Reducing ActiveRecord Queries to Speed Up Your Requests

Software Engineer

Ruby on Rails

2025-10-06 09:52:56 +0700 · 2 mins read

In Rails, ActiveRecord is one of the things that makes the framework so enjoyable to use. It reads almost like natural language and lets you write database logic in a clean, Ruby-style way.

But every line of Active Record still turns into real SQL behind the scenes. Every SQL query comes with a fixed cost:

Network latency between your app and the database (even on localhost)
Query parsing and planning time inside PostgreSQL
Serialization of results back to Ruby
Context switching inside your Rails process

By reducing the number of SQL queries, you minimize these fixed costs: fewer round-trips, less time spent parsing and deserializing, and fewer blocking waits inside your Ruby threads, which directly improves your app’s performance.

1. Aggregate Metrics

A common pattern is calculating both sum and count separately:

// language: ruby
# Before
pipeline_sum   = PipelineRun.sum(:time_to_results_finalized)
pipeline_count = PipelineRun.count

Each call triggers a separate query. You can combine both metrics in a single SQL scan:

// language: ruby
# After
pipeline_sum, pipeline_count = PipelineRun.pick(
  Arel.sql("SUM(time_to_results_finalized)"),
  Arel.sql("COUNT(*)")
)

✅ Only 1 query
✅ Postgres calculates both in the same scan

2. Counting Conditional Subsets

If you’re counting filtered subsets, don’t run one query per condition.

// language: ruby
# Before
active_count   = User.where(active: true).count
inactive_count = User.where(active: false).count

Use Postgres FILTER to compute both in a single pass:

// language: ruby
# After
active_count, inactive_count = User.pick(Arel.sql("
  COUNT(*) FILTER (WHERE active = TRUE),
  COUNT(*) FILTER (WHERE active = FALSE)
"))

✅ Only 1 query
✅ Postgres calculates both in the same scan

We can use it with more complex conditions:

// language: ruby
User.pick(Arel.sql("
  COUNT(*) FILTER (WHERE last_login_at > NOW() - INTERVAL '30 days') AS recent_users,
  COUNT(*) FILTER (WHERE confirmed = FALSE) AS unconfirmed_users
"))

3. Using .group for Grouped Metrics

Grouping is another easy win. We often loop over values and count them separately:

// language: ruby
# Before
statuses = {}
["draft", "published"].each do |s|
  statuses[s] = Post.where(status: s).count
end

Rails can do it all in one query:

// language: ruby
# After
statuses = Post.group(:status).count
# => { "draft" => 12, "published" => 42 }

✅ Only 1 query
✅ Returns a hash directly
✅ Scales linearly even with many groups

4. Loading Metadata & Associated Totals Together

When showing project stats, we often query separately:

// language: ruby
# Before
project       = Project.find(1)
tasks_count   = project.tasks.count
members_count = project.members.count

Three queries total. You can aggregate directly with joins:

// language: ruby
# After
project = Project
  .left_joins(:tasks, :members)
  .where(id: 1)
  .select("projects.*,
           COUNT(DISTINCT tasks.id)   AS tasks_count,
           COUNT(DISTINCT members.id) AS members_count")
  .group("projects.id")
  .first

✅ Only 1 query
✅ You can access project.tasks_count directly

Conclusion

Rails makes it easy to express queries cleanly, but small inefficiencies add up fast. When you notice multiple .sum, .count, or .where calls in a row, ask yourself:
"Can this be done in one SQL pass?"

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