Query Django model trees using adjacency lists and recursive common table expressions. Supports PostgreSQL, sqlite3 (3.8.3 or higher) and MariaDB (10.2.2 or higher) and MySQL (8.0 or higher, if running without ONLY_FULL_GROUP_BY).

Supports Django 3.2 or better, Python 3.8 or better. See the GitHub actions build for more details.

• Supports only integer and UUID primary keys (for now).
• Allows specifying ordering among siblings.
• Uses the correct definition of depth, where root nodes have a depth of zero.
• The parent foreign key must be named "parent" at the moment (but why would you want to name it differently?)
• The fields added by the common table expression always are tree_depth, tree_path and tree_ordering. The names cannot be changed. tree_depth is an integer, tree_path an array of primary keys representing the path from the root to the current node (including the current node itself), and tree_ordering an array of values used for ordering nodes within their siblings at each level of the tree hierarchy. Note that the contents of the tree_path and tree_ordering are subject to change. You shouldn't rely on their contents.
• Besides adding the fields mentioned above the package only adds queryset methods for ordering siblings and filtering ancestors and descendants. Other features may be useful, but will not be added to the package just because it's possible to do so.
• Little code, and relatively simple when compared to other tree management solutions for Django. No redundant values so the only way to end up with corrupt data is by introducing a loop in the tree structure (making it a graph). The TreeNode abstract model class has some protection against this.
• Supports only trees with max. 50 levels on MySQL/MariaDB, since those databases do not support arrays and require us to provide a maximum length for the tree_path and tree_ordering upfront.
• Performance optimization: The library automatically detects simple cases (single field ordering, no tree filters, no custom tree fields) and uses an optimized CTE that avoids creating a rank table, significantly improving performance for basic tree queries.

Here's a blog post offering some additional insight (hopefully) into the reasons for django-tree-queries' existence.

• Install django-tree-queries using pip.
• Extend tree_queries.models.TreeNode or build your own queryset and/or manager using tree_queries.query.TreeQuerySet. The TreeNode abstract model already contains a parent foreign key for your convenience and also uses model validation to protect against loops.
• Call the with_tree_fields() queryset method if you require the additional fields respectively the CTE.
• Call the order_siblings_by("field_name") queryset method if you want to order tree siblings by a specific model field. Note that Django's standard order_by() method isn't supported -- nodes are returned according to the depth-first search algorithm.
• Use tree_filter() and tree_exclude() for better performance when working with large tables - these filter the base table before building the tree structure.
• Use tree_fields() to aggregate ancestor field values into arrays.
• Create a manager using TreeQuerySet.as_manager(with_tree_fields=True) if you want to add tree fields to queries by default.
• Until documentation is more complete I'll have to refer you to the test suite for additional instructions and usage examples, or check the recipes below.

The following two examples both extend the TreeNode which offers a few agreeable utilities and a model validation method that prevents loops in the tree structure. The common table expression could be hardened against such loops but this would involve a performance hit which we don't want -- this is a documented limitation (non-goal) of the library after all.

from tree_queries.models import TreeNode

class Node(TreeNode):
    name = models.CharField(max_length=100)

Nodes with the same parent may be ordered among themselves. The default is to order siblings by their primary key but that's not always very useful.

Manual position management:

from tree_queries.models import TreeNode

class Node(TreeNode):
    name = models.CharField(max_length=100)
    position = models.PositiveIntegerField(default=0)

    class Meta:
        ordering = ["position"]

Automatic position management:

For automatic position management, use OrderableTreeNode which automatically assigns sequential position values to new nodes:

from tree_queries.models import OrderableTreeNode

class Category(OrderableTreeNode):
    name = models.CharField(max_length=100)
    # position field and ordering are inherited from OrderableTreeNode

When creating new nodes without an explicit position, OrderableTreeNode automatically assigns a position value 10 units higher than the maximum position among siblings. The increment of 10 (rather than 1) makes it explicit that the position values themselves have no inherent meaning - they are purely for relative ordering, not a sibling counter or index.

If you need to customize the Meta class (e.g., to add verbose names or additional ordering fields), inherit from OrderableTreeNode.Meta:

from tree_queries.models import OrderableTreeNode

class Category(OrderableTreeNode):
    name = models.CharField(max_length=100)

    class Meta(OrderableTreeNode.Meta):
        verbose_name = "category"
        verbose_name_plural = "categories"
        # ordering = ["position"] is inherited from OrderableTreeNode.Meta

# Create nodes - positions are assigned automatically
root = Category.objects.create(name="Root")  # position=10
child1 = Category.objects.create(name="Child 1", parent=root)  # position=10
child2 = Category.objects.create(name="Child 2", parent=root)  # position=20
child3 = Category.objects.create(name="Child 3", parent=root)  # position=30

# Manual reordering is still possible
child3.position = 15  # Move between child1 and child2
child3.save()

This approach is identical to the pattern used in feincms3's AbstractPage.

from tree_queries.models import TreeNode
from tree_queries.query import TreeQuerySet

class NodeQuerySet(TreeQuerySet):
    def active(self):
        return self.filter(is_active=True)

class Node(TreeNode):
    is_active = models.BooleanField(default=True)

    objects = NodeQuerySet.as_manager()

All examples assume the Node class from above.

# Basic usage, disregards the tree structure completely.
nodes = Node.objects.all()

# Fetch nodes in depth-first search order. All nodes will have the
# tree_path, tree_ordering and tree_depth attributes.
nodes = Node.objects.with_tree_fields()

# Fetch any node.
node = Node.objects.order_by("?").first()

# Fetch direct children and include tree fields. (The parent ForeignKey
# specifies related_name="children")
children = node.children.with_tree_fields()

# Fetch all ancestors starting from the root.
ancestors = node.ancestors()

# Fetch all ancestors including self, starting from the root.
ancestors_including_self = node.ancestors(include_self=True)

# Fetch all ancestors starting with the node itself.
ancestry = node.ancestors(include_self=True).reverse()

# Fetch all descendants in depth-first search order, including self.
descendants = node.descendants(include_self=True)

# Temporarily override the ordering by siblings.
nodes = Node.objects.order_siblings_by("id")

# Revert to a queryset without tree fields (improves performance).
nodes = Node.objects.with_tree_fields().without_tree_fields()

When using with_tree_fields(), each node gets three additional attributes:

• ``tree_depth``: An integer representing the depth of the node in the tree (root nodes have depth 0)
• ``tree_path``: An array containing the primary keys of all ancestors plus the current node itself, representing the path from root to current node
• ``tree_ordering``: An array containing the ordering/ranking values used for sibling ordering at each level of the tree hierarchy

The key difference between tree_path and tree_ordering:

# Example tree structure:
#   Root (pk=1, order=0)
#   ├── Child A (pk=2, order=10)
#   │   └── Grandchild (pk=4, order=5)
#   └── Child B (pk=3, order=20)

# For the Grandchild node:
grandchild = Node.objects.with_tree_fields().get(pk=4)

# tree_path shows the route through primary keys: Root -> Child A -> Grandchild
assert grandchild.tree_path == [1, 2, 4]  # [root.pk, child_a.pk, grandchild.pk]

# tree_ordering shows ordering values at each level: Root's order, Child A's order, Grandchild's order
assert grandchild.tree_ordering == [0, 10, 5]  # [root.order, child_a.order, grandchild.order]

Important note: When not using an explicit ordering (like a position field), siblings are ordered by their primary key by default. This means tree_path and tree_ordering will contain the same values. While this may be fine for your use case consider adding an explicit ordering field:

class Node(TreeNode):
    id = models.UUIDField(primary_key=True, default=uuid.uuid4)
    name = models.CharField(max_length=100)
    position = models.PositiveIntegerField(default=0)

    class Meta:
        ordering = ["position"]

Tree fields (tree_depth, tree_path, tree_ordering) are only available on objects returned by queries that use with_tree_fields() or a manager configured with with_tree_fields=True. They are NOT available after Model.objects.create(), instance.save(), or instance.refresh_from_db().

Why? Tree fields are calculated by the recursive CTE at query time and are not stored in the database. They only exist as annotations on the queryset results.

# Tree fields are NOT available after creation
node = Node.objects.create(name="New Node", parent=root)
# node.tree_depth  # AttributeError: 'Node' object has no attribute 'tree_depth'

# refresh_from_db() only updates database fields, not tree fields
node.refresh_from_db()
# node.tree_depth  # Still AttributeError

# To get tree fields, re-query the object with with_tree_fields()
node = Node.objects.with_tree_fields().get(pk=node.pk)
print(node.tree_depth)  # Now it works! e.g., 1

# Or use a manager with tree fields enabled by default
class Node(TreeNode):
    name = models.CharField(max_length=100)
    objects = TreeQuerySet.as_manager(with_tree_fields=True)

# Now tree fields are available on all queries automatically
node = Node.objects.get(pk=some_pk)
print(node.tree_depth)  # Works!

# But still not after create/save/refresh_from_db
new_node = Node.objects.create(name="Another")
# new_node.tree_depth  # Still AttributeError - need to re-query

Common pattern when creating nodes:

# Create a new node
new_node = Node.objects.create(name="New Child", parent=parent_node)

# Re-query to get tree fields if you need them
new_node = Node.objects.with_tree_fields().get(pk=new_node.pk)

# Now you can access tree fields
print(f"Depth: {new_node.tree_depth}")
print(f"Path: {new_node.tree_path}")

IMPORTANT: For large tables, always use tree_filter() or tree_exclude() to limit which nodes are processed by the recursive CTE. Without these filters, the database evaluates the entire table, which can be extremely slow.

# Get a specific tree from a forest by filtering on root category
product_tree = Node.objects.with_tree_fields().tree_filter(category="products")

# Get organizational chart for a specific department
engineering_tree = Node.objects.with_tree_fields().tree_filter(department="engineering")

# Exclude entire trees/sections you don't need
content_trees = Node.objects.with_tree_fields().tree_exclude(category="archived")

# Chain multiple tree filters for more specific trees
recent_products = (Node.objects.with_tree_fields()
                  .tree_filter(category="products")
                  .tree_filter(created_date__gte=datetime.date.today()))

# Get descendants within a filtered tree subset
product_descendants = (Node.objects.with_tree_fields()
                      .tree_filter(category="products")
                      .descendants(some_product_node))

# Filter by site/tenant in multi-tenant applications
site_content = Node.objects.with_tree_fields().tree_filter(site_id=request.site.id)

Performance note: tree_filter() and tree_exclude() filter the base table before the recursive CTE processes relationships, dramatically improving performance for large datasets compared to using regular filter() after with_tree_fields(). Best used for selecting complete trees or tree sections rather than scattered nodes.

The tree queryset doesn't support all types of queries Django supports.

Updating all descendants directly isn't supported because the recursive CTE isn't added to the UPDATE query correctly. Use a subquery workaround instead:

# Doesn't work:
node.descendants().update(is_active=False)

# Use this workaround instead:
Node.objects.filter(pk__in=node.descendants()).update(is_active=False)

Using select_related() works when querying from the tree model to fetch related objects. However, querying from a related model and trying to get tree fields on the tree model via select_related() is not supported.

# This works - tree model is the base, select_related fetches the category
nodes = Node.objects.with_tree_fields().select_related("category")
for node in nodes:
    print(node.tree_depth, node.category.name)

# This doesn't work - ReferenceModel is the base, tree fields won't be present
# on the related Node objects
references = ReferenceModel.objects.select_related("tree_field")
for ref in references:
    # ref.tree_field.tree_depth  # AttributeError - tree fields not available

Workaround: Query from the tree model and use prefetch_related() or Prefetch() to fetch the related objects:

from django.db.models import Prefetch

# Approach 1: Query from tree model, prefetch references
nodes = Node.objects.with_tree_fields().prefetch_related("referencemodel_set")
for node in nodes:
    print(node.tree_depth)
    for ref in node.referencemodel_set.all():
        print(f"  Reference: {ref.id}")

# Approach 2: Query references, then fetch tree nodes separately
references = ReferenceModel.objects.all()
tree_node_ids = [ref.tree_field_id for ref in references]
nodes_by_id = {
    node.pk: node
    for node in Node.objects.with_tree_fields().filter(pk__in=tree_node_ids)
}
for ref in references:
    node = nodes_by_id[ref.tree_field_id]
    print(f"Reference {ref.id}: tree depth = {node.tree_depth}")

Set operations like union(), intersection(), and difference() are not currently supported with tree querysets due to how Django passes the elide_empty parameter. Attempting to use these operations will result in errors.

# This doesn't work:
qs1 = Node.objects.with_tree_fields().filter(name__startswith="A")
qs2 = Node.objects.with_tree_fields().filter(name__startswith="B")
combined = qs1.union(qs2)  # Will raise an error

Workaround: Use regular Django querysets without tree fields, then add tree fields after the set operation:

# Perform the set operation without tree fields
qs1 = Node.objects.filter(name__startswith="A")
qs2 = Node.objects.filter(name__startswith="B")
combined_ids = qs1.union(qs2).values_list("pk", flat=True)

# Then fetch with tree fields
result = Node.objects.with_tree_fields().filter(pk__in=combined_ids)

For more details and discussion about adding support for these operations, see GitHub issue #55.

To limit the depth of the tree returned by a query, use .extra() with a WHERE clause on the tree_depth field:

# Get only nodes up to depth 2 (root is depth 0)
nodes = Node.objects.with_tree_fields().extra(
    where=["__tree.tree_depth <= %s"],
    params=[2],
)

# Get nodes within a depth range
nodes = Node.objects.with_tree_fields().extra(
    where=["__tree.tree_depth BETWEEN %s AND %s"],
    params=[1, 3],  # Only depths 1, 2, and 3
)

To enforce a maximum number of children per node, implement validation in your model's clean() method:

from django.core.exceptions import ValidationError

class Node(TreeNode):
    def clean(self):
        super().clean()
        max_children = 10

        # Check if this would exceed the limit
        if self.parent_id:
            sibling_count = (
                Node.objects
                .filter(parent_id=self.parent_id)
                .exclude(pk=self.pk)
                .count()
            )
            if sibling_count >= max_children:
                raise ValidationError(
                    f"Parent already has {sibling_count} children "
                    f"(maximum {max_children})"
                )

If you need to validate or check depth without tree fields:

# Check depth by counting ancestors
depth = node.ancestors().count()

# Or check depth manually by traversing parent chain
def get_depth(node):
    depth = 0
    current = node
    while current.parent_id:
        depth += 1
        current = current.parent
    return depth

The recursive CTE used by django-tree-queries builds the tree top-down from root nodes to descendants. This makes certain operations, like aggregating values bottom-up (from descendants to ancestors) or using tree fields in complex subqueries, difficult or impossible.

# This doesn't work - can't use tree fields in subqueries with OuterRef
Branch.objects.annotate(
    descendant_sum=Subquery(
        Branch.objects.descendants(of=OuterRef('pk')).aggregate(Sum('amount'))
    )
)

Workaround for small trees: Load the tree into memory and compute in Python:

# For small trees, load root nodes and calculate in Python
roots = Branch.objects.filter(parent=None)
for root in roots:
    descendants = root.descendants(include_self=True)
    total = sum(node.amount for node in descendants)
    # Use the total...

For complex query requirements: If you need extensive CTE customization, subqueries with tree fields, or bottom-up aggregations, consider using django-cte which provides more control over recursive CTE construction, or evaluate whether a different tree storage approach (like django-treebeard with nested sets) better fits your needs.

For discussion of this limitation, see GitHub issue #75.

Nobody wants breadth-first search but if you still want it you can achieve it as follows:

nodes = Node.objects.with_tree_fields().extra(
    order_by=["__tree.tree_depth", "__tree.tree_ordering"]
)

If you only want nodes from the top two levels:

nodes = Node.objects.with_tree_fields().extra(
    where=["__tree.tree_depth <= %s"],
    params=[1],
)

By default, .values() only returns model fields, not tree fields. If you need tree fields in a .values() call, you can access them using RawSQL:

from django.db.models.expressions import RawSQL

# Include tree fields in values() output
data = Node.objects.with_tree_fields().values(
    "name",
    tree_depth=RawSQL("tree_depth", ()),
    tree_path=RawSQL("tree_path", ()),
)
# Returns: [{'name': 'root', 'tree_depth': 0, 'tree_path': [1]}, ...]

Important caveats:

• PostgreSQL only: tree_path returns a proper array only on PostgreSQL. Other databases return the internal string representation used by django-tree-queries (subject to change).
• Not guaranteed stable: The internal representation of tree fields may change in future versions. Avoid relying on the exact format of these values in application logic.
• Performance: Using .values() with tree fields doesn't provide performance benefits over regular querysets with tree fields. Use this only when you specifically need dictionary output.

If you need tree field values for application logic, prefer accessing them as attributes on model instances rather than through .values():

# Preferred approach
nodes = Node.objects.with_tree_fields()
for node in nodes:
    depth = node.tree_depth
    path = node.tree_path  # Consistent across all databases

# Only use RawSQL with values() when you need dictionary output
data = Node.objects.with_tree_fields().values(
    "name",
    tree_depth=RawSQL("tree_depth", ()),
)

Use tree_fields() to aggregate values from ancestor nodes into arrays. This is useful for collecting paths, permissions, categories, or any field that should be inherited down the tree hierarchy.

# Aggregate names from all ancestors into an array
nodes = Node.objects.with_tree_fields().tree_fields(
    tree_names="name",
)
# Each node now has a tree_names attribute: ['root', 'parent', 'current']

# Aggregate multiple fields
nodes = Node.objects.with_tree_fields().tree_fields(
    tree_names="name",
    tree_categories="category",
    tree_permissions="permission_level",
)

# Build a full path string from ancestor names
nodes = Node.objects.with_tree_fields().tree_fields(tree_names="name")
for node in nodes:
    full_path = " > ".join(node.tree_names)  # "Root > Section > Subsection"

# Combine with tree filtering for better performance
active_nodes = (Node.objects.with_tree_fields()
                .tree_filter(is_active=True)
                .tree_fields(tree_names="name"))

The aggregated fields contain values from all ancestors (root to current node) in hierarchical order, including the current node itself.

django-tree-queries ships a model field and some form fields which augment the default foreign key field and the choice fields with a version where the tree structure is visualized using dashes etc. Those fields are tree_queries.fields.TreeNodeForeignKey, tree_queries.forms.TreeNodeChoiceField, tree_queries.forms.TreeNodeMultipleChoiceField.

django-tree-queries includes template tags to help render tree structures in Django templates. These template tags are designed to work efficiently with tree querysets and respect queryset boundaries.

Add tree_queries to your INSTALLED_APPS setting:

INSTALLED_APPS = [
    # ... other apps
    'tree_queries',
]

Then load the template tags in your template:

{% load tree_queries %}

The tree_info filter provides detailed information about each node's position in the tree structure. It's useful when you need fine control over the tree rendering.

{% load tree_queries %}
<ul>
{% for node, structure in nodes|tree_info %}
    {% if structure.new_level %}<ul><li>{% else %}</li><li>{% endif %}
    {{ node.name }}
    {% for level in structure.closed_levels %}</li></ul>{% endfor %}
{% endfor %}
</ul>

The filter returns tuples of (node, structure_info) where structure_info contains:

• new_level: True if this node starts a new level, False otherwise
• closed_levels: List of levels that close after this node
• ancestors: List of ancestor node representations from root to immediate parent

Example showing ancestor information:

{% for node, structure in nodes|tree_info %}
    {{ node.name }}
    {% if structure.ancestors %}
        (Path: {% for ancestor in structure.ancestors %}{{ ancestor }}{% if not forloop.last %} > {% endif %}{% endfor %})
    {% endif %}
{% endfor %}

The recursetree tag provides recursive rendering similar to django-mptt's recursetree tag, but optimized for django-tree-queries. It only considers nodes within the provided queryset and doesn't make additional database queries.

Basic usage:

{% load tree_queries %}
<ul>
{% recursetree nodes %}
    <li>
        {{ node.name }}
        {% if children %}
            <ul>{{ children }}</ul>
        {% endif %}
    </li>
{% endrecursetree %}
</ul>

The recursetree tag provides these context variables within the template:

• node: The current tree node
• children: Rendered HTML of child nodes (from the queryset)
• is_leaf: True if the node has no children in the queryset

Using is_leaf for conditional rendering:

{% recursetree nodes %}
    <div class="{% if is_leaf %}leaf-node{% else %}branch-node{% endif %}">
        <span class="node-name">{{ node.name }}</span>
        {% if children %}
            <div class="children">{{ children }}</div>
        {% elif is_leaf %}
            <span class="leaf-indicator">🍃</span>
        {% endif %}
    </div>
{% endrecursetree %}

Advanced example with depth information:

{% recursetree nodes %}
    <div class="node depth-{{ node.tree_depth }}"
         data-id="{{ node.pk }}"
         data-has-children="{{ children|yesno:'true,false' }}">
        <h{{ node.tree_depth|add:1 }}>{{ node.name }}</h{{ node.tree_depth|add:1 }}>
        {% if children %}
            <div class="node-children">{{ children }}</div>
        {% endif %}
    </div>
{% endrecursetree %}

Both template tags respect queryset boundaries and work efficiently with filtered or limited querysets:

# Only nodes up to depth 2
limited_nodes = Node.objects.with_tree_fields().extra(
    where=["__tree.tree_depth <= %s"], params=[2]
)

# Only specific branches
branch_nodes = Node.objects.descendants(some_node, include_self=True)

When using these limited querysets:

• recursetree will only render nodes from the queryset
• is_leaf reflects whether nodes have children in the queryset, not in the full tree
• No additional database queries are made
• Nodes whose parents aren't in the queryset are treated as root nodes

Example with depth-limited queryset:

<!-- Template -->
{% recursetree limited_nodes %}
    <li>
        {{ node.name }}
        {% if is_leaf %}
            <small>(leaf in limited view)</small>
        {% endif %}
        {{ children }}
    </li>
{% endrecursetree %}

This is particularly useful for creating expandable tree interfaces or rendering only portions of large trees for performance.

django-tree-queries includes a TreeAdmin class for Django's admin interface that provides an intuitive tree management experience with drag-and-drop style node moving capabilities.

To use the admin functionality, install with the admin extra:

pip install django-tree-queries[admin]

With automatic position management:

For the best admin experience with proper ordering, use OrderableTreeNode:

from django.contrib import admin
from tree_queries.admin import TreeAdmin
from tree_queries.models import OrderableTreeNode

class Category(OrderableTreeNode):
    name = models.CharField(max_length=100)
    # position field and ordering are inherited from OrderableTreeNode

@admin.register(Category)
class CategoryAdmin(TreeAdmin):
    list_display = [*TreeAdmin.list_display, "name"]
    position_field = "position"  # Enables sibling ordering controls

With manual position management:

If you prefer to manage positions yourself:

from django.contrib import admin
from django.db.models import Max
from tree_queries.admin import TreeAdmin
from tree_queries.models import TreeNode

class Category(TreeNode):
    name = models.CharField(max_length=100)
    position = models.PositiveIntegerField(default=0)

    class Meta:
        ordering = ["position"]

    def save(self, *args, **kwargs):
        # Custom position logic here
        if not self.position:
            self.position = (
                10
                + (
                    self.__class__._default_manager.filter(parent_id=self.parent_id)
                    .order_by()
                    .aggregate(p=Max("position"))["p"]
                    or 0
                )
            )
        super().save(*args, **kwargs)

    save.alters_data = True

@admin.register(Category)
class CategoryAdmin(TreeAdmin):
    list_display = [*TreeAdmin.list_display, "name"]
    position_field = "position"

The TreeAdmin provides:

• Tree visualization: Nodes are displayed with indentation and visual tree structure
• Collapsible nodes: Click to expand/collapse branches for better navigation
• Node moving: Cut and paste nodes to reorganize the tree structure
• Flexible ordering: Supports both ordered (with position field) and unordered trees
• Root moves: Direct "move to root" buttons for trees without sibling ordering

Configuration:

• Set position_field to the field name used for positioning siblings (e.g., "position", "order")
• Leave position_field = None for trees positioned by other criteria (pk, name, etc.)
• The admin automatically adapts its interface based on whether positioning is controllable

Required list_display columns:

• collapse_column: Shows expand/collapse toggles
• indented_title: Displays the tree structure with indentation
• move_column: Provides move controls (cut, paste, move-to-root)

These are included by default in TreeAdmin.list_display.

When migrating from django-mptt to django-tree-queries, you'll need to populate the position field (or whatever field you use for sibling ordering) based on the existing MPTT lft values. Here's an example migration:

def fill_position(apps, schema_editor):
    ModelWithMPTT = apps.get_model("your_app", "ModelWithMPTT")
    db_alias = schema_editor.connection.alias
    position_map = ModelWithMPTT.objects.using(db_alias).annotate(
        lft_rank=Window(
            expression=RowNumber(),
            partition_by=[F("parent_id")],
            order_by=["lft"],
        ),
    ).in_bulk()
    # Update batches of 2000 objects.
    batch_size = 2000
    qs = ModelWithMPTT.objects.all()
    batches = (qs[i : i + batch_size] for i in range(0, qs.count(), batch_size))
    for batch in batches:
        for obj in batch:
            obj.position = position_map[obj.pk].lft_rank
        ModelWithMPTT.objects.bulk_update(batch, ["position"])

class Migration(migrations.Migration):

    dependencies = [...]

    operations = [
        migrations.RunPython(
            code=fill_position,
            reverse_code=migrations.RunPython.noop,
        )
    ]

This migration uses Django's Window function with RowNumber() to assign position values based on the original MPTT lft ordering, ensuring that siblings maintain their relative order after the migration.

Note that the position field is used purely for ordering siblings and is not an index. By default, django-tree-queries' admin interface starts with a position value of 10 and increments by 10 (10, 20, 30, etc.) to make it explicit that the position values themselves have no inherent meaning - they are purely for relative ordering, not a sibling counter or index.

Replacing add_related_count():

django-mptt's add_related_count() method for cumulative related object counts is not directly supported in django-tree-queries. The implementation would be complex and database-specific. For community-contributed solutions and discussion of alternative approaches, see mptt-related issues on GitHub.