spree-performance

name	spree-performance
description	Profile and optimize a Spree application — N+1 queries with bullet/scout, database indexing strategy for Spree's polymorphic associations, Rails fragment + Russian doll caching, ActiveStorage variant pre-generation, Sidekiq queue tuning, MeiliSearch vs Postgres FTS tradeoffs, Puma worker/thread sizing, CDN strategy for catalog pages, asset precompile time, and load testing. Use when Spree is slow, the database is hot, or you're preparing for a traffic spike (Black Friday, launch).
allowed-tools	Read, Write, Edit, Bash, Grep, Glob, WebSearch, WebFetch

Spree Performance

Before writing code

Fetch live docs:

1. Search the Spree blog at https://spreecommerce.org/blog for performance posts (Black Friday case studies, scaling guides).
2. Inspect Spree::OrderUpdater and other "hot path" service objects in the live source for known bottlenecks.
3. Cross-reference Rails 7+ performance guides for caching primitives.
4. For Sidekiq tuning, fetch https://github.com/sidekiq/sidekiq/wiki/Best-Practices.
5. Check the v5.4 announcement (https://spreecommerce.org/announcing-spree-commerce-5-4/) for the API v3 "10x faster than v2" claim and what changed.

Conceptual Architecture

Where Spree Spends Time

Profiling typical Spree requests shows hotspots at:

1. N+1 queries in catalog views (variants, prices, images)
2. Order recalculation on cart updates (OrderUpdater traverses line items, adjustments, taxes)
3. Tax calculation (Spree::TaxCalculator per-line)
4. Promotion eligibility (rules evaluated per applicable promotion per order)
5. Image processing (ActiveStorage variants generated on first request)
6. Search (Postgres FTS or MeiliSearch query)
7. Adapter overhead on heavy use cases (JSON:API in v2 was a serializer bottleneck — fixed in v3 flat-JSON)

v3 API is ~10× Faster than v2

The v5.4 v3 API rewrite cites 10× perf vs v2:

• Flat JSON serialization beats JSON:API
• Prefixed IDs lookup uses index
• Fewer DB round-trips per response
• Better Russian-doll caching support

If migrating from v2 → v3, expect significant catalog-page speedups.

Common N+1 Sources

# BAD — N+1 on variants, prices, images
Spree::Product.all.each { |p| puts p.master.price }

# GOOD
Spree::Product.includes(master: :prices).each { |p| puts p.master.price }

# Better for views — preload images and stores too
Spree::Product.includes(:images, :stores, master: [:prices, :stock_items])

Use bullet in dev/test:

# Gemfile
group :development, :test do
  gem 'bullet'
end

# config/environments/development.rb
config.after_initialize do
  Bullet.enable = true
  Bullet.alert = true
  Bullet.rails_logger = true
  Bullet.add_footer = true
end

Fragment Caching

<% cache product do %>
  <%= render product %>
<% end %>

Cache key auto-busts on product.updated_at. Combine with touch: true on associations for Russian-doll caching:

class Spree::Variant < ApplicationRecord
  belongs_to :product, touch: true
end

Catalog Page Caching

For PLPs that don't change per user:

class CatalogController < ApplicationController
  def show
    expires_in 5.minutes, public: true   # CDN-cacheable
    @products = current_store.products.active.includes(:taxons, master: :prices)
  end
end

For per-user PDP (price varies):

• Cache the immutable bits as fragments
• Render user-specific bits without cache

Database Indexing

Spree ships indexes for most common access patterns. Verify your queries hit them — EXPLAIN ANALYZE in psql. Common missing indexes after schema changes:

CREATE INDEX index_spree_orders_on_user_id_and_state ON spree_orders(user_id, state);
CREATE INDEX index_spree_adjustments_on_adjustable ON spree_adjustments(adjustable_type, adjustable_id);

For multi-store, scope indexes to store_id columns.

ActiveStorage Variant Pre-Generation

# Generate variants in background after upload
class Spree::Image < ApplicationRecord
  has_one_attached :attachment do |attachable|
    attachable.variant :small, resize_to_limit: [120, 120], preprocessed: true
    attachable.variant :large, resize_to_limit: [1200, 1200], preprocessed: true
  end
end

Without preprocessed: true, the first user request generates the variant — slow.

Sidekiq Queue Strategy

# config/sidekiq.yml
:concurrency: 25
:queues:
  - [critical, 5]
  - [webhooks, 3]
  - [mailers, 2]
  - [default, 1]

Tune :concurrency against DB pool size — each Sidekiq thread takes one DB connection.

Puma Sizing

# config/puma.rb
workers ENV.fetch('WEB_CONCURRENCY') { 2 }
threads_count = ENV.fetch('RAILS_MAX_THREADS') { 5 }
threads threads_count, threads_count
preload_app!

Math: workers × threads_count ≤ DB connection pool. Set pool in database.yml to threads_count.

Reporting Queries

Reports over Spree::Order can scan millions of rows. Strategies:

• Materialized views for daily/monthly aggregates
• Replica DB for reporting (avoid contention on primary)
• Pre-computed via Sidekiq jobs cached in Redis
• Pagination with cursors (starting_after) not OFFSET

Image CDN

Front ActiveStorage with a CDN:

# config/storage.yml
amazon:
  service: S3
  access_key_id: <%= ENV['AWS_ACCESS_KEY_ID'] %>
  secret_access_key: <%= ENV['AWS_SECRET_ACCESS_KEY'] %>
  bucket: my-spree-bucket
  region: us-east-1
  public: true   # if you front with CDN

Set CloudFront / Cloudflare in front. URLs become https://cdn.example.com/....

MeiliSearch vs Postgres FTS

Backend	Latency	Faceting	Typo tolerance
MeiliSearch	<50ms typical	Yes	Yes (built-in)
Algolia	<50ms typical	Yes	Yes
Postgres FTS	100-500ms	Limited	Trigram-based, manual

MeiliSearch wins for catalog search above ~10k products.

Order Recalc Cost

Spree::OrderUpdater runs on every line-item change. If profiling shows it dominant:

• Batch updates (Spree::OrderMutations.batch { |order| ... } — verify API)
• Defer to a Sidekiq job if not needed synchronously
• Swap Spree::Dependencies.order_updater_class with a leaner implementation if you don't use all features

Implementation Guidance

Profiling Setup

# Gemfile
group :development do
  gem 'bullet'
  gem 'rack-mini-profiler'
  gem 'flamegraph'
  gem 'stackprof'
  gem 'memory_profiler'
end

Hit a slow page with ?pp=help to see profiler menu.

Identifying Slow Queries

Enable pg_stat_statements:

CREATE EXTENSION pg_stat_statements;

SELECT calls, mean_exec_time, query
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;

Cache Key Versioning

After a Spree minor upgrade, bust caches:

# config/initializers/cache_version.rb
Rails.application.config.cache_classes_version = 'spree_5.4.2_v1'

(Add as fragment cache version suffix.)

Black Friday Prep Playbook

1. Capacity test 4 weeks before — synthetic load = 5× expected peak
2. DB read replica for reporting queries
3. Cache warming — pre-generate ActiveStorage variants for all live products
4. CDN cache TTLs raised on catalog pages
5. Sidekiq scaling test — webhook backlog handling at burst
6. Database connection pool raised on primary
7. Read-only mode plan in case of overload
8. War room runbook — who is on call for what
9. Disable expensive features during peak (e.g., live carrier rate calls — fall back to flat rate)

Common Pitfalls

• Spree::Order.all in a controller — full table scan; always scope.
• includes not matching what the view uses — N+1 sneaks back in.
• Fragment cache without touch: true — stale cards on inventory change.
• Sidekiq concurrency exceeding DB pool — connection-pool errors under load.
• Image processing on web tier — blocks request thread; do in Sidekiq.
• OFFSET pagination over large tables — slow; use cursors.
• MeiliSearch index out of sync — events not firing or job failures; monitor lag.
• Ransack filters scanning unindexed columns — admin order search slow over millions of rows.
• Logging full response bodies — log volume explodes; filter.
• spree:install rake task in CI on every build — slow; cache the test app.

Always profile before optimizing — assumptions about where the time goes are usually wrong. Spree's hot paths are well-known; community blog posts and Spree's own benchmarks are the best starting point.