index.html

---
layout: presentation
title: Server-side Caching
---

class: center, middle

# {{page.title}}

## CS291A: Scalable Internet Services

---

# Caching HTTP Responses: Motivation

A single web server process repeatedly responds to HTTP requests from a variety
of clients.

--

Responding to each request requires computation and I/O to be performed, both
of which can be expensive.

--

In practice, there is a significant amount of similarity between HTTP
responses.

--

With client-side caching we looked at optimizing scenarios where
requests for the same resource would result in an identical response.

--

In this lecture we will look at optimizing scenarios where repeated responses
are not identical, but similar.

---

# Caching HTTP Responses

> Which parts of an HTTP response are similar to responses for other resources?

--

* View fragments

--

* Rarely modified ORM objects

--

* Expensive to compute data

---

# View Fragments

View fragments are items that are similar across pages such as the header,
footer, sidebar, or even an item listing that may appear in multiple locations
(product listings, search results, product suggestions).

Sidebar Fragment (shown on all cs291.com pages)

```html
<div class="col-md-3 hidden-xs">
  <div class="sidebar well">
    Scalable Internet Services, CS291A, Fall 2017
  </div>

  <div class="sidebar well">
    <h1 id="course-information">Course Information</h1>
    <h2 id="instructor">Instructor</h2>
    <p><a href="https://cs.ucsb.edu/~bboe">Dr. Bryce Boe</a>

    <h2 id="lecture">Lecture</h2>
    <p>Tuesday and Thursday<br />
       3:00pm – 4:50pm</p>

    ...

  </div>
</div>
```

---

# Rarely Modified ORM Objects

* User permissions

* Configuration options

* Product details (on a shopping site)

* In general, any persisted data (via a database, file store, external server, etc) that seldom changes

---

# Expensive to Compute Data

Expensive to compute data is exactly what the name suggests. Expensive could
mean:

* it takes a significant amount of time to compute

* it is best suited for a background worker due to hardware constraints on the
  application servers.

--

## Examples

* The complete diff between two commits on GitHub

* The list of suggested contacts on LinkedIn

---

# Semi-Expensive Operations

View fragments are produced by extensive string manipulation. In ruby, many
string manipulations with portions loaded from disk can result in a significant
amount of time.

Every query to the database utilizes the database's resources. Optimizing for
similar queries can minimize the chance of a database bottleneck.

--

> Assuming we want to keep previously computed results around between requests,
> how can we do it?

--

> Where can we store the cached results?

---

# Storing Cached Results

Option 1: In memory on the application server

--

Option 2: On the file system

--

Option 3: In memory on another machine

--

Now, let's look into each of these options. But first a look at latency.

---

# Latency Numbers (ns and μs)

![Latency Numbers Every Programmer Should Know](latency_1_2019.png)

Source: <https://colin-scott.github.io/personal_website/research/interactive_latency.html>

---

# Latency Numbers (μs and ms)

![Latency Numbers Every Programmer Should Know](latency_2_2019.png)

Source: <https://colin-scott.github.io/personal_website/research/interactive_latency.html>

---

# Latency Numbers for Caching

* Storing in memory and reading later is fast
    * Random read: `0.1μs`

    * Reading 1MB: `4μs`

--

* Storing on disk is slow without SSD (solid state drive):
    * Disk seek: `3000μs`

    * Reading 1MB: `947μs`

--

* Storing on disk with SSD is much more reasonable
    * Random read: `16μs`

    * Reading 1MB: `62μs`

--

* Storing on another machine is reasonable

    * Round trip within data center is `500μs`

---

# Latency Numbers Summary

## Summary

* In memory: single digit `μs`

* On SSD: tens of `μs`

* On magnetic disk: thousands of `μs`

* On remote machine: __add__ hundreds of `μs`

## Conclusions

* Prefer SSD over magnetic storage for speed

* Memory > SSD > Remote

> Are these conclusions always true?

---

# Caching Locations

> What effect on the cache hit rate does each of these designs have?

* In memory: cache per process

--

* On SSD: cache per machine

--

* On remote machine: cache per application server pool (cluster)

---

# Caching Location Trade-offs

* In memory
    * highest performance
    * lowest hit rate (if applicable to only one process)

* On SSD
    * lower performance
    * higher hit rate

* On remote machine
    * lowest performance
    * highest hit rate

There is no silver bullet.

???

* In memory: cache per process
    * If the cache is per process, then the cache hit rate is low. This is
      because each process has its own cache and the cache is not shared
      between processes.
    * This could be a consideration in using a single process multi threaded
      server.
* On SSD: cache per machine
    * If the cache is per machine, then the cache hit rate is higher. This is
      because the cache is shared between all processes on the machine.
    * This could be a consideration in using a multi process server vs a single
      process multi threaded server.
* On remote machine: cache per application server pool (cluster)
    * If the cache is per application server pool, then the cache hit rate is
      highest. This is because the cache is shared between all processes on all
      machines in the pool.
    * This could be a consideration if you plan to load balance many servers.

---

# Memcached

__memcached__ is a commonly used remote cache server. It...

* keeps a cache in memory

* provides a simple TCP protocol to return responses to look up requests

* is a distributed key-value store
    * keys can be up to 250 bytes

    * values can be up to 1MB

    * scales horizontally

* uses a simple LRU (least recently used) to make space for new items when full

* performs all operations in constant time

???

* Memcached vs Redis
    * Memcached is a simple key-value store
    * Redis is a more complex data structure store
    * Redis is more feature rich
    * Memcached is slightly faster
* Redis
  * Features is a key value store with additional features
    * Persistence to disk
    * Clustering
    * Data structures (lists, sets, sorted sets, hashes, ...)
    * Lua scripting to extend functionality

---

# Server-side Caching in Rails

Rails provides excellent support for server-side caching.

The three primary interfaces to caching in rails are:

* HTTP caching

* Fragment caching

* Low level caching

We already covered HTTP caching. Today we'll discuss the latter two.

---

# Enabling Caching in Rails

By default, caching is disabled in development and test modes, and enabled only
in production mode.

If you want to enable caching in development mode you must make the following
change to your Rails environment:

```ruby
config.action_controller.perform_caching = true
```

Rails can be configured to store cached data in a few different places:

* In memory
* On a _local_ file system
* In a remote in-memory store

<https://edgeguides.rubyonrails.org/caching_with_rails.html>

---

# Configuring Rails Cache Stores

* ActiveSupport::Cache::MemoryStore

* ActiveSupport::Cache::FileStore

* ActiveSupport::Cache::MemcacheStore

---

# ActiveSupport::Cache::MemoryStore

* Cached data is stored in memory, in the same address space as the ruby
  process.

* Defaults to `32MB`, configurable

---

# ActiveSupport::Cache::FileStore

* Cached data is stored on the _local_ file system.

* Can configure the location of the storage in the Rails environment:

```ruby
config.cache_store = :file_store, '/path/to/cache/'
```

---

# ActiveSupport::Cache::MemcacheStore

* Cached data is stored in memory on another machine (could also be local).

* Can configure the location of the server in the Rails environment:

```ruby
config.cache_store = :mem_cache_store, 'cache-1.example.com'
```

---

# Fragment Caching in Rails

Fragment caching caches a portion of a rendered view for reuse with future
requests.

Let's look at fragment caching in the context of the demo app.

---

# Fragment Caching Submission Partials

.left-column30[
We can cache each submission listing.

With that done, then regardless of any other changes on the page, we can
re-render the submission view for all submissions that haven't been updated.
]
.right-column70[
![Submissions Index View](demo_submissions_index.png)
]

???

* Contrast with client-side caching where the entire page is cached.
  * Caching on client side only benefits the client that requested the page.
  * Caching on the server side benefits all clients that request the page.

---

# Submission Listing (without cache)

```erb
<% @submissions.each do |submission| %>



  <tr>
    <td><%= link_to(submission.title, submission.url) %></td>
    <td><%= submission.url %></td>
    <td><%= submission.community.name %></td>
    <td>
      <%= link_to("#{submission.comments.size} comments",
                  submission, class: 'btn btn-primary btn-xs') %>
    </td>
  </tr>



<% end %>
```

---

# Submission Listing (with cache)

```erb
<% @submissions.each do |submission| %>

* <% cache(cache_key_for_submission(submission)) do %>

  <tr>
    <td><%= link_to(submission.title, submission.url) %></td>
    <td><%= submission.url %></td>
    <td><%= submission.community.name %></td>
    <td>
      <%= link_to("#{submission.comments.size} comments",
                  submission, class: 'btn btn-primary btn-xs') %>
    </td>
  </tr>

* <% end %>

<% end %>
```

---

# Choosing a Cache Key

> How should we choose a cache key for a submission?

--

```ruby
module SubmissionHelper
  def cache_key_for_submission(submission)
    "submission/#{submission.id}"
  end
end
```

> What are the weaknesses with the above approach?

--

* Invalidation will need to be explicit as the submission ID typically will
  never be updated for an object.

* Explicitly invalidating cache items can easily make a mess of the code.

---

# Choosing a Better Cache Key

```ruby
module SubmissionHelper
  def cache_key_for_submission(sub)
    "submission/#{sub.id}/#{sub.updated_at}/#{sub.comments.count}"
  end
end
```

With the above, a submission's fragment cache is invalidated anytime the
submission is updated (`updated_at` is automatically updated on change), or
when the number of comments associated with the submission changes.

_Note_: An Active Record model can be used directly as the key. It calls an
over-writable method `cache_key` on the model. By default that method returns a
key that "includes the model name, the id and finally the updated_at
timestamp".

Reference:
<https://guides.rubyonrails.org/caching_with_rails.html#fragment-caching>

???

* Creating a cache key and checking the cache may be more expensive than
  rendering the view.  This is especially true if the cache key is complex.

---

# More Fragment Caching

.left-column30[
Now we are caching a fragment for each submission.

> What else can we cache?
]
.right-column70[
![Submissions Index View](demo_submissions_index.png)
]

--

We can cache the entire submission listing!

---

# Community Listing (without cache)

```erb


<h3>Submissions</h3>
<table class="table">
  <thead>
    <tr>
      <th>Title</th>
      <th>Url</th>
      <th>Community</th>
      <th colspan="3"></th>
    </tr>
  </thead>

  <tbody>
    <% @submissions.each do |submission| %>
      <% cache(...) do %> ... <% end %>
    <% end %>
  </tbody>
</table>

...
```

---

# Community Listing (with cache)

```erb
*<% cache(cache_key_for_submission_table) do %>

<h3>Submissions</h3>
<table class="table">
  <thead>
    <tr>
      <th>Title</th>
      <th>Url</th>
      <th>Community</th>
      <th colspan="3"></th>
    </tr>
  </thead>

  <tbody>
    <% @submissions.each do |submission| %>
      <% cache(...) do %> ... <% end %>
    <% end %>
  </tbody>
</table>

...

*<% end %>
```

---

# cache_key_for_submission_table

```ruby
module SubmissionHelper
  def cache_key_for_submission(sub)
    "submission/#{sub.id}/#{sub.updated_at}/#{sub.comments.count}"
  end

* def cache_key_for_submission_table
*   ("submission_table/#{Submission.maximum(:updated_at)}/"
*    + Comment.maximum(:updated_at).to_s)
* end
end
```

---

# Russian Doll Caching

This technique of nesting cache fragments is known as __Russian Doll Caching__.

.center[![Imperial Russian Dolls](russian_dolls.jpg)]

---

# Low-level Rails Caching

You can use the same built-in mechanisms to manually cache anything:

```ruby
class Product < ActiveRecord::Base
  def competing_price
    Rails.cache.fetch("#{cache_key}/competing_price",
                      expires_in: 12.hours) do
      Competitor::API.find_price(id)
    end
  end
end
```

---

# Performance Comparison

Let's compare the performance of the demo app with and without caching.

The subsequent graphs were generated using Tsung against a deployment of the
`main` branch (without caching) and against a deployment of the
`server_side_caching` branch (with server-side caching) using the default rails
caching mechanism (memory).

The `main` branch intentionally includes no optimizations.

---

# Caching Test: Simulated Users

We will use an m3-medium instance with the usual workload deployed using
passenger as an NGINX module.

Using Tsung (erlang-based test framework) we will simulate multiple users
visiting the Demo App web service. Each user will:

```
Visit the homepage (/)
  Wait randomly between 0 and 2 seconds
Request community creation form
  Wait randomly between 0 and 2 seconds
Submit new community form
Request new link submission form
  Wait randomly between 0 and 2 seconds
Submit new link submission form
  Wait randomly between 0 and 2 seconds
Delete the link
  Wait randomly between 0 and 2 seconds
Delete the community
```

---

# Caching Test: Phases

This time we have six phases of testing each lasting 60 seconds:

```
   (0-59s) Every second a new simulated user arrives
 (60-119s) Every second 1.5 new simulated users arrive
(120-179s) Every second 2 new simulated users arrive
(180-239s) Every second 4 new simulated users arrive
(240-299s) Every second 6 new simulated users arrive
(300-359s) Every second 10 new simulated users arrive
```

---

class: center middle

# Performance without Caching

![Performance without caching](performance_no_caching.png)

---

class: center middle

# Performance with Caching

![Performance with caching](performance_server_cache.png)

---

# Performance Comparison

Without caching the server struggled to handle a single new arrival each
second.

With server-side caching the server can easily handle up to two
new arrivals a second.