Phase E: Soft deletes (tombstones) for compliance-grade row deletion

Implements GDPR/CCPA-compatible row-level deletion without rewriting
the underlying Parquet. Tombstone markers live beside each dataset and
are applied at query time via a DataFusion view that excludes the
deleted row_key_values.

Schema (shared::types):
- Tombstone { dataset, row_key_column, row_key_value, deleted_at,
              actor, reason }
- All tombstones for a dataset must share one row_key_column —
  enforced at write so the query-time filter remains a single
  WHERE NOT IN (...) clause

Storage (catalogd::tombstones):
- Per-dataset AppendLog at _catalog/tombstones/{dataset}/
- flush_threshold=1 + explicit flush after every append — tombstones
  are high-value, low-frequency; durability on return is the contract
- Reuses storaged::append_log infra so compaction is already wired
  (POST .../tombstones/compact will work once we expose it)

Catalog (catalogd::registry):
- add_tombstone validates dataset exists + key column compatibility
- list_tombstones for the GET endpoint
- TombstoneStore exposed via Registry::tombstones() for queryd

HTTP (catalogd::service):
- POST /catalog/datasets/by-name/{name}/tombstone
    { row_key_column, row_key_values[], actor, reason }
  Returns rows_tombstoned count + per-value failure list (207 on
  partial success).
- GET same path lists active tombstones with full audit info.

Query layer (queryd::context):
- Snapshot tombstones-by-dataset before registering tables
- Tombstoned tables: raw goes to "__raw__{name}", public "{name}"
  becomes DataFusion view with
  SELECT * FROM "__raw__{name}" WHERE CAST(col AS VARCHAR) NOT IN (...)
- CAST AS VARCHAR handles both string and integer key columns
- Untombstoned tables register as before — zero overhead

End-to-end on candidates (100K rows):
- Pick CAND-000001/2/3 (Linda/Charles/Kimberly)
- POST tombstone -> rows_tombstoned: 3
- COUNT(*) drops 100000 -> 99997
- WHERE candidate_id IN (those 3) -> 0 rows
- candidates_safe view transitively excludes them
  (Linda+Denver: __raw__candidates=159, candidates_safe=158)
- Restart: COUNT still 99997, 3 tombstones reload from disk

Reversibility: tombstones are reversible deletes, not destruction.
Power users can still query "__raw__{name}" to see deleted rows.
Phase 13 access control is what stops a non-admin from accessing
__raw__* tables.

Limits / follow-up:
- Physical compaction not yet integrated — Phase 8's compact_files
  doesn't read tombstones during merge. Tombstoned rows are still
  on disk until that integration ships.
- Phase 9 journald event emission for tombstones not wired —
  tombstone records carry their own actor+reason+timestamp so the
  audit trail is intact, but cross-referencing with the mutation
  event log would help compliance reporting.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

crates/catalogd/src/lib.rs

@@ -1,3 +1,4 @@
 pub mod registry;
 pub mod service;
 pub mod grpc;
+pub mod tombstones;

crates/catalogd/src/registry.rs

@@ -1,7 +1,9 @@
 use shared::types::{
     AiView, ColumnMeta, DatasetId, DatasetManifest, FreshnessContract, Lineage, ObjectRef,
-    RefreshPolicy, SchemaFingerprint, Sensitivity,
+    RefreshPolicy, SchemaFingerprint, Sensitivity, Tombstone,
 };
+
+use crate::tombstones::TombstoneStore;
 use std::collections::HashMap;
 use std::sync::Arc;
 use tokio::sync::RwLock;
@@ -46,10 +48,12 @@ const VIEW_PREFIX: &str = "_catalog/views";
 
 /// In-memory dataset registry backed by manifest persistence in object storage.
 /// Also tracks AiViews (Phase D) — safe projections over base datasets.
+/// And tombstones (Phase E) — soft-delete markers applied at query time.
 #[derive(Clone)]
 pub struct Registry {
     datasets: Arc<RwLock<HashMap<DatasetId, DatasetManifest>>>,
     views: Arc<RwLock<HashMap<String, AiView>>>,
+    tombstones: TombstoneStore,
     store: Arc<dyn ObjectStore>,
 }
 
@@ -58,10 +62,44 @@ impl Registry {
         Self {
             datasets: Arc::new(RwLock::new(HashMap::new())),
             views: Arc::new(RwLock::new(HashMap::new())),
+            tombstones: TombstoneStore::new(store.clone()),
             store,
         }
     }
 
+    pub fn tombstones(&self) -> &TombstoneStore {
+        &self.tombstones
+    }
+
+    /// Add a tombstone for a dataset row. Validates that the dataset
+    /// exists. Idempotent at the endpoint layer — callers dedup if needed.
+    pub async fn add_tombstone(
+        &self,
+        dataset: &str,
+        row_key_column: &str,
+        row_key_value: &str,
+        actor: &str,
+        reason: &str,
+    ) -> Result<Tombstone, String> {
+        if self.get_by_name(dataset).await.is_none() {
+            return Err(format!("dataset not found: {dataset}"));
+        }
+        let ts = Tombstone {
+            dataset: dataset.to_string(),
+            row_key_column: row_key_column.to_string(),
+            row_key_value: row_key_value.to_string(),
+            deleted_at: chrono::Utc::now(),
+            actor: actor.to_string(),
+            reason: reason.to_string(),
+        };
+        self.tombstones.append(&ts).await?;
+        Ok(ts)
+    }
+
+    pub async fn list_tombstones(&self, dataset: &str) -> Result<Vec<Tombstone>, String> {
+        self.tombstones.list(dataset).await
+    }
+
     /// Rebuild in-memory index from persisted manifests + views on startup.
     pub async fn rebuild(&self) -> Result<usize, String> {
         let keys = ops::list(&self.store, Some(MANIFEST_PREFIX)).await?;

crates/catalogd/src/service.rs

@@ -25,6 +25,8 @@ pub fn router(registry: Registry) -> Router {
         // Phase D: AI-safe views
         .route("/views", post(create_view).get(list_views))
         .route("/views/{name}", get(get_view).delete(delete_view))
+        // Phase E: soft-delete tombstones
+        .route("/datasets/by-name/{name}/tombstone", post(tombstone_rows).get(list_tombstones))
         .with_state(registry)
 }
 
@@ -269,3 +271,70 @@ async fn delete_view(
         Err(e) => Err((StatusCode::INTERNAL_SERVER_ERROR, e)),
     }
 }
+
+// --- Phase E: soft-delete tombstones ---
+
+#[derive(Deserialize)]
+struct TombstoneRequest {
+    row_key_column: String,
+    row_key_values: Vec<String>,
+    #[serde(default)]
+    actor: String,
+    #[serde(default)]
+    reason: String,
+}
+
+#[derive(Serialize)]
+struct TombstoneResponse {
+    dataset: String,
+    row_key_column: String,
+    rows_tombstoned: usize,
+    failures: Vec<String>,
+}
+
+async fn tombstone_rows(
+    State(registry): State<Registry>,
+    Path(name): Path<String>,
+    Json(req): Json<TombstoneRequest>,
+) -> impl IntoResponse {
+    if req.row_key_values.is_empty() {
+        return Err((StatusCode::BAD_REQUEST, "row_key_values is empty".to_string()));
+    }
+
+    let mut ok = 0;
+    let mut failures = Vec::new();
+    for value in &req.row_key_values {
+        match registry
+            .add_tombstone(&name, &req.row_key_column, value, &req.actor, &req.reason)
+            .await
+        {
+            Ok(_) => ok += 1,
+            Err(e) => failures.push(format!("{value}: {e}")),
+        }
+    }
+
+    let status = if ok > 0 && failures.is_empty() {
+        StatusCode::CREATED
+    } else if ok > 0 {
+        StatusCode::MULTI_STATUS
+    } else {
+        StatusCode::BAD_REQUEST
+    };
+
+    Ok((status, Json(TombstoneResponse {
+        dataset: name,
+        row_key_column: req.row_key_column,
+        rows_tombstoned: ok,
+        failures,
+    })))
+}
+
+async fn list_tombstones(
+    State(registry): State<Registry>,
+    Path(name): Path<String>,
+) -> impl IntoResponse {
+    match registry.list_tombstones(&name).await {
+        Ok(ts) => Ok(Json(ts)),
+        Err(e) => Err((StatusCode::INTERNAL_SERVER_ERROR, e)),
+    }
+}

crates/catalogd/src/tombstones.rs

@@ -0,0 +1,124 @@
+//! Soft-delete tombstone storage (Phase E).
+//!
+//! One append-log per dataset at `_catalog/tombstones/{dataset}/batch_*.jsonl`.
+//! Uses the shared `storaged::append_log::AppendLog` pattern so appends
+//! are write-once (never rewrites existing files) and can be compacted.
+//!
+//! The store exposes a per-dataset cache of active tombstones for the
+//! hot path (queryd filter construction) — that's why events are pulled
+//! into an in-memory map on every call rather than scanning object
+//! storage repeatedly.
+
+use object_store::ObjectStore;
+use shared::types::Tombstone;
+use std::collections::HashMap;
+use std::sync::Arc;
+use storaged::append_log::{AppendLog, CompactStats};
+use tokio::sync::RwLock;
+
+const TOMBSTONE_PREFIX: &str = "_catalog/tombstones";
+
+#[derive(Clone)]
+pub struct TombstoneStore {
+    store: Arc<dyn ObjectStore>,
+    logs: Arc<RwLock<HashMap<String, Arc<AppendLog>>>>,
+}
+
+impl TombstoneStore {
+    pub fn new(store: Arc<dyn ObjectStore>) -> Self {
+        Self {
+            store,
+            logs: Arc::new(RwLock::new(HashMap::new())),
+        }
+    }
+
+    fn prefix_for(dataset: &str) -> String {
+        // Sanitize dataset name for filesystem safety.
+        let safe: String = dataset
+            .chars()
+            .map(|c| if c.is_ascii_alphanumeric() || c == '_' || c == '-' { c } else { '_' })
+            .collect();
+        format!("{TOMBSTONE_PREFIX}/{}", safe)
+    }
+
+    async fn log_for(&self, dataset: &str) -> Arc<AppendLog> {
+        if let Some(log) = self.logs.read().await.get(dataset) {
+            return log.clone();
+        }
+        let mut guard = self.logs.write().await;
+        if let Some(log) = guard.get(dataset) {
+            return log.clone();
+        }
+        // Threshold of 1 — every tombstone is high-value. Compliance/audit
+        // doesn't tolerate "lost on restart"; we trade a small file count
+        // for guaranteed durability. Compaction merges later if volume grows.
+        let log = Arc::new(
+            AppendLog::new(self.store.clone(), Self::prefix_for(dataset))
+                .with_flush_threshold(1),
+        );
+        guard.insert(dataset.to_string(), log.clone());
+        log
+    }
+
+    /// Append one tombstone. Validates that the `row_key_column` matches
+    /// the column already used for this dataset (all tombstones for a
+    /// dataset share one key column so the query filter is well-defined).
+    /// Forces a flush so the tombstone is durable before this call returns.
+    pub async fn append(&self, ts: &Tombstone) -> Result<(), String> {
+        let existing = self.list(&ts.dataset).await?;
+        if let Some(prior) = existing.first() {
+            if prior.row_key_column != ts.row_key_column {
+                return Err(format!(
+                    "dataset '{}' already uses '{}' as tombstone key; cannot mix with '{}'",
+                    ts.dataset, prior.row_key_column, ts.row_key_column,
+                ));
+            }
+        }
+        let line = serde_json::to_vec(ts).map_err(|e| e.to_string())?;
+        let log = self.log_for(&ts.dataset).await;
+        log.append(line).await?;
+        // Belt-and-suspenders: explicit flush in case the threshold is
+        // ever raised. Tombstones must be durable on return.
+        log.flush().await
+    }
+
+    /// All tombstones for a dataset (chronological).
+    pub async fn list(&self, dataset: &str) -> Result<Vec<Tombstone>, String> {
+        let log = self.log_for(dataset).await;
+        let lines = log.read_all().await?;
+        let mut out = Vec::with_capacity(lines.len());
+        for line in lines {
+            match serde_json::from_slice::<Tombstone>(&line) {
+                Ok(t) => out.push(t),
+                Err(e) => tracing::warn!("tombstones/{}: skip malformed entry: {e}", dataset),
+            }
+        }
+        Ok(out)
+    }
+
+    /// Per-dataset grouped view used by queryd — returns a map of
+    /// `{dataset -> (row_key_column, set_of_values)}` for every dataset
+    /// that has any tombstones.
+    pub async fn all_grouped(
+        &self,
+        datasets: &[String],
+    ) -> Result<HashMap<String, (String, Vec<String>)>, String> {
+        let mut grouped = HashMap::new();
+        for dataset in datasets {
+            let ts = match self.list(dataset).await {
+                Ok(ts) => ts,
+                Err(_) => continue,
+            };
+            if ts.is_empty() { continue; }
+            let col = ts[0].row_key_column.clone();
+            let values: Vec<String> = ts.iter().map(|t| t.row_key_value.clone()).collect();
+            grouped.insert(dataset.clone(), (col, values));
+        }
+        Ok(grouped)
+    }
+
+    pub async fn compact(&self, dataset: &str) -> Result<CompactStats, String> {
+        let log = self.log_for(dataset).await;
+        log.compact().await
+    }
+}