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docs/design_docs_of_journalsync_2nd.md
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### Goal
- Build a robust and memory-efficient replication foundation that decouples the physical storage layer by leveraging the Web Streams API.
- Maintain strict compliance with the data consistency and replication protocols of CouchDB/PouchDB.
- Support "Connection Strings" to easily extend compatibility to various object storages (e.g. S3, MinIO).
- Support 'Connection Strings' to easily extend compatibility to various object storages (e.g., S3 and MinIO).
### Motivation
- The original Journal Replicator used a custom queue mechanism called `Trench` to manage backpressure, which had limitations regarding memory efficiency when dealing with a massive number of files.
- The storage operation logic was tightly coupled with `JournalSyncAbstract`, making it difficult to swap out the physical storage layer (e.g., S3, WebDAV).
- The storage operation logic was tightly coupled with `JournalSyncAbstract`, making it difficult to swap out the physical storage layer (e.g., S3 and WebDAV).
- The transfer of revision trees (`_revisions`) conforming to PouchDB's replication protocol was implicitly managed. There was a need for a stricter, more deterministic application of document histories.
### Methods and implementations
#### Pipeline Construction using Web Streams API
We replaced `Trench` with standard Web Streams APIs (`ReadableStream`, `TransformStream`, `WritableStream`) to build the sending and receiving pipelines.
- **Sending Pipeline**: Reads documents from the PouchDB changes stream, passes them through a compression `TransformStream`, and pipes them to an upload `WritableStream`. This enables automatic backpressure, keeping memory consumption stable even during large-scale synchronization.
We replaced `Trench` with standard Web Streams APIs (`ReadableStream`, `TransformStream`, and `WritableStream`) to build the sending and receiving pipelines.
- **Sending Pipeline**: Reads documents from the PouchDB changes stream, passes them through a compression `TransformStream`, and pipes them to an upload `WritableStream`. This enables automatic backpressure, keeping memory consumption stable even during large-scale synchronisation.
- **Receiving Pipeline**: Processes storage file listing, downloading/decompression, and bulk application to PouchDB in a streamlined manner.
#### Decoupling the Physical Layer via IJournalStorage
To detach the storage operations from the core synchronization logic (`JournalSyncCore`), we introduced the `IJournalStorage` interface.
When adding new backend storages in the future (e.g., R2, WebDAV), developers only need to add an Adapter that implements this interface, without modifying the core replicator.
To detach the storage operations from the core synchronisation logic (`JournalSyncCore`), we introduced the `IJournalStorage` interface.
When adding new backend storages in the future (e.g., R2 and WebDAV), developers only need to add an Adapter that implements this interface, without modifying the core replicator.
#### Strict Application of PouchDB Replication Protocols
To synchronize precisely according to the CouchDB/PouchDB protocol, the following steps were optimized:
To synchronise precisely according to the CouchDB/PouchDB protocol, the following steps were optimised:
1. **Transferring History**: Using `bulkGet({ revs: true })`, the replicator transfers not only the latest revision of a document but its entire history tree (`_revisions`) alongside the deletion flag (`_deleted`).
2. **Applying History**: On the receiving end, the replicator uses `revsDiff` to identify which incoming revisions are missing locally. It then applies them using `bulkDocs(saveDocs, { new_edits: false })`.
By specifying `new_edits: false`, PouchDB integrates the received history exactly as it is without treating them as new local edits. This prevents unexpected conflicts and redundant branching of the revision tree.
@@ -36,14 +36,14 @@ The connection string acts as a user-friendly configuration. Each Storage Adapte
By migrating from the previous `Trench` architecture to the Web Streams API and strict PouchDB protocol compliance, the replication speed characteristics have changed in the following ways:
1. **Consistent Throughput via Backpressure**:
The `Trench` mechanism occasionally loaded too many items into memory or stalled during massive transfers. The Web Streams API applies automatic backpressure across the pipeline (Read `changes` -> Compress -> Upload). While peak burst speeds might appear slightly smoothed out, the **sustained throughput is far more stable**, preventing out-of-memory crashes on mobile devices and keeping network utilization optimal.
The `Trench` mechanism occasionally loaded too many items into memory or stalled during massive transfers. The Web Streams API applies automatic backpressure across the pipeline (Read `changes` -> Compress -> Upload). While peak burst speeds might appear slightly smoothed out, the **sustained throughput is far more stable**, preventing out-of-memory crashes on mobile devices and keeping network utilisation optimal.
2. **Faster Receive-Side Application (`new_edits: false`)**:
In the previous version, incoming documents were sometimes evaluated as new local edits. By utilizing PouchDB's `bulkDocs({ new_edits: false })` alongside the proper `_revisions` tree, we bypass unnecessary conflict generation and local revision hashing. This drastically **speeds up the document insertion process** on the receiving end.
In the previous version, incoming documents were sometimes evaluated as new local edits. By utilising PouchDB's `bulkDocs({ new_edits: false })` alongside the proper `_revisions` tree, we bypass unnecessary conflict generation and local revision hashing. This drastically **speeds up the document insertion process** on the receiving end.
3. **Optimized Network Traffic**:
Because conflicts are resolved deterministically and revision trees are replicated exactly as they exist, the system avoids generating "echoes" (redundant syncs triggered by a device misunderstanding a history tree). This reduces unnecessary background traffic significantly.
3. **Optimised Network Traffic**:
Because conflicts are resolved deterministically and revision trees are replicated exactly as they exist, the system avoids generating 'echoes' (redundant synchronisations triggered by a device misunderstanding a history tree). This reduces unnecessary background traffic significantly.
### Consideration and Conclusion
The Journal Replicator 2nd Edition achieves robust and scalable storage synchronization through enhanced memory efficiency (via Web Streams), decoupled extensibility (via IJournalStorage), and strict protocol compliance (via `new_edits: false`).
The Journal Replicator 2nd Edition achieves robust and scalable storage synchronisation through enhanced memory efficiency (via Web Streams), decoupled extensibility (via IJournalStorage), and strict protocol compliance (via `new_edits: false`).
Moving forward, this foundation will make it much easier to officially support a wider variety of backend storages.