mirror of https://github.com/buster-so/buster.git
refactor metric metadata
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# Refactoring Metric Data Metadata Storage
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## Problem Statement ✅
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The `MetricYml` structure currently contains a `data_metadata` field that stores column metadata information as part of the metric definition. This approach has several issues:
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1. The data metadata is derived from query results rather than being an intrinsic property of the metric definition
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2. It can become outdated when underlying data changes, as it's not automatically updated
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3. Including it in the metric definition adds unnecessary complexity to the metric YAML schema
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4. It requires redundant validation logic in multiple places
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By moving data metadata from the metric definition to a database column on the `metric_files` table, we'll separate the metric definition from its execution results, maintain cleaner YAML schema for metrics, and provide a more reliable caching mechanism for metadata.
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### Current Limitations
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- Data metadata in MetricYml can become stale when source data changes
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- Validation requires redundant computation of metadata in multiple places
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- `METRIC_YML_SCHEMA` includes unnecessary complexity for data_metadata validation
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- Users may be confused about whether to update data_metadata manually
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### Impact
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- User Impact: Cleaner metric definition format, more reliable metadata information in UI
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- System Impact: More efficient caching of query metadata, reduced redundancy
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- Business Impact: Improved reliability of metric visualizations and metadata analysis
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## Requirements
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### Functional Requirements ✅
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#### Core Functionality
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- Remove `data_metadata` field from `MetricYml` struct
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- Details: Field should be completely removed from the structure
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- Acceptance Criteria: All metric-related code builds and functions without the field
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- Dependencies: None
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- Add `data_metadata` column to `metric_files` table
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- Details: A new JSONB column added to the metric_files table to store cached metadata
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- Acceptance Criteria: Successful migration that adds the column
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- Dependencies: None
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- Update query_engine to return data metadata alongside results
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- Details: The query_engine should compute and return metadata for every query
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- Acceptance Criteria: query_engine returns both results and metadata
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- Dependencies: None
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- Update validate_sql to store computed metadata in the metric file
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- Details: When validating SQL, save the computed metadata to the metric file record
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- Acceptance Criteria: Validation updates the data_metadata field with current metadata
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- Dependencies: data_metadata column added to metric_files
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- Update create_metrics.rs and update_metric.rs to handle data_metadata in the database
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- Details: Any SQL changes should trigger metadata recalculation and storage
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- Acceptance Criteria: Metadata is correctly updated when SQL is modified
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- Dependencies: query_engine metadata calculation
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- Continue saving data_metadata in version_history for metrics
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- Details: Include data_metadata in version history to preserve historical metadata
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- Acceptance Criteria: Version history preserves data_metadata for each version
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- Dependencies: None
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### Non-Functional Requirements ✅
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- Performance Requirements
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- Metadata computation should not significantly impact query performance
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- Accessing cached metadata should be faster than recomputing it
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- Security Requirements
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- No additional security requirements (using existing table and permissions)
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- Scalability Requirements
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- Metadata should be compact enough to not significantly increase database size
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## Technical Design ✅
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### System Architecture
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```mermaid
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graph TD
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A[Client Request] --> B[REST API]
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B --> C[Handlers]
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C --> D[query_engine]
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D -->|Query Results & Metadata| C
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C -->|Store Metadata| E[metric_files table]
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E -->|Cached Metadata| C
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C -->|Response| B
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B --> A
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```
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### Core Components ✅
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#### Component 1: Updated MetricYml Structure
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```rust
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// Remove data_metadata from MetricYml
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#[derive(Debug, Serialize, Deserialize, Clone, FromSqlRow, AsExpression)]
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#[diesel(sql_type = Jsonb)]
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pub struct MetricYml {
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pub name: String,
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pub description: Option<String>,
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pub time_frame: String,
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pub sql: String,
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pub chart_config: ChartConfig,
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// data_metadata field removed
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pub dataset_ids: Vec<Uuid>,
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}
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```
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#### Component 2: Updated metric_files Schema
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```rust
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// Updated MetricFile model
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#[derive(Queryable, Insertable, Identifiable, Debug, Clone, Serialize)]
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#[diesel(table_name = metric_files)]
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pub struct MetricFile {
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pub id: Uuid,
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pub name: String,
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pub file_name: String,
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pub content: MetricYml,
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pub verification: Verification,
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pub evaluation_obj: Option<Value>,
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pub evaluation_summary: Option<String>,
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pub evaluation_score: Option<f64>,
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pub organization_id: Uuid,
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pub created_by: Uuid,
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pub created_at: DateTime<Utc>,
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pub updated_at: DateTime<Utc>,
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pub deleted_at: Option<DateTime<Utc>>,
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pub publicly_accessible: bool,
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pub publicly_enabled_by: Option<Uuid>,
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pub public_expiry_date: Option<DateTime<Utc>>,
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pub version_history: VersionHistory,
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pub data_metadata: Option<Value>, // New field for cached metadata
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}
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```
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// DataMetadata structure definition
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#[derive(Debug, Serialize)]
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pub struct DataMetadata {
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pub column_count: i64,
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pub row_count: i64,
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pub column_metadata: Vec<ColumnMetaData>,
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}
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#### Component 3: Updated query_engine Interface
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```rust
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// Define a QueryResult structure to hold both results and metadata
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#[derive(Debug, Clone)]
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pub struct QueryResult {
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pub data: Vec<IndexMap<String, DataType>>,
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pub metadata: DataMetadata,
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}
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// Enhance existing query_engine to include metadata in response
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pub async fn query_engine(
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data_source_id: &Uuid,
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sql: &str,
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limit: Option<i64>,
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) -> Result<QueryResult> {
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let corrected_sql = sql.to_owned();
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let secure_sql = corrected_sql.clone();
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if let Some(warning) = query_safety_filter(secure_sql.clone()).await {
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return Err(anyhow!(warning));
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}
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let results = match route_to_query(data_source_id, &secure_sql, limit).await {
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Ok(results) => results,
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Err(e) => {
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tracing::error!(
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"There was an issue while querying the data source: {}",
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e
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);
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return Err(anyhow!(e));
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}
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};
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// Compute metadata from results
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let column_count = results.first().map(|row| row.len()).unwrap_or(0) as i64;
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let row_count = results.len() as i64;
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let column_metadata = compute_column_metadata(&results);
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// Return both results and metadata in the QueryResult structure
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Ok(QueryResult {
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data: results,
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metadata: DataMetadata {
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column_count,
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row_count,
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column_metadata,
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}
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})
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}
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// Helper function to compute column metadata from query results
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fn compute_column_metadata(results: &[IndexMap<String, DataType>]) -> Vec<ColumnMetaData> {
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let mut column_metadata = Vec::new();
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if let Some(first_row) = results.first() {
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for (col_name, sample_value) in first_row.iter() {
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let mut value_map = HashSet::new();
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let mut min_value = None;
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let mut max_value = None;
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// Analyze column data
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for row in results {
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if let Some(value) = row.get(col_name) {
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value_map.insert(format!("{:?}", value));
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update_min_max(value, &mut min_value, &mut max_value);
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}
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}
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let (simple_type, column_type) = determine_types(sample_value);
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// Only include min/max for numeric and date types
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let (min_value, max_value) = match simple_type {
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SimpleType::Number | SimpleType::Date => (
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min_value.unwrap_or(serde_json::Value::Null),
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max_value.unwrap_or(serde_json::Value::Null),
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),
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_ => (serde_json::Value::Null, serde_json::Value::Null),
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};
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column_metadata.push(ColumnMetaData {
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name: col_name.clone(),
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min_value,
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max_value,
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unique_values: value_map.len() as i32,
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simple_type,
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column_type,
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});
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}
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}
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column_metadata
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}
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```
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#### Component 4: Updated validate_sql Function
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```rust
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pub async fn validate_sql(
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sql: &str,
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dataset_id: &Uuid,
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) -> Result<(String, Vec<IndexMap<String, DataType>>, Option<Value>)> {
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// Get data source for the dataset
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let mut conn = get_pg_pool().get().await?;
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let data_source_id = datasets::table
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.filter(datasets::id.eq(dataset_id))
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.select(datasets::data_source_id)
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.first::<Uuid>(&mut conn)
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.await?;
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// Execute query and get results with metadata
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let query_result = query_engine(&data_source_id, sql, None).await?;
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let num_records = query_result.data.len();
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let message = if num_records == 0 {
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"No records were found".to_string()
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} else {
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format!("{} records were returned", num_records)
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};
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// Return records (limited to 13), message, and metadata
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let return_records = if num_records <= 13 {
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query_result.data
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} else {
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Vec::new()
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};
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Ok((message, return_records, Some(serde_json::to_value(query_result.metadata)?)))
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}
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```
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// Updated MetricDataResponse structure to use DataMetadata
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#[derive(Debug, Serialize)]
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pub struct MetricDataResponse {
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pub metric_id: Uuid,
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pub data: Vec<IndexMap<String, DataType>>,
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pub data_metadata: DataMetadata, // Changed from MetricData to DataMetadata
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}
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### Database Changes
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```sql
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-- Add data_metadata column to metric_files table
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ALTER TABLE metric_files
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ADD COLUMN data_metadata JSONB;
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-- Create index for faster queries
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CREATE INDEX metric_files_data_metadata_idx ON metric_files USING GIN (data_metadata);
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```
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### File Changes
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#### Modified Files
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- `libs/database/src/types/metric_yml.rs`
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- Changes: Remove data_metadata field and related functionality
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- Impact: Simplifies metric definition structure
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- Dependencies: None
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- `libs/database/src/models.rs`
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- Changes: Add data_metadata field to MetricFile struct
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- Impact: Enables storage of metadata in the database
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- Dependencies: Migration to add column
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- `libs/database/src/schema.rs`
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- Changes: Update metric_files table definition to include data_metadata
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- Impact: Reflects database schema change
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- Dependencies: Migration to add column
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- `libs/agents/src/tools/categories/file_tools/common.rs`
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- Changes: Update METRIC_YML_SCHEMA to remove data_metadata
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- Impact: Simplifies schema validation for metrics
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- Dependencies: None
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- `libs/agents/src/tools/categories/file_tools/common.rs`
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- Changes: Update validate_sql to return metadata and update process_metric_file to save it
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- Impact: Enables metadata computation and storage
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- Dependencies: query_engine changes
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- `libs/query_engine/src/data_source_query_routes/query_engine.rs`
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- Changes: Add metadata computation functionality
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- Impact: Enables computation of metadata along with query results
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- Dependencies: None
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- `libs/handlers/src/metrics/get_metric_data_handler.rs`
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- Changes: Use cached metadata if available, compute if not
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- Impact: Improves performance by using cached values
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- Dependencies: data_metadata column in metric_files
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- `libs/handlers/src/metrics/update_metric_handler.rs`
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- Changes: Update data_metadata when SQL changes
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- Impact: Ensures metadata is updated when metric definition changes
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- Dependencies: validate_sql changes
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- `libs/agents/src/tools/categories/file_tools/create_metrics.rs`
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- Changes: Add data_metadata to created metric files
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- Impact: Ensures metadata is stored during metric creation
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- Dependencies: validate_sql changes
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#### New Files
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- `migrations/YYYY-MM-DD-HHMMSS_add_data_metadata_to_metric_files/up.sql`
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- Purpose: Add data_metadata column to metric_files table
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- Key components: SQL to add column and index
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- Dependencies: None
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- `migrations/YYYY-MM-DD-HHMMSS_add_data_metadata_to_metric_files/down.sql`
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- Purpose: Remove data_metadata column from metric_files table
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- Key components: SQL to drop column and index
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- Dependencies: None
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## Implementation Plan
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### Phase 1: Database Schema Update 🔜 (Not Started)
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1. Create migration to add data_metadata column
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- [ ] Generate migration files using diesel CLI:
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```bash
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cd /path/to/project
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diesel migration generate add_data_metadata_to_metric_files
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```
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- [ ] Add the following SQL to the up.sql file:
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```sql
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-- Add data_metadata column to metric_files table
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ALTER TABLE metric_files ADD COLUMN data_metadata JSONB;
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-- Create index for faster queries
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CREATE INDEX metric_files_data_metadata_idx ON metric_files USING GIN (data_metadata);
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```
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- [ ] Add the following SQL to the down.sql file:
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```sql
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-- Drop index first
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DROP INDEX IF EXISTS metric_files_data_metadata_idx;
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-- Drop column
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ALTER TABLE metric_files DROP COLUMN IF EXISTS data_metadata;
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```
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- [ ] Run the migration:
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```bash
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diesel migration run
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```
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2. Update database-related structs
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- [ ] Update models.rs with new field
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- [ ] Update version_history.rs to include data_metadata for metric_file versions
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### Phase 2: Metadata Computation 🔜 (Not Started)
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1. Enhance query_engine
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- [ ] Create QueryResult structure to hold results and metadata
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- [ ] Update query_engine function to compute and return metadata
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- [ ] Implement compute_column_metadata helper function
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- [ ] Add tests for metadata computation
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2. Update validate_sql function
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- [ ] Modify to use enhanced query_engine and extract metadata
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- [ ] Add code to return metadata for metric storage
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- [ ] Update tests
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### Phase 3: Metric Definition Update 🔜 (Not Started)
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1. Update MetricYml structure
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- [ ] Remove data_metadata field
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- [ ] Update related methods and tests
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- [ ] Update METRIC_YML_SCHEMA to remove data_metadata
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2. Update handlers to use cached metadata
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- [ ] Modify get_metric_data_handler to use cached metadata
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- [ ] Update update_metric_handler to recalculate metadata when SQL changes
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- [ ] Update tests
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### Phase 4: Agent Tool Updates 🔜 (Not Started)
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1. Update metric creation and modification tools
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- [ ] Modify process_metric_file to include metadata
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- [ ] Update create_metrics.rs to store metadata
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- [ ] Update tests
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2. Migration for existing metrics
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- [ ] Create script to calculate and store metadata for existing metrics
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- [ ] Test migration on staging
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- [ ] Run migration in production
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3. Documentation and cleanup
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- [ ] Update API documentation
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- [ ] Add comments explaining metadata handling
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- [ ] Remove any leftover references to data_metadata in yml
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## Testing Strategy
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### Unit Tests
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- Test metadata computation with various data types
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- Test validate_sql with both valid and invalid queries
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- Test MetricYml serialization and deserialization without data_metadata
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### Integration Tests
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- Test end-to-end flow from query to metadata storage
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- Test metric creation with metadata computation
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- Test metric update with metadata recalculation
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### Database Migration Tests
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- Test migration on a copy of production data
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- Verify data_metadata can be computed for existing metrics
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- Test rollback procedure
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## Migration Procedure
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### Database Migration
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1. **Generate Migration Files**
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```bash
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cd /path/to/project
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diesel migration generate add_data_metadata_to_metric_files
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```
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2. **Implement Migration Files**
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**up.sql**:
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```sql
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-- Add data_metadata column to metric_files table
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ALTER TABLE metric_files ADD COLUMN data_metadata JSONB;
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-- Create index for faster queries
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CREATE INDEX metric_files_data_metadata_idx ON metric_files USING GIN (data_metadata);
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```
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**down.sql**:
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```sql
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-- Drop index first
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DROP INDEX IF EXISTS metric_files_data_metadata_idx;
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-- Drop column
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ALTER TABLE metric_files DROP COLUMN IF EXISTS data_metadata;
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```
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3. **Run Migration**
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```bash
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diesel migration run
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```
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4. **Verify Migration**
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```sql
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SELECT column_name, data_type
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FROM information_schema.columns
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WHERE table_name = 'metric_files' AND column_name = 'data_metadata';
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```
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### Code Updates
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1. **Update Diesel Schema**
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```bash
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diesel print-schema > libs/database/src/schema.rs
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```
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2. **Adapt Database Models**
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- Add data_metadata field to MetricFile struct in models.rs
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- Remove data_metadata field from MetricYml in metric_yml.rs
|
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- Update version_history.rs to handle data_metadata in versions
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|
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3. **Update Query Engine**
|
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- Create QueryResult structure in query_engine.rs
|
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- Enhance existing query_engine function to calculate and return metadata
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- Implement compute_column_metadata helper function
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- Add tests for metadata computation
|
||||
|
||||
4. **Update Handlers and Tools**
|
||||
- Update validate_sql to use enhanced query_engine and extract metadata
|
||||
- Modify get_metric_data_handler to use cached metadata when available
|
||||
- Update update_metric_handler to recalculate metadata when SQL changes
|
||||
- Update create_metrics.rs to store metadata for new metrics
|
||||
|
||||
### Backfill Data for Existing Metrics
|
||||
|
||||
A migration script will be needed to populate the data_metadata column for existing metrics. This script will:
|
||||
|
||||
1. **Query for Existing Metrics**
|
||||
```rust
|
||||
let metrics = metric_files::table
|
||||
.filter(metric_files::deleted_at.is_null())
|
||||
.load::<MetricFile>(&mut conn)
|
||||
.await?;
|
||||
```
|
||||
|
||||
2. **Process Each Metric**
|
||||
```rust
|
||||
for metric in metrics {
|
||||
// Parse YAML content
|
||||
let metric_yml: MetricYml = metric.content;
|
||||
|
||||
// Get dataset IDs
|
||||
let dataset_id = metric_yml.dataset_ids.first().unwrap_or(default_dataset_id);
|
||||
|
||||
// Get data source for dataset
|
||||
let data_source_id = get_data_source_for_dataset(dataset_id).await?;
|
||||
|
||||
// Execute query with metadata calculation
|
||||
let query_result = query_engine(&data_source_id, &metric_yml.sql, None).await?;
|
||||
|
||||
// Update metric record with metadata
|
||||
diesel::update(metric_files::table)
|
||||
.filter(metric_files::id.eq(metric.id))
|
||||
.set(metric_files::data_metadata.eq(serde_json::to_value(query_result.metadata)?))
|
||||
.execute(&mut conn)
|
||||
.await?;
|
||||
}
|
||||
```
|
||||
|
||||
3. **Deployment Strategy**
|
||||
- Run script during off-peak hours
|
||||
- Use transaction batching (for example, process 100 metrics per transaction)
|
||||
- Add logging and resumability in case of interruption
|
||||
- Monitor database performance during migration
|
||||
|
||||
### Deployment Strategy
|
||||
|
||||
1. **Pre-Deployment**
|
||||
- Run all tests on staging environment
|
||||
- Verify migrations work correctly
|
||||
- Ensure backfill script completes successfully
|
||||
- Check for performance issues
|
||||
|
||||
2. **Deployment Order**
|
||||
1. Deploy database migration
|
||||
2. Deploy code changes (compatible with both old and new schema)
|
||||
3. Run backfill script
|
||||
4. Deploy final code changes (removing old schema support)
|
||||
|
||||
3. **Rollback Strategy**
|
||||
- If issues are detected after migration but before backfill: roll back code changes, then run down.sql
|
||||
- If issues are detected after backfill: restore from backup or reverse data changes programmatically
|
||||
|
||||
## Security Considerations
|
||||
- No additional security concerns (using existing database permissions)
|
||||
|
||||
## Rollback Plan
|
||||
- Database migration includes down.sql for rollback
|
||||
- Code can be reverted if issues are found
|
||||
- Temporary dual support for both approaches during transition
|
||||
|
||||
## Monitoring
|
||||
- Add logs for metadata computation and caching
|
||||
- Monitor query performance to ensure minimal impact
|
||||
- Track database size to ensure metadata doesn't cause significant growth
|
||||
|
||||
## Success Criteria
|
||||
- All metrics have data_metadata stored in the database
|
||||
- MetricYml structures no longer contain data_metadata field
|
||||
- Visualization in UI uses cached metadata correctly
|
||||
- Performance equal to or better than previous implementation
|
||||
|
||||
// Updated MetricDataResponse structure to use DataMetadata
|
||||
#[derive(Debug, Serialize)]
|
||||
pub struct MetricDataResponse {
|
||||
pub metric_id: Uuid,
|
||||
pub data: Vec<IndexMap<String, DataType>>,
|
||||
pub data_metadata: DataMetadata, // Changed from MetricData to DataMetadata
|
||||
}
|
Loading…
Reference in New Issue