%%% @doc Extended Relational Database Operations (RM/T)
%%%
%%% This module implements core database operations for Domino, an extended
%%% relational database engine based on E. F. Codd's RM/T (1979).
%%%
%%% == Architecture ==
%%%
%%% The system uses:
%%% <ul>
%%%   <li><b>Content-Addressed Storage</b> - All entities identified by SHA-256 hashes</li>
%%%   <li><b>Immutable Data Structures</b> - Operations create new versions</li>
%%%   <li><b>Merkle Trees</b> - Efficient versioning and diffing via merklet</li>
%%%   <li><b>Mnesia Backend</b> - ACID transactions and persistent storage</li>
%%%   <li><b>Volcano Iterator Model</b> - Lazy evaluation with process-based iterators</li>
%%% </ul>
%%%
%%% == Data Model ==
%%%
%%% The system maintains four tables in Mnesia:
%%% <ul>
%%%   <li>`attribute' - Stores atomic values with their hashes</li>
%%%   <li>`tuple' - Stores tuples as maps from attribute names to value hashes</li>
%%%   <li>`relation' - Stores relations with merkle trees of tuple hashes</li>
%%%   <li>`database_state' - Stores database snapshots with relation merkle trees</li>
%%% </ul>
%%%
%%% == RM/T Integrity Rules ==
%%%
%%% <ul>
%%%   <li><b>Rule 3 (Entity Integrity)</b> - E-relations accept insertions/deletions, not updates</li>
%%%   <li><b>Rule 4 (Property Integrity)</b> - Tuples require entity existence in E-relation</li>
%%% </ul>
%%%
%%% @author Nekoma Team
%%% @copyright 2025
%%% @version 0.1.0
%%% @reference E. F. Codd (1979). "Extending the Database Relational Model to Capture More Meaning"
%%% @reference E. F. Codd (1970). "A Relational Model of Data for Large Shared Data Banks"

-module(operations).
-include("../include/operations.hrl").
-export([
	 create_database/1,
	 create_relation/3,
	 create_immutable_relation/2,
	 create_tuple/3,
	 create_all_tuples/3,
	 retract_tuple/3,
	 retract_tuple/4,
	 clear_relation/2,
	 clear_relation/3,
	 retract_relation/2,
	 retract_relation/3,
	 get_relation_hash/2,
	 get_relations/1,
	 hashes_from_tuple/1,
	 get_tuples_iterator/2,
	 get_iterator_from_generator/2,
	 next_tuple/1,
	 close_iterator/1,
	 collect_all/1,
	 %% Iterator operators (lazy)
	 select_iterator/2,
	 project_iterator/2,
	 sort_iterator/2,
	 take_iterator/2,
	 %% Join operators
	 equijoin_iterator/3,
	 theta_join_iterator/3,
	 %% Materialization (eager)
	 materialize/3
	]).

%% @private
%% @doc Compute SHA-256 hash of an Erlang term.
%%
%% This is the foundational hashing function used throughout the system for
%% content addressing. All entities (attributes, tuples, relations, databases)
%% are identified by hashes computed with this function.
%%
%% @param Value Any Erlang term to hash
%% @returns Binary SHA-256 hash (32 bytes)
%%
%% @see create_tuple/3
%% @see create_relation/3
hash(Value) ->
    crypto:hash(sha256, term_to_binary(Value)).

%% @doc Insert a tuple into a relation and update the database state.
%%
%% This is the primary operation for adding data to the database. It:
%% <ol>
%%   <li>Stores each attribute value with its hash (automatic deduplication)</li>
%%   <li>Creates a tuple record mapping attribute names to value hashes</li>
%%   <li>Inserts the tuple hash into the relation's merkle tree</li>
%%   <li>Computes new relation hash from updated tree</li>
%%   <li>Updates database merkle tree with new relation hash</li>
%%   <li>Computes new database hash</li>
%% </ol>
%%
%% All operations execute within a Mnesia transaction for ACID guarantees.
%%
%% == Example ==
%% <pre>
%% DB = operations:create_database(my_db),
%% {DB1, _} = operations:create_relation(DB, users, #{name => string, age => integer}),
%% {DB2, _} = operations:create_tuple(DB1, users, #{name => "Alice", age => 30}).
%% </pre>
%%
%% @param Database `#database_state{}' record
%% @param RelationName Atom naming the relation
%% @param Tuple Map of attribute names to values (e.g., `#{name => "Alice", age => 30}')
%% @returns `{UpdatedDatabase, UpdatedRelation}' tuple
%%
%% @see create_relation/3
%% @see get_tuples_iterator/2
create_tuple(Database, RelationName, Tuple) when is_map(Tuple), is_record(Database, database_state) ->
    %% Check if relation is immutable before attempting insert
    CurrentRelationHash = maps:get(RelationName, Database#database_state.relations),
    [RelationRecord] = mnesia:dirty_read(relation, CurrentRelationHash),

    %% Validate tuple against relation schema before insertion
    case constraint:validate_tuple(Tuple, RelationRecord) of
        ok ->
            create_tuple_internal(Database, RelationName, Tuple, RelationRecord);
        {error, Reason} ->
            {error, {constraint_violation, Reason}}
    end.

%% @private
%% Internal function for tuple creation
create_tuple_internal(Database, RelationName, Tuple, _RelationRecord) ->
    F = fun() ->
        %% Step 1: Store each attribute value and build attribute_map
        AttributeMap = maps:map(fun(_AttrName, Value) ->
            ValueHash = hash(Value),
            mnesia:write(attribute, #attribute{hash = ValueHash, value = Value}, write),
            ValueHash
        end, Tuple),
        
        %% Step 2: Compute tuple hash and store tuple
        TupleHash = hash(Tuple),
        mnesia:write(tuple, #tuple{hash = TupleHash, relation = RelationName, attribute_map = AttributeMap}, write),
        
        %% Step 3: Get current relation hash from database
        CurrentRelationHash = maps:get(RelationName, Database#database_state.relations),
        
        %% Step 4: Read current relation
        [RelationRecord] = mnesia:read(relation, CurrentRelationHash),
        
        %% Step 5: Insert tuple hash into relation merkle tree
        %% We don't need to store the content of a tuple hash, just the key
        %% merklet can spot the difference in both keys and values
        %% sets:set follows the same strategy of an empty value
        NewRelationTree = merklet:insert({TupleHash, <<>>}, RelationRecord#relation.tree),
        
        %% Step 6: Compute new relation hash as the root of the new merkle tree and store
        {_,NewRelationTreeHash,_,_} = NewRelationTree,
	NewRelationHash = hash({RelationRecord#relation.name, RelationRecord#relation.schema, NewRelationTreeHash}),

        %% Update cardinality: increment by 1
        NewCardinality = case RelationRecord#relation.cardinality of
            {finite, N} -> {finite, N + 1};
            Other -> Other  % Shouldn't happen for finite relations
        end,

        UpdatedRelation = #relation{
            hash = NewRelationHash,
            name = RelationName,
            tree = NewRelationTree,
            schema = RelationRecord#relation.schema,
            constraints = RelationRecord#relation.constraints,
            cardinality = NewCardinality,
            generator = RelationRecord#relation.generator,
            provenance = RelationRecord#relation.provenance,
            lineage = RelationRecord#relation.lineage,
            membership_criteria = RelationRecord#relation.membership_criteria
        },
        %% Delete old relation record before writing new one
        %% This ensures only one relation record exists per name
        mnesia:delete({relation, CurrentRelationHash}),
        mnesia:write(relation, UpdatedRelation, write),
        
        %% Step 7: Update database relations map
        NewRelations = maps:put(RelationName, NewRelationHash, Database#database_state.relations),
        
        %% Step 8: Update database tree (for diffing)
        RelationKey = atom_to_binary(RelationName, utf8),
        NewDatabaseTree = merklet:insert({RelationKey, NewRelationHash}, Database#database_state.tree),
        
        %% Step 9: Compute new database hash and store
        {_,NewDatabaseHash,_,_} = NewDatabaseTree,
        UpdatedDatabase = #database_state{
            name = Database#database_state.name,
            hash = NewDatabaseHash,
            tree = NewDatabaseTree,
            relations = NewRelations,
            timestamp = erlang:timestamp()
        },
        mnesia:write(database_state, UpdatedDatabase, write),
        
        {UpdatedDatabase, UpdatedRelation}
    end,
    
    {atomic, Result} = mnesia:transaction(F),
    Result.

%% @doc Clear all tuples from a relation (truncate).
%%
%% Wrapper for `clear_relation/3' with transactions enabled.
%%
%% @param Database `#database_state{}' record
%% @param RelationHash Binary hash identifying the relation
%% @returns `{atomic, {UpdatedDatabase, UpdatedRelation}}' or `{aborted, Reason}'
%%
%% @see clear_relation/3
%% @see retract_relation/2
clear_relation(Database, RelationHash) ->
    clear_relation(Database, RelationHash, true).

%% @doc Clear all tuples from a relation with optional transaction control.
%%
%% Removes all tuples from the relation but preserves its schema. The relation's
%% merkle tree is set to `undefined', and a new relation hash is computed.
%%
%% @param Database `#database_state{}' record
%% @param RelationHash Binary hash identifying the relation
%% @param Transact Boolean - if `true', wraps operation in Mnesia transaction
%% @returns When `Transact =:= true': `{atomic, {UpdatedDatabase, UpdatedRelation}}'
%%          When `Transact =:= false': `{UpdatedDatabase, UpdatedRelation}'
%%
%% @see retract_relation/2
clear_relation(Database, RelationHash, Transact) ->
    [Relation] = mnesia:dirty_read(relation, RelationHash),
    NewRelationHash = hash({Relation#relation.name, Relation#relation.schema, undefined}),
    UpdatedRelation =
	Relation#relation{hash = NewRelationHash,
			  tree = undefined},
    DatabaseTreeWithoutTuples = merklet:insert({atom_to_binary(Relation#relation.name, utf8), NewRelationHash}, Database#database_state.tree),
    {_,NewDatabaseHash,_,_} = DatabaseTreeWithoutTuples,
    NewRelations = maps:put(Relation#relation.name, NewRelationHash, Database#database_state.relations),
    UpdatedDatabase =
	Database#database_state{timestamp = erlang:timestamp(),
			        hash = NewDatabaseHash,
			        tree = DatabaseTreeWithoutTuples,
			        relations = NewRelations},
     TX = fun () ->
		 mnesia:write(relation, UpdatedRelation, write),
		 mnesia:write(database_state, UpdatedDatabase, write),
		 {UpdatedDatabase, UpdatedRelation}
	 end,
    case Transact of
	true -> mnesia:transaction(TX);
	false -> {UpdatedDatabase, UpdatedRelation}
    end.

%% @doc Remove a tuple from a relation.
%%
%% Wrapper for `retract_tuple/4' with transactions enabled.
%%
%% @param Database `#database_state{}' record
%% @param RelationHash Binary hash identifying the relation
%% @param TupleHash Binary hash identifying the tuple to remove
%% @returns `{atomic, {UpdatedDatabase, UpdatedRelation}}' or `{aborted, Reason}'
%%
%% @see retract_tuple/4
%% @see create_tuple/3
retract_tuple(Database, RelationHash, TupleHash) ->
    retract_tuple(Database, RelationHash, TupleHash, true).

%% @doc Remove a tuple from a relation with optional transaction control.
%%
%% Deletes a specific tuple from the relation's merkle tree and recomputes
%% the relation and database hashes.
%%
%% @param Database `#database_state{}' record
%% @param RelationHash Binary hash identifying the relation
%% @param TupleHash Binary hash identifying the tuple to remove
%% @param Transact Boolean - if `true', wraps operation in Mnesia transaction
%% @returns When `Transact =:= true': `{atomic, {UpdatedDatabase, UpdatedRelation}}'
%%          When `Transact =:= false': `{UpdatedDatabase, UpdatedRelation}'
%%
%% @see create_tuple/3
retract_tuple(Database, RelationHash, TupleHash, Transact)
  when is_record(Database, database_state) ->
    [Relation] = mnesia:dirty_read(relation, RelationHash),
    RelationTreeWithoutTuple = merklet:delete(TupleHash, Relation#relation.tree),
    {_,NewRelationTreeHash,_,_} = RelationTreeWithoutTuple,
    NewRelationHash = hash({Relation#relation.name, Relation#relation.schema, NewRelationTreeHash}),
    DatabaseTreeWithoutTuple = merklet:insert({atom_to_binary(Relation#relation.name, utf8), NewRelationHash}, Database#database_state.tree),
    {_,NewDatabaseHash,_,_} = DatabaseTreeWithoutTuple,
    NewRelations = maps:put(Relation#relation.name, NewRelationHash, Database#database_state.relations),
    UpdatedDatabase =
	Database#database_state{timestamp = erlang:timestamp(),
				hash = NewDatabaseHash,
			        tree = DatabaseTreeWithoutTuple,
			        relations = NewRelations},
    UpdatedRelation =
	Relation#relation{hash = NewRelationHash,
			  tree = RelationTreeWithoutTuple},
    TX = fun () ->
		 mnesia:write(relation, UpdatedRelation, write),
		 mnesia:write(database_state, UpdatedDatabase, write),
		 {UpdatedDatabase, UpdatedRelation}
	 end,
    case Transact of
	true -> mnesia:transaction(TX);
	false -> {UpdatedDatabase, UpdatedRelation}
    end.

%% @doc Remove a relation from the database.
%%
%% Wrapper for `retract_relation/3' with transactions enabled.
%%
%% @param Database `#database_state{}' record
%% @param Name Atom naming the relation to remove
%% @returns `{atomic, UpdatedDatabase}' or `{aborted, Reason}'
%%
%% @see retract_relation/3
%% @see clear_relation/2
retract_relation(Database, Name) ->
    retract_relation(Database, Name, true).

%% @doc Remove a relation from the database with optional transaction control.
%%
%% First clears all tuples from the relation, then removes the relation entirely
%% from the database's relation map and merkle tree.
%%
%% @param Database `#database_state{}' record
%% @param Name Atom naming the relation to remove
%% @param Transact Boolean - if `true', wraps operation in Mnesia transaction
%% @returns When `Transact =:= true': `{atomic, UpdatedDatabase}'
%%          When `Transact =:= false': `UpdatedDatabase'
%%
%% @see create_relation/3
retract_relation(Database, Name, Transact) ->
    RelationHash = maps:get(Name, Database#database_state.relations, {error, retract_relation_could_not_find}),
    {DatabaseWithClearedRelation, _ClearedRelation} = clear_relation(Database, RelationHash, false),
    NewTree = merklet:delete(atom_to_binary(Name, utf8), DatabaseWithClearedRelation#database_state.tree),
    NewHash = case NewTree of
                  undefined -> <<>>;
                  {_,H,_,_} -> H
              end,
    UpdatedDatabase =
	DatabaseWithClearedRelation#database_state{relations = maps:remove(Name, DatabaseWithClearedRelation#database_state.relations),
						   hash = NewHash,
						   tree = NewTree,
						   timestamp = erlang:timestamp()},
    TX = fun () ->
		 mnesia:write(database_state, UpdatedDatabase, write),
		 UpdatedDatabase
	 end,
    case Transact of
	true -> mnesia:transaction(TX);
	false -> UpdatedDatabase
    end.

%% @doc Create a new relation in the database with a schema definition.
%%
%% Creates an empty relation (no tuples) with the specified schema. The schema
%% is stored but not yet enforced - it serves as documentation and provides a
%% foundation for future domain validation.
%%
%% <b>Important:</b> The relation's identity is tied to its name. Two relations
%% with identical schemas but different names are semantically distinct and can
%% contain different tuples.
%%
%% == Example ==
%% <pre>
%% DB = operations:create_database(my_db),
%% Schema = #{name => string, age => integer, email => string},
%% {DB1, _Rel} = operations:create_relation(DB, users, Schema).
%% </pre>
%%
%% @param Database `#database_state{}' record
%% @param Name Atom naming the new relation
%% @param Definition Map specifying schema (e.g., `#{name => string, age => integer}')
%% @returns `{UpdatedDatabase, NewRelation}' tuple
%%
%% @see create_database/1
%% @see create_tuple/3
%% @see retract_relation/2
create_relation(Database, Name, Definition) when is_record(Database, database_state) ->
    %% Validate schema before creating relation
    case constraint:validate_schema(Definition) of
        {error, Reason} ->
            {error, {invalid_schema, Reason}};
        ok ->
            create_relation_internal(Database, Name, Definition)
    end.

%% @private
%% Internal function for relation creation after schema validation
create_relation_internal(Database, Name, Definition) ->
    F = fun() ->
        %% Create relation with empty tree
        %% The meaning of a relation needs to be attached to its name
        %% It may sound nominalistic but that is a physical concern.
        %% Two relations might share definitions but their name gives another interpretation
        %% and therefore different tuples
        RelationHash = hash({Name, Definition, undefined}),

        %% Returns a generator function (not a PID) that yields tuples one at a time
        %% TODO: investigate if a generator should or should not be bound to a specific DATABASE VERSION. If so, create the fun passing the DB as an attribute
        GeneratorFun = fun() ->
            [CurrentRelation] = mnesia:dirty_index_read(relation, Name, #relation.name),
            CurrentHash = CurrentRelation#relation.hash,
            TupleHashes = case CurrentRelation#relation.tree of
                undefined -> [];
                _ -> hashes_from_tuple(CurrentHash)
            end,
            InnerGen = make_finite_generator(TupleHashes),
            spawn(fun() -> generator_iterator_loop(InnerGen) end)
        end,

        %% Build membership criteria from schema (1OPiC declarations)
        MembershipCriteria = constraint:build_membership_criteria(Definition),

        NewRelation = #relation{
            hash = RelationHash,
            name = Name,
            tree = undefined,
            schema = Definition,
            constraints = constraint:empty_constraints(),  % No constraints by default
            cardinality = {finite, 0},      % Empty relation
            generator = GeneratorFun,       % Function generator for finite relations
            membership_criteria = MembershipCriteria,  % Domain membership from schema
            provenance = build_base_provenance(Definition, Name), % Base relation provenance
            lineage = {base, Name}          % Base relation lineage
        },
        mnesia:write(relation, NewRelation, write),
        
        %% Update database relations map
        %% TODO: maybe we can purge this later after including the name on the merkle trees
        NewRelations = maps:put(Name, RelationHash, Database#database_state.relations),
        
        %% Update database tree (for diffing)
	%% Note that the name here is important as a key because
        %% we gotta go from a name to a hash within a database state
        NewTree = merklet:insert({atom_to_binary(Name), RelationHash}, Database#database_state.tree),
        {_,NewHash,_,_} = NewTree,
        
        UpdatedDatabase = #database_state{
            name = Database#database_state.name,
            hash = NewHash,
            tree = NewTree,
            relations = NewRelations,
            timestamp = erlang:timestamp()
        },
        mnesia:write(database_state, UpdatedDatabase, write),
        
        {UpdatedDatabase, NewRelation}
    end,
    {atomic, Result} = mnesia:transaction(F),
    Result.

%% @doc Create a new database state.
%%
%% Initializes an empty database with no relations. The database starts with:
%% <ul>
%%   <li>Empty relation map</li>
%%   <li>Undefined merkle tree (will be created when first relation is added)</li>
%%   <li>Empty hash</li>
%%   <li>Timestamp of creation</li>
%% </ul>
%%
%% All changes are committed to Mnesia within a transaction.
%%
%% == Example ==
%% <pre>
%% DB = operations:create_database(my_app).
%% </pre>
%%
%% @param Name Atom identifying the database
%% @returns `#database_state{}' record
%%
%% @see setup/0
%% @see create_relation/3
create_database(Name) ->
    DB = #database_state{
        name = Name,
        hash = <<>>,
        tree = undefined,
        relations = #{},
        timestamp = erlang:timestamp()
    },
    %% Standard immutable relations (domains and their predicates)
    StandardTypeRelations = [
        %% Boolean domain - finite, immutable
        #domain{
            name = boolean,
            schema = #{value => boolean},
            generator = fun() -> spawn(fun() -> generator_iterator_loop(generators:boolean(#{})) end) end,
            membership_criteria = #{value => {in, [true, false]}},
            cardinality = {finite, 2}
        },
        %% Natural numbers - infinite, immutable
        #domain{
            name = natural,
            schema = #{value => natural},
            generator = fun() -> spawn(fun() -> generator_iterator_loop(generators:natural(#{})) end) end,
            membership_criteria = #{value => {'and', {gte, 0}, is_integer}},
            cardinality = aleph_zero
        },
        %% Integers - infinite, immutable
        #domain{
            name = integer,
            schema = #{value => integer},
            generator = fun() -> spawn(fun() -> generator_iterator_loop(generators:integer(#{})) end) end,
            membership_criteria = #{value => is_integer},
            cardinality = aleph_zero
        },
        %% Rationals - infinite, immutable
        #domain{
            name = rational,
            schema = #{numerator => integer, denominator => integer},
            generator = fun() -> spawn(fun() -> generator_iterator_loop(generators:rational(#{})) end) end,
            membership_criteria = #{denominator => {neq, 0}},
            cardinality = aleph_zero
        }
        %% Reals - TODO: implement
        %% #domain{
        %%     name = reals,
        %%     schema = #{value => reals},
        %%     generator = fun() -> spawn(fun() -> generator_iterator_loop(generators:reals(#{})) end) end,
        %%     membership_criteria = #{value => is_float},
        %%     cardinality = continuum
        %% }
    ],
    Folder = fun (Elem, AccDB) ->
		     {NextDB, _} = operations:create_immutable_relation(AccDB, Elem),
		     NextDB
	     end,
    StandardDB = lists:foldl(Folder, DB, StandardTypeRelations),
    {atomic, ok} = mnesia:transaction(fun () ->
					      mnesia:write(database_state, StandardDB, write)
				      end),
    StandardDB.

%% @doc Create an infinite relation with a generator.
%%
%% Creates a relation with infinite cardinality (aleph_zero or continuum).
%% The relation is defined by a generator function that produces tuples
%% lazily based on constraints.
%%
%% == Primitive Infinite Relations ==
%%
%% <ul>
%%   <li>`naturals' - Natural numbers: {0, 1, 2, 3, ...}</li>
%%   <li>`integers' - Integers: {0, 1, -1, 2, -2, ...}</li>
%%   <li>`rationals' - Rationals via Stern-Brocot tree</li>
%% </ul>
%%
%% == Example ==
%% <pre>
%% %% Create immutable relation of natural numbers
%% {DB1, Naturals} = operations:create_immutable_relation(DB, #domain{
%%     name = naturals,
%%     schema = #{value => naturals},
%%     generator = {generators, naturals},
%%     membership_criteria = #{value => {'and', {gte, 0}, is_integer}},
%%     cardinality = aleph_zero
%% }).
%%
%% %% Query with bounds
%% Iterator = operations:get_tuples_iterator(DB1, naturals,
%%     #{value => {range, 0, 100}}).
%% </pre>
%%
%% @param Database `#database_state{}' record
%% @param Specification `#domain{}' record
%% @returns `{UpdatedDatabase, NewRelation}'
%%
%% @see create_relation/3
%% @see get_tuples_iterator/3
create_immutable_relation(Database, Specification)
  when is_record(Database, database_state)
       andalso is_record(Specification, domain) ->
    Name = Specification#domain.name,
    Schema = Specification#domain.schema,
    Generator = Specification#domain.generator,
    MembershipCriteria = Specification#domain.membership_criteria,
    Cardinality = Specification#domain.cardinality,

    F = fun() ->
        RelationHash = hash({Name, Schema, undefined}),

        NewRelation = #relation{
            hash = RelationHash,
            name = Name,
            tree = undefined,           % Immutable relations have no merkle tree
            schema = Schema,
            constraints = constraint:empty_constraints(),  % No constraints by default
            cardinality = Cardinality,
            generator = Generator,
            membership_criteria = MembershipCriteria,
            provenance = build_base_provenance(Schema, Name), % Base relation provenance
            lineage = {base, Name}      % Base relation lineage
        },
        mnesia:write(relation, NewRelation, write),

        %% Update database relations map
        NewRelations = maps:put(Name, RelationHash, Database#database_state.relations),

        %% Update database tree
        NewTree = merklet:insert({atom_to_binary(Name), RelationHash}, Database#database_state.tree),
        {_,NewHash,_,_} = NewTree,

        UpdatedDatabase = #database_state{
            name = Database#database_state.name,
            hash = NewHash,
            tree = NewTree,
            relations = NewRelations,
            timestamp = erlang:timestamp()
        },
        mnesia:write(database_state, UpdatedDatabase, write),

        {UpdatedDatabase, NewRelation}
    end,
    {atomic, Result} = mnesia:transaction(F),
    Result.

%% @doc Get relation hash by name.
%%
%% Looks up a relation's hash in the database's relation map. This hash
%% can be used to retrieve the full relation record from Mnesia.
%%
%% @param Database `#database_state{}' record
%% @param RelationName Atom naming the relation
%% @returns `{ok, Hash}' on success, `{error, not_found}' if relation doesn't exist
%%
%% @see get_relations/1
%% @see hashes_from_tuple/1
get_relation_hash(Database, RelationName) ->
    case maps:find(RelationName, Database#database_state.relations) of
        error -> {error, not_found};
        X -> X
    end.

%% @doc Get list of all relation names in the database.
%%
%% Returns a list of all relation names (atoms) currently stored in the database.
%% This includes both user-defined relations and built-in infinite relations.
%%
%% @param Database `#database_state{}' record
%% @returns List of relation names (atoms)
-spec get_relations(#database_state{}) -> [atom()].
get_relations(Database) ->
    maps:keys(Database#database_state.relations).

%% @doc Resolve a tuple hash to actual values.
%%
%% This is an internal function that materializes a tuple by:
%% <ol>
%%   <li>Reading the tuple record from Mnesia (contains attribute_map)</li>
%%   <li>For each attribute, reading the actual value using its hash</li>
%%   <li>Building a map with attribute names and materialized values</li>
%% </ol>
%%
%% Uses dirty reads since all data is immutable.
%%
%% @param TupleHash Binary hash identifying the tuple
%% @returns Map with attribute names and actual values (e.g., `#{name => "Alice", age => 30}')
%%
%% @see get_tuples_iterator/2
%% @see create_tuple/3
resolve_tuple(TupleHash) ->
    [#tuple{attribute_map = AttributeMap}] = mnesia:dirty_read(tuple, TupleHash),
    maps:map(fun(_AttrName, ValueHash) ->
        [#attribute{value = Value}] = mnesia:dirty_read(attribute, ValueHash),
        Value
    end, AttributeMap).

%% @doc Extract all tuple hashes from a relation.
%%
%% Retrieves the list of all tuple hashes stored in the relation's merkle tree.
%% Returns an empty list if the relation has no tuples (tree is `undefined').
%%
%% Uses dirty reads since the relation data is immutable.
%%
%% @param RelationHash Binary hash identifying the relation
%% @returns List of binary tuple hashes, or empty list `[]' if relation is empty
%%
%% @see get_tuples_iterator/2
%% @see create_tuple/3
hashes_from_tuple(RelationHash) ->
    [Relation] = mnesia:dirty_read(relation, RelationHash),
    case Relation#relation.tree of
        undefined -> [];
        Tree -> merklet:keys(Tree)
    end.

%% @doc Create an iterator for streaming tuples from a relation with constraints.
%%
%% Extended version of get_tuples_iterator/2 that accepts boundedness constraints.
%% For infinite relations, constraints are mandatory to ensure termination.
%%
%% <b>All tuples include metadata tracking</b> with a nested `meta' field containing:
%% <ul>
%%   <li>`provenance' - Tracks immediate source of each attribute</li>
%%   <li>`lineage' - Tracks complete operational history</li>
%% </ul>
%%
%% == Options Format ==
%%
%% Nested format (preferred):
%% <pre>
%% #{constraints => #{age => {gt, 30}}}
%% </pre>
%%
%% Flat format (backward compatible):
%% <pre>
%% #{age => {gt, 30}}
%% </pre>
%%
%% == Constraints ==
%%
%% Constraints bound the iteration over infinite relations:
%% <ul>
%%   <li>`{range, Min, Max}' - Range constraint</li>
%%   <li>`{eq, Value}' - Equality constraint</li>
%%   <li>`{lt, Value}', `{lte, Value}' - Upper bounds</li>
%%   <li>`{gt, Value}', `{gte, Value}' - Lower bounds</li>
%%   <li>`{in, List}' - Membership constraint</li>
%% </ul>
%%
%% == Example ==
%% <pre>
%% %% Iterate employees with age > 30 (metadata always included)
%% Iterator = operations:get_tuples_iterator(DB, employees,
%%     #{constraints => #{age => {gt, 30}}}).
%%
%% %% Results will have:
%% %% #{id => 1, name => "Alice", age => 35,
%% %%   meta => #{
%% %%     provenance => #{id => {employees, id}, ...},
%% %%     lineage => {select, Fun, {base, employees}}
%% %%   }}
%% </pre>
%%
%% @param Database `#database_state{}' record
%% @param RelationName Atom naming the relation to iterate
%% @returns Pid of iterator process
%%
%% @see get_tuples_iterator/2
%% @see take/3
get_tuples_iterator(Database, RelationName) when is_record(Database, database_state) ->
    case maps:get(RelationName, Database#database_state.relations, error) of
        error ->
            erlang:error({error_tuple_iterator_init, relation_not_found, RelationName});
        RelationHash ->
            [Relation] = mnesia:dirty_read(relation, RelationHash),
            case Relation#relation.cardinality of
                {finite, _} when Relation#relation.tree =/= undefined ->
                    TupleHashes = hashes_from_tuple(RelationHash),
                    spawn(fun() -> tuple_iterator_loop(TupleHashes) end);
                _ ->
                    get_iterator_from_generator(Relation#relation.name, Relation#relation.generator)
            end
    end.

%% @doc Get an iterator out of a generator.
get_iterator_from_generator(RelationName, GeneratorFun) ->
    case GeneratorFun of
	undefined -> spawn(fun() -> tuple_iterator_loop([]) end);
	Generator when is_function(Generator) -> Generator();
        _ -> erlang:error({error_tuple_iterator_init, non_function_generator, RelationName})
    end.

%% @doc Get next tuple from iterator.
%%
%% Sends a `{next, self()}' message to the iterator process and waits for
%% the response. The iterator will materialize the tuple on demand.
%%
%% Timeout is set to 5 seconds to prevent indefinite blocking.
%%
%% @param IteratorPid Pid returned from `get_tuples_iterator/2'
%% @returns `{ok, Tuple}' with materialized tuple map,
%%          `done' when no more tuples remain, or
%%          `{error, timeout}' if iterator doesn't respond
%%
%% @see get_tuples_iterator/2
%% @see close_iterator/1
next_tuple(IteratorPid) ->
    IteratorPid ! {next, self()},
    receive
        {tuple, Tuple} -> {ok, Tuple};
        done -> done;
        {error, Reason} -> {error, Reason}
    after 5000 ->
        {error, timeout}
    end.

%% @doc Close iterator process.
%%
%% Sends a `stop' message to terminate the iterator process. This should be
%% called when you're done consuming tuples to avoid leaving processes running.
%%
%% @param IteratorPid Pid returned from `get_tuples_iterator/2'
%% @returns `ok'
%%
%% @see get_tuples_iterator/2
%% @see collect_all/1
close_iterator(IteratorPid) ->
    IteratorPid ! stop,
    ok.

%% @private
%% Iterator loop implementation (internal).
%% This is the process loop for the Volcano Iterator Model. It maintains a list
%% of tuple hashes and resolves them lazily when requested.
%%
%% Protocol:
%% <ul>
%%   <li>`{next, Caller}' - Resolve next tuple and send `{tuple, ResolvedTuple}' or `done'</li>
%%   <li>`stop' - Terminate the process</li>
%% </ul>
%%
%% @param TupleHashes List of binary tuple hashes to stream
tuple_iterator_loop([]) ->
    receive
        {next, Caller} ->
            Caller ! done;
        stop ->
            ok
    end;
tuple_iterator_loop([TupleHash | Rest]) ->
    receive
        {next, Caller} ->
            %% Resolve tuple on demand
            ResolvedTuple = resolve_tuple(TupleHash),
            Caller ! {tuple, ResolvedTuple},
            tuple_iterator_loop(Rest);
        stop ->
            ok
    end.

%% @doc Helper to collect all tuples from an iterator into a list.
%%
%% This is a convenience function primarily used for testing. It exhausts the
%% iterator by repeatedly calling `next_tuple/1' until `done' is received,
%% accumulating all tuples into a list.
%%
%% <b>Warning:</b> This materializes all tuples in memory, defeating the purpose
%% of lazy evaluation. Only use for small relations or testing.
%%
%% == Example ==
%% <pre>
%% Iterator = operations:get_tuples_iterator(DB, users),
%% AllTuples = operations:collect_all(Iterator).
%% %% Iterator is automatically closed
%% </pre>
%%
%% @param IteratorPid Pid returned from `get_tuples_iterator/2'
%% @returns List of tuple maps, `{error, Reason, PartialResults}' on timeout
%%
%% @see get_tuples_iterator/2
%% @see next_tuple/1
collect_all(IteratorPid) ->
    collect_all(IteratorPid, []).

collect_all(IteratorPid, Acc) ->
    case next_tuple(IteratorPid) of
        {ok, Tuple} -> collect_all(IteratorPid, [Tuple | Acc]);
        done ->
            close_iterator(IteratorPid),
            lists:reverse(Acc);  % Preserve order
        {error, Reason} ->
            close_iterator(IteratorPid),
            {error, Reason, lists:reverse(Acc)}
    end.

%% @doc Select iterator: Filters tuples based on a predicate.
%%
%% Lazy operator that wraps a child iterator and only emits tuples that
%% satisfy the predicate function.
%%
%% == Example ==
%% <pre>
%% BaseIter = get_tuples_iterator(DB, employees, #{}),
%% FilteredIter = select_iterator(BaseIter, fun(E) -&gt; maps:get(age, E) &gt; 30 end),
%% Tuples = collect_all(FilteredIter).
%% </pre>
%%
%% @param ChildIterator Iterator process to filter
%% @param Predicate Function: tuple() -> boolean()
%% @returns Iterator process (Pid)
select_iterator(ChildIterator, Predicate) when is_function(Predicate, 1) ->
    spawn(fun() -> select_loop(ChildIterator, Predicate) end).

select_loop(ChildPid, Predicate) ->
    receive
        {next, Caller} ->
            case next_tuple(ChildPid) of
                {ok, Tuple} ->
                    case Predicate(Tuple) of
                        true ->
                            Caller ! {tuple, Tuple},
                            select_loop(ChildPid, Predicate);
                        false ->
                            self() ! {next, Caller},
                            select_loop(ChildPid, Predicate)
                    end;
                done ->
                    close_iterator(ChildPid),
                    Caller ! done;
                {error, Reason} ->
                    close_iterator(ChildPid),
                    Caller ! {error, Reason}
            end;
        stop ->
            close_iterator(ChildPid),
            ok
    end.

%% @doc Project iterator: Projects tuples onto specified attributes.
%%
%% Lazy operator that transforms tuples by keeping only the specified
%% attributes. This is the relational projection operator (π).
%%
%% == Example ==
%% <pre>
%% BaseIter = get_tuples_iterator(DB, employees, #{}),
%% ProjectedIter = project_iterator(BaseIter, [name, age]),
%% Tuples = collect_all(ProjectedIter).
%% </pre>
%%
%% @param ChildIterator Iterator process to project
%% @param Attributes List of attribute names to keep
%% @returns Iterator process (Pid)
project_iterator(ChildIterator, Attributes) when is_list(Attributes) ->
    spawn(fun() -> project_loop(ChildIterator, Attributes) end).

project_loop(ChildPid, Attributes) ->
    receive
        {next, Caller} ->
            case next_tuple(ChildPid) of
                {ok, Tuple} ->
                    % Project data attributes only - no metadata handling
                    ProjectedTuple = maps:with(Attributes, Tuple),
                    Caller ! {tuple, ProjectedTuple},
                    project_loop(ChildPid, Attributes);
                done ->
                    close_iterator(ChildPid),
                    Caller ! done;
                {error, Reason} ->
                    close_iterator(ChildPid),
                    Caller ! {error, Reason}
            end;
        stop ->
            close_iterator(ChildPid),
            ok
    end.

%% @doc Sort iterator: Sorts all tuples from child iterator.
%%
%% <b>Blocking operator</b> - must consume ALL tuples from child before
%% emitting any output. Materializes and sorts tuples in memory.
%%
%% <b>Warning:</b> Do not use on unbounded streams! Ensure child iterator
%% produces finite tuples (via constraints or take).
%%
%% == Example ==
%% <pre>
%% BaseIter = get_tuples_iterator(DB, employees, #{}),
%% SortedIter = sort_iterator(BaseIter, fun(A, B) -&gt;
%%     maps:get(age, A) =&lt; maps:get(age, B)
%% end),
%% Tuples = collect_all(SortedIter).
%% </pre>
%%
%% @param ChildIterator Iterator process to sort
%% @param CompareFun Function: (tuple(), tuple()) -> boolean()
%% @returns Iterator process (Pid)
sort_iterator(ChildIterator, CompareFun) when is_function(CompareFun, 2) ->
    spawn(fun() ->
        %% Sync all state blocking the pipeline
        AllTuples = collect_all(ChildIterator),
        %% Sort in memory
        SortedTuples = lists:sort(CompareFun, AllTuples),
        sorted_emit_loop(SortedTuples)
    end).

sorted_emit_loop([]) ->
    receive
        {next, Caller} -> Caller ! done;
        stop -> ok
    end;
sorted_emit_loop([Tuple | Rest]) ->
    receive
        {next, Caller} ->
            Caller ! {tuple, Tuple},
            sorted_emit_loop(Rest);
        stop -> ok
    end.

%% @doc Take iterator: Limits output to N tuples.
%%
%% Lazy operator that emits at most N tuples from the child iterator,
%% then closes the child and signals done.
%%
%% == Example ==
%% <pre>
%% BaseIter = get_tuples_iterator(DB, naturals, #{value => {range, 0, 1000}}),
%% LimitedIter = take_iterator(BaseIter, 10),
%% Tuples = collect_all(LimitedIter).  % Gets exactly 10 tuples
%% </pre>
%%
%% @param ChildIterator Iterator process to limit
%% @param N Maximum number of tuples to emit
%% @returns Iterator process (Pid)
take_iterator(ChildIterator, N) when is_integer(N), N > 0 ->
    spawn(fun() -> take_iter_loop(ChildIterator, N, 0, N) end).

take_iter_loop(ChildPid, Limit, Count, OriginalN) when Count < Limit ->
    receive
        {next, Caller} ->
            case next_tuple(ChildPid) of
                {ok, Tuple} ->
                    %% Pass through tuple without metadata injection  
                    Caller ! {tuple, Tuple},
                    take_iter_loop(ChildPid, Limit, Count + 1, OriginalN);
                done ->
                    close_iterator(ChildPid),
                    Caller ! done;
                {error, Reason} ->
                    close_iterator(ChildPid),
                    Caller ! {error, Reason}
            end;
        stop ->
            close_iterator(ChildPid),
            ok
    end;
take_iter_loop(ChildPid, _Limit, _Count, _OriginalN) ->
    %% Reached limit - close child and signal done
    close_iterator(ChildPid),
    receive
        {next, Caller} -> Caller ! done;
        stop -> ok
    end.

%%% ============================================================================
%%% Join Operators
%%% ============================================================================

%% @doc Equijoin iterator: Natural join on matching attribute values.
%%
%% Performs an equijoin between two relations on a common attribute.
%% For each tuple from the left iterator, finds all matching tuples from
%% the right iterator where the specified attributes are equal.
%%
%% The result tuples merge attributes from both sides. If attribute names
%% conflict, right side attributes are prefixed with "right_".
%%
%% Implementation: Nested loop join (simple but works for small-medium datasets).
%% Future: Hash join for better performance.
%%
%% == Example ==
%% <pre>
%% %% Join employees with departments on dept_id
%% LeftIter = operations:get_tuples_iterator(DB, employees, #{}),
%% RightIter = operations:get_tuples_iterator(DB, departments, #{}),
%% JoinIter = operations:equijoin_iterator(LeftIter, RightIter, dept_id)
%% </pre>
%%
%% @param LeftIterator Iterator for left relation
%% @param RightIterator Iterator for right relation
%% @param JoinAttribute Attribute name to join on (must exist in both relations)
%% @returns Iterator process that emits joined tuples
equijoin_iterator(LeftIterator, RightIterator, JoinAttribute) ->
    spawn(fun() -> equijoin_loop(LeftIterator, RightIterator, JoinAttribute) end).

%% @doc Theta-join iterator: Join with arbitrary predicate.
%%
%% Performs a theta-join between two relations using a user-provided
%% predicate function. For each pair of tuples (left, right), the predicate
%% is evaluated. If true, the tuples are merged and emitted.
%%
%% == Example ==
%% <pre>
%% %% Join where employee.age &gt; department.min_age
%% Predicate = fun(EmpTuple, DeptTuple) -&gt;
%%     maps:get(age, EmpTuple) &gt; maps:get(min_age, DeptTuple)
%% end,
%% JoinIter = operations:theta_join_iterator(LeftIter, RightIter, Predicate)
%% </pre>
%%
%% @param LeftIterator Iterator for left relation
%% @param RightIterator Iterator for right relation
%% @param Predicate Function `fun(LeftTuple, RightTuple) -> boolean()'
%% @returns Iterator process that emits joined tuples
theta_join_iterator(LeftIterator, RightIterator, Predicate) ->
    spawn(fun() -> theta_join_loop(LeftIterator, RightIterator, Predicate) end).

%%% ============================================================================
%%% Materialization (Eager - Relation Creation)
%%% ============================================================================

%% @doc Materialize an iterator pipeline into a new relation.
%%
%% This is the boundary between lazy evaluation (iterator pipeline) and
%% eager materialization (stored relation). Consumes all tuples from the
%% iterator and stores them as a new finite relation in the database.
%%
%% == Example ==
%% <pre>
%% %% Build lazy pipeline
%% Pipeline = get_tuples_iterator(DB, employees, #{})
%%            |&gt; select_iterator(fun(E) -&gt; maps:get(age, E) &gt; 30 end)
%%            |&gt; sort_iterator(fun(A, B) -&gt; maps:get(age, A) =&lt; maps:get(age, B) end)
%%            |&gt; take_iterator(10)
%%            |&gt; project_iterator([name, age]),
%%
%% %% Materialize into new relation
%% {DB1, SeniorEmployees} = materialize(DB, Pipeline, senior_employees).
%% </pre>
%%
%% @param Database Current database state
%% @param SourceIterator Iterator process (pipeline endpoint)
%% @param ResultName Atom naming the new relation
%% @returns `{UpdatedDatabase, NewRelation}' tuple
materialize(Database, SourceIterator, ResultName) when is_record(Database, database_state) ->
    %% 1. Pull all tuples from pipeline
    AllTuples = collect_all(SourceIterator),

    case AllTuples of
        [] ->
            %% Empty result - create empty relation
            Schema = #{},  % Empty schema
            create_relation(Database, ResultName, Schema);

        [FirstTuple | _] ->
            %% 2. Infer schema from first tuple
            Schema = infer_schema_from_tuple(FirstTuple),

            %% 3. Create new relation
            {DB1, _Relation} = create_relation(Database, ResultName, Schema),

            %% 4. Insert all tuples
            {FinalDB, FinalRelation} = create_all_tuples(DB1, ResultName, AllTuples),

            {FinalDB, FinalRelation}
    end.

%% @private
%% Infer schema from a tuple.
infer_schema_from_tuple(Tuple) when is_map(Tuple) ->
    maps:map(fun(_AttrName, Value) -> infer_type(Value) end, Tuple).

%% @private
%% Infer type from value.
infer_type(Value) when is_integer(Value) -> integer;
infer_type(Value) when is_float(Value) -> float;
infer_type(Value) when is_binary(Value) -> binary;
infer_type(Value) when is_list(Value) -> string;
infer_type(Value) when is_atom(Value) -> atom;
infer_type(Value) when is_boolean(Value) -> boolean;
infer_type(_) -> term.

%% @private
%% Insert multiple tuples into a relation.
create_all_tuples(DB, RelationName, Tuples) ->
    lists:foldl(
        fun(Tuple, {DBacc, _RelAcc}) ->
            create_tuple(DBacc, RelationName, Tuple)
        end,
        {DB, undefined},
        Tuples
    ).

%%% ============================================================================
%%% Generator Support Functions
%%% ============================================================================


%% @private
%% Generator iterator loop for infinite relations.
%% Similar to tuple_iterator_loop but works with generator functions instead
%% of tuple hashes.
generator_iterator_loop(Generator) ->
    receive
        {next, Caller} ->
            case Generator(next) of
                done ->
                    Caller ! done;
                {value, Tuple, NextGenerator} ->
                    Caller ! {tuple, Tuple},
                    generator_iterator_loop(NextGenerator);
                {error, Reason} ->
                    Caller ! {error, Reason}
            end;
        stop ->
            ok
    end.

%% @private
%% Equijoin loop: nested loop join on attribute equality.
%% Strategy:
%% 1. Collect all tuples from right iterator into memory
%% 2. For each left tuple, scan right tuples for matches
%% 3. Emit merged tuple when join attribute values match
%%
%% @param LeftIter Left iterator process
%% @param RightIter Right iterator process
%% @param JoinAttr Attribute to join on
equijoin_loop(LeftIter, RightIter, JoinAttr) ->
    % Materialize right side (for nested loop)
    RightTuples = collect_all(RightIter),
    equijoin_emit_loop(LeftIter, RightTuples, JoinAttr).

equijoin_emit_loop(LeftIter, RightTuples, JoinAttr) ->
    receive
        {next, Caller} ->
            case next_tuple(LeftIter) of
                done ->
                    Caller ! done,
                    ok;  % Stop after sending done
                {ok, LeftTuple} ->
                    % Find all right tuples that match on join attribute
                    case maps:get(JoinAttr, LeftTuple, undefined) of
                        undefined ->
                            % Left tuple doesn't have join attribute - try next
                            self() ! {next, Caller},
                            equijoin_emit_loop(LeftIter, RightTuples, JoinAttr);
                        LeftValue ->
                            % Find matching right tuples
                            Matches = [merge_tuples(LeftTuple, RightTuple)
                                      || RightTuple <- RightTuples,
                                         maps:get(JoinAttr, RightTuple, undefined) =:= LeftValue],
                            case Matches of
                                [] ->
                                    % No matches - try next left tuple
                                    self() ! {next, Caller},
                                    equijoin_emit_loop(LeftIter, RightTuples, JoinAttr);
                                [FirstMatch | RestMatches] ->
                                    % Emit first match, buffer rest
                                    Caller ! {tuple, FirstMatch},
                                    equijoin_emit_buffered(LeftIter, RightTuples, JoinAttr, RestMatches)
                            end
                    end;
                {error, Reason} ->
                    close_iterator(LeftIter),
                    Caller ! {error, Reason},
                    ok  % Stop after sending error
            end;
        stop ->
            close_iterator(LeftIter),
            ok
    end.

equijoin_emit_buffered(LeftIter, RightTuples, JoinAttr, [Match | Rest]) ->
    receive
        {next, Caller} ->
            Caller ! {tuple, Match},
            equijoin_emit_buffered(LeftIter, RightTuples, JoinAttr, Rest);
        stop ->
            close_iterator(LeftIter),
            ok
    end;
equijoin_emit_buffered(LeftIter, RightTuples, JoinAttr, []) ->
    % Buffer empty - continue with next left tuple
    equijoin_emit_loop(LeftIter, RightTuples, JoinAttr).

%% @private
%% Theta-join loop: nested loop join with predicate.
%% @param LeftIter Left iterator process
%% @param RightIter Right iterator process
%% @param Pred Predicate function fun(LeftTuple, RightTuple) -> boolean()
theta_join_loop(LeftIter, RightIter, Pred) ->
    % Materialize right side
    RightTuples = collect_all(RightIter),
    theta_join_emit_loop(LeftIter, RightTuples, Pred).

theta_join_emit_loop(LeftIter, RightTuples, Pred) ->
    receive
        {next, Caller} ->
            case next_tuple(LeftIter) of
                done ->
                    Caller ! done;
                {ok, LeftTuple} ->
                    % Find all right tuples that satisfy predicate
                    Matches = [merge_tuples(LeftTuple, RightTuple)
                              || RightTuple <- RightTuples,
                                 Pred(LeftTuple, RightTuple)],
                    case Matches of
                        [] ->
                            % No matches - continue
                            self() ! {next, Caller},
                            theta_join_emit_loop(LeftIter, RightTuples, Pred);
                        [FirstMatch | RestMatches] ->
                            Caller ! {tuple, FirstMatch},
                            theta_join_emit_buffered(LeftIter, RightTuples, Pred, RestMatches)
                    end;
                {error, Reason} ->
                    close_iterator(LeftIter),
                    Caller ! {error, Reason}
            end;
        stop ->
            close_iterator(LeftIter),
            ok
    end.

theta_join_emit_buffered(LeftIter, RightTuples, Pred, [Match | Rest]) ->
    receive
        {next, Caller} ->
            Caller ! {tuple, Match},
            theta_join_emit_buffered(LeftIter, RightTuples, Pred, Rest);
        stop ->
            close_iterator(LeftIter),
            ok
    end;
theta_join_emit_buffered(LeftIter, RightTuples, Pred, []) ->
    theta_join_emit_loop(LeftIter, RightTuples, Pred).

%% @private
%% @doc Simple tuple merge for joins
%% Equally named and typed attributes are not repeated.
%%
%% @param LeftTuple Left side tuple
%% @param RightTuple Right side tuple  
merge_tuples(LeftTuple, RightTuple) ->
    maps:fold(
        fun(RightKey, RightValue, LeftTupleAcc) ->
            case maps:get(RightKey, LeftTupleAcc, undefined) of
                LeftValue when LeftValue =:= RightValue ->
                    % Equal attributes, keep single value
                    LeftTupleAcc;
                undefined ->
                    % No conflict, add right attribute
                    maps:put(RightKey, RightValue, LeftTupleAcc);
                _ ->
                    % Different attributes
                    maps:put(RightKey, RightValue, LeftTupleAcc)
            end
        end,
        LeftTuple,
        RightTuple
    ).

%% @private
%% Build base provenance mapping for a relation.
%% Maps each attribute to its source relation and attribute name.
%%
%% @param Schema Map of attribute names to types
%% @param RelationName Name of the source relation
%% @returns Provenance map #{attr => {relation, attr}}
build_base_provenance(Schema, RelationName) ->
    maps:fold(
        fun(AttrName, _Type, Acc) ->
            maps:put(AttrName, {RelationName, AttrName}, Acc)
        end,
        #{},
        Schema
    ).

%% The issue here is that the creation of a generator on the create_relation is wrong.
%% I assume it was because of the fun(Constraints), which I believe should receive an
%% optional database version, or just build from the current relation attributes as a factory.

%% @private
%% @doc Create a generator function from a list of tuple hashes.
%%
%% Converts a list of tuple hashes into a generator function compatible
%% with the generator protocol used by infinite relations. The generator
%% reads tuples from Mnesia on demand.
%%
%% @param TupleHashes List of tuple hashes to iterate over
%% @returns Generator function that accepts 'next' and returns {value, Tuple, NextGen} or done
make_finite_generator([]) ->
    fun(_) -> done end;
make_finite_generator([Hash | Rest]) ->
    fun(next) ->
        ResolvedTuple = resolve_tuple(Hash),
        NextGen = make_finite_generator(Rest),
        {value, ResolvedTuple, NextGen}
    end.