wehub-resource-sync
59 lines
1.9 KiB
Markdown
59 lines
1.9 KiB
Markdown
# Client/Server replicated
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<img src="./architecture.sub2.svg" />
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_For medium-sized, real-time, synchronized and/or editable data sets with many
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concurrent users._
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The dataset is instantiated in-memory with a Python or Node.js Perspective
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server, and web applications create duplicates of these tables in a local
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WebAssembly client in the browser, synchonized efficiently to the server via
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Apache Arrow. This design scales well with additional concurrent users, as
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browsers only need to download the initial data set and subsequent update
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deltas, while operations like scrolling, pivots, sorting, etc. are performed on
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the client.
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Python servers can make especially good use of additional threads, as
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Perspective will release the GIL for almost all operations. Interactive
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performance on the client is very good and identical to client-only
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architecture. Updates and edits are seamlessly synchonized across clients via
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their virtual server counterparts using websockets and Apache Arrow.
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## Python and Tornado server
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```python
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from perspective import Server, PerspectiveTornadoHandler
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server = Server()
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client = server.new_local_client()
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client.table(csv, name="my_table")
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routes = [(
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r"/websocket",
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perspective.handlers.tornado.PerspectiveTornadoHandler,
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{"perspective_server": server},
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)]
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app = tornado.web.Application(routes)
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app.listen(8080)
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loop = tornado.ioloop.IOLoop.current()
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loop.start()
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```
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## Javascript client
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Perspective's websocket client interfaces with the Python server, then
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_replicates_ the server-side Table.
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```javascript
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const websocket = await perspective.websocket("ws://localhost:8080");
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const server_table = await websocket.open_table("my_table");
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const server_view = await server_table.view();
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const worker = await perspective.worker();
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const client_table = await worker.table(server_view);
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const viewer = document.createElement("perspective-viewer");
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document.body.appendChild(viewer);
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await viewer.load(client_table);
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```
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