Move annotations to the test file; add a link to docs.

crates/core/src/sync_local.rs

@@ -186,35 +186,28 @@ impl<'a> SyncOperation<'a> {
         Ok(match &self.partial {
             None => {
                 // Complete sync
+                // See dart/test/sync_local_performance_test.dart for an annotated version of this query.
                 self.db
                     .prepare_v2(
                         "\
--- 1. Filter oplog by the ops added but not applied yet (oplog b).
---    We do not do any DISTINCT operation here, since that introduces a temp b-tree.
---    We filter out duplicates using the GROUP BY below.
 WITH updated_rows AS (
     SELECT b.row_type, b.row_id FROM ps_buckets AS buckets
         CROSS JOIN ps_oplog AS b ON b.bucket = buckets.id
         AND (b.op_id > buckets.last_applied_op)
     UNION ALL SELECT row_type, row_id FROM ps_updated_rows
 )
 
--- 2. Find *all* current ops over different buckets for those objects (oplog r).
 SELECT
     b.row_type,
     b.row_id,
     (
-        -- 3. For each unique row, select the data from the latest oplog entry.
-        -- The max(r.op_id) clause is used to select the latest oplog entry.
-        -- The iif is to avoid the max(r.op_id) column ending up in the results.
         SELECT iif(max(r.op_id), r.data, null)
                  FROM ps_oplog r
                 WHERE r.row_type = b.row_type
                   AND r.row_id = b.row_id
 
     ) as data
     FROM updated_rows b
-    -- Group for (2)
     GROUP BY b.row_type, b.row_id;",
                     )
                     .into_db_result(self.db)?

dart/test/sync_local_performance_test.dart

@@ -119,6 +119,7 @@ COMMIT;
   // standard test suite.
 
   test('sync_local new query', () {
+    // This is the query we're using now.
     // This query only uses a single TEMP B-TREE for the GROUP BY operation,
     // leading to fairly efficient execution.
 
@@ -134,27 +135,40 @@ COMMIT;
     // |--USE TEMP B-TREE FOR GROUP BY
     // `--CORRELATED SCALAR SUBQUERY 3
     //    `--SEARCH r USING INDEX ps_oplog_row (row_type=? AND row_id=?)
+    //
+    // For details on the max(r.op_id) clause, see:
+    // https://sqlite.org/lang_select.html#bare_columns_in_an_aggregate_query
+    // > If there is exactly one min() or max() aggregate in the query, then all bare columns in the result
+    // > set take values from an input row which also contains the minimum or maximum.
 
     var timer = Stopwatch()..start();
     final q = '''
+-- 1. Filter oplog by the ops added but not applied yet (oplog b).
+--    We do not do any DISTINCT operation here, since that introduces a temp b-tree.
+--    We filter out duplicates using the GROUP BY below.
 WITH updated_rows AS (
     SELECT b.row_type, b.row_id FROM ps_buckets AS buckets
         CROSS JOIN ps_oplog AS b ON b.bucket = buckets.id
         AND (b.op_id > buckets.last_applied_op)
     UNION ALL SELECT row_type, row_id FROM ps_updated_rows
 )
 
+-- 2. Find *all* current ops over different buckets for those objects (oplog r).
 SELECT
     b.row_type,
     b.row_id,
     (
+        -- 3. For each unique row, select the data from the latest oplog entry.
+        -- The max(r.op_id) clause is used to select the latest oplog entry.
+        -- The iif is to avoid the max(r.op_id) column ending up in the results.
         SELECT iif(max(r.op_id), r.data, null)
                  FROM ps_oplog r
                 WHERE r.row_type = b.row_type
                   AND r.row_id = b.row_id
 
     ) as data
     FROM updated_rows b
+    -- Group for (2)
     GROUP BY b.row_type, b.row_id;
 ''';
     db.select(q);