Using data tiering to squeeze scale out of SQL

As traffic grows, some of the data structures our application has to manipulate gets contended. Ours is an unusual, but effective solution: segregate data into read-mostly and write-mostly.

I’ll use HouseTrip as an example. We’re a holiday rental marketplace, and information about whether the properties we list are available or not for given dates is backed by a table that looks like this:

│ availabilities │
│ id             │
│ property_id    │
│ start_date     │
│ end_date       │

At the time of writing, this table has shy of 1M rows.

Searching for available properties takes the form of a faily elaborate query, with multiple joins, on various tables including availabilities. This is plenty fast and scales well horizontally: so far our searches, and most other read-y queries, are almost always handled by one of our MySQL read replicas.

The problem is this scales poorly when the tables also take a lot of writes.

People searching for properties end up booking them, thus changing their availability.

Corollary: For the core tables (properties, availabilities, rates), our peak traffic is both a read and a write peak.

The consequence is read contention. At some point, there are so many writes that the readers (search queries) end up waiting too long for read lock.

This can be resolved in a number of ways:

  • scale out: adding read slaves helps a bit, but you get diminishing returns; if you’re saturating on writes, no matter how thinly spread the reads, they will disrupt your table. Also note that read replicas take exactly as many writes as your master.
  • scale up: bigger servers, better IO. This pushes the problem back, but it’s a single-barrel shotgun. Amazon (or whoever your host is) only has so many higher servier tiers.
  • use technology X: some “architects”, faced with data performance problems, have a single answer: It’s NoSQL’o’Clock!. It might well be, but rebuilding a core system on top of Mongo (Riak, Redis, Couch, whatever rocks your boat) is a huge investment.
  • go async: if reading slightly out-of-date information is good enough (a typical situation for search engines), segregates your reads from your writes. That’s what we’ve tried out.

Data tiering

“Data tiering” is the mechanism we currently use to reduce database contention for our search. It’s inspired by the double buffering used in video cards.

The idea is to have search queries not hit the main database tables and compete with updates and other queries, but instead have copies of relevant tables. Those tables will be refreshed every few minutes. We use it for all tables that get heavy writes and reads. Every one of these regular tables now also has two clones: The front table and the back table.

  • Most parts of the application will keep using the original table for reads and writes.
  • Any read-intensive part of the application (e.g. searching) will use the front table.
  • No part of the application will ever use the back table.
  • Every few minutes, a task (errand in our jargon) will sync all recent changes from the regular to the inactive table. Then the front and the back table will be swapped.


   read <- │ availabilities_front │
           ┌──────────────────────┐            ┌────────────────┐
           │ availabilities_back  │ <- update  │ availabilities │ <-> read/︎write
           └──────────────────────┘            └────────────────┘

Syncing data

To detect changes we use MySQL timestamps (row_touched_at) that get updated automatically by the database, regardless of whether you’ve done a normal save, or some mass update. We don’t use them for anything else in our code as Rail’s timezone handling does not work properly.

The column spec looks like:

`row_touched_at` timestamp NOT NULL

This allows us to easily do partial updates of the back table instead of copying all the data in bulk from the original table.

Swapping tables

While we could atomically rename the front and back tables to implement swapping (although it’s unclear whether RENAME TABLE is atomic when swapping), there’s a simpler solution: pointers.

We use a place we can atomically write to (currently, another SQL table) to store what table we mean when we say “front” or “back”. The real tables are named availabilities_secondary_[01].

Dealing with migrations

Our sync engine detects schema changes, by comparing the output of SHOW CREATE TABLE (minus the AUTO_INCREMENT part, if any).

If you migrate one of the involved tables, the next sync will migrate those as well, automatically.

If your migration doesn’t touch the schema, but modifies the data, the row_touched_at field will be updated—so changes, again, will be propagated in the next sync.


When we introduced data tiering, search query time at peak traffic dropped by 30%.

The whole project took us less than 2 man-weeks to implement, test, and deliver. It bought us time, and in that sense was a sensible solution to scrape more performance out of our mostly-relational storage instead of going through the massive investment of a foray into SOA and/or NoSQL lands.


Parts of this article are directly taken from the internal documentation @kratob has written for us.

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