📊 Exporters

A flexible and extensible data export utility for exporting Django model data in multiple formats (CSV, JSON, XLSX).

🎯 Overview

The exporters module provides a schema-based approach to exporting data with support for:

📄 Multiple formats: CSV, JSON, and XLSX (Excel)
🔒 Type-safe field definitions: StringField, NumberField, DateField, DateTimeField, BooleanField, ListField, JSONField
⚡ Custom transformations: Field-level transformations and custom preparer methods
🔗 Dotted path notation: Easy access to nested attributes and related models
🎨 Format-specific handling: Automatic formatting based on export format (e.g., lists as arrays in JSON, comma-separated in CSV)

🚀 Quick Start

Basic Usage

python

from plane.utils.exporters import Exporter, ExportSchema, StringField, NumberField

# Define a schema
class UserExportSchema(ExportSchema):
    name = StringField(source="username", label="User Name")
    email = StringField(source="email", label="Email Address")
    posts_count = NumberField(label="Total Posts")

    def prepare_posts_count(self, obj):
        return obj.posts.count()

# Export data - just pass the queryset!
users = User.objects.all()
exporter = Exporter(format_type="csv", schema_class=UserExportSchema)
filename, content = exporter.export("users_export", users)

Exporting Issues

python

from plane.utils.exporters import Exporter, IssueExportSchema

# Get issues with prefetched relations
issues = Issue.objects.filter(project_id=project_id).prefetch_related(
    'assignee_details',
    'label_details',
    'issue_module',
    # ... other relations
)

# Export as XLSX - pass the queryset directly!
exporter = Exporter(format_type="xlsx", schema_class=IssueExportSchema)
filename, content = exporter.export("issues", issues)

# Export with custom fields only
exporter = Exporter(format_type="json", schema_class=IssueExportSchema)
filename, content = exporter.export("issues_filtered", issues, fields=["id", "name", "state_name", "assignees"])

Exporting Multiple Projects Separately

python

# Export each project to a separate file
for project_id in project_ids:
    project_issues = issues.filter(project_id=project_id)
    exporter = Exporter(format_type="csv", schema_class=IssueExportSchema)
    filename, content = exporter.export(f"issues-{project_id}", project_issues)
    # Save or upload the file

📝 Schema Definition

Field Types

📝 StringField

Converts values to strings.

python

name = StringField(source="name", label="Name", default="N/A")

🔢 NumberField

Handles numeric values (int, float).

python

count = NumberField(source="items_count", label="Count", default=0)

📅 DateField

Formats date objects as %a, %d %b %Y (e.g., "Mon, 01 Jan 2024").

python

start_date = DateField(source="start_date", label="Start Date")

⏰ DateTimeField

Formats datetime objects as %a, %d %b %Y %I:%M:%S %Z%z.

python

created_at = DateTimeField(source="created_at", label="Created At")

✅ BooleanField

Converts values to boolean.

python

is_active = BooleanField(source="is_active", label="Active", default=False)

📋 ListField

Handles list/array values. In CSV/XLSX, lists are joined with a separator (default: ", "). In JSON, they remain as arrays.

python

tags = ListField(source="tags", label="Tags")
assignees = ListField(label="Assignees")  # Custom preparer can populate this

🗂️ JSONField

Handles complex JSON-serializable objects (dicts, lists of dicts). In CSV/XLSX, they're serialized as JSON strings. In JSON, they remain as objects.

python

metadata = JSONField(source="metadata", label="Metadata")
comments = JSONField(label="Comments")

⚙️ Field Parameters

All field types support these parameters:

source: Dotted path string to the attribute (e.g., "project.name")
default: Default value when field is None
label: Display name in export headers

🔗 Dotted Path Notation

Access nested attributes using dot notation:

python

project_name = StringField(source="project.name", label="Project")
owner_email = StringField(source="created_by.email", label="Owner Email")

🎯 Custom Preparers

For complex logic, define prepare_{field_name} methods:

python

class MySchema(ExportSchema):
    assignees = ListField(label="Assignees")

    def prepare_assignees(self, obj):
        return [f"{u.first_name} {u.last_name}" for u in obj.assignee_details]

Preparers take precedence over field definitions.

⚡ Custom Transformations with Preparer Methods

For any custom logic or transformations, use prepare_<field_name> methods:

python

class MySchema(ExportSchema):
    name = StringField(source="name", label="Name (Uppercase)")
    status = StringField(label="Status")

    def prepare_name(self, obj):
        """Transform the name field to uppercase."""
        return obj.name.upper() if obj.name else ""

    def prepare_status(self, obj):
        """Compute status based on model state."""
        return "Active" if obj.is_active else "Inactive"

📦 Export Formats

📊 CSV Format

Fields are quoted with QUOTE_ALL
Lists are joined with ", " (customizable with list_joiner option)
JSON objects are serialized as JSON strings
File extension: .csv

python

exporter = Exporter(
    format_type="csv",
    schema_class=MySchema,
    options={"list_joiner": "; "}  # Custom separator
)

📋 JSON Format

Lists remain as arrays
Objects remain as nested structures
Preserves data types
File extension: .json

python

exporter = Exporter(format_type="json", schema_class=MySchema)
filename, content = exporter.export("data", records)
# content is a JSON string: '[{"field": "value"}, ...]'

📗 XLSX Format

Creates Excel-compatible files using openpyxl
Lists are joined with ", " (customizable with list_joiner option)
JSON objects are serialized as JSON strings
File extension: .xlsx
Returns binary content (bytes)

python

exporter = Exporter(format_type="xlsx", schema_class=MySchema)
filename, content = exporter.export("data", records)
# content is bytes

🔧 Advanced Usage

📦 Using Context for Pre-fetched Data

Pass context data to schemas to avoid N+1 queries. Override get_context_data() in your schema:

python

class MySchema(ExportSchema):
    attachment_count = NumberField(label="Attachments")

    def prepare_attachment_count(self, obj):
        attachments_dict = self.context.get("attachments_dict", {})
        return len(attachments_dict.get(obj.id, []))

    @classmethod
    def get_context_data(cls, queryset):
        """Pre-fetch all attachments in one query."""
        attachments_dict = get_attachments_dict(queryset)
        return {"attachments_dict": attachments_dict}

# The Exporter automatically uses get_context_data() when serializing
queryset = MyModel.objects.all()
exporter = Exporter(format_type="csv", schema_class=MySchema)
filename, content = exporter.export("data", queryset)

🔌 Registering Custom Formatters

Add support for new export formats:

python

from plane.utils.exporters import Exporter, BaseFormatter

class XMLFormatter(BaseFormatter):
    def format(self, filename, records, schema_class, options=None):
        # Implementation
        return (f"{filename}.xml", xml_content)

# Register the formatter
Exporter.register_formatter("xml", XMLFormatter)

# Use it
exporter = Exporter(format_type="xml", schema_class=MySchema)

✅ Checking Available Formats

python

formats = Exporter.get_available_formats()
# Returns: ['csv', 'json', 'xlsx']

🔍 Filtering Fields

Pass a fields parameter to export only specific fields:

python

# Export only specific fields
exporter = Exporter(format_type="csv", schema_class=MySchema)
filename, content = exporter.export(
    "filtered_data",
    queryset,
    fields=["id", "name", "email"]
)

🎯 Extending Schemas

Create extended schemas by inheriting from existing ones and overriding get_context_data():

python

class ExtendedIssueExportSchema(IssueExportSchema):
    custom_field = JSONField(label="Custom Data")

    def prepare_custom_field(self, obj):
        # Use pre-fetched data from context
        return self.context.get("custom_data", {}).get(obj.id, {})

    @classmethod
    def get_context_data(cls, queryset):
        # Get parent context (attachments, etc.)
        context = super().get_context_data(queryset)

        # Add your custom pre-fetched data
        context["custom_data"] = fetch_custom_data(queryset)

        return context

💾 Manual Serialization

If you need to serialize data without exporting, you can use the schema directly:

python

# Serialize a queryset to a list of dicts
data = MySchema.serialize_queryset(queryset, fields=["id", "name"])

# Or serialize a single object
schema = MySchema()
obj_data = schema.serialize(obj)

💡 Example: IssueExportSchema

The IssueExportSchema demonstrates a complete implementation:

python

from plane.utils.exporters import Exporter, IssueExportSchema

# Simple export - just pass the queryset!
issues = Issue.objects.filter(project_id=project_id)
exporter = Exporter(format_type="csv", schema_class=IssueExportSchema)
filename, content = exporter.export("issues", issues)

# Export specific fields only
filename, content = exporter.export(
    "issues_filtered",
    issues,
    fields=["id", "name", "state_name", "assignees", "labels"]
)

# Export multiple projects to separate files
for project_id in project_ids:
    project_issues = issues.filter(project_id=project_id)
    filename, content = exporter.export(f"issues-{project_id}", project_issues)
    # Save or upload each file

Key features:

🔗 Access to related models via dotted paths
🎯 Custom preparers for complex fields
📎 Context-based attachment handling via get_context_data()
📋 List and JSON field handling
📅 Date/datetime formatting

✨ Best Practices

🚄 Avoid N+1 Queries: Override get_context_data() to pre-fetch related data:

python

@classmethod
def get_context_data(cls, queryset):
    return {
        "attachments": get_attachments_dict(queryset),
        "comments": get_comments_dict(queryset),
    }

🏷️ Use Labels: Provide descriptive labels for better export headers:
python
```
created_at = DateTimeField(source="created_at", label="Created At")
```
🛡️ Handle None Values: Set appropriate defaults for fields that might be None:
python
```
count = NumberField(source="count", default=0)
```
🎯 Use Preparers for Complex Logic: Keep field definitions simple and use preparers for complex transformations:
python
```
def prepare_assignees(self, obj):
    return [f"{u.first_name} {u.last_name}" for u in obj.assignee_details]
```

⚡ Pass QuerySets Directly: Let the Exporter handle serialization:

python

# Good - Exporter handles serialization
exporter.export("data", queryset)

# Avoid - Manual serialization unless needed
data = MySchema.serialize_queryset(queryset)
exporter.export("data", data)

📦 Filter QuerySets, Not Data: For multiple exports, filter the queryset instead of the serialized data:

python

# Good - efficient, only serializes what's needed
for project_id in project_ids:
    project_issues = issues.filter(project_id=project_id)
    exporter.export(f"project-{project_id}", project_issues)

# Avoid - serializes all data upfront
all_data = MySchema.serialize_queryset(issues)
for project_id in project_ids:
    project_data = [d for d in all_data if d['project_id'] == project_id]
    exporter.export(f"project-{project_id}", project_data)

📚 API Reference

📊 Exporter

__init__(format_type, schema_class, options=None)

format_type: Export format ('csv', 'json', 'xlsx')
schema_class: Schema class defining fields
options: Optional dict of format-specific options

export(filename, data, fields=None)

filename: Filename without extension
data: Django QuerySet or list of dicts
fields: Optional list of field names to include
Returns: (filename_with_extension, content)
content is str for CSV/JSON, bytes for XLSX

get_available_formats() (class method)

Returns: List of available format types

register_formatter(format_type, formatter_class) (class method)

📝 ExportSchema

__init__(context=None)

context: Optional dict accessible in preparer methods via self.context for pre-fetched data

serialize(obj, fields=None)

Returns: Dict of serialized field values for a single object

serialize_queryset(queryset, fields=None) (class method)

queryset: QuerySet of objects to serialize
fields: Optional list of field names to include
Returns: List of dicts with serialized data

get_context_data(queryset) (class method)

Override to pre-fetch related data for the queryset
Returns: Dict of context data

🔧 ExportField

Base class for all field types. Subclass to create custom field types.

get_value(obj, context)

Returns: Formatted value for the field

_format_value(raw)

Override in subclasses for type-specific formatting

🧪 Testing

python

# Test exporting a queryset
queryset = MyModel.objects.all()
exporter = Exporter(format_type="json", schema_class=MySchema)
filename, content = exporter.export("test", queryset)
assert filename == "test.json"
assert isinstance(content, str)

# Test with field filtering
filename, content = exporter.export("test", queryset, fields=["id", "name"])
data = json.loads(content)
assert all(set(item.keys()) == {"id", "name"} for item in data)

# Test manual serialization
data = MySchema.serialize_queryset(queryset)
assert len(data) == queryset.count()