fix: memoize JsonSchemaGenerator output per connector

obp-api/src/main/scala/code/api/util/JsonSchemaGenerator.scala

@@ -3,8 +3,10 @@ package code.api.util
 import org.json4s._
 import code.api.util.APIUtil.MessageDoc
 import com.openbankproject.commons.util.ReflectUtils
+import com.tesobe.CacheKeyFromArguments
 import org.json4s.JsonDSL._
 
+import scala.concurrent.duration._
 import scala.reflect.runtime.universe._
 
 /**
@@ -15,9 +17,34 @@ import scala.reflect.runtime.universe._
 object JsonSchemaGenerator {
 
   /**
-   * Convert a list of MessageDoc to a complete JSON Schema document
+   * Convert a list of MessageDoc to a complete JSON Schema document.
+   *
+   * Memoized in-process, keyed by connectorName: for a given connector the message docs
+   * (and therefore the schema) are static for the lifetime of the JVM, but building it
+   * walks every message type's full field tree via Scala runtime reflection (`<:<`/`=:=`
+   * subtype checks), which is expensive and -- unlike a plain field lookup -- leaves behind
+   * long-lived reflection bookkeeping objects (TypeConstraint/UndoPair/Symbol) that don't
+   * get reclaimed promptly. Recomputing this on every request under sustained polling is
+   * what drove a production old-gen heap to exhaustion. The caller (Http4s600) also has a
+   * Redis-backed cache in front of this, but that one silently falls through to a full
+   * recompute if Redis is unreachable or slow -- this in-memory layer doesn't depend on
+   * Redis at all, so it stays a working safety net even when Redis is the one struggling.
    */
   def messageDocsToJsonSchema(messageDocs: List[MessageDoc], connectorName: String): JObject = {
+    // This 3-tuple of random UUIDs is a placeholder only -- CacheKeyFromArguments is a macro
+    // that replaces it at compile time with a real key derived from this method's owner,
+    // name and arguments (regardless of this method's own arity; the convention throughout
+    // this codebase is always a 3-tuple here). See:
+    // https://github.com/OpenBankProject/scala-macros/blob/master/macros/src/main/scala/com/tesobe/CacheKeyFromArgumentsMacro.scala#L49
+    var cacheKey = (java.util.UUID.randomUUID().toString, java.util.UUID.randomUUID().toString, java.util.UUID.randomUUID().toString)
+    CacheKeyFromArguments.buildCacheKey {
+      code.api.cache.Caching.memoizeSyncWithImMemory(Some(cacheKey.toString()))(100000.days) {
+        messageDocsToJsonSchemaUncached(messageDocs, connectorName)
+      }
+    }
+  }
+
+  private def messageDocsToJsonSchemaUncached(messageDocs: List[MessageDoc], connectorName: String): JObject = {
     val allDefinitions = scala.collection.mutable.Map[String, JObject]()
 
     val messages = messageDocs.map { messageDoc =>

obp-api/src/test/scala/code/api/util/JsonSchemaGeneratorCacheTest.scala

@@ -0,0 +1,116 @@
+package code.api.util
+
+import code.api.util.APIUtil.MessageDoc
+import org.scalatest.{FlatSpec, Matchers}
+
+/**
+ * Guards the in-process cache added to JsonSchemaGenerator.messageDocsToJsonSchema.
+ *
+ * Building the schema walks the full field tree of every message type via Scala runtime
+ * reflection (recursive <:</=:= subtype checks), which is the expensive, GC-pressure-generating
+ * part this cache exists to avoid paying on every call. A test that only checks the returned
+ * JSON is unchanged would pass even if the cache silently never hit (wrong key, wrong TTL
+ * handling, etc.) - that exact class of mistake happened once already while writing this fix (a
+ * cache-key placeholder tuple with the wrong arity, caught by the compiler, not by output
+ * correctness). So this asserts on both: the second call must be dramatically faster, and it
+ * must return the identical JSON as the first.
+ *
+ * Each scenario uses its own unique connector name (via a nanoTime suffix) so it always starts
+ * from a genuinely cold cache entry, independent of whatever else has run in this JVM.
+ */
+class JsonSchemaGeneratorCacheTest extends FlatSpec with Matchers {
+
+  // A case class tree wide and deep enough that the reflection cost this cache exists to avoid
+  // clearly dominates JVM/JIT noise -- a handful of fields on a single small type (tried first)
+  // measures in the same single-digit milliseconds as scheduling jitter, which made the timing
+  // assertion below flaky. This shape is closer to a real connector's message docs (see
+  // MessageDocsJsonSchemaTest, which asserts real connectors have 100+ definitions).
+  case class SchemaCacheTestAddress(city: String, postcode: Option[String], country: String)
+  case class SchemaCacheTestContact(email: String, phone: Option[String], address: SchemaCacheTestAddress)
+  case class SchemaCacheTestLineItem(sku: String, quantity: Int, unitPrice: BigDecimal, tags: List[String])
+  case class SchemaCacheTestOrder(
+    id: String, status: String, createdAt: String, contact: SchemaCacheTestContact,
+    items: List[SchemaCacheTestLineItem], notes: Option[String]
+  )
+  case class SchemaCacheTestBatch(
+    batchId: String, orders: List[SchemaCacheTestOrder], primaryContact: SchemaCacheTestContact,
+    secondaryContacts: List[SchemaCacheTestContact], metadata: Map[String, String]
+  )
+  case class SchemaCacheTestPayload(
+    id: String,
+    amount: BigDecimal,
+    tags: List[String],
+    address: SchemaCacheTestAddress,
+    batch: SchemaCacheTestBatch
+  )
+
+  private def freshConnectorName(name: String): String =
+    s"JsonSchemaGeneratorCacheTest-$name-${System.nanoTime()}"
+
+  private def samplePayload(): SchemaCacheTestPayload = {
+    val address = SchemaCacheTestAddress("Berlin", Some("13359"), "DE")
+    val contact = SchemaCacheTestContact("a@example.com", Some("+49 30 1234567"), address)
+    val order = SchemaCacheTestOrder(
+      "order-1", "PENDING", "2026-09-23T00:00:00Z", contact,
+      List(SchemaCacheTestLineItem("sku-1", 2, BigDecimal(9.99), List("x", "y"))),
+      Some("note")
+    )
+    val batch = SchemaCacheTestBatch("batch-1", List(order), contact, List(contact), Map("k" -> "v"))
+    SchemaCacheTestPayload("id-1", BigDecimal(1), List("a", "b"), address, batch)
+  }
+
+  private def sampleMessageDocs(connectorName: String): List[MessageDoc] = List(
+    MessageDoc(
+      process = s"obp.$connectorName.getThing",
+      messageFormat = "JSON",
+      description = "test message doc for cache verification",
+      exampleOutboundMessage = samplePayload(),
+      exampleInboundMessage = samplePayload()
+    )
+  )
+
+  "JsonSchemaGenerator.messageDocsToJsonSchema" should "return identical output on a cache hit as on the initial cold call" in {
+    val connectorName = freshConnectorName("identical-output")
+    val docs = sampleMessageDocs(connectorName)
+
+    val first = JsonSchemaGenerator.messageDocsToJsonSchema(docs, connectorName)
+    val second = JsonSchemaGenerator.messageDocsToJsonSchema(docs, connectorName)
+
+    second should equal(first)
+  }
+
+  it should "be dramatically faster on a cache hit than on the initial cold call" in {
+    val connectorName = freshConnectorName("timing")
+    val docs = sampleMessageDocs(connectorName)
+
+    val coldStart = System.nanoTime()
+    JsonSchemaGenerator.messageDocsToJsonSchema(docs, connectorName)
+    val coldNanos = System.nanoTime() - coldStart
+
+    // A handful of warm calls, not just one: a single fast call could be a lucky JIT/scheduling
+    // blip rather than an actual cache hit. Comparing the median (not every sample, and not the
+    // fastest) against the cold call keeps this robust to a single GC pause or JIT hiccup landing
+    // on one warm sample while still requiring a real, consistent improvement.
+    val warmNanosSamples = (1 to 7).map { _ =>
+      val start = System.nanoTime()
+      JsonSchemaGenerator.messageDocsToJsonSchema(docs, connectorName)
+      System.nanoTime() - start
+    }
+    val medianWarmNanos = warmNanosSamples.sorted.apply(warmNanosSamples.size / 2)
+
+    withClue(s"cold=${coldNanos}ns warm=${warmNanosSamples.sorted.mkString(",")}ns median=${medianWarmNanos}ns: ") {
+      medianWarmNanos should be < (coldNanos / 2)
+    }
+  }
+
+  it should "isolate different connector names as independent cache entries" in {
+    val connectorA = freshConnectorName("connector-a")
+    val connectorB = freshConnectorName("connector-b")
+
+    val schemaA = JsonSchemaGenerator.messageDocsToJsonSchema(sampleMessageDocs(connectorA), connectorA)
+    val schemaB = JsonSchemaGenerator.messageDocsToJsonSchema(sampleMessageDocs(connectorB), connectorB)
+
+    (schemaA \ "title") should equal(org.json4s.JString(s"$connectorA Message Schemas"))
+    (schemaB \ "title") should equal(org.json4s.JString(s"$connectorB Message Schemas"))
+  }
+}