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Languages & runtimes · #jupyter
The goal is to confirm that a Jupyter Notebook backed up in format version 5 retains its original cell execution order, output, and custom metadata after being restored on a newer Jupyter environment. Verification involves opening the restored file, inspecting the execution counts, and ensuring that outputs are intact and that extensions’ metadata survive th
Goal: Determine whether JupyterLab 4.0 allows an extension to declare both the legacy `jupyter_server_extension` entry point and the new `jupyter_server.extension` entry point simultaneously during the migration from the Server Extension system to the Application Extension system. Constraints: The release notes state that legacy server extensions will be dep
Background DataSpell 2023.3 (build 233.11799) fixed a memory leak where Jupyter kernel connections were not released after notebook closure, allowing heap growth up to the configured -Xmx limit (default 2 GB). The fix adds a background monitor that detects orphaned kernels and cleans them up on notebook close. Remaining constraint The IDE still relies on a u
DataSpell ↔ Jupyter Kernel Integration JetBrains DataSpell connects to Jupyter kernels via the Jupyter client library, enabling notebook execution inside the IDE. The client currently declares support for the messaging protocol up to version 5.7.1. When a kernel advertises protocol 6.0 or higher, DataSpell may abort the handshake or emit a compatibility warn
The goal is to rely on the %%sql magic from ipython-sql to apply LIMIT and OFFSET directly in the database query, thereby avoiding the transfer of large result sets to the notebook kernel. However, the documentation does not guarantee server‑side push‑down for all adapters; SQLite may fetch the full set and filter in Python, and behavior can shift with ipyth
Goal: determine whether stdout and stderr character-level truncation behaves consistently between the classic Jupyter Notebook interface and JupyterLab, and whether NotebookApp.truncated_output_size is reliably enforced in Jupyter Notebook 7.x. Uncertainty: classic Notebook reportedly truncates long output near 1000 characters by default, while JupyterLab us
Goal: maintain identical figure state across all Jupyter clients connected to the same kernel when using Plotly FigureWidget for live updates. Constraint: state synchronization relies on ipywidgets WebSocket messages; batching reduces traffic but does not guarantee ordering or deduplication when retries occur, especially with multiple subscribers. Uncertaint
DataSpell connects to remote Jupyter servers using authentication tokens or password-based methods. However, the interface does not currently expose the specific permission scopes for these connections, raising concerns regarding least-privilege access enforcement. When a remote Jupyter token expires during runtime, the IDE often reports a generic connection
Context Jupyter Server 2.x introduced KernelManager.cull_connected to control whether kernels with active frontend connections but no recent code execution are culled, complementing the existing cull_idle_timeout and cull_interval settings. However, these idle-culling parameters only affect kernels that have gone completely inactive; they do not interrupt a
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