feat(checkpoint-sqlite): override get_delta_channel_history with streaming walk (#7702)

## Summary

Adds a sqlite-specific override of
`BaseCheckpointSaver.get_delta_channel_history` (and async). Before this
PR, `SqliteSaver` / `AsyncSqliteSaver` inherited the default impl, which
calls `get_tuple` once per ancestor — N round-trips, full pending-writes
fetch per step regardless of channel relevance.

The override mirrors the postgres two-stage shape (ancestor walk +
per-channel UNION ALL writes fetch) but adapted for sqlite:

- **No JSONB** → stage 1 streams the cursor row-by-row in
`checkpoint_id` DESC order. The merged walk advances one row at a time,
deserializing only on-path checkpoints and dropping each before
advancing — peak in-flight is one deserialized checkpoint, no
`fetchall()` materialization.
- **No separate blob table** → `channel_values` lives inline in the
checkpoint blob, so seeds come back from stage 1 with no second fetch.
- **Single merged walk (not K independent walks)**: each visited cid is
deserialized exactly once, regardless of how many channels are still
seeking their seed.
- **Stage 2** stays per-channel UNION ALL to avoid over-fetching writes
when channels have different chain depths — same rationale as postgres.

`AsyncSqliteSaver.get_delta_channel_history` bridges to its async form
via `run_coroutine_threadsafe`, matching the same cross-thread guard
used by `get_tuple` / `delete_thread`.

## Tests

- New `tests/test_delta_channel_migration.py`: covers the
`BinaryOperatorAggregate -> DeltaChannel` migration path on sqlite (sync
round-trip, sync continuation with post-migration delta folding, async
round-trip). Mirrors
`libs/langgraph/tests/test_delta_channel_migration.py` (which covered
`InMemorySaver`); without these, the override's behavior on
pre-migration threads was unverified — the override has to identify a
plain accumulated `channel_values[ch]` at a pre-migration ancestor as a
valid `seed`, not just `_DeltaSnapshot` sentinels.
- Existing `tests/test_get_delta_channel_history.py` (7 tests) continues
to pass and now exercises the optimized override end-to-end (previously
hit the inherited default impl).
- `make format`, `make lint`, `make test`: clean. 97/97 in the non-flaky
sqlite suite (the one ignored test, `test_async_asearch_refresh_ttl`, is
a known TTL-store timing flake on a separate module unrelated to this
PR).

## Benchmarks

### `get_delta_channel_history` micro-bench (override vs inherited
default impl)

1000-turn synthetic threads with sentinel snapshots + per-step writes;
`bench_sqlite_delta_history.py`. Per-call latency in microseconds.

| Scenario | min | median | mean |
|---|---:|---:|---:|
| S1 single channel, root-only snapshot | **4.60x** | **4.90x** |
**5.13x** |
| S2 mixed cadence (every-50 + root-only), 2 channels | **6.08x** |
**6.37x** | **6.84x** |
| S3 K=8 channels, root-only snapshot | 1.23x | 1.27x | 0.90x |

S2 wins biggest because per-channel UNION ALL avoids over-fetching
writes for the shallow channel. S3 is the worst case for sqlite (8
channels all walking to root, 1000 deserializations either way) — the
override still wins on min/median.

### Long-running thread mem/storage bench (delta vs no-delta)

`bench_sqlite_delta_memory.py`. `delta` mode uses `DeltaChannel` + the
override; `no_delta` uses `Annotated[list, _messages_delta_reducer]`
(full state in every blob). Same workload, file-backed sqlite. Latency
measured untraced (30 iterations); peak heap measured separately under
tracemalloc.

| Scenario | Turns | Storage Δ | Peak heap Δ | Read latency Δ |
|---|---:|---|---|---|
| K=1, freq=50 | 200 | **-96%** (942 KB vs 25.1 MB) | +21% (504 KB vs
418 KB) | **+13%** |
| K=1, freq=50 | 500 | **-98%** (2.9 MB vs 152.3 MB) | +20% (1.2 MB vs
1.0 MB) | **-6%** (delta wins) |
| K=3, freq=50 uniform | 200 | **-98%** (1.7 MB vs 73.5 MB) | +7% (1.3
MB vs 1.2 MB) | **+10%** |
| K=3, freq=50 uniform | 500 | **-99%** (6.0 MB vs 452.5 MB) | +7% (3.3
MB vs 3.0 MB) | **+6%** |
| K=3, freq=mixed | 200 | **-98%** (1.4 MB vs 73.5 MB) | +5% (1.3 MB vs
1.2 MB) | +190% (5.1 ms vs 1.7 ms abs) |
| K=3, freq=mixed | 500 | **-99%** (4.1 MB vs 452.5 MB) | +8% (3.3 MB vs
3.0 MB) | +377% (20.9 ms vs 4.4 ms abs) |

- **Storage**: -96 to -99% on long threads (a 500-turn K=3 thread
shrinks from 452 MB to 6 MB on disk). This is the headline win.
- **Peak heap**: within +5 to +21% of the no-delta path — the streaming
cursor + merged walk + drop-after-deserialize keep peak in-flight at one
checkpoint at a time.
- **Read latency**: equivalent-ish (within ~15%) on uniform-cadence
scenarios; at K=1/500 turns delta even wins by 6%. The mixed-cadence
rows have one channel with `snapshot_frequency=1000` walking to root on
a 500-turn thread — by configuration. Absolute mixed-delta latency is
still 5-21 ms per read.

Bench scripts (not committed; workspace-root convention matches other
`bench_*.py` files):
- `bench_sqlite_delta_history.py`
- `bench_sqlite_delta_memory.py`

## Test plan

- [x] `cd libs/checkpoint-sqlite && make format` clean
- [x] `cd libs/checkpoint-sqlite && make lint` clean
- [x] `cd libs/checkpoint-sqlite && make test` — 97 passed (1 known
flake unrelated)
- [x] `tests/test_get_delta_channel_history.py` — 7/7 (now exercises the
override)
- [x] `tests/test_delta_channel_migration.py` — 3/3 (new)

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py

@@ -4,7 +4,7 @@ import json
 import random
 import sqlite3
 import threading
-from collections.abc import AsyncIterator, Iterator, Sequence
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from contextlib import closing, contextmanager
 from typing import Any, cast
 
@@ -16,12 +16,19 @@ from langgraph.checkpoint.base import (
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    DeltaChannelHistory,
     SerializerProtocol,
     get_checkpoint_id,
     get_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 
+from langgraph.checkpoint.sqlite._delta import (
+    DELTA_STAGE1_SQL,
+    build_delta_channels_writes_history,
+    build_delta_stage2_sql,
+    step_walk_with_row,
+)
 from langgraph.checkpoint.sqlite.utils import search_where
 
 _AIO_ERROR_MSG = (
@@ -493,6 +500,88 @@ class SqliteSaver(BaseCheckpointSaver[str]):
                 (str(thread_id),),
             )
 
+    def get_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Fast-path override of `BaseCheckpointSaver.get_delta_channel_history`.
+
+        Two-stage query:
+
+        * Stage 1 (paged): newest-first slice of `checkpoints` returning
+          `(checkpoint_id, parent_checkpoint_id, type, checkpoint)` per
+          ancestor. Sqlite has no JSONB, so we ship the full serialized
+          checkpoint blob and inspect `channel_values` in Python. Pages
+          newest-first by `checkpoint_id` with a `< cursor` predicate;
+          page size is `DELTA_PAGE_SIZE`. Stops paging when every channel
+          has found its seed or the chain is exhausted.
+
+        * Stage 2 (per-channel UNION ALL): one branch per channel reading
+          `writes` filtered to that channel's specific `chain_cids`. No
+          separate seed-blob fetch — sqlite stores `channel_values` inline
+          in the checkpoint blob, so seeds come back from stage 1.
+        """
+        if not channels:
+            return {}
+        channels = list(channels)
+        thread_id = str(config["configurable"]["thread_id"])
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = self.get_tuple(config)
+            if target is None:
+                return {ch: {"writes": []} for ch in channels}
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+
+        chain_by_ch: dict[str, list[str]] = {ch: [] for ch in channels}
+        seed_val_by_ch: dict[str, Any] = {}
+        walk_state: dict[str, Any] = {}
+        seeded: set[str] = set()
+
+        with self.cursor(transaction=False) as cur:
+            cur.execute(DELTA_STAGE1_SQL, (thread_id, checkpoint_ns, checkpoint_id))
+            for row in cur:
+                cid, parent_cid, type_tag, blob = row
+                if step_walk_with_row(
+                    cid=cid,
+                    parent_cid=parent_cid,
+                    type_tag=type_tag,
+                    blob=blob,
+                    target_id=checkpoint_id,
+                    serde=self.serde,
+                    chain_by_ch=chain_by_ch,
+                    seed_val_by_ch=seed_val_by_ch,
+                    walk_state=walk_state,
+                    seeded=seeded,
+                    channels=channels,
+                ):
+                    break
+
+            channels_with_chain = [ch for ch in channels if chain_by_ch[ch]]
+            stage2_sql = build_delta_stage2_sql(
+                chain_lens=[len(chain_by_ch[ch]) for ch in channels_with_chain],
+            )
+            if stage2_sql:
+                stage2_params: list[Any] = []
+                for ch in channels_with_chain:
+                    stage2_params.extend(
+                        [thread_id, checkpoint_ns, ch, *chain_by_ch[ch]]
+                    )
+                cur.execute(stage2_sql, stage2_params)
+                stage2_rows = cast(
+                    "list[tuple[str, str, str, int, str, bytes]]", cur.fetchall()
+                )
+            else:
+                stage2_rows = []
+
+        return build_delta_channels_writes_history(
+            channels=channels,
+            chain_by_ch=chain_by_ch,
+            seed_val_by_ch=seed_val_by_ch,
+            seeded=seeded,
+            stage2_rows=stage2_rows,
+            serde=self.serde,
+        )
+
     async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database asynchronously.