despite errors.

Question 12

Weak consistency

Answer 12

After a write, reads may or may not see it. A best effort approach is taken.

Examples: VoIP, video chat, realtime multiplayer games. If you lose reception for a few minutes, when you reconnect you don’t hear what you missed.

Question 13

Eventual consistency

Answer 13

After a write, reads will eventually see it. Data is replicated async. Works well in highly available systems.

Examples: DNS, Email

Question 14

Strong consistency

Answer 14

After a write, reads with see it. Data is replicated synchronously.

Examples: transaction based systems

Question 15

High Availability Patterns

Answer 15

Fail-over and replication

These are complimentary, not mutually exclusive

Question 16

Active-passive failover

Answer 16

Also referred to as master slave

If the active server stops sending heartbeats, the passive server takes over.

Only the active server handles traffic

Question 17

Active-active failover

Answer 17

Both servers are managing traffic with the load spread between them.

Also called master-master failover

Question 18

99.9% uptime (three nines)

Answer 18

Just under 9 hours downtime a year (8h 45 min)

Question 19

99.999% uptime (five nines)

Answer 19

5 minutes per year

Question 20

99.99% uptime (four nines)

Answer 20

Just under 1 hour a year (52 min)

Question 21

Spanner

Answer 21

GCP high availability global SQL database

Not truly CAP but truly high availability (5 9s). One failure in 10^5 reads or writes.

In the event of partitions, Spanner becomes CP.

Question 22

Vertical partitioning

Answer 22

• Some columns are moved to new tables. Each table contains the same number of rows but fewer column
• Can be used to isolate rarely used columns

Question 23

Horizontal partitioning

Answer 23

• also known as sharding
• it divides a table into multiple smaller tables. Each table is a separate data store, and it contains the same number of columns, but fewer rows

Question 24

Horizontal Sharding Pros & Cons

Answer 24

Pros
- Facilitate horizontal scaling. Sharding facilitates the possibility of adding more machines to spread out the load.
- Reduces response time. By sharding one table into multiple tables, queries go over fewer rows, and results are returned much more quickly.

Cons
- The order by operation is more complicated. Usually, we need to fetch data from different shards and sort the data in the application’s code.
- Uneven distribution. Hot spots. Some shards may contain more data than others (this is also called the hotspot).

Question 25

Sharding routing algorithms

Answer 25

• Range-based sharding. This algorithm uses ordered columns, such as integers, longs, timestamps, to separate the rows.
• Hash-based sharding. This algorithm applies a hash function to one column or several columns to decide which row goes to which table.

Question 26

p99 latency

Answer 26

99% of requests should be faster
Only 1% of requests are expected to the slower than this number

Question 27

Web socket

Answer 27

• Most common solution for sending async updates from client to server
• The connection is initiated by the client. After that, it is bidirectional and persistent.

Question 28

CDN

Answer 28

Content Delivery Network
Content Distribution Network
Geolocates data closer to user to reduce load speeds

Question 29

BLOB

Answer 29

Binary large object
A collection of binary data stored as a single entity in a database

Question 30

Video encoding / transcoding

Answer 30

It is the process of converting a video format to other formats (MPEG, HLS, etc), which provide the best video streams possible for different devices and bandwidth capabilities.

Question 31

codecs

Answer 31

These are compression and decompression algorithms aim to reduce the video size while preserving the video quality.

Question 32

Bloom Filter

Answer 32

• space-efficient probabilistic data structure
• used to test whether an element is a member of a set.
• False positive matches are possible, but false negatives are not – in other words, a query returns either “possibly in set” or “definitely not in set”.

Question 33

Consistent hashing

Answer 33

Consistent hashing is a special kind of hashing such that when a hash table is re-sized and consistent hashing is used, only k/n keys need to be remapped on average, where k is the number of keys, and n is the number of slots. In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all keys to be remapped