Designing Data Intensive Applications - Ch 1 - Reliability, Scalability, Maintainability

Reliability

Scalability 

Maintainability

Data Engines as developers see it; database, ccaches, search index, stream processing, batch processing.

How the data is distributed in disks

encoding data

Reliability - anticipate faults and can tolerate them. Faults - component failure. failure - system failure. hardware faults. software faults. human errors. 

telemetry

using interfaces, decoupling, testing

Scalability - 

Load parameters

- request to web server

- read or write to database

- cache hit rate

- active users

could be average case or small number of extreme cases

Twitter example :

4.6K to max 12k writes per user, but 300K reads per user. So work is done pushing writes to individual users caches at write time so read time can be faster. write times become a challenge when they involve so much leg work. still done within 5 seconds.

Now twitter does a hybrid model where most tweets follow above approacch, but celebrity tweets are sent at read time.

Performance :

throughput

response time

latency - the time that a request is latent OR waiting to be handled

percentiles - median / 99th percentile better than mean

head of line blocking

tail latency amplification

details of response times ; naive methods like t-digest, forward decay. hdrHistogram

how to deal with increase in loads

shared-nothing or scaling out

scaling up - replacing new systems\

manual scaling or elastic

usually stateful services are kept on single node

stateless services are distributed

reads, writes, data, data complexity, response time, access patterns = architecture is tailored to this

10000 req per sec * 1KB is not = to 3 req per minute = 2GB in size even though same throughput.

Maintainability

Operability + Simplicity + Evolvability