What's New in DataJoint 2.1¶

DataJoint 2.1 adds PostgreSQL as a production backend, enhanced diagram visualization, and singleton tables.

Upgrading from 2.0? No breaking changes. All existing code continues to work. New features are purely additive.

Citation: Yatsenko D, Nguyen TT. DataJoint 2.0: A Computational Substrate for Agentic Scientific Workflows. arXiv:2602.16585. 2026. doi:10.48550/arXiv.2602.16585

PostgreSQL Backend¶

DataJoint now supports PostgreSQL 15+ as a production database backend alongside MySQL 8+. The adapter architecture generates backend-specific SQL while maintaining a consistent API — the same table definitions, queries, and pipeline logic work on both backends.

export DJ_BACKEND=postgresql
export DJ_HOST=localhost
export DJ_PORT=5432

Or configure programmatically:

dj.config['database.backend'] = 'postgresql'

All core types (int32, float64, varchar, uuid, json), codec types (<blob>, <attach>, <object@>), query operations, foreign keys, indexes, and auto-populate work identically across backends. Backend-specific differences are handled internally by the adapter layer.

See Database Backends for the full specification.

Diagram Enhancements¶

dj.Diagram gains several visualization features for working with complex, multi-schema pipelines.

Layout Direction¶

Control the flow direction of diagrams:

# Horizontal layout
dj.config.display.diagram_direction = "LR"

# Or temporarily
with dj.config.override(display__diagram_direction="LR"):
    dj.Diagram(schema).draw()

Value	Description
`"TB"`	Top to bottom (default)
`"LR"`	Left to right

Mermaid Output¶

Generate Mermaid syntax for embedding diagrams in Markdown, GitHub, or web documentation:

print(dj.Diagram(schema).make_mermaid())

Save directly to .mmd or .mermaid files:

dj.Diagram(schema).save("pipeline.mmd")

Schema Grouping¶

Multi-schema diagrams automatically group tables into visual clusters by database schema. The cluster label shows the Python module name when available, following the DataJoint convention of one module per schema.

combined = dj.Diagram(schema1) + dj.Diagram(schema2)
combined.draw()  # tables grouped by schema

Collapsing Schemas¶

For high-level pipeline views, collapse entire schemas into single nodes:

# Show schema1 expanded, schema2 as a single node with table count
dj.Diagram(schema1) + dj.Diagram(schema2).collapse()

The "expanded wins" rule applies: if a table appears in both a collapsed and non-collapsed diagram, it stays expanded. This allows showing specific tables while collapsing the rest:

# Subject is expanded, rest of analysis schema is collapsed
dj.Diagram(Subject) + dj.Diagram(analysis).collapse()

See Diagram Specification for the full reference.

Singleton Tables¶

A singleton table holds at most one row. Declare it with no attributes in the primary key section:

@schema
class Config(dj.Lookup):
    definition = """
    # Global configuration
    ---
    setting1 : varchar(100)
    setting2 : int32
    """

Operation	Result
Insert	Works without specifying a key
Second insert	Raises `DuplicateError`
`fetch1()`	Returns the single row

Useful for global configuration, pipeline parameters, and summary statistics.

See Table Declaration for details.