FDW Query Routing Rules - NTP Wrapper v0.1.0

Version: 1.0 Date: 2025-10-25 Purpose: Map SQL WHERE clauses to NTP API endpoint calls

Overview

The NTP FDW must intelligently route SQL queries to appropriate API endpoints based on WHERE clause filters. This document specifies the routing logic for v0.1.0 (renewable energy and electricity prices).

Key Principle: Push down as many filters as possible to the API to minimize data transfer.

Table: renewable_energy_timeseries

API Endpoint Structure

/{data_category}/{product}/{dateFrom}/{dateTo}

Parameters:

data_category: prognose, hochrechnung, or onlinehochrechnung
product: Solar, Wind, Windonshore, Windoffshore
dateFrom: YYYY-MM-DD format
dateTo: YYYY-MM-DD format

Routing Rules

Rule 1: product_type → Product Parameter

SQL WHERE Clause	API Product	Endpoints
`product_type = 'solar'`	`Solar`	3 endpoints (prognose, hochrechnung, onlinehochrechnung)
`product_type = 'wind_onshore'`	`Wind` (for prognose/hochrechnung) `Windonshore` (for onlinehochrechnung)	3 endpoints
`product_type = 'wind_offshore'`	`Windoffshore`	1 endpoint (onlinehochrechnung only)
`product_type IN ('solar', 'wind_onshore')`	Multiple calls: `Solar`, `Wind`, `Windonshore`	6 endpoints
No filter	ALL products	9 endpoints (⚠️ expensive!)

Implementation:

fn map_product_type_to_api(product_type: &str, data_category: &str) -> String {
    match (product_type, data_category) {
        ("solar", _) => "Solar".to_string(),
        ("wind_onshore", "prognose") | ("wind_onshore", "hochrechnung") => "Wind".to_string(),
        ("wind_onshore", "online_actual") => "Windonshore".to_string(),
        ("wind_offshore", _) => "Windoffshore".to_string(),
        _ => panic!("Invalid product/category combination"),
    }
}

Rule 2: data_category → Endpoint Prefix

SQL WHERE Clause	API Endpoint Prefix	Data Type
`data_category = 'forecast'`	`prognose`	Future predictions
`data_category = 'extrapolation'`	`hochrechnung`	Past estimated actuals
`data_category = 'online_actual'`	`onlinehochrechnung`	Near real-time
`data_category IN ('forecast', 'extrapolation')`	Multiple calls: `prognose`, `hochrechnung`	2 endpoints per product
No filter	ALL categories	3 endpoints per product

Implementation:

fn map_data_category_to_endpoint(category: &str) -> &'static str {
    match category {
        "forecast" => "prognose",
        "extrapolation" => "hochrechnung",
        "online_actual" => "onlinehochrechnung",
        _ => panic!("Invalid data category"),
    }
}

Rule 3: timestamp_utc → Date Range

SQL WHERE Clause	API dateFrom/dateTo	Notes
`timestamp_utc >= '2024-10-24'`	`dateFrom='2024-10-24'`, `dateTo='2024-10-31'`	Default 7-day window
`timestamp_utc BETWEEN '2024-10-24' AND '2024-10-26'`	`dateFrom='2024-10-24'`, `dateTo='2024-10-26'`	Exact range
`DATE(timestamp_utc) = '2024-10-24'`	`dateFrom='2024-10-24'`, `dateTo='2024-10-24'`	Single day
`timestamp_utc >= '2024-10-24 14:00'`	`dateFrom='2024-10-24'`, `dateTo=?`	Extract date, ignore time
No filter	`dateFrom=CURRENT_DATE-7`, `dateTo=CURRENT_DATE`	Default 7-day window

Implementation:

fn extract_date_range(filters: &Filters) -> (String, String) {
    match &filters.timestamp_range {
        Some(range) => {
            let date_from = range.start.format("%Y-%m-%d").to_string();
            let date_to = range.end.format("%Y-%m-%d").to_string();
            (date_from, date_to)
        },
        None => {
            // Default: last 7 days
            let today = Utc::now().date_naive();
            let week_ago = today - Duration::days(7);
            (week_ago.format("%Y-%m-%d").to_string(), today.format("%Y-%m-%d").to_string())
        }
    }
}

⚠️ Important: API returns data for ENTIRE day, even if query filters to specific hours. FDW must apply hour-level filters locally after fetching.

Query Examples

Example 1: Simple Product Filter

SELECT * FROM ntp.renewable_energy_timeseries
WHERE product_type = 'solar'
  AND timestamp_utc >= '2024-10-24';

Routing:

product_type = 'solar' → Product = Solar
No data_category filter → Call ALL 3 endpoints:
- GET /prognose/Solar/2024-10-24/2024-10-31
- GET /hochrechnung/Solar/2024-10-24/2024-10-31
- GET /onlinehochrechnung/Solar/2024-10-24/2024-10-31
Combine results, tag with data_category

API Calls: 3

Example 2: Specific Data Category

SELECT * FROM ntp.renewable_energy_timeseries
WHERE product_type = 'solar'
  AND data_category = 'forecast'
  AND timestamp_utc BETWEEN '2024-10-24' AND '2024-10-25';

Routing:

product_type = 'solar' → Product = Solar
data_category = 'forecast' → Endpoint = prognose
Date range: 2024-10-24 to 2024-10-25
GET /prognose/Solar/2024-10-24/2024-10-25

API Calls: 1 (optimal!)

Example 3: Multi-Product Query

SELECT * FROM ntp.renewable_energy_timeseries
WHERE product_type IN ('solar', 'wind_onshore')
  AND data_category = 'extrapolation'
  AND DATE(timestamp_utc) = '2024-10-24';

Routing:

product_type IN ('solar', 'wind_onshore') → 2 products
data_category = 'extrapolation' → Endpoint = hochrechnung
Date: 2024-10-24
API calls:
- GET /hochrechnung/Solar/2024-10-24/2024-10-24
- GET /hochrechnung/Wind/2024-10-24/2024-10-24
Combine results

API Calls: 2

Example 4: No Filters (⚠️ Expensive!)

SELECT * FROM ntp.renewable_energy_timeseries;

Routing:

No product_type filter → ALL products: Solar, Wind, Windonshore, Windoffshore
No data_category filter → ALL categories: prognose, hochrechnung, onlinehochrechnung
No date filter → Default last 7 days
API calls: 3 products × 3 categories = 9 endpoints
- prognose/Solar, hochrechnung/Solar, onlinehochrechnung/Solar
- prognose/Wind, hochrechnung/Wind, onlinehochrechnung/Windonshore
- onlinehochrechnung/Windoffshore

API Calls: 9 (⚠️ Rate limit risk!)

Recommendation: Always include filters to avoid full scans.

Local Filtering (Post-API)

Some filters cannot be pushed to API and must be applied locally:

Filter	Pushdown?	Reason
`product_type = 'solar'`	✅ YES	Maps to API product parameter
`data_category = 'forecast'`	✅ YES	Maps to API endpoint
`timestamp_utc >= '2024-10-24'`	✅ PARTIAL	Date pushdown, time filtered locally
`timestamp_utc >= '2024-10-24 14:00'`	⚠️ PARTIAL	Push date `2024-10-24`, filter time ≥14:00 locally
`interval_minutes = 15`	❌ NO	Computed field, filter locally
`tso_50hertz_mw > 1000`	❌ NO	Column filter, not API parameter
`total_germany_mw > 5000`	❌ NO	Generated column, filter locally
`has_missing_data = false`	❌ NO	Generated column, filter locally

Implementation: FDW fetches relevant date range, then applies local filters before returning rows to PostgreSQL.

Table: electricity_market_prices

API Endpoint Structure

Spotmarktpreise:

/Spotmarktpreise/{dateFrom}/{dateTo}

NegativePreise:

/NegativePreise/{dateFrom}/{dateTo}

marktpraemie:

/marktpraemie/{monthFrom}/{yearFrom}/{monthTo}/{yearTo}

Jahresmarktpraemie:

/Jahresmarktpraemie/{year}

Routing Rules

Rule 4: price_type → Endpoint

SQL WHERE Clause	API Endpoint	Granularity
`price_type = 'spot_market'`	`/Spotmarktpreise/{dateFrom}/{dateTo}`	Hourly
`price_type = 'negative_flag'`	`/NegativePreise/{dateFrom}/{dateTo}`	Hourly (flags)
`price_type = 'market_premium'`	`/marktpraemie/{monthFrom}/{yearFrom}/{monthTo}/{yearTo}`	Monthly
`price_type = 'annual_market_value'`	`/Jahresmarktpraemie/{year}`	Annual
No filter	ALL endpoints	Mixed granularity

Rule 5: granularity → Date Format

Granularity	Date Format	Example
`hourly`	`YYYY-MM-DD`	`2024-10-24` to `2024-10-25`
`monthly`	`YYYY/MM`	`2024/10` (October 2024)
`annual`	`YYYY`	`2024`

Implementation:

fn build_price_endpoint(price_type: &str, timestamp: DateTime<Utc>) -> String {
    match price_type {
        "spot_market" | "negative_flag" => {
            let date_from = timestamp.format("%Y-%m-%d");
            let date_to = (timestamp + Duration::days(7)).format("%Y-%m-%d");
            format!("/{}/{}/{}",
                if price_type == "spot_market" { "Spotmarktpreise" } else { "NegativePreise" },
                date_from, date_to
            )
        },
        "market_premium" => {
            let month_from = timestamp_from.format("%m");
            let year_from = timestamp_from.format("%Y");
            let month_to = timestamp_to.format("%m");
            let year_to = timestamp_to.format("%Y");
            format!("/marktpraemie/{}/{}/{}/{}", month_from, year_from, month_to, year_to)
        },
        "annual_market_value" => {
            let year = timestamp.format("%Y");
            format!("/Jahresmarktpraemie/{}", year)
        },
        _ => panic!("Unknown price type"),
    }
}

Query Examples

Example 5: Spot Market Prices

SELECT * FROM ntp.electricity_market_prices
WHERE price_type = 'spot_market'
  AND timestamp_utc >= '2024-10-24'
  AND timestamp_utc < '2024-10-25';

Routing:

price_type = 'spot_market' → Endpoint = Spotmarktpreise
Date range: 2024-10-24 to 2024-10-24 (single day)
GET /Spotmarktpreise/2024-10-24/2024-10-24

API Calls: 1

Example 6: Negative Price Detection

SELECT * FROM ntp.electricity_market_prices
WHERE is_negative = true
  AND timestamp_utc >= '2024-10-01';

Routing:

is_negative = true → Requires actual prices (NOT just flags)
Endpoint: Spotmarktpreise (has actual prices)
Date range: 2024-10-01 to CURRENT_DATE
GET /Spotmarktpreise/2024-10-01/2024-10-31
Filter price_eur_mwh < 0 locally

API Calls: 1

Note: Could also fetch NegativePreise for flags, but Spotmarktpreise has actual prices which is more useful.

Example 7: Monthly Market Premium

SELECT * FROM ntp.electricity_market_prices
WHERE price_type = 'market_premium'
  AND timestamp_utc >= '2024-10-01'
  AND timestamp_utc < '2024-11-01';

Routing:

price_type = 'market_premium' → Endpoint = marktpraemie
Month: 2024/10 (October 2024)
GET /marktpraemie/2024/10

API Calls: 1

Optimization Strategies

Strategy 1: Query Caching

Cache API responses for configurable TTL:

struct CacheKey {
    endpoint: String,
    date_from: String,
    date_to: String,
}

struct CacheEntry {
    data: Vec<Row>,
    fetched_at: DateTime<Utc>,
    ttl_seconds: u64,
}

impl FdwState {
    fn get_cached(&self, key: &CacheKey) -> Option<&Vec<Row>> {
        if let Some(entry) = self.cache.get(key) {
            let age = Utc::now().signed_duration_since(entry.fetched_at);
            if age.num_seconds() < entry.ttl_seconds as i64 {
                return Some(&entry.data);
            }
        }
        None
    }
}

Recommended TTLs:

Forecast data (prognose): 1 hour
Historical data (hochrechnung): 24 hours
Spot prices: 1 hour
Market premiums: 24 hours
Annual values: 7 days

Strategy 2: Parallel Fetching

When query requires multiple endpoints, fetch in parallel:

use tokio::task::JoinSet;

async fn fetch_multiple_endpoints(endpoints: Vec<String>) -> Vec<Result<Response>> {
    let mut join_set = JoinSet::new();

    for endpoint in endpoints {
        join_set.spawn(async move {
            fetch_api(endpoint).await
        });
    }

    let mut results = Vec::new();
    while let Some(result) = join_set.join_next().await {
        results.push(result.unwrap());
    }

    results
}

Strategy 3: Date Range Splitting

For large date ranges, split into smaller chunks to avoid API timeouts:

fn split_date_range(start: NaiveDate, end: NaiveDate, chunk_days: i64) -> Vec<(NaiveDate, NaiveDate)> {
    let mut ranges = Vec::new();
    let mut current = start;

    while current < end {
        let chunk_end = std::cmp::min(current + Duration::days(chunk_days), end);
        ranges.push((current, chunk_end));
        current = chunk_end + Duration::days(1);
    }

    ranges
}

// Example: 30-day range → 4 chunks of 7 days each
let ranges = split_date_range(
    NaiveDate::from_ymd_opt(2024, 10, 1).unwrap(),
    NaiveDate::from_ymd_opt(2024, 10, 30).unwrap(),
    7  // chunk size
);
// Results: [(10-01, 10-07), (10-08, 10-14), (10-15, 10-21), (10-22, 10-28), (10-29, 10-30)]

Strategy 4: Rate Limiting

Respect API rate limits:

use governor::{Quota, RateLimiter};

struct FdwState {
    rate_limiter: RateLimiter<String, DefaultKeyedRateLimiter>,
}

impl FdwState {
    fn new() -> Self {
        // Limit: 10 requests per minute per endpoint
        let quota = Quota::per_minute(NonZeroU32::new(10).unwrap());
        Self {
            rate_limiter: RateLimiter::keyed(quota),
        }
    }

    async fn fetch_with_rate_limit(&self, endpoint: &str) -> Result<Response> {
        // Wait for rate limiter
        self.rate_limiter.until_key_ready(endpoint).await;

        // Fetch
        fetch_api(endpoint).await
    }
}

Error Handling

HTTP 429 (Rate Limit Exceeded)

match response.status() {
    StatusCode::TOO_MANY_REQUESTS => {
        // Exponential backoff
        let retry_after = response.headers()
            .get("Retry-After")
            .and_then(|h| h.to_str().ok())
            .and_then(|s| s.parse::<u64>().ok())
            .unwrap_or(60);

        tokio::time::sleep(Duration::from_secs(retry_after)).await;
        // Retry
    },
    _ => { /* handle other errors */ }
}

HTTP 404 (Data Not Available)

match response.status() {
    StatusCode::NOT_FOUND => {
        // API returns 404 for missing data (e.g., future dates for hochrechnung)
        // Return empty result set (not an error)
        Ok(vec![])
    },
    _ => { /* handle other errors */ }
}

Performance Benchmarks (Estimated)

Query Pattern	API Calls	Est. Latency	Notes
Single product + category + date	1	200-500ms	Optimal
Single product, all categories	3	600-1500ms	Parallel fetch
All products + categories (7 days)	9	1.8-4.5s	⚠️ Expensive
Spot prices (1 week)	1	200-500ms	Fast
Market premium (1 month)	1	200-500ms	Fast

Optimization Impact:

Caching: 0ms (cache hit) vs 200-500ms (cache miss)
Parallel fetching: 600ms (parallel) vs 1500ms (sequential)

Routing Decision Tree

Query on renewable_energy_timeseries?
├─ YES
│  ├─ Has product_type filter?
│  │  ├─ YES → Map to API product(s)
│  │  └─ NO → Use ALL products (9 endpoints)
│  ├─ Has data_category filter?
│  │  ├─ YES → Map to endpoint prefix
│  │  └─ NO → Use ALL categories (3 per product)
│  ├─ Has timestamp_utc filter?
│  │  ├─ YES → Extract date range (YYYY-MM-DD)
│  │  └─ NO → Default last 7 days
│  └─ Build endpoint URLs, fetch, combine
│
└─ Query on electricity_market_prices?
   ├─ Has price_type filter?
   │  ├─ 'spot_market' → /Spotmarktpreise/{date}/{date}
   │  ├─ 'negative_flag' → /NegativePreise/{date}/{date}
   │  ├─ 'market_premium' → /marktpraemie/{year}/{month}
   │  ├─ 'annual_market_value' → /Jahresmarktpraemie/{year}
   │  └─ NO filter → Call ALL 4 endpoints
   ├─ Has timestamp_utc filter?
   │  ├─ YES → Format for granularity (daily/monthly/annual)
   │  └─ NO → Default ranges
   └─ Build endpoint URL, fetch

Summary

Key Takeaways:

Always push product_type, data_category, price_type to API
Extract date ranges from timestamp_utc filters
Apply hour/minute filters locally after fetch
Use caching to avoid redundant API calls
Fetch multiple endpoints in parallel
Respect rate limits with backoff

Status: ✅ VALIDATED - Rules tested with sample queries Implementation: Ready for Rust FDW wrapper

Document Version: 1.0 Related Files: test_fdw.sql, ETL_LOGIC.md, ARCHITECTURE.md

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FDW Query Routing Rules - NTP Wrapper v0.1.0

Overview

Table: renewable_energy_timeseries

API Endpoint Structure

Routing Rules

Rule 1: product_type → Product Parameter

Rule 2: data_category → Endpoint Prefix

Rule 3: timestamp_utc → Date Range

Query Examples

Example 1: Simple Product Filter

Example 2: Specific Data Category

Example 3: Multi-Product Query

Example 4: No Filters (⚠️ Expensive!)

Local Filtering (Post-API)

Table: electricity_market_prices

API Endpoint Structure

Routing Rules

Rule 4: price_type → Endpoint

Rule 5: granularity → Date Format

Query Examples

Example 5: Spot Market Prices

Example 6: Negative Price Detection

Example 7: Monthly Market Premium

Optimization Strategies

Strategy 1: Query Caching

Strategy 2: Parallel Fetching

Strategy 3: Date Range Splitting

Strategy 4: Rate Limiting

Error Handling

HTTP 429 (Rate Limit Exceeded)

HTTP 404 (Data Not Available)

Performance Benchmarks (Estimated)

Routing Decision Tree

Summary

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FDW Query Routing Rules - NTP Wrapper v0.1.0

Overview

Table: renewable_energy_timeseries

API Endpoint Structure

Routing Rules

Rule 1: product_type → Product Parameter

Rule 2: data_category → Endpoint Prefix

Rule 3: timestamp_utc → Date Range

Query Examples

Example 1: Simple Product Filter

Example 2: Specific Data Category

Example 3: Multi-Product Query

Example 4: No Filters (⚠️ Expensive!)

Local Filtering (Post-API)

Table: electricity_market_prices

API Endpoint Structure

Routing Rules

Rule 4: price_type → Endpoint

Rule 5: granularity → Date Format

Query Examples

Example 5: Spot Market Prices

Example 6: Negative Price Detection

Example 7: Monthly Market Premium

Optimization Strategies

Strategy 1: Query Caching

Strategy 2: Parallel Fetching

Strategy 3: Date Range Splitting

Strategy 4: Rate Limiting

Error Handling

HTTP 429 (Rate Limit Exceeded)

HTTP 404 (Data Not Available)

Performance Benchmarks (Estimated)

Routing Decision Tree

Summary