`nexyron.snapshot_materialized_read`

Generic Description

nexyron.snapshot_materialized_read reads one cached materialized feature snapshot artifact.

CALL nexyron.procedures()
YIELD name, description, parameters, output_columns
WHERE name = 'nexyron.snapshot_materialized_read'
RETURN name, description, parameters, output_columns

Abstraction-platform runtime procedures build, materialize, refresh, inspect, and execute artifacts derived from registered abstraction contracts.

Consumer-Level Explanation

This procedure reads cached feature snapshot rows from the abstraction artifact surface. The row shape is reconstructed from the stream-backed feature histories written during materialization, so exact snapshot reads and feature-history reads use the same stored values.

Parameters

• name: Snapshot definition name Required.
• scenario: Optional scenario name used when the snapshot was materialized Optional.
• at: Optional ASOF timestamp in epoch milliseconds. Use this to select one daily or historical materialized snapshot for the same contract Optional.
• system_at: Optional system-time cutoff in epoch milliseconds for dual-time snapshots Optional.

Output Columns

• subject_id
• snapshot_ts
• features

Example Contract Prerequisite

The executable Cypher blocks on this page query nexyron.procedures() so they work in an empty database and stay synchronized with the live procedure registry. A direct CALL nexyron.snapshot_materialized_read(...) requires the named abstraction contracts, artifacts, features, snapshots, datasets, models, or policies referenced by that call to exist first; otherwise the runtime correctly fails with an unknown-contract error rather than inventing state.

Conceptual Explanation

The important thing about nexyron.snapshot_materialized_read is that it stays inside the same Cypher and abstraction-contract runtime as the rest of Nexyron. That means it can compose with named abstractions, time-aware snapshot contracts, document-style payloads, graph-native identity, and later model or policy layers without forcing you to move into a separate tool first.

Materialized snapshots are selected by name, optional scenario, optional at, and optional system_at. If daily automatic snapshots exist, pass the requested_at_ms value reported by nexyron.snapshot_materialized_list as at to read that specific day. Use nexyron.feature_history when the goal is one feature across many timestamps.

More Detailed Explanation

In practical queries, start by deciding the row grain you want after the call: one row per node, one row per path, one row per registry object, one row per artifact, or one row per summary. Then keep that grain explicit with YIELD and named projections. That is the difference between a useful planner-facing example and a vague call that downstream tooling cannot safely compose. For contract-driven abstraction procedures, the executable examples on these pages intentionally inspect procedure metadata unless the required named artifacts are created in the same example.

Advanced Example

CALL nexyron.procedures()
YIELD name, parameters, output_columns
WHERE name = 'nexyron.snapshot_materialized_read'
RETURN name,
       [p IN parameters | p.name] AS parameter_names,
       output_columns
ORDER BY name

Real Use Cases

• Build or refresh quarter-specific training artifacts close to the graph so analysts can inspect the exact rows, labels, and split behavior before promoting a model.
• Audit artifact freshness, dependency shape, and rebuild backlog when a late membership backfill or facility schedule correction invalidates downstream abstraction assets.
• Read the exact ASOF feature row used by a daily snapshot after a model score or policy decision needs explanation.
• Reconstruct the grouped snapshot row for compatibility with dataset, model, and policy surfaces while keeping the canonical stored values in feature streams.

Real Limitations And Tradeoffs

• This surface is contract-driven. If the registered feature, snapshot, dataset, model, or policy definition is weak, the procedure will faithfully execute that weak contract rather than silently repairing it.
• Catalog procedures define or expose metadata. They do not by themselves build data, score models, or apply interventions.
• Materialized feature streams improve reuse, but they add freshness and invalidation concerns that on-demand execution avoids.
• Reading without at selects only the non-ASOF materialization for that snapshot. Use nexyron.snapshot_materialized_list first when multiple daily or historical entries exist.