Struct MockDatabase

pub struct MockDatabase {
    pub projections: RwLock<HashMap<String, MockProjection>>,
    pub should_fail_on: Option<String>,
    pub call_count: RwLock<HashMap<String, usize>>,
}

Simple in-memory mock database for testing

Fields

projections: RwLock<HashMap<String, MockProjection>>

should_fail_on: Option<String>

call_count: RwLock<HashMap<String, usize>>

Implementations

impl MockDatabase

pub fn new() -> Self

pub fn with_projection(self, name: &str, nodes: Vec<NodeMetrics>) -> Self

Add a test projection with predefined data

pub fn fail_on(self, operation: &str) -> Self

Configure the mock to fail on a specific operation

pub fn get_call_count(&self, method: &str) -> usize

Get the number of times a method was called

Trait Implementations

impl AnalysisDatabase for MockDatabase

fn create_graph_projection<'life0, 'life1, 'async_trait>( &'life0 self, params: &'life1 GraphProjectionParams, ) -> Pin<Box<dyn Future<Output = Result<(), AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Creates or recreates a GDS graph projection. If a projection with the same name exists, it should ideally be dropped and recreated.

fn delete_graph_projection<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<(), AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Deletes an existing GDS graph projection if it exists. Should succeed even if the projection does not exist (making it idempotent).

fn check_graph_projection_exists<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<bool, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Checks if a GDS graph projection with the given name exists.

fn calculate_node_degrees<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<Vec<NodeMetrics>, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Calculates node-level degree metrics for all nodes in a given GDS projection. Returns a vector of NodeMetrics containing in-degree, out-degree, and total degree for each node in the graph.

fn calculate_graph_metrics<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<GraphMetrics, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Calculates comprehensive graph metrics for a given GDS projection including basic counts, degree distribution, and degree statistics.

fn calculate_weakly_connected_components<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<ComponentAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Calculates weakly connected components using Neo4j GDS WCC algorithm. Returns analysis results containing components, sizes, and statistics.

fn calculate_strongly_connected_components<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<ComponentAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Calculates strongly connected components using Neo4j GDS SCC algorithm. Returns analysis results containing components, sizes, and statistics.

fn calculate_louvain_communities<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, projection_name: &'life1 str, _params: &'life2 LouvainConfig, ) -> Pin<Box<dyn Future<Output = Result<ComponentAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Calculates communities using Neo4j GDS Louvain algorithm. Returns analysis results containing communities, sizes, and statistics.

fn calculate_label_propagation_communities<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, projection_name: &'life1 str, _params: &'life2 LabelPropagationConfig, ) -> Pin<Box<dyn Future<Output = Result<ComponentAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Calculates communities using Neo4j GDS Label Propagation algorithm. Returns analysis results containing communities, sizes, and statistics.

fn calculate_modularity<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, projection_name: &'life1 str, _community_property: &'life2 str, ) -> Pin<Box<dyn Future<Output = Result<f64, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Calculates modularity score for a given community assignment. Higher modularity indicates better community structure.

fn classify_components_by_geography<'life0, 'life1, 'async_trait>( &'life0 self, components: &'life1 [ConnectedComponent], ) -> Pin<Box<dyn Future<Output = Result<PartitionClassificationResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Classifies connected components by geographic location (country). Groups relays by country and analyzes their distribution across components.

fn classify_components_by_asn<'life0, 'life1, 'async_trait>( &'life0 self, components: &'life1 [ConnectedComponent], ) -> Pin<Box<dyn Future<Output = Result<PartitionClassificationResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Classifies connected components by ASN. Groups relays by ASN and analyzes their distribution across components.

fn classify_components_by_family<'life0, 'life1, 'async_trait>( &'life0 self, components: &'life1 [ConnectedComponent], ) -> Pin<Box<dyn Future<Output = Result<PartitionClassificationResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Classifies connected components by relay family relationships. Groups relays by family membership and analyzes their distribution across components.

fn calculate_betweenness_centrality<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, sampling_size: Option<usize>, sampling_seed: Option<u64>, ) -> Pin<Box<dyn Future<Output = Result<CentralityAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Calculates betweenness centrality for nodes in a GDS projection. Uses Neo4j GDS betweenness centrality algorithm to identify nodes that act as bridges between other nodes.

fn calculate_closeness_centrality<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, use_wasserman_faust: Option<bool>, ) -> Pin<Box<dyn Future<Output = Result<CentralityAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Calculates closeness centrality for nodes in a GDS projection. Uses Neo4j GDS closeness centrality algorithm to identify nodes that are close to all other nodes in the network.

fn calculate_combined_centrality<'life0, 'life1, 'async_trait>( &'life0 self, projection_name: &'life1 str, betweenness_sampling_size: Option<usize>, betweenness_sampling_seed: Option<u64>, use_wasserman_faust: Option<bool>, ) -> Pin<Box<dyn Future<Output = Result<CentralityAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Calculates both betweenness and closeness centrality

fn analyze_paths_between_communities<'life0, 'life1, 'life2, 'life3, 'async_trait>( &'life0 self, projection_name: &'life1 str, source_nodes: &'life2 [String], target_nodes: &'life3 [String], ) -> Pin<Box<dyn Future<Output = Result<PathAnalysisResult, AnalysisError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait, 'life3: 'async_trait,

Analyzes shortest paths between nodes from different communities. Uses Neo4j GDS shortest path algorithms to understand inter-community connectivity.

impl Debug for MockDatabase

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for MockDatabase

fn default() -> Self

Returns the “default value” for a type.