Everything is a trade off!
Performance vs scalability
A service is scalable if it results in increased performance in a manner proportional to resources added. Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow.
Another way to look at performance vs scalability:
- If you have a performance problem, your system is slow for a single user.
- If you have a scalability problem, your system is fast for a single user but slow under heavy load.
Latency vs throughput
- Latency is the time to perform some action or to produce some result.
- Throughput is the number of such actions or results per unit of time.
Generally, you should aim for maximal throughput with acceptable latency.
Availability vs consistency
CAP Theorem
In a distributed computer system, you can only support two of the following guarantees:
- Consistency - Every read receives the most recent write or an error
- Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
- Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures
Networks aren’t reliable, so you’ll need to support partition tolerance. You’ll need to make a software tradeoff between consistency and availability.
CP - consistency and partition tolerance
Waiting for a response from the partitioned node might result in a timeout error. CP is a good choice if your business needs require atomic reads and writes.AP - availability and partition tolerance
Responses return the most recent version of the data available on the a node, which might not be the latest. Writes might take some time to propagate when the partition is resolved.AP is a good choice if the business needs allow for eventual consistency or when the system needs to continue working despite external errors.
Consistency patterns
With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the CAP theorem - Every read receives the most recent write or an error.
Weak consistency
After a write, reads may or may not see it. A best effort approach is taken.
This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss.
Eventual consistency
After a write, reads will eventually see it (typically within milliseconds). Data is replicated asynchronously.
This approach is seen in systems such as DNS and email. Eventual consistency works well in highly available systems.
Strong consistency
After a write, reads will see it. Data is replicated synchronously.
This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions.
Availability patterns
There are two main patterns to support high availability: fail-over and replication.
Fail-over
Active-passive
With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active’s IP address and resumes service.
The length of downtime is determined by whether the passive server is already running in ‘hot’ standby or whether it needs to start up from ‘cold’ standby. Only the active server handles traffic.
Active-passive failover can also be referred to as master-slave failover.
Active-active
In active-active, both servers are managing traffic, spreading the load between them.
If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers.
Active-active failover can also be referred to as master-master failover.
Disadvantage(s): failover
- Fail-over adds more hardware and additional complexity.
- There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive.
Replication
Master-slave and master-master
This topic is further discussed in the Database section:
Master-slave replication
Master-master replication
Load Balancing
What is HAProxy
HAProxy (High Availability Proxy) is a TCP/HTTP load balancer and proxy server that allows a webserver to spread incoming requests across multiple endpoints. This is useful in cases where too many concurrent connections over-saturate the capability of a single server. Instead of a client connecting to a single server which processes all of the requests, the client will connect to an HAProxy instance, which will use a reverse proxy to forward the request to one of the available endpoints, based on a load-balancing algorithm.
Horizontal scaling
Load balancers can also help with horizontal scaling, improving performance and availability. Scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware, called Vertical Scaling. It is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems.
Disadvantage(s): horizontal scaling
Scaling horizontally introduces complexity and involves cloning servers
- Servers should be stateless: they should not contain any user-related data like sessions or profile pictures
- Sessions can be stored in a centralized data store such as a database (SQL, NoSQL) or a persistent cache (Redis, Memcached)
- Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out
###Disadvantage(s): load balancer - The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
- Introducing a load balancer to help eliminate single points of failure results in increased complexity.
- A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.
References
- https://github.com/donnemartin/system-design-primer#system-design-topics-start-here