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NOTES.md
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NOTES.md
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# Notes
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This file records working notes from the recent Geolog / backend design discussion.
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## Current State of `chase-rs`
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- `chase-rs` currently runs the minimal `.chase` frontend language, not the
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richer `.geolog` example language.
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- The current CLI supports `repl`, `gui`, and `script` over the minimal command
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language.
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- The project is best described as a chase engine for TGDs / existential rules,
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not a narrow classical Datalog implementation.
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- It can execute Datalog-like programs, but it also supports existential head
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variables via labeled null generation.
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## Geolog and `geolog-lite`
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- The `.geolog` files in `examples/geolog/` appear to define a richer DSL that
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is not currently wired into the executable frontend.
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- A practical direction is to extract a smaller, well-defined core named
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`geolog-lite`.
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- `geolog-lite` should focus on the positive relational fragment:
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- theories
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- instances
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- predicates
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- conjunctive rule bodies
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- conjunctive rule heads
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- existential variables in heads
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- conjunctive queries
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- Surface features such as record arguments, qualified names, field projection,
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and function-like syntax should be desugared into a flat relational IR.
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## Using `chase-rs` as a Processor
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- `chase-rs` is a good fit as a backend processor for `geolog-lite` once the
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language is lowered to flat predicates, facts, and TGD-style rules.
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- A compilation pipeline could be:
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- parse `geolog-lite`
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- elaborate names and parameters
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- lower to relational IR
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- compile to `Instance` + `Rule`
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- run chase
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- answer conjunctive queries over the materialized instance
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- This works well for the positive existential fragment.
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- It does **not** fully cover richer Geolog features such as equality reasoning,
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solver-oriented unsatisfiability, disjunction, or other advanced semantics.
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## Most Critical Missing Capability in `chase-rs`
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- The most semantically important missing feature is equality support:
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- EGDs
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- congruence closure / equality saturation
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- A close second is full query-answering support beyond the current materialized
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instance matching behavior.
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## Relational Database as Backend
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- A relational database can be used as a backend for `geolog-lite`.
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- However, a plain relational database is **not** a drop-in replacement for a
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chase engine.
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- If the language includes existential rules and repeated rule application to
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fixpoint, the chase logic still needs to exist somewhere.
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- Therefore, the preferred model is:
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- database = storage + joins + dedup + persistence
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- Rust engine = chase coordination + witness generation + trigger tracking
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## Preferred Architecture: DB-Backed Chase Engine
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- Strongest direction discussed:
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- store facts in a relational database
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- let SQL perform joins and candidate generation
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- implement the chase loop in Rust
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- let Rust handle restricted-chase triggers and existential witnesses
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- This keeps semantics explicit while benefiting from database execution.
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## Recommended MVP Scope
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Start with a deliberately small fragment:
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- positive rules only
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- flat predicates only after lowering
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- conjunctive bodies and heads
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- existential variables in heads
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- conjunctive queries
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- no equality
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- no negation
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- no disjunction
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- no solver-style unsat features
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This should be enough for a useful first vertical slice.
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## Suggested Data Model
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- Use one database table per predicate.
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- Do not use SQL `NULL` as labeled nulls.
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- Generate labeled null identities in Rust.
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- Qualified Geolog names should lower to stable SQL-safe predicate names.
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Example lowering ideas:
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- `Edge : [src: V, tgt: V] -> Prop` -> predicate table `edge(src, tgt)`
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- `src : E -> V` -> relation `src(e, v)` after desugaring
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- `R/data : R -> [x: A, y: B]` -> relation `r_data(r, x, y)`
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## Restricted Chase in a DB-Backed Engine
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- The key mechanism is trigger tracking.
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- For each rule application, compute a canonical frontier binding.
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- Store applied triggers in a dedicated table.
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- Skip any body match whose frontier binding has already been applied.
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- For existential heads, generate fresh labeled nulls in Rust and insert the
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corresponding derived facts.
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High-level loop:
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1. Find body matches using SQL.
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2. Project frontier bindings.
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3. Filter out already-applied triggers.
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4. Generate existential witnesses in Rust when needed.
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5. Insert derived facts with dedup.
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6. Record applied triggers.
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7. Repeat until fixpoint.
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## Clean Backend Interface
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- A clean backend trait is preferable to embedding raw SQL everywhere.
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- The backend abstraction should be chase-shaped rather than a generic
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`execute_sql` wrapper.
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Suggested responsibilities:
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- ensure predicate storage exists
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- load / append base facts
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- evaluate a compiled rule body
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- filter unseen triggers
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- insert derived facts with dedup
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- record applied triggers
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- evaluate conjunctive queries over materialized facts
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Also keep an in-memory backend as the semantic reference implementation.
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## DuckDB as the First Database Backend
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- DuckDB is a strong candidate for the first backend:
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- embedded / in-process
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- good analytical join performance
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- simple deployment model
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- good fit for batched chase rounds
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- Recommended usage model:
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- one engine process owns the database connection
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- work in batches rather than many tiny transactions
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- keep chase semantics in Rust
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- This makes DuckDB a good first backend behind a clean database trait.
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## Suggested Implementation Order
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1. Define a relational IR for predicates, rules, and queries.
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2. Define a clean backend trait for fact storage and rule evaluation.
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3. Keep an in-memory backend as the reference implementation.
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4. Implement a `DuckDbBackend`.
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5. Build a minimal `geolog-lite` parser and lowering pipeline.
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6. Run a first end-to-end example such as transitive closure.
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## Good First Example
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- `examples/geolog/transitive_closure.geolog` is an ideal first target.
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- It exercises:
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- theory parsing
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- predicate lowering
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- basic chase materialization
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- recursive closure
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- simple conjunctive querying
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@ -428,65 +428,6 @@ Some example comments refer to interactive commands such as:
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Those commands appear to belong to an external REPL or tool environment rather
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than to the `.geolog` file grammar itself.
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## Querying
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The examples do **not** show a stable, implemented query form inside `.geolog`
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files in the same way they show `theory` and `instance` declarations.
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What they do show is:
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### 1. External interactive commands
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Some files suggest that querying or inspection happens through an external tool
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or REPL:
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```text
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:source examples/geolog/transitive_closure.geolog
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:inspect Chain
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:chase Chain
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:solve EmptyModel
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```
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Based on the example comments, these commands appear to mean roughly:
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- `:source` or `:load`: load Geolog definitions from files
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- `:inspect`: inspect a declared instance
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- `:chase`: materialize or display the closure of a `= chase` instance
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- `:solve`: ask a solver to construct a satisfying instance for a theory
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These are tool commands, not part of the confirmed `.geolog` declaration syntax.
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### 2. A sketched future query form
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One file contains a commented-out example of a possible query block:
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```text
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query can_reach_B_from_A {
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? : ExampleNet problem0 Solution instance;
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}
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```
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This suggests an aspirational style where querying is expressed as asking for a
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witness inhabiting some instance type. However, in the current examples this is
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only a comment, not an observed live construct.
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### 3. Current implemented querying elsewhere in the repo
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The currently implemented query syntax in this repository belongs to the minimal
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frontend language, not to Geolog. That language supports forms such as:
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```text
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query Ancestor(?X, ?Y)?
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explain Ancestor(alice, carol)?
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```
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So the safest summary is:
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- Geolog examples define theories, instances, and chase-oriented structure.
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- Querying appears to be external, REPL-driven, or still in design.
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- The only clearly implemented query syntax in this repo today is the minimal
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`.chase` frontend query language.
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## Best Example Files by Feature
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- `transitive_closure.geolog`: basic theory, axiom, and `= chase` usage
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