Blog

Dual Release Channels

Jan 23, 2026

Stephen Haberman

Joist now supports dual release channels, allowing you to choose between a stable and a bleeding-edge version of the ORM.

Bringing Back Unnest

Jan 11, 2026

Stephen Haberman

The latest Joist release brings back leveraging the Postgres unnest function (see the PR), for a nice 9% bump on our alpha latency-oriented benchmarks (joist_v2 is the merged PR):

What is unnest?

unnest is a Postgres function that takes an array and returns a set of rows, one for each element in the array.

The simplest example is converting one array and turning it into a set of rows:

-- Pass in 1 array, get back 3 rows
select unnest(array[1, 2, 3]) as i;
 i
---
 1
 2
 3
(3 rows)

But you can also use multiple unnest statements to get multiple columns on those rows:

select
  unnest(array[1, 2, 3]) as id,
  unnest(array['foo', 'bar', 'zaz']) as first_name;
 id | first_name
----+------------
  1 | foo
  2 | bar
  3 | zaz
(3 rows)

Why unnest is useful

unnest is great for bulk SQL statements, such as inserting 10 authors in one INSERT; without INSERT you might have 4 columns * 10 authors = 40 query parameters:

INSERT INTO authors (first_name, last_name, publisher_id, favorite_color)
  VALUES (?, ?, ?, ?). -- #a1
    (?, ?, ?, ?), -- #a2
    (?, ?, ?, ?), -- #a3
    (?, ?, ?, ?), -- #a4
    (?, ?, ?, ?), -- #a5
    (?, ?, ?, ?), -- #a6
    (?, ?, ?, ?), -- #a7
    (?, ?, ?, ?), -- #a8
    (?, ?, ?, ?), -- #a9
    (?, ?, ?, ?) -- #a10

Where we have 10 first_name parameters, 10 last_name parameters, etc., but with unnest we can instead send up “just 1 array per column”:

INSERT INTO authors (first_name, last_name, publisher_id, favorite_color)
  SELECT * FROM unnest(
    $1::varchar[], -- first_names
    $2::varchar[], -- last_names
    $3::int[],     -- publisher_ids
    $3::varchar[] -- colors
  )

The benefits of fewer query parameters are:

• Smaller SQL statements going over the wire (one of our benchmarks saw 41kb of SQL without unnest, and 350 bytes with unnest)
• “Stable” SQL statement that don’t change for each “number of authors being updated”, so will have better prepared statement cache hit rates
• Observability tools like Datadog will also better group “stable” SQL statements with a fixed number of parameters

This is generally a well-known approach, i.e. TimeScale had a blog post highlighting a 2x performance increase, albeit you have to get to fairly large update sizes to have this much impact.

Bringing it back?

Joist had previously used unnest in our INSERTs and UPDATEs, but we’d removed it because it turns out unnest is finicky with array columns—it “overflattens” and requires reactangular arrays.

(I.e. array columns like varchar[] for storing multiple values favorite_colors = ['red', 'blue'] in a single column.)

The 1st unfortunate thing with unnest is that it “overflattens”, i.e. if we want to update two author’s favorite_colors columns using unnest, we’d intuitively think “let’s just pass an array of arrays”, one array for each author:

-- Pass two arrays, with two elements each
-- We expect to get back two rows of {red,blue} and {green,purple}
select * from unnest(array[array['red','blue'],array['green','purple']]);
 unnest
--------
 red
 blue
 green
 purple
(4 rows)

…wait, we got 4 rows instead.

Unfortunately this is just how unnest works—when given 2-dimensional arrays (like a matrix), it creates a row per each value/cell in matrix.

Another unfortunate wrinkle with unnest is that our intuitive “array of arrays” creates fundamentally invalid arrays if the authors have a different number of favorite colors:

-- Try to create 1 array of {red,blue} and 1 array of {purple}
select * from unnest(array[array['red','blue'],array['purple']]);
-- ERROR:  multidimensional arrays must have array expressions with matching dimensions

Our error is treating the varchar[][] as “an array of arrays”, when fundamentally Postgres treats it as “a single array, of two dimensions”, like mathematical n-dimensional arrays or matrices: they must be “rectangular” i.e. every row of our m x n matrix must be the same length (we’ve been trying to create “jagged” multidimensional arrays, which is not supported).

One final wrinkle is, not only must all rows be the same length, but think about nullable columns—how could we set a1 favorite_colors='red', 'blue'] but then set a2 favorite_colors=null? With unnests strict array limitations we cannot.

The combination of these issues is why we’d previously removed unnest usage, but now have introducing our own unnest_arrays custom function that solves each of these problems.

unnest_arrays Custom Function

Our custom unnest_arrays function works around unnests limitations by coordinating with the Joist runtime to create 2-dimensional arrays that satisfy Postgres’s requirements, but still produce the desired values:

• When updating favorite_colors for multiple authors with different number of colors, we pad trailing nulls to the end of each author’s colors array, until the array is rectangular
• When updating favorite_colors to null, we also pad a single leading null to indicate the desired nullness (and pad a “not-null marker” for other rows).

This is simpler to see with an example, of updating three authors:

• Author 1 should update favorite_colors=red,green,blue
• Author 2 should update favorite_colors=green
• Author 3 should update favorite_colors=null

We are able to issue a SQL UPDATE like:

WITH data AS (
  SELECT
    unnest($1) as id
    unnest_arrays($2) as favorite_colors,
)
UPDATE authors SET favorite_colors = data.favorite_colors
FROM data WHERE authors.id = data.id

And our favorite_colors array looks like:

-- Created by the Joist runtime by reading the Author's favoriteColors
-- property and then adding padding as needed to a rectangular 2D array
array[
  array['', 'red', 'green', 'blue'], -- a:1
  array['', 'green', null, null], -- a:2
  array[null, null, null, null]] -- a:3
]

This array is passed our unnest_arrays custom function that knows about each of these conventions:

CREATE OR REPLACE FUNCTION unnest_arrays(arr ANYARRAY, nullable BOOLEAN = false, OUT a ANYARRAY)
  RETURNS SETOF ANYARRAY
  LANGUAGE plpgsql IMMUTABLE STRICT AS
$func$
BEGIN
  FOREACH a SLICE 1 IN ARRAY arr LOOP
    -- When invoked for nullable columns, watch for the is-null/is-not-null marker
    IF nullable THEN
      -- If we should be null, drop all values and  return null
      IF a[1] IS NULL THEN a := NULL;
      -- Otherwise drop the is-not-null  marker
      ELSE a := a[2:array_length(a, 1)];
      END IF;
    END IF;
    -- Drop all remaining/trailing nulls
    a := array_remove(a, NULL);
    RETURN NEXT;
  END LOOP;
END

And that’s it; we get out the other side our desired rows:

select unnest_arrays(array[
  array['', 'red', 'green', 'blue'], -- a:1
  array['', 'green', null, null], -- a:2
  array[null, null, null, null]] -- a:3
, true);
  unnest_arrays
------------------
  {red,green,blue}
  {green}

(3 rows)

Pros/Cons

Our solution has a few pros/cons:

• Pro: We’ve restored our ability to use unnest for all of our batched SELECTs, UPDATEs, and INSERTs 🎉
• Con: Joist users with array columns in their schemas will need to create the unnest_arrays function
  • This is a one-time migration so seems reasonable
• Con: With all the null padding tricks, we’re giving up the ability to have null values within our array values
  • I.e. we cannot have favorite_colors=[red, null, blue]
  • For our domain modeling purposes, this is a fine/acceptable tradeoff, b/c we’ve always modeled varchar[] columns as string[] and not Array<string | null> — we actively don’t want nulls in our varchar[] columns anyway

So far these pros/cons are worth it 🚀; but, as always, we’ll continue adjusting our approach as we learn more from real-world use cases & usage.

Using CTEs and Query Rewriting for Versioning

Dec 5, 2025

Stephen Haberman

Joist is an ORM primarily developed for Homebound’s GraphQL majestic monolith, and we recently shipped a long-awaited Joist feature, SQL query rewriting via an ORM plugin API, to deliver a key component of our domain model: aggregate level versioning.

We’ll get into the nuanced details below, but “aggregate level versioning” is a fancy name for providing this minor “it’s just a dropdown, right? 😰” feature of a version selector across several major subcomponents of our application:

Where the user can:

• “Time travel” back to a previous version of what they’re working on ⌛,
• Draft new changes (collaboratively with other users) that are not seen until they click “Publish” to make those changes active ✅

And have the whole UI “just work” while they flip between the two.

As a teaser, after some fits & painful spurts, we achieved having our entire UI (and background processes) load historical “as of” values with just a few lines of setup/config per endpoint—and literally no other code changes. 🎉

Read on to learn about our approach!

Aggregate What Now?

Besides just “versioning”, I called this “aggregate versioning”—what is that?

It’s different from traditional database-wide, system time-based versioning, that auditing solutions like cyanaudit or temporal FOR SYSTEM_TIME AS OF queries provide (although we do use cyanaudit for our audit trail & really like it!).

Let’s back up and start with the term “aggregate”. An aggregate is a cluster of ~2-10+ “related entities” in your domain model (or “related tables” in your database schema). The cluster of course depends on your specific domain—examples might be “an author & their books”, or “a customer & their bank accounts & profile information”.

Typically there is “an aggregate parent” (called the “aggregate root”, since it sits at the root of the aggregate’s subgraph) that naturally “owns” the related children within the aggregate; i.e. the Author aggregate root owns the Book and BookReview children; the Customer aggregate root owns the CustomerBankAccount and CustomerProfile entities.

Tip

In your own domain model, if you see a naming pattern of Customer, and then lots of CustomerFoo, CustomerBar, CustomerZaz entities, all starting with a Customer... prefix, that is a hint that Customer is the aggregate root for that cluster (aggregate) of entities.

Historically, Aggregate Roots are a pattern from Domain Driven Design, and mostly theoretically useful—they serve as a logical grouping, which is nice, but don’t always manifest as specific outcomes/details in the implementation (at least from what I’ve seen).

Tip

Unless you are sharding! At which point the aggregate root’s primary key, i.e. Customer.id, makes a really great shard key for all the child entities within the aggregate root.

Why Version An Aggregate?

Normally Joist blog posts don’t focus on specific domains or verticals, but for the purposes of this post, it helps to know the problem we are solving.

At Homebound, we’re a construction company that builds residential homes; our primary domain model supports the planning and execution of our procurement & construction ops field teams.

The domain model is large (currently 500+ tables), but two key components are:

• The architectural plans for a specific model of home (called a PlanPackage), and
• The design scheme for products that go into a group of similar plans (called a DesignPackage)—i.e. a “Modern Farmhouse” design package might be shared across ~4-5 separate, but similarly-sized/laid out, architectural plans

Both of these PlanPackages and DesignPackages are aggregate roots that encompass many child PlanPackage... or DesignPackage... entities within them:

• What rooms are in the plan? PlanPackageRooms
• What materials & labor are required (bricks, lumber, quantities)? PlanPackageScopeLines
• What structural/floor plan options do we offer to customers? PlanPackageOptions
  • How do these options change the plan’s materials & labor? A m2m between PlanPackageScopeLines and PlanPackageOptions
• What are the appliances in the kitchen? DesignPackageProducts
• What spec levels, of Essential/Premium/etc, do we offer? DesignPackageOptions
  • How do these spec levels change the home’s products? A m2m between DesignPackageProducts and DesignPackageOptions

This web of interconnected data can all be modeled successfully (albeit somewhat tediously)—but we also want it versioned! 😬

Change management is extremely important in construction—what was v10 of the PlanPackage last week? What is v15 of the PlanPackage this week? What changed in each version between v10 and v15? Are there new options available to homebuyers? What scope changed? Do we need new bids? Etc.

And, pertintent to this blog post, we want each of the PlanPackages and DesignPackages respective “web of interconnected data” (the aggregate root & its children) versioned together as a group with application-level versioning: multiple users collaborate on the data (making simultaneous edits across the PlanPackage or DesignPackage), co-creating the next draft, and then we “Publish” the next active version with a changelog of changes.

After users draft & publish the plans, and potentially start working on the next draft, our application needs to be able to load a complete “aggregate-wide snapshot” for “The Cabin Plan” PlanPackage v10 (that was maybe published yesterday) and a complete “aggregate-wide snapshot” of “Modern Design Scheme” DesignPackage v8 (that was maybe published a few days earlier), and glue them together in a complete home.

Hopefully this gives you an idea of the problem—it’s basically like users are all collaborating on “the next version of the plan document”, but instead of it being a single Google Doc-type artifact that gets copy/pasted to create “v1” then “v2” then “v3”, the collaborative/versioned artifact is our deep, rich domain model (relational database tables) of construction data.

Schema Approach

After a few cough “prototypes” cough of database schemas for versioning in the earlier days of our app, we settled on a database schema we like: two tables per entity, a main “identity” table, and a “versions” table to store snapshots, i.e. something like:

• authors table: stores the “current” author data (first_name, last_name, etc), with 1 row per author (we call this the “identity” table)
• author_versions table: stores snapshots of the author over time (author_id, version_id, first_name, last_name), with 1 row per version of each author (we call this the “version” table)

This is an extremely common approach for versioning schemas, i.e. it’s effectively the same schema suggested by PostgreQL’s SQL2011Temporal docs, albeit technically they’re tracking system time, like an audit trail, and not our user-driven versioning.

This leads to a few differences: the SQL2011Temporal history tables use a _system_time daterange column to present “when each row was applicable in time” (tracking system time), while we use two FKs that also form “an effective range”, but the range is not time-based, it’s version-based: “this row was applicable starting at first=v5 until final=v10”.

So if we had three versions of an Author in our author_versions table, it would look like:

• id=10 author_id=1 first_id=v10 final_id=v15 first_name=bob last_name=smith
  • From versions v10 to v15, the author:1 had a firstName=bob
• id=11 author_id=1 first_id=v15 final_id=v20 first_name=fred last_name=smith
  • From versions v15 to v20, the author:1 had a firstName=fred
• id=11 author_id=1 first_id=v20 final_id=null first_name=fred last_name=brown
  • From versions v20 to now, the author:1 had a lastName=brown

We found this _versions table strikes a good balance of tradeoffs:

• It only stores changed rows—if v20 of the aggregate root (i.e. our PlanPackage) only changed the Authors and two of its Books, there will only be 1 author_versions and two book_versions, even if the Author has 100s of books (i.e. we avoid making full copies of the aggregate root on every version)
• When a row does change, we snapshot the entire row, instead of tracking only specific columns changes (storing the whole row takes more space, but makes historical/versioned queries much easier)
  • Technically we only include mutable columns in the _versions table, i.e. our entities often have immutable columns (like type flags or parent references) and we don’t bother copying these into the _versions tables.
• We only store 1 version row “per version”—i.e. if, while making PlanPackage v20, an Authors first name changes multiple times, we only keep the latest value in the draft author_versionss row.
  • This is different from auditing systems like SQL2011Temporal, where _history rows are immutable, and every change must create a new row—we did not need/want this level of granularity for our application-level versioning.

With this approach, we can reconstruct historical versions by finding the singular “effective version” for each entity, with queries like:

select * from author_versions av
join authors a on av.author_id = a.id
where a.id in ($1)
  and av.first_id <= $2
  and (av.final_id is null or av.final_id < $3)

Where:

• $1 is whatever authors we’re looking for (here a simple “id matches”, but it could be an arbitrarily complex WHERE condition)
• $2 is the version of the aggregate root we’re “as of”, i.e. v10
• $3 is also the same version “as of”, i.e. v10

The condition of first_id <= v10 < final_id finds the singular author_versions row that is “effective” or “active” in v10, even if the version itself was created in v5 (and either never replaced, or not replaced until “some version after v10” like v15).

…but what is “Current”?

The authors and author_versions schema is hopefully fairly obvious/intuitive, but I left out a wrinkle: what data is stored in the authors table itself?

Obviously it should be “the data for the author”…but which version of the author? The latest published data? Or latest/WIP draft data?

Auditing solutions always put “latest draft” in authors, but that’s because the application itself is always reading/writing data from authors, and often doesn’t even know that the auditing author_versions tables exist—but in our app, we need workflows & UIs to regularly read “the published data” & ignore the WIP draft changes.

So we considered the two options:

1. The authors table stores the latest draft version (as in SQL2011Temporal), or
2. The authors table stores the latest published version,

We sussed out pros/cons in a design doc:

• Option 1. Store “latest draft” data in authors
  • Pro: Writes with business logic “just read the Author and other entities” for validation
  • Con: All readers, even those that just want the published data, must use the _versions table to “reconstruct the published/active author”
• Option 2. Store the “latest published” data in authors
  • Pro: The safest b/c readers that “just read from authors” will not accidentally read draft/unpublished data (a big concern for us)
  • Pro: Any reader that wants to “read latest” doesn’t have to know about versioning at all, and can just read the authors table as-is (also a big win)
  • Con: Writes that have business logic must first “reconstruct the draft author” (and its draft books and its draft book reviews if apply cross-entity business rules/invariants) from the _versions tables

Tip

By “reconstruct the [published or draft] author”, what we mean is that instead of “boring CRUD code” that just SELECTs from the authors, books, etc. tables, version-aware code must:

• a) actually read from the author_verisons, book_versions tables,
• b) know “which version” it should use to find those authors/books
  • …and we might be looking for “PlanPackage v10” but “DesignPackage v8” so track multiple, contextual versions within a single request, not just a single “as of” timestamp
• c) do version-aware graph traversal, i.e. “boring walk the graph” code like for (const book in author.books) that is ubiquitous in our codebase needs the author.books relation to use the version-aware book_versions.author_id instead of the books.author_id, b/c the Book might have changed its Author over time.
  • So nearly every graph navigation needs checked for “is this relation actually versioned?”, and if so, opt-into the more complex, version-aware codepath.

This “reconstruction” problem seemed very intricate/complicated, and we did not want to update our legacy callers (which were mostly reads) to “do all this crazy version resolution”, so after the usual design doc review, group comments, etc., we decide to store latest published in authors.

The key rationale being:

• We really did not want to instrument our legacy readers to know about _versions tables to avoid seeing draft data (we were worried about draft data leaking into existing UIs/reporting)
• We thought “teaching the writes to ‘reconstruct’ the draft subgraph” when applying validation rules would be the lesser evil.

(You can tell I’m foreshadowing this was maybe not the best choice.)

Initial Approach: Not so great

With our versioning scheme in place, we started a large project for our first versioning-based feature: allowing home buyers to deeply personalize the products (sinks, cabinet hardware, wall paint colors, etc) in the home their buying (i.e. in v10 of Plan 1, we offered sinks 1/2/3, but in v11 of Plan 1, we now offer sinks 3/4/5).

And it was a disaster.

This new feature set was legitimately complicated, but “modeling complicated domain models” is supposed to be Joist’s sweet spot—what was happening?

We kept having bugs, regressions, and accidental complexity—that all boiled down to the write path (mutations) having to constantly “reconstruct the drafts” that it was reading & writing.

Normally in Joist, a saveAuthor mutation just “loads the author, sets some fields, and saves”. Easy!

But with this versioning scheme:

• the saveAuthor mutation has to first “reconstruct the latest-draft Author from the author_versions (if it exists)
• any writes cannot be “just update the Author row” (that would immediately publish the change), they need to be staged into the draft AuthorVersion
• any Joist hooks or validation rules also need to do the same thing:
  • validation rules have to “reconstruct the draft” view of the author + books + book reviews subgraph of data they’re validating,
  • hooks that want to make reactive changes must also “stage the change” into a draft BookVersion or BookReviewVersion instead of “just setting some Book fields”.

We had helper methods for most of these incantations—but it was still terrible.

After a few months of this, we were commiserating about “why does this suck so much?” and finally realized—well, duh, we were wrong:

We had chosen to “optimize reads” (they could “just read from authors” table).

But, in doing so, we threw writes under the bus—they needed to “read & write from drafts”—and it was actually our write paths that are the most complicated part of our application—validation rules, side effects, business process, all happen on the write path.

We needed the write path to be easy.

New Approach: Write to Identities

We were fairly ecstatic about reversing direction, and storing drafts/writes directly in authors, books, etc.

This would drastically simplify all of our write paths (GraphQL mutations) back to “boring CRUD” code:

// Look, no versioning code!
const author = await em.load(Author, "a:1");
if (author.shouldBeAllowedToUpdateFoo) {
  author.foo = 1;
}
await em.flush();

…but what about those reads? Wouldn’t moving the super-complicated “reconstruct the draft” code out of the writes (yay!), over into “reconstruct the published” reads (oh wait), be just as bad, or worse (which was the rationale for our original decision)?

We wanted to avoid making the same mistake twice, and just “hot potato-ing” the write path disaster over to the read path.

Making Reads not Suck

We spent awhile brainstorming “how to make our reads not suck”, specifically avoiding manually updating all our endpoints’ SQL queries/business logic to do tedious/error-prone “remember to (maybe) do version-aware reads”.

If you remember back to that screenshot from the beginning, we need our whole app/UI, at the flip of a dropdown, to automatically change every SELECT query from:

-- simple CRUD query
SELECT * FROM authors WHERE id IN (?) -- or whatever WHERE clause

To a “version-aware replacement”:

-- find the right versions row
SELECT * FROM author_versions av
WHERE av.author_id in (?) -- same WHERE clause
  AND av.first <= v10 -- and versioning
  AND (av.final IS NULL or av.final > v10)

And not only for top-level SELECTs but anytime authors is used in a query, i.e. in JOINs:

-- simple CRUD query that joins into `authors`
SELECT b.* FROM books
  JOIN authors a on b.author_id = a.id
  WHERE a.first_name = 'bob';

-- version-aware replacement, uses books_versions *and* author_versions
-- for the join & where clause
SELECT bv.* FROM book_versions bv
  -- get the right author version
  JOIN author_versions av ON
    bv.author_id = av.author_id AND (av.first <= v10 AND (av.final IS NULL or a.final > v10))
  -- get the right book version
  WHERE bv.first <= v10 AND (bv.final IS NULL or bv.final > v10)
  -- predicate should use the version table
  AND av.first_name = 'bob';

1. Initial Idea: Using CTEs

When it’s articulated like “we want every table access to routed to the versions table”, a potential solution starts to emerge…

Ideally we want to magically “swap out” authors with “a virtual authors table” that automatically has the right version-aware values. How could we do this, as easily as possible?

It turns out a CTE is a great way of structuring this:

-- create the fake/version-aware authors table
WITH _authors (
  SELECT
    -- make the columns look exactly like the regular table
    av.author_id as id,
    av.first_name as first_name
    -- but read the data from author_versions behind the scenes
  FROM author_versions
  WHERE (...version matches...)
)
-- now the rest of the application's SQL query as normal, but we swap out
-- the `authors` table with our `_authors` CTE
SELECT * FROM _authors a
WHERE a.first_name = 'bob'

And that’s (almost) it!

If anytime our app wants to “read authors”, regardless of the SQL query it’s making, we swapped authors-the-table to _authors-the-CTE, the SQL query would “for free” be using/returning the right version-aware values.

So far this is just a prototype; we have three things left to do:

1. Add the request-specific versioning config to the query
2. Inject this rewriting as seamlessly as possible
3. Evaluate the performance impact

2. Adding Request Config via CTEs

We have a good start, in terms of a hard-coded prototype SQL query, but now we need to get the first <= v10 AND ... pseudo code in the previous SQL snippets actually working.

Instead of a hard-coded v10, we need queries to use:

• Dynamic request-specific versioning (i.e. the user is currently looking at, or “pinning to”, PlanPackage v10), and
• Support pinning multiple different aggregate roots in the same request (i.e. the user is looking at PlanPackage v10 but DesignPackage v15)

CTEs are our new hammer 🔨—let’s add another for this, calling it _versions and using the VALUES syntax to synthesize a table:

WITH _versions (
  SELECT plan_id, version_id FROM
   -- parameters added to the query, one "row" per pinned aggregate
   -- i.e. `[1, 10, 2, 15]` means this request wants `plan1=v10,plan2=v15`
  VALUES (?, ?), (?, ?)
), _authors (
  -- the CTE from before but joins into _versions for versioning config
  SELECT
    av.author_id as id,
    av.first_name as first_name
  FROM authors a
  JOIN _versions v ON a.plan_id = v.plan_id
  -- now we know:
  -- a) what plan the author belongs to (a.plan_id, i.e. its aggregate root)
  -- b) what version of the plan we're pinned to (v.version_id)
  -- so we can use them in our JOIN clause
  JOIN author_versions av ON (
    av.author_id = a.id AND
    av.first_id >= v.version_id AND (
      av.final_id IS NULL OR av.final_id < v.version_id
    )
  )
)
SELECT * FROM _authors a WHERE a.first_name = 'bob'

Now our application can “pass in the config” (i.e. for this request, plan:1 uses v10, plan:2 uses v15) as extra query parameters into the query, they’ll be added as rows to the _versions CTE table, and then the rest of the query will resolve versions using that data.

Getting closer!

Tip

It’s easy to miss, but a core aspect of our approach is that each row in the database “knows its parent aggregate root”. Because the core version config is “per package” (the aggregate root), but there are many child tables that we’ll be reading from, we use a strong/required convention that every child table must have a plan_id (or parent_id) foreign key that lets us join directly from the child to the parent’s version config.

One issue with the query so far is that we must ahead-of-time “pin” every plan we want to read (by adding it to our _versions config table), b/c if a plan doesn’t have a row in the _versions CTE, then the INNER JOINs will not find any p.version_id available, and none of its data will match.

Ideally, any plan that is not explicitly pinned should have all its child entities read their active/published data (which will actually be in their author_versions tables, and not the primary authors tables); which we can do with (…wait for it…) another CTE:

WITH _versions (
  -- the injected config _versions stays the same as before
  SELECT plan_id, version_id FROM VALUES (?, ?), (?, ?)
), _plan_versions (
   -- we add an additional CTE that defaults all plans to active unless pinned in _versions
  SELECT
    plans.id as plan_id,
    -- prefer `v.version_id` but fallback on `active_version_id`
    COALESCE(v.version_id, plans.active_version_id) as version_id,
  FROM plans
  LEFT OUTER JOIN _versions v ON v.plan_id = plans.id
)
_authors (
  -- this now joins on _plan_versions instead _versions directly
  SELECT
    av.author_id as id,
    av.first_name as first_name
  FROM authors a
  JOIN _plan_versions pv ON a.plan_id = pv.id
  JOIN author_versions av ON (
    av.author_id = a.id AND
    av.first_id >= pv.version_id AND (
      av.final_id IS NULL OR av.final_id < pv.version_id
    )
  )
)
-- automatically returns & filters against the versioned data
SELECT * FROM _authors a WHERE a.first_name = 'bob'

We’ve basically got it, in terms of a working prototype—now we just need to drop it into our application code, ideally as easily as possible.

3. Injecting the Rewrite Automatically

We’ve discovered a scheme to make reads automatically version-aware—now we want our application to use it, basically all the time, without us messing up or forgetting the rewrite incantation.

Given that a) we completely messed this up the 1st time around 😕, and b) this seems like a very mechanical translation 🤖, what if we could automate it? For every read?

Our application already does all reads through Joist (of course 😅), as EntityManager calls:

// Becomes SELECT * FROM authors WHERE id = 1
const a = em.load(Author, "a:1");
// Becomes SELECT * FROM authors WHERE first_name = ?
const as = em.find(Author, { firstName: "bob" });
// Also becomes SELECT * FROM authors WHERE id = ?
const a = await book.author.load();

It would be really great if these em.load and em.find SQL queries were all magically rewritten—so that’s what we did. 🪄

We built a Joist plugin that intercepts all em.load or em.find queries (in a new plugin hook called beforeFind), and rewrites the query’s ASTs to be version-aware, before they are turned into SQL and sent to the database.

So now what our endpoint/GraphQL query code does is:

function planPackageScopeLinesQuery(args) {
  // At the start of any version-aware endpoint, setup our VersioningPlugin...
  const plugin = new VersioningPlugin();
  // Read the REST/GQL params to know which versions to pin
  const { packageId, versionId } = args;
  plugin.pin(packageId, versionId);
  // Install the plugin into the EM, for all future em.load/find calls
  em.addPlugin(plugin);

  // Now just use the em as normal and all operations will automatically
  // be tranlated into the `...from _versions...` SQL
  const { filter } = args;
  return em.find(PlanPackageScopeLine, {
    // apply filter logic as normal, pseudo-code...
    quantity: filter.quantity,
  });
}

How does this work?

Internally, Joist parses the arguments of em.find into an AST, ParsedFindQuery, that is a very simplified AST/ADT version of a SQL query, i.e. the shape looks like:

// An AST/ADT for an `em.find` call that will become a SQL `SELECT`
type ParsedFindQuery = {
  // I.e. arrays of `a.*` or `a.first_name`
  selects: ParsedSelect[];
  // I.e. the `authors` table, with its alias, & any inner/outer joins
  tables: ParsedTable[];
  // I.e. `WHERE` clauses
  condition?: ParsedExpressionFilter;
  groupBys?: ParsedGroupBy[];
  orderBys: ParsedOrderBy[];
  ctes?: ParsedCteClause[];
};

After Joist takes the user’s “fluent DSL” input to em.find and parses it into this ParsedFindQuery AST, plugins can now inspect & modify the query:

class VersioningPlugin {
  // List of pinned plan=version tuples, populated per request
  #versions = []

  // Called for any em.load or em.find call
  beforeFind(meta: EntityMetadata, query: ParsedFindQuery): void {
    let didRewrite = false;
    for (const table of [...query.tables]) {
      // Only rewrite tables that are versioned
      if (hasVersionsTable(table)) {
        this.#rewriteTable(query, table);
        didRewrite = true;
      }
    }
    // Only need to add these once/query
    if (didRewrite) {
      this.#addCTEs(query);
    }
  }

  #rewriteTable(query, table) {
    // this `table` will initially be the `FROM authors AS a` from a query;
    // leave the alias as-is, but swap the table from "just `authors`" to
    //  the `_authors` CTE we'll add later
    table.table = `_${table.table}`;

    // If `table.table=author`, get the AuthorMetadata that knows the columns
    const meta = getMetadatFromTableName(table.table);

    // Now inject the `_authors` CTE that to be our virtual table
    query.ctes.push({
      alias: `_${table.table}`,
      columns: [...],
      query: {
        kind: "raw",
        // put our "read the right author_versions row" query here; in this blog
        // post it is hard-coded to `authors` but in the real code would get
        // dynamically created based on the `meta` metadta
        sql: `
          SELECT
            av.author_id as id,
            av.first_name as first_name,
            -- all other author columns...
          FROM authors a
          JOIN _plan_versions pv ON a.plan_id = pv.id
          JOIN author_versions av ON (
            av.author_id = a.id AND
            av.first_id >= pv.version_id AND (
              av.final_id IS NULL OR av.final_id < pv.version_id
            )
          )
        `
      }
    })
  }

  // Inject the _versions and _plan_versions CTEs
  #addCTEs(query) {
    query.ctes.push( {
      alias: "_versions",
      columns: [
        { columnName: "plan_id", dbType: "int" },
        { columnName: "version_id", dbType: "int" },
      ],
      query: {
        kind: "raw",
        bindings: this.#versions.flatMap(([pId, versionId]) => unsafeDeTagIds([pId, versionId])),
        sql: `SELECT * FROM (VALUES ${this.#versions.map(() => "(?::int,?::int)").join(",")}) AS t (plan_id, version_id)`,
      },
    },
    // Do the same thing for _plan_versions
    query.ctes.push(...);
  }
}

This is very high-level pseudo-code but the gist is:

• We’ve mutated the query to use our _authors CTE instead of the authors table
• We’ve injected the _authors CTE, creating it dynamically based on the AuthorMetadata
• We’ve injected our _versions and _plan_versions config tables

After the plugin’s beforeFind is finished, Joist takes the updated query and turns it into SQL, just like it would any em.find query, but now the SQL it generates automatically reads the right versioned values.

4. Performance Evaluation

Now that we have everything working, how was the performance?

It was surprisingly good, but not perfect—we unfortunately saw a regression for reads “going through the CTE”, particularly when doing filtering, like:

WITH _versions (...),
 _plan_versions (...),
 _authors (...),
)
-- this first_name is evaluating against the CTE results
SELECT * FROM _authors a WHERE a.first_name = 'bob'

We were disappointed b/c we thought since our _authors CTE is “only used once” in the SQL query, that ideally the PG query planner would essentially “inline” the CTE and pretend it was not even there, for planning & indexing purposes.

Contrast this with a CTE that is “used twice” in a SQL query, which our understanding is that then Postgres executes it once, and materializes it in memory (basically caches it, instead of executing it twice). This materialization would be fine for smaller CTEs like _versions or _plan_versions, but on a potentially huge table like authors or plan_package_scope_lines, we definitely don’t want those entire tables sequentially scanned and creating “versioned copies” materialized in-memory before any WHERE clauses were applied.

So we thought our “only used once” _authors CTE rewrite would be performance neutral, but it was not—we assume because many of the CTE’s columns are not straight mappings, but due to some nuances with handling drafts, ended up being non-trivial CASE statements that look like:

-- example of the rewritten select clauses in the `_authors` CTE
SELECT
  a.id as id,
  a.created_at as created_at,
  -- immutable columns
  a.type_id as type_id,
  -- versioned columns
  (CASE WHEN _a_version.id IS NULL THEN a.first_name) ELSE _a_version.first_name END) as first_name,
  (CASE WHEN _a_version.id IS NULL THEN a.last_name) ELSE _a_version.last_name END) as last_name,

And we suspected these CASE statements were not easy/possible for the query planner to “see through” and push filtering & indexing statistics through the top-level WHERE clause.

So, while so far our approach has been “add yet another CTE”, for this last stretch, we had to remove the _authors CTE and start “hard mode” rewriting the query by adding JOINs directly to the query itself, i.e. we’d go from a non-versioned query like:

-- previously we'd "just swap" the books & authors tables to
-- versioned _books & _authors CTEs
SELECT b.* FROM books
  JOIN authors a ON b.author_id = a.id
  WHERE a.first_name = 'bob';

To:

SELECT
  -- rewrite the top-level select
  b.id as id,
  -- any versioned columns need `CASE` statements
  (CASE WHEN bv.id IS NULL THEN b.title ELSE bv.title) AS title,
  (CASE WHEN bv.id IS NULL THEN b.notes ELSE bv.notes) AS notes,
  -- ...repeat for each versioned column...
  -- ...also special for updated_at...
FROM books
  -- keep the `books` table & add a `book_versions` JOIN directly to the query
  JOIN book_versions bv ON (bv.book_id = b.id AND bv.first_id >= pv.version_id AND ...)
  -- rewrite the `ON` to use `bv.author_id` (b/c books can change authors)
  JOIN authors a ON bv.author_id = a.id
  -- add a author_version join directly to the query
  JOIN author_versions av ON (av.author_id = a.id AND av.first_id >= pv.version_id AND ...)
  -- rewrite the condition from `a.first_name` to `av.first_name`
  WHERE av.first_name = 'bob';

This is a lot more rewriting!

While the CTE approach let us just “swap the table”, and leave the rest of the query “reading from the alias a” & generally being none-the-wiser, now we have to find every b alias usage or a alias usage, and evaluate if the SELECT or JOIN ON or WHERE clause is touching a versioned column, and if so rewrite that usage to the bv or av respective versioned column.

There are pros/cons to this approach:

• Con: Obviously the query is much trickier to rewrite
• Pro: But since our rewriting algorithm is isolated to the VersioningPlugin file, we were able to “refactor our versioned query logic” just once and have it apply everywhere which was amazing 🎉
• Pro: The “more complicated to us” CTE-less query is actually “simpler to the Postgres query planner” b/c there isn’t a CTE “sitting in the way” and so all the usual indexing/filtering performance optimizations kicked in, and got us back to baseline performance

Removing the _authors / _books CTEs and doing “inline rewriting” (basically what we’d hoped Postgres would do for us with the “used once” CTEs, but now we’re doing by hand) gave us a ~10x performance increase, and returned us to baseline performance, actually beating the performance of our original “write to drafts” approach. 🏃

Skipping Some Details

It would make the post even longer, so I’m skipping some of the nitty-gritty details like:

• Soft deletions—should entities “disappear” if they were added in v10 and the user pins to v8? Or “disappear” if they were deleted in v15 and the user is looking at v20?
  • Initially we had our VersionPlugin plugin auto-filter these rows, but in practice this was too strict for some of our legacy code paths, so in both “not yet added” and “previously deleted” scenarios, we return rows anyway & then defer to application-level filtering
• Versioning m2m collections, both in the database (store full copies or incremental diffs?), and teaching the plugin to rewrite m2m joins/filters accordingly.
• Reading updated_at from the right identity table vs. the versions table to avoid oplock errors when drafts issue UPDATEs using plugin-loaded data
• Ensuring endpoints make the pin and addPlugin calls without accidentally loading “not versioned” copies of the data they want to read into the EntityManager, which would cache the non-versioned data, & prevent future “should be versioned” reads for working as expected.
• Migrating our codebase from the previous “by hand” / “write to drafts” initial versioning approach, to the new plugin + “write to identities” approach, which honestly was a lot of fun—lots of red code that was deleted & simplified by the new approach. 🔪

Thankfully we were able to solve each of these, and none turned into dealbreakers that compromised the overall approach. 😅

Wrapping Up

This was definitely a long-form post, as we explored the Homebound problem space that drove our solution, rather than just a shorter announcement post of “btw Joist now has a plugin API”.

Which, yes, Joist does now have a plugin API for query rewriting 🎉, but we think it’s important to show how/why it was useful to us, and potentially inspire ideas for how it might be useful to others as well (i.e. an auth plugin that does ORM/data layer auth 🔑 is also on our todo list).

That said, we anticipate readers wondering “wow this solution seems too complex” (and, yes, our production VersionPlugin code is much more complicated than the pseudocode we’ve used in this post), “why didn’t you hand-write the queries”, etc 😰.

We can only report that we tried “just hand-write your versioning queries”, in the spirit of KISS & moving quickly while building our initial set of version-aware features, for about 6-9 months, and it was terrible. 😢

Today, we have versioning implemented as “a cross-cutting concern” (anyone remember Aspect Oriented Programming? 👴), primarily isolated to a single file/plugin, and the rest of our code went back to “boring CRUD” with “boring reads” and “boring writes”.

Our velocity has increased, bugs have decreased, and overall DX/developer happiness is back to our usual “this is a pleasant codebase” levels. 🎉

If you have any questions, feel free to drop by our Discord to chat.

Thanks

Thanks to the Homebound engineers who worked on this project: Arvin, for bearing the brunt of the tears & suffering, fixing bugs during our pre-plugin “write to drafts” approach (mea cupla! 😅), ZachG for owning the rewriting plugin, both Joist’s new plugin API & our internal VersioningPlugin implementation 🚀, and Roberth, Allan, and ZachO for all pitching in to get our refactoring landed in the limited, time-boxed window we had for the initiative ⏰🎉.

Lazy Fields for 30x speedup without Decorators or Transforms

Oct 6, 2025

Stephen Haberman

Joist leverages JavaScript’s prototypes to blend great developer ergonomics/DX with performance.

Avoiding ORM Decorators

Jul 27, 2025

Stephen Haberman

Entity-based ORMs, going back to Java’s Hibernate & earlier, often use decorators or annotations like @PrimaryKey to define the domain model; Joist pushes back on this pattern, and prefers schema-driven code generation.

Older posts