Logistics

• Magic Code:

Review: Achieving Scale

• Measuring performance:
  • How many Xs per second?
  • and/orhow long does it take to Y?
• Analysis
  • Instrumentation (basically logging)
  • Deep thought
  • Identify the bottle neck
• Action
  • Remove the bottleneck
• Remember: One of the cardinal “sins” is optimizing early
  • Instead, optimize based on measurement
  • Discover which parts of your product’s features is causing a scaling problem
  • Consider which of your techniques might be brought to bear

Scalability Pattern: General Caching

What is caching?

• Save the result of a request with a given set of parameters.
• In a future request with the same parameter (maybe) return the same result
• System level caching. Storage:
  1. In ‘local’ memory
  2. In ‘remote’ memory
  3. In database
  4. In Cloud

Review of storage system architectural hierarchy

• Processor
  1. Cores
  2. Caches
  3. On board memory
• Offboard Memory
  1. Very different speeds depending on cost
  2. On a special very fast connection (bus)
• External local storage
  1. USB connected SSD
• Ethernet Connected storage
  1. On local LAN
• Ethernet connected storage
  1. On separate network (internet/cloud)

Cost of operations

• Awareness of order of magnitude speed of operations:
• Access registers inside CPU
• Access CPU caches
• Access standard RAM
• Access local disk
  1. Access files
  2. Access local database
• Access over network
  1. To a nearby server
  2. To a nearby database server
• Access over the internet
  1. To a remote server
  2. To a remote database server
  3. To a remote Web Service

Memoization:

• caching applied to an individual method
• A basic programming technique
• Simple

Name-value databases

• Very fast searches and lookups
• Distributed searches and distributed databases
• Robust across system and application failures

Database Caching

• To a certaine extent, it’s what databases do
• Caching both at the server (postgres itself)
• And at the client (the postgres and activerecord subsystems)
• Yet a lot more can be done

HTML page caching

• Done at the web server
• Don’t regenerate the page if it’s requested again
• As long as you know it hasn’t changed
• Page fragment caching, including “russian doll caching”
• A key feature of good frameworks

Caching with “Redis”

Advantages

• Blindingly fast
• Many data types: list, set, sorted set and hashes
• Atomic operations
• Has many uses: caching, message queue, publish subscribe, sharing application global state

An instance of “network caching”

• Evolved from the original cached
• Typical structure is a key-value store
• A nosql database. But in memory!
• Ruby bindings gem redis

Wait, where’s the data actually stored?

• A redis host, accessible by tcp/ip: dns name + port number
• You can run it: $ redis-server
• Heroku can run it for you with Redis to go. Nano size is free!
• In all cases, if the host dies, the data is gone (not 100% true)

It has some interesting characteristics

• ATOMIC operations, e.g. “INCR” operation
• keys that expire (TTL)
• Supports other values: lists, sets, hashes
• And many many more

Heroku

• Can provide a basic free instance of it
• Remember it has a URL and can be shared across applications
• heroku redis:cli

Redis Concepts

• Play with Redis
• Keys
  • are text with colons, e.g. global:usercount by convention
  • but can be anything. You decide your structure. Colons are recommended.
• Values
  • Are strings
  • Or compoounds: lists, sets, sorted sets, hashes
• Note we play with commands (a kind of a REPL)
• But you will be doing API calls

SET key value                   # store a singular key and give it a value
GET key                         # retrieve its value
INCR key                        # add one to key value, should be an integer. Atomic!
DECR key                        # delete one from key value
EXPIRE key seconds              # key will cease to exsit `seconds` later
RPUSH key value                 # Append value to list
LPUSH key value                 # Prepend value to list
LPOP key                        # Remove first value
RPOP                            # Remvoe last value
SADD key value                  # Add value to set
SREM key                        # Remove value from set
SISMEMBER key value             # Is value a member of set
SUNION key1 key2                # Get union of two sets
ZADD key score value            # Add value with score to sorted set
HSET hashname hashkey value     # Add entry to hash
HHET hashname hashkey           # Retrieve a value from hash

• Ahem: That is not all: Redis Commands

Putting the it together

• Redis is a power tool!
• Don’t be scared of putting lots of information into it
• Remember that it’s a cache. You need to have real persistent store to recover
• Example:
  1. Display list of 50 most recent posts for users who are followed by user uid
  2. Key is: 50_tweets_for_user:uid
  3. Value is: ordered list of tweet ids
• Processing:
  1. When list is displayed
  2. When user :u tweets

Thank you. Questions?  (random Image from picsum.photos)