8000+ SEO Location Pages in AEM Without JCR Nodes: 3 Solutions Compared

The Context

A client requested dedicated pages for every Italian region, province, and municipality to map local resources and improve SEO rankings.

The Numbers

• 20 regions
• ~110 provinces
• ~8000 municipalities
• Total: ~8130 pages

Requirements

✅ Real server-side HTML (not SPA/React) to be indexed by search engines ✅ Clean SEO-friendly URLs: /locations/lombardia/milano/navigli.html ✅ Dynamic content based on location (region/province/municipality name) ❌ Zero JCR nodes (or bare minimum) to avoid bloating authoring

The Problem

Creating 8130 physical pages in /content would be a disaster:

• ❌ Degraded siteadmin performance
• ❌ Very slow backup/restore
• ❌ Complex deployments
• ❌ Impossible to maintain at scale
• ❌ Risk of timeouts when rendering the tree

Question: How to generate thousands of SEO-friendly HTML pages without creating thousands of JCR nodes?

---

Solution 1: ISTAT JSON + In-Memory Service (Implemented)

This is the solution I implemented in the real project.

Strategy

1. ISTAT Data: Download official ISTAT CSV with regions/provinces/municipalities
2. JSON Conversion: Script to convert CSV → structured JSON
3. In-memory Service: Load JSON into RAM at AEM startup
4. Dispatcher rewrite: Convert clean URLs into Sling selectors
5. Filter + Session: Validate location and pass data via session
6. Sling Rewriter: Replace HTML placeholders with actual data

1. ISTAT Data - Official Source

Data Source: ISTAT - Codes of Municipalities, Provinces and Regions

ISTAT (Italian National Institute of Statistics) provides the official dataset with all Italian municipalities in CSV format. The file contains:

• Official name (with spaces, apostrophes, special characters)
• ISTAT code
• Province affiliation
• Region affiliation

Problem: Names contain non-URL-safe characters (Valle d'Aosta, Reggio nell'Emilia, Saint-Vincent).

Solution: Conversion script CSV → JSON with automatic URL-safe slug generation.

Target JSON Structure

The ISTAT CSV contains official names with spaces and special characters. I created a script to generate URL-safe slugs:

{
  "regioni": [
    {
      "name": "Valle d'Aosta",           // Official name
      "slug": "valle-d-aosta",            // URL-safe
      "province": [
        {
          "name": "Aosta",
          "slug": "aosta",
          "comuni": [
            {
              "name": "Saint-Vincent",
              "slug": "saint-vincent"
            }
          ]
        }
      ]
    },
    {
      "name": "Emilia-Romagna",
      "slug": "emilia-romagna",
      "province": [
        {
          "name": "Reggio nell'Emilia",
          "slug": "reggio-emilia",
          "comuni": [
            {
              "name": "Reggio nell'Emilia",
              "slug": "reggio-emilia"
            }
          ]
        }
      ]
    }
  ]
}

File saved in: src/main/resources/data/comuni-italia.json

CSV → JSON Conversion Script

// convert-istat-csv.js
// Download CSV from: https://www.istat.it/classificazione/...

const fs = require('fs');
const csv = require('csv-parser');

const results = {
  regioni: []
};

const regioniMap = new Map();
const provinceMap = new Map();

// Function to create URL-safe slugs
function slugify(text) {
  return text
    .toLowerCase()
    .normalize('NFD')                   // Decompose accented characters
    .replace(/[\u0300-\u036f]/g, '')    // Remove accents
    .replace(/['']/g, '')               // Remove apostrophes
    .replace(/[^a-z0-9]+/g, '-')        // Replace non-alphanumeric with -
    .replace(/^-+|-+$/g, '');           // Remove leading/trailing -
}

fs.createReadStream('comuni-istat.csv')
  .pipe(csv({ separator: ';' }))
  .on('data', (row) => {

    const regioneName = row['Denominazione regione'];
    const provinciaName = row['Denominazione provincia'];
    const comuneName = row['Denominazione comune'];

    // Skip headers
    if (!regioneName || regioneName === 'Denominazione regione') return;

    const regioneSlug = slugify(regioneName);
    const provinciaSlug = slugify(provinciaName);
    const comuneSlug = slugify(comuneName);

    // Create region if not exists
    if (!regioniMap.has(regioneSlug)) {
      const regione = {
        name: regioneName,
        slug: regioneSlug,
        province: []
      };
      regioniMap.set(regioneSlug, regione);
      results.regioni.push(regione);
    }

    const regione = regioniMap.get(regioneSlug);

    // Create province if not exists
    const provinciaKey = `${regioneSlug}/${provinciaSlug}`;
    if (!provinceMap.has(provinciaKey)) {
      const provincia = {
        name: provinciaName,
        slug: provinciaSlug,
        comuni: []
      };
      provinceMap.set(provinciaKey, provincia);
      regione.province.push(provincia);
    }

    const provincia = provinceMap.get(provinciaKey);

    // Add municipality
    provincia.comuni.push({
      name: comuneName,
      slug: comuneSlug
    });
  })
  .on('end', () => {
    // Save JSON
    fs.writeFileSync(
      'comuni-italia.json',
      JSON.stringify(results, null, 2),
      'utf-8'
    );

    console.log('✅ Conversion completed!');
    console.log(`   Regions: ${results.regioni.length}`);
    console.log(`   Provinces: ${provinceMap.size}`);

    let totaleComuni = 0;
    results.regioni.forEach(r =>
      r.province.forEach(p =>
        totaleComuni += p.comuni.length
      )
    );
    console.log(`   Municipalities: ${totaleComuni}`);
  });

Execution:

npm install csv-parser
node convert-istat-csv.js
# Output: comuni-italia.json (~1.5 MB)

Generated slug examples:

• Valle d'Aosta → valle-d-aosta
• Reggio nell'Emilia → reggio-emilia
• Bolzano/Bozen → bolzano-bozen
• Forlì-Cesena → forli-cesena

2. In-Memory Service with Nested JSON

@Component(service = LocationDataService.class, immediate = true)
public class LocationDataServiceImpl implements LocationDataService {

    private static final Logger LOG = LoggerFactory.getLogger(LocationDataServiceImpl.class);

    private JSONObject locationsData;

    @Activate
    protected void activate() throws Exception {
        LOG.info("Loading ISTAT locations data...");

        // Load JSON from classpath
        InputStream jsonStream = getClass().getResourceAsStream("/data/comuni-italia.json");
        String jsonString = IOUtils.toString(jsonStream, StandardCharsets.UTF_8);

        // Parse native JSON (org.json or Gson)
        this.locationsData = new JSONObject(jsonString);

        // Count total locations for logging
        int count = countLocations(locationsData);
        LOG.info("✅ Loaded {} locations in memory (~{}MB)", count,
                 Runtime.getRuntime().totalMemory() / 1024 / 1024);
    }

    @Override
    public LocationData get(String regionSlug, String provinciaSlug, String comuneSlug) {

        try {
            // Hierarchical lookup: regions → province → municipality
            JSONArray regioni = locationsData.getJSONArray("regioni");

            // Find region
            JSONObject regione = findBySlug(regioni, regionSlug);
            if (regione == null) return null;

            // Find province
            JSONArray province = regione.getJSONArray("province");
            JSONObject provincia = findBySlug(province, provinciaSlug);
            if (provincia == null) return null;

            // Find municipality
            JSONArray comuni = provincia.getJSONArray("comuni");
            JSONObject comune = findBySlug(comuni, comuneSlug);
            if (comune == null) return null;

            // Build LocationData
            return new LocationData(
                comune.getString("name"),
                provincia.getString("name"),
                regione.getString("name")
            );

        } catch (JSONException e) {
            LOG.warn("Location not found: {}/{}/{}", regionSlug, provinciaSlug, comuneSlug);
            return null;
        }
    }

    @Override
    public boolean exists(String regionSlug, String provinciaSlug, String comuneSlug) {
        return get(regionSlug, provinciaSlug, comuneSlug) != null;
    }

    // Helper: find object in array by slug
    private JSONObject findBySlug(JSONArray array, String slug) throws JSONException {
        for (int i = 0; i < array.length(); i++) {
            JSONObject obj = array.getJSONObject(i);
            if (slug.equals(obj.getString("slug"))) {
                return obj;
            }
        }
        return null;
    }

    private int countLocations(JSONObject data) throws JSONException {
        int count = 0;
        JSONArray regioni = data.getJSONArray("regioni");
        for (int i = 0; i < regioni.length(); i++) {
            JSONArray province = regioni.getJSONObject(i).getJSONArray("province");
            for (int j = 0; j < province.length(); j++) {
                count += province.getJSONObject(j).getJSONArray("comuni").length();
            }
        }
        return count;
    }
}

Nested approach advantages:

• ✅ Simplicity: Mirrors the natural data hierarchy
• ✅ No composite keys: Direct navigation region → province → municipality
• ✅ Memory footprint: ~2-5 MB (identical to HashMap)
• ✅ Performance: O(1) for region + O(1) for province + O(1) for municipality = <1ms

Note: The findBySlug() array lookup is O(n), but given:

• Regions = 20 → O(20) = negligible
• Provinces per region ≤ 12 → O(12) = negligible
• Municipalities per province = variable, but in-memory lookup is blazing fast

Total performance: ~0.5-1ms (same as flat HashMap)

3. Dispatcher Rewrite Rules

Goal: Convert SEO-friendly URLs into Sling selectors

# dispatcher.any or httpd vhost

# Region + Province + Municipality
RewriteRule ^/locations/([a-z0-9-]+)/([a-z0-9-]+)/([a-z0-9-]+)\.html$ \
            /content/mysite/locations.$1.$2.$3.html [PT,L]

# Region + Province only
RewriteRule ^/locations/([a-z0-9-]+)/([a-z0-9-]+)\.html$ \
            /content/mysite/locations.$1.$2.html [PT,L]

# Region only
RewriteRule ^/locations/([a-z0-9-]+)\.html$ \
            /content/mysite/locations.$1.html [PT,L]

Examples:

• /locations/lombardia/milano/navigli.html → /content/mysite/locations.lombardia.milano.navigli.html

• /locations/emilia-romagna/bologna.html → /content/mysite/locations.emilia-romagna.bologna.html

4. Sling Filter - Validation and Session

@Component(
    service = Filter.class,
    property = {
        EngineConstants.SLING_FILTER_SCOPE + "=" + EngineConstants.FILTER_SCOPE_REQUEST,
        ServiceConstants.SERVICE_RANKING + ":Integer=1000"
    }
)
public class LocationDataFilter implements Filter {

    private static final String SESSION_KEY = "location.current.data";
    private static final String LOCATION_PAGE_PATH = "/content/mysite/locations";

    @Reference
    private LocationDataService locationService;

    @Override
    public void doFilter(ServletRequest request, ServletResponse response, FilterChain chain)
            throws IOException, ServletException {

        SlingHttpServletRequest slingRequest = (SlingHttpServletRequest) request;

        // Check if it's a location page
        String path = slingRequest.getRequestPathInfo().getResourcePath();

        if (path.startsWith(LOCATION_PAGE_PATH)) {

            // Read selectors: .lombardia.milano.navigli
            String[] selectors = slingRequest.getRequestPathInfo().getSelectors();

            if (selectors.length >= 1) {

                String regionSlug = selectors[0];
                String provinciaSlug = selectors.length >= 2 ? selectors[1] : null;
                String comuneSlug = selectors.length >= 3 ? selectors[2] : null;

                // Determine location type and validate
                LocationData locationData = null;

                if (comuneSlug != null) {
                    // Municipality
                    if (locationService.exists(regionSlug, provinciaSlug, comuneSlug)) {
                        locationData = locationService.get(regionSlug, provinciaSlug, comuneSlug);
                    }
                } else if (provinciaSlug != null) {
                    // Province (similar logic)
                } else {
                    // Region (similar logic)
                }

                if (locationData != null) {
                    // Save to session for template
                    slingRequest.getSession().setAttribute(SESSION_KEY, locationData);
                } else {
                    // Location not found → 404
                    ((SlingHttpServletResponse) response).sendError(404, "Location not found");
                    return;
                }
            }
        }

        chain.doFilter(request, response);
    }
}

5. Template Page Component

A single template page in /content/mysite/locations reused for all 8130 locations.

Sling Model:

@Model(adaptables = SlingHttpServletRequest.class)
public class LocationPageModel {

    private static final String SESSION_KEY = "location.current.data";

    @SlingObject
    private SlingHttpServletRequest request;

    public LocationData getLocationData() {
        return (LocationData) request.getSession().getAttribute(SESSION_KEY);
    }

    public String getPageTitle() {
        LocationData data = getLocationData();
        return data != null ? "Resources in " + data.getComuneName() : "Location";
    }
}

HTL Template:

<div data-sly-use.model="com.mysite.models.LocationPageModel">
    <h1>{{COMUNE_NAME}}</h1>
    <p>Province: {{PROVINCIA_NAME}}</p>
    <p>Region: {{REGIONE_NAME}}</p>

    <div class="resources">
        <!-- Standard AEM components for content -->
    </div>
</div>

6. Sling Rewriter - Placeholder Replacement

Rewriter Pipeline Configuration:

<?xml version="1.0" encoding="UTF-8"?>
<jcr:root xmlns:sling="http://sling.apache.org/jcr/sling/1.0"
          xmlns:jcr="http://www.jcp.org/jcr/1.0"
    jcr:primaryType="sling:Folder">

    <location-placeholder-rewriter
        jcr:primaryType="nt:unstructured"
        enabled="{Boolean}true"
        generatorType="htmlparser"
        order="{Long}1"
        serializerType="htmlwriter"
        transformerTypes="[location-placeholder-replacer]"
        paths="[/content/mysite/locations]"/>

</jcr:root>

Transformer Implementation:

@Component(
    service = TransformerFactory.class,
    property = {
        "pipeline.type=location-placeholder-replacer"
    }
)
public class LocationPlaceholderTransformerFactory implements TransformerFactory {

    @Override
    public Transformer createTransformer() {
        return new AbstractTransformer() {

            private LocationData locationData;

            @Override
            public void init(ProcessingContext context, ProcessingComponentConfiguration config) {
                // Retrieve data from session
                SlingHttpServletRequest request = context.getRequest();
                locationData = (LocationData) request.getSession()
                    .getAttribute("location.current.data");
            }

            @Override
            public void characters(char[] chars, int offset, int length) throws SAXException {

                if (locationData != null) {
                    String text = new String(chars, offset, length);

                    // Replace placeholders
                    text = text.replace("{{COMUNE_NAME}}", locationData.getComuneName());
                    text = text.replace("{{PROVINCIA_NAME}}", locationData.getProvinciaName());
                    text = text.replace("{{REGIONE_NAME}}", locationData.getRegioneName());

                    char[] replaced = text.toCharArray();
                    super.characters(replaced, 0, replaced.length);
                } else {
                    super.characters(chars, offset, length);
                }
            }
        };
    }
}

Final HTML result:

<!-- HTL template had: -->
<h1>{{COMUNE_NAME}}</h1>

<!-- Browser receives: -->
<h1>Navigli</h1>

Solution 1 Advantages

✅ Zero JCR nodes (only the template /content/mysite/locations) ✅ Excellent performance (in-memory HashMap, <1ms lookup) ✅ Perfect SEO (clean URLs, server-side HTML) ✅ Official ISTAT data (authoritative and updated source) ✅ Simple to implement (uses native AEM features) ✅ Dispatcher cacheable (once rendered, cached for hours)

Solution 1 Disadvantages

❌ Hard-coded data in bundle (update = redeploy) ❌ Not authorable (authors don't see pages in siteadmin) ❌ Manual sitemap (requires custom servlet to generate it)

---

Solution 2: /var Nodes with Tuples + Cache

Alternative: Store data under /var instead of in-memory.

/var Structure

/var/locations (nt:unstructured)

  /lombardia (nt:unstructured)
    name = "Lombardia"
    slug = "lombardia"

    /milano (nt:unstructured)
      name = "Milano"
      slug = "milano"
      comuni (String[]) = [
        "milano|Milano",
        "monza|Monza",
        "rho|Rho",
        "sesto-san-giovanni|Sesto San Giovanni",
        ...
      ]

Total nodes: ~130 (20 regions + 110 provinces) Municipalities: Saved as tuple arrays "slug|Official Name"

Service with Cache

@Component(service = LocationDataService.class)
public class LocationDataServiceImpl implements LocationDataService {

    @Reference
    private ResourceResolverFactory resolverFactory;

    // Cache only ~110 provinces
    private LoadingCache<String, ProvinciaData> provinciaCache;

    @Activate
    protected void activate() {
        this.provinciaCache = CacheBuilder.newBuilder()
            .maximumSize(150)
            .expireAfterWrite(1, TimeUnit.HOURS)
            .build(new CacheLoader<String, ProvinciaData>() {
                @Override
                public ProvinciaData load(String key) {
                    return loadProvinciaFromJcr(key);
                }
            });
    }

    @Override
    public LocationData get(String regionSlug, String provinciaSlug, String comuneSlug) {

        String key = regionSlug + "/" + provinciaSlug;
        ProvinciaData provincia = provinciaCache.get(key);

        // Search municipality in tuple array
        String tuple = provincia.getComuni().stream()
            .filter(t -> t.startsWith(comuneSlug + "|"))
            .findFirst()
            .orElse(null);

        if (tuple != null) {
            String[] parts = tuple.split("\\|");
            return new LocationData(parts[1], provincia.getName(), regionSlug);
        }

        return null;
    }

    private ProvinciaData loadProvinciaFromJcr(String key) {
        try (ResourceResolver resolver = getServiceResolver()) {
            Resource res = resolver.getResource("/var/locations/" + key);

            if (res != null) {
                ValueMap props = res.getValueMap();
                return new ProvinciaData(
                    props.get("name", String.class),
                    Arrays.asList(props.get("comuni", String[].class))
                );
            }
        }
        return null;
    }
}

ISTAT Import Script → /var

public void importIstatToVar(InputStream jsonStream) throws Exception {
    try (ResourceResolver resolver = getServiceResolver()) {

        ObjectMapper mapper = new ObjectMapper();
        IstatData data = mapper.readValue(jsonStream, IstatData.class);

        Resource varRoot = resolver.getResource("/var");
        Resource locationsRoot = resolver.create(varRoot, "locations",
            Map.of("jcr:primaryType", "nt:unstructured"));

        for (RegionData region : data.getRegioni()) {

            Resource regionNode = resolver.create(locationsRoot, region.getSlug(),
                Map.of("name", region.getName(), "slug", region.getSlug()));

            for (ProvinciaData provincia : region.getProvince()) {

                // Create tuple array "slug|name"
                String[] comuniTuples = provincia.getComuni().stream()
                    .map(c -> c.getSlug() + "|" + c.getName())
                    .toArray(String[]::new);

                resolver.create(regionNode, provincia.getSlug(),
                    Map.of(
                        "name", provincia.getName(),
                        "slug", provincia.getSlug(),
                        "comuni", comuniTuples
                    ));
            }
        }

        resolver.commit();
        LOG.info("✅ Import completed: ~130 /var nodes created");
    }
}

Solution 2 Advantages

✅ Persistent data (not lost on restart) ✅ Simple updates (modify /var nodes, no redeploy) ✅ Intelligent cache (only accessed provinces) ✅ Automatic backup (part of repository) ✅ JCR queries possible (if needed)

Solution 2 Disadvantages

❌ ~130 additional nodes in JCR (minimal, but still present) ❌ Slightly slower than pure in-memory (cache miss = JCR query) ❌ Requires initial import script

---

Solution 3: External Backend + API Cache

Alternative: Data on external backend (microservice, DB, headless CMS).

Architecture

AEM → HTTP Client → Backend API → ISTAT Database
                         ↓
                    Cache Layer
                    (Redis/Memcached)

Service with HTTP Client

@Component(service = LocationDataService.class)
public class LocationDataServiceImpl implements LocationDataService {

    @Reference
    private HttpClient httpClient;

    private static final String API_BASE = "https://api.mycompany.com/locations";

    // Guava in-memory cache
    private LoadingCache<String, LocationData> cache;

    @Activate
    protected void activate() {
        this.cache = CacheBuilder.newBuilder()
            .maximumSize(1000)  // Cache 1000 most accessed locations
            .expireAfterWrite(6, TimeUnit.HOURS)
            .build(new CacheLoader<String, LocationData>() {
                @Override
                public LocationData load(String key) throws Exception {
                    return fetchFromBackend(key);
                }
            });
    }

    @Override
    public LocationData get(String regionSlug, String provinciaSlug, String comuneSlug) {
        String key = regionSlug + "/" + provinciaSlug + "/" + comuneSlug;
        return cache.get(key);
    }

    private LocationData fetchFromBackend(String key) throws Exception {

        String apiUrl = API_BASE + "/" + key + ".json";

        HttpResponse response = httpClient.execute(new HttpGet(apiUrl));

        if (response.getStatusLine().getStatusCode() == 200) {
            String json = EntityUtils.toString(response.getEntity());
            return new ObjectMapper().readValue(json, LocationData.class);
        }

        return null;
    }
}

Backend API Endpoint (Node.js example)

// Express.js API
app.get('/locations/:region/:provincia/:comune.json', async (req, res) => {
    const { region, provincia, comune } = req.params;

    // Query database
    const location = await db.query(
        'SELECT * FROM comuni WHERE slug = ? AND provincia_slug = ?',
        [comune, provincia]
    );

    if (location) {
        res.json(location);
    } else {
        res.status(404).json({ error: 'Not found' });
    }
});

Solution 3 Advantages

✅ Centralized data (shareable across multiple systems) ✅ Real-time updates (no AEM redeploy) ✅ Independent scalability (backend can scale separately) ✅ Dynamic data (can change frequently)

Solution 3 Disadvantages

❌ External dependency (if backend down, AEM fails) ❌ Network latency (even with cache, first access is slow) ❌ Architectural complexity (more systems to maintain) ❌ Infrastructure costs (database, API server, Redis cache)

---

Comparison of 3 Solutions

Aspect	Sol. 1: In-Memory JSON	Sol. 2: /var Nodes	Sol. 3: Backend API
Performance	🚀 Very fast (<1ms)	⚡ Fast (2-5ms)	🐢 Depends (10-50ms first hit)
JCR Nodes	✅ Zero	⚠️ ~130	✅ Zero
Data Update	♻️ Bundle redeploy	✏️ Update nodes	⚡ Real-time
Persistence	❌ Lost on restart	✅ Persistent	✅ Persistent
Dependencies	✅ Zero	✅ Only AEM	❌ External backend
Complexity	🟢 Low	🟡 Medium	🔴 High
Costs	💰 Zero	💰 Zero	💰💰 External infra
Scalability	⚠️ Limited (RAM)	✅ Good	✅ Excellent
Authoring	❌ No	❌ No	❌ No

---

Conclusions and Recommendations

When to use Solution 1 (In-Memory JSON)

✅ Small/medium projects (~10k locations max) ✅ Static data (rarely changes) ✅ Limited budget (no extra infrastructure) ✅ Fast time-to-market (quick implementation)

When to use Solution 2 (/var Nodes)

✅ Enterprise projects with strong governance ✅ Periodically changing data (monthly updates) ✅ Team prefers JCR as source of truth ✅ Backup/restore important (part of repository)

When to use Solution 3 (Backend API)

✅ Shared data across multiple systems (AEM, mobile app, etc.) ✅ Frequent real-time updates ✅ Maximum scalability (millions of locations) ✅ Microservices architecture already in place

---

My Final Choice

In the real project I chose Solution 1 (In-Memory JSON) because:

1. ✅ Stable ISTAT data (updated 1-2 times per year)
2. ✅ Critical performance (high-traffic e-commerce)
3. ✅ Limited budget (no external backend)
4. ✅ 8130 locations = ~5 MB RAM (negligible)
5. ✅ Fast implementation (2 days development)

Results

• 🚀 8130 SEO pages with zero JCR nodes
• ⚡ <5ms TTFB (Time To First Byte)
• 📈 +300% organic traffic in 6 months
• 💾 Dispatcher cache hit >95%
• 👨‍💻 Zero overhead for authors

---

Sitemap.xml Bonus

For SEO, a sitemap with all 8130 URLs is needed:

@Component(service = Servlet.class, ...)
public class LocationSitemapServlet extends SlingSafeMethodsServlet {

    @Reference
    private LocationDataService locationService;

    @Override
    protected void doGet(SlingHttpServletRequest request,
                        SlingHttpServletResponse response) throws IOException {

        response.setContentType("application/xml");
        PrintWriter out = response.getWriter();

        out.println("<?xml version=\"1.0\" encoding=\"UTF-8\"?>");
        out.println("<urlset xmlns=\"http://www.sitemaps.org/schemas/sitemap/0.9\">");

        // Loop through all locations
        for (LocationData location : locationService.getAllLocations()) {
            out.printf("<url><loc>%s</loc><priority>0.7</priority></url>%n",
                location.getUrl());
        }

        out.println("</urlset>");
    }
}

Endpoint: /locations-sitemap.xml Submit to: Google Search Console

---

Key Takeaways

1. 8000+ SEO pages in AEM without JCR nodes is possible
2. Dispatcher rewrite + Selectors = clean URLs
3. Sling Rewriter = powerful but underused feature
4. ISTAT = official source for Italian geographic data
5. In-memory HashMap = unbeatable performance
6. Solution choice depends on: budget, update frequency, scalability

---

Have you implemented similar solutions? Which approach did you choose? Share your experience in the comments!

Related articles:

• Session Pattern for data sharing
• AEM Best Practices: Component Development