Codex Task Logs

Task ID: task_e_682fe05306688323a7de4e2e796170f6

Environment setup
Configuring language runtimes... + rm -- /tmp/QQWxfJ-setup_script.sh + source ./codex_setup.sh ++ uv sync --frozen Using CPython 3.12.3 interpreter at: /usr/bin/python3.12 Creating virtual environment at: .venv Building scubaduck @ file:///workspace/scubaduck Downloading pyright (5.3MiB) Downloading ruff (11.0MiB) Downloading duckdb (19.3MiB) Downloading playwright (43.1MiB) Downloading ruff Built scubaduck @ file:///workspace/scubaduck Downloading duckdb Downloading playwright Downloading pyright Prepared 33 packages in 2.59s Installed 33 packages in 107ms + blinker==1.9.0 + certifi==2025.4.26 + charset-normalizer==3.4.2 + click==8.2.0 + duckdb==1.2.2 + execnet==2.1.1 + flask==3.1.1 + greenlet==3.2.2 + idna==3.10 + iniconfig==2.1.0 + itsdangerous==2.2.0 + jinja2==3.1.6 + markupsafe==3.0.2 + nodeenv==1.9.1 + packaging==25.0 + playwright==1.52.0 + pluggy==1.6.0 + pyee==13.0.0 + pyright==1.1.400 + pytest==8.3.5 + pytest-base-url==2.1.0 + pytest-playwright==0.7.0 + pytest-xdist==3.6.1 + python-dateutil==2.9.0.post0 + python-slugify==8.0.4 + requests==2.32.3 + ruff==0.11.10 + scubaduck==0.1.0 (from file:///workspace/scubaduck) + six==1.17.0 + text-unidecode==1.3 + typing-extensions==4.13.2 + urllib3==2.4.0 + werkzeug==3.1.3 ++ source .venv/bin/activate +++ '[' -z '' ']' +++ '[' -n x ']' +++ SCRIPT_PATH=.venv/bin/activate +++ '[' .venv/bin/activate = /tmp/QQWxfJ-setup_script.sh ']' +++ deactivate nondestructive +++ unset -f pydoc +++ '[' -z '' ']' +++ '[' -z '' ']' +++ hash -r +++ '[' -z '' ']' +++ unset VIRTUAL_ENV +++ unset VIRTUAL_ENV_PROMPT +++ '[' '!' nondestructive = nondestructive ']' +++ VIRTUAL_ENV=/workspace/scubaduck/.venv +++ '[' linux-gnu = cygwin ']' +++ '[' linux-gnu = msys ']' +++ export VIRTUAL_ENV +++ '[' -z '' ']' +++ unset SCRIPT_PATH +++ _OLD_VIRTUAL_PATH=/root/.cargo/bin:/root/.rbenv/shims:/root/.rbenv/bin:/root/.rbenv/shims:/root/.local/share/swiftly/bin:/root/.bun/bin:/root/.nvm/versions/node/v22.16.0/bin:/root/.pyenv/shims:3441PYENV_ROOT/shims:/root/.pyenv/bin:/usr/local/go/bin:/root/go/bin:/root/.rbenv/bin:/root/.rbenv/shims:/root/.bun/bin:/root/.local/bin:/root/.pyenv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin +++ PATH=/workspace/scubaduck/.venv/bin:/root/.cargo/bin:/root/.rbenv/shims:/root/.rbenv/bin:/root/.rbenv/shims:/root/.local/share/swiftly/bin:/root/.bun/bin:/root/.nvm/versions/node/v22.16.0/bin:/root/.pyenv/shims:3441PYENV_ROOT/shims:/root/.pyenv/bin:/usr/local/go/bin:/root/go/bin:/root/.rbenv/bin:/root/.rbenv/shims:/root/.bun/bin:/root/.local/bin:/root/.pyenv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin +++ export PATH +++ '[' xscubaduck '!=' x ']' +++ VIRTUAL_ENV_PROMPT=scubaduck +++ export VIRTUAL_ENV_PROMPT +++ '[' -z '' ']' +++ '[' -z '' ']' +++ _OLD_VIRTUAL_PS1= +++ PS1='(scubaduck) ' +++ export PS1 +++ alias pydoc +++ true +++ hash -r ++ python -c 'import os; import duckdb; con = duckdb.connect(); con.execute(f"SET http_proxy = '\''{os.getenv("HTTP_PROXY")}'\''"); con.execute("INSTALL '\''sqlite'\'';")' ++ playwright install chromium Downloading Chromium 136.0.7103.25 (playwright build v1169) from https://cdn.playwright.dev/dbazure/download/playwright/builds/chromium/1169/chromium-linux.zip 167.7 MiB [] 0% 0.0s167.7 MiB [] 0% 46.4s167.7 MiB [] 0% 52.9s167.7 MiB [] 0% 36.9s167.7 MiB [] 0% 30.3s167.7 MiB [] 0% 24.4s167.7 MiB [] 0% 17.6s167.7 MiB [] 0% 13.0s167.7 MiB [] 1% 10.5s167.7 MiB [] 1% 7.8s167.7 MiB [] 2% 6.7s167.7 MiB [] 4% 4.3s167.7 MiB [] 5% 3.6s167.7 MiB [] 5% 3.5s167.7 MiB [] 6% 3.4s167.7 MiB [] 7% 3.1s167.7 MiB [] 8% 2.9s167.7 MiB [] 8% 3.0s167.7 MiB [] 9% 3.0s167.7 MiB [] 10% 3.0s167.7 MiB [] 12% 2.6s167.7 MiB [] 12% 2.7s167.7 MiB [] 13% 2.7s167.7 MiB [] 14% 2.7s167.7 MiB [] 14% 2.8s167.7 MiB [] 15% 2.8s167.7 MiB [] 15% 2.7s167.7 MiB [] 16% 2.6s167.7 MiB [] 17% 2.5s167.7 MiB [] 18% 2.4s167.7 MiB [] 19% 2.3s167.7 MiB [] 20% 2.3s167.7 MiB [] 21% 2.3s167.7 MiB [] 23% 2.2s167.7 MiB [] 23% 2.1s167.7 MiB [] 25% 2.0s167.7 MiB [] 26% 2.0s167.7 MiB [] 27% 1.9s167.7 MiB [] 28% 1.8s167.7 MiB [] 29% 1.7s167.7 MiB [] 30% 1.8s167.7 MiB [] 30% 1.7s167.7 MiB [] 32% 1.6s167.7 MiB [] 33% 1.6s167.7 MiB [] 35% 1.5s167.7 MiB [] 36% 1.5s167.7 MiB [] 37% 1.4s167.7 MiB [] 38% 1.4s167.7 MiB [] 39% 1.4s167.7 MiB [] 40% 1.3s167.7 MiB [] 41% 1.3s167.7 MiB [] 42% 1.3s167.7 MiB [] 43% 1.2s167.7 MiB [] 44% 1.2s167.7 MiB [] 45% 1.2s167.7 MiB [] 47% 1.1s167.7 MiB [] 48% 1.1s167.7 MiB [] 49% 1.1s167.7 MiB [] 50% 1.0s167.7 MiB [] 51% 1.0s167.7 MiB [] 52% 1.0s167.7 MiB [] 53% 1.0s167.7 MiB [] 54% 0.9s167.7 MiB [] 55% 0.9s167.7 MiB [] 57% 0.9s167.7 MiB [] 58% 0.8s167.7 MiB [] 59% 0.8s167.7 MiB [] 60% 0.8s167.7 MiB [] 61% 0.8s167.7 MiB [] 62% 0.7s167.7 MiB [] 63% 0.7s167.7 MiB [] 65% 0.7s167.7 MiB [] 66% 0.7s167.7 MiB [] 67% 0.6s167.7 MiB [] 68% 0.6s167.7 MiB [] 69% 0.6s167.7 MiB [] 70% 0.6s167.7 MiB [] 71% 0.5s167.7 MiB [] 72% 0.5s167.7 MiB [] 74% 0.5s167.7 MiB [] 75% 0.5s167.7 MiB [] 76% 0.4s167.7 MiB [] 77% 0.4s167.7 MiB [] 78% 0.4s167.7 MiB [] 79% 0.4s167.7 MiB [] 80% 0.4s167.7 MiB [] 81% 0.3s167.7 MiB [] 82% 0.3s167.7 MiB [] 83% 0.3s167.7 MiB [] 84% 0.3s167.7 MiB [] 85% 0.3s167.7 MiB [] 86% 0.2s167.7 MiB [] 87% 0.2s167.7 MiB [] 88% 0.2s167.7 MiB [] 89% 0.2s167.7 MiB [] 90% 0.2s167.7 MiB [] 91% 0.2s167.7 MiB [] 92% 0.1s167.7 MiB [] 93% 0.1s167.7 MiB [] 95% 0.1s167.7 MiB [] 96% 0.1s167.7 MiB [] 97% 0.0s167.7 MiB [] 98% 0.0s167.7 MiB [] 99% 0.0s167.7 MiB [] 100% 0.0s Chromium 136.0.7103.25 (playwright build v1169) downloaded to /root/.cache/ms-playwright/chromium-1169 Downloading FFMPEG playwright build v1011 from https://cdn.playwright.dev/dbazure/download/playwright/builds/ffmpeg/1011/ffmpeg-linux.zip 2.3 MiB [] 0% 0.0s2.3 MiB [] 2% 0.7s2.3 MiB [] 5% 0.8s2.3 MiB [] 10% 0.5s2.3 MiB [] 15% 0.4s2.3 MiB [] 22% 0.3s2.3 MiB [] 36% 0.2s2.3 MiB [] 51% 0.1s2.3 MiB [] 80% 0.0s2.3 MiB [] 100% 0.0s FFMPEG playwright build v1011 downloaded to /root/.cache/ms-playwright/ffmpeg-1011 Downloading Chromium Headless Shell 136.0.7103.25 (playwright build v1169) from https://cdn.playwright.dev/dbazure/download/playwright/builds/chromium/1169/chromium-headless-shell-linux.zip 101.4 MiB [] 0% 0.0s101.4 MiB [] 0% 29.6s101.4 MiB [] 0% 32.0s101.4 MiB [] 0% 22.3s101.4 MiB [] 0% 18.6s101.4 MiB [] 0% 13.7s101.4 MiB [] 1% 10.3s101.4 MiB [] 1% 7.5s101.4 MiB [] 2% 5.4s101.4 MiB [] 3% 3.8s101.4 MiB [] 6% 2.6s101.4 MiB [] 7% 2.1s101.4 MiB [] 9% 1.9s101.4 MiB [] 9% 2.0s101.4 MiB [] 11% 1.8s101.4 MiB [] 13% 1.6s101.4 MiB [] 16% 1.4s101.4 MiB [] 18% 1.2s101.4 MiB [] 21% 1.1s101.4 MiB [] 23% 1.0s101.4 MiB [] 25% 1.0s101.4 MiB [] 27% 0.9s101.4 MiB [] 29% 0.9s101.4 MiB [] 31% 0.8s101.4 MiB [] 31% 0.9s101.4 MiB [] 32% 0.9s101.4 MiB [] 34% 0.9s101.4 MiB [] 35% 0.8s101.4 MiB [] 38% 0.8s101.4 MiB [] 40% 0.7s101.4 MiB [] 41% 0.7s101.4 MiB [] 43% 0.7s101.4 MiB [] 45% 0.6s101.4 MiB [] 47% 0.6s101.4 MiB [] 49% 0.6s101.4 MiB [] 51% 0.6s101.4 MiB [] 53% 0.5s101.4 MiB [] 55% 0.5s101.4 MiB [] 56% 0.5s101.4 MiB [] 58% 0.5s101.4 MiB [] 60% 0.4s101.4 MiB [] 63% 0.4s101.4 MiB [] 65% 0.4s101.4 MiB [] 66% 0.4s101.4 MiB [] 69% 0.3s101.4 MiB [] 70% 0.3s101.4 MiB [] 73% 0.3s101.4 MiB [] 74% 0.3s101.4 MiB [] 76% 0.2s101.4 MiB [] 79% 0.2s101.4 MiB [] 80% 0.2s101.4 MiB [] 83% 0.2s101.4 MiB [] 85% 0.2s101.4 MiB [] 87% 0.1s101.4 MiB [] 89% 0.1s101.4 MiB [] 91% 0.1s101.4 MiB [] 93% 0.1s101.4 MiB [] 96% 0.0s101.4 MiB [] 99% 0.0s101.4 MiB [] 100% 0.0s Chromium Headless Shell 136.0.7103.25 (playwright build v1169) downloaded to /root/.cache/ms-playwright/chromium_headless_shell-1169 ++ echo 'source .venv/bin/activate'
Internet turned off
If we try to interpret an integral column as a time, but with the wrong unit, we currently get an error like "Invalid time value 13384414800000000 for column time_slot with time_unit s". In this case, we should look at the value and heuristically determine what the correct unit is, and suggest it to the user. (We WON'T select it by default, because that would require us to snoop data, and we don't want to snoop data unless the user Dives.)
shell
bash: .venv/bin/activate: No such file or directory root@141de59f5186:/workspace# ls scubaduck
root@141de59f5186:/workspace# cd scubaduck root@141de59f5186:/workspace/scubaduck# ls AGENTS.md codex_setup.sh scubaduck tests README.md pyproject.toml stubs uv.lock

I'll begin by checking the AGENTS.md file in the root directory.

shell
root@141de59f5186:/workspace/scubaduck# cat AGENTS.md - Use "source .venv/bin/activate" to activate the virtual environment before running any commands - ONLY use non-interactive commands like cat, sed, apply_patch to do edits. Do NOT use interactive editors. - Do NOT attempt to install packages. Only the packages specified in pyproject.toml are available. You cannot add new packages. If you desperately want another package, make a note of it in the final PR description. - Use conventional commits to format PR title - There are no nested AGENTS.md files, this is the only agents file - When using Playwright, ONLY use chromium browser. This browser is already installed. - Use "ruff check" to check lint, "ruff format" to autoformat files and "pyright" to typecheck. - When writing the PR description, include the original prompt supplied by the user. - When you add functionality to the server, add server tests to tests/test_server.py. When you add features to the frontend, add frontend tests to tests/test_web.py

Looks like the root instructions are clear: activate the environment before running commands!

shell
root@141de59f5186:/workspace/scubaduck# cat README.md I really like Scuba (Meta's internal real-time database system). The distributed , real-time database part of Scuba is quite difficult (and expensive) to replica te, but I also really like Scuba's UI for doing queries, and I have found myself wishing that I have access to it even for "small" databases, e.g., I have a sql ite dataset I want to explore. Pivotal ideas: * Time series by default. In the dedicated "time series" view, there are many fe atures specifically oriented towards working towards tables that represent event s that occurred over time: the start, end, compare, aggregate and granularity fi elds all specially privilege the timestamp field. In fact, you can't log events to Scuba's backing data store without a timestamp, they always come with one. (S cuba also supports other views that don't presuppose a time series, but the time series is the most beloved and well used view.) This is in contrast to typical software which tries to generalize to arbitrary data first, with time series bei ng added on later. * It's all about exploration. Scuba is predicated on the idea that you don't kno w what you're looking for, that you are going to spend time tweaking queries and changing filters/grouping as part of an investigation to figure out why a syste m behaves the way it is. So the filters/comparisons/groupings you want to edit a re always visible on the left sidebar, with the expectation that you're going to tweak the query to look at something else. Similarly, all the parameters of you r query get saved into your URL, so your browser history can double up as a quer y history / you can easily share a query with someone else. This is contrast to typical software which is often oriented to making pretty dashboards and reports . (This function is important too, but it's not what I want in exploration mode! ) * You can fix data problems in the query editor. It's pretty common to have mess ed up and ended up with a database that doesn't have exactly the columns you nee d, or some columns that are corrupted in some way. Scuba has pretty robust suppo rt for defining custom columns with arbitrary SQL functions, grouping over them as if they were native functions, and doing so with minimal runtime cost (Scuba aims to turn around your query in milliseconds!) Having to go and run a huge dat a pipeline to fix your data is a big impediment to exploration; quick and easy c ustom columns means you can patch over problems when you're investigating and fi x them for real later. We're going to build a exploratory data analysis tool like Scuba for time series database (i.e., a database with a mandatory timestamp representing the time an event occurred). We'll use DuckDB as the underlying SQL engine served from a Py thon server, and render the GUI/results as a webpage with vanilla HTML and JS. W e'll use choices.js to support token inputs. We define a token input to mean a text input element where as you type a dropdown displays with valid values, and if you select one or press enter, the selection turns into a token/chip that can only be deleted as one unit. To start, we are going to support one views: samples. The samples view only all ows you to view individual samples from the database, subject to a filter. Our m ain UI concept is that there is a left sidebar that is the query editor, and the right side that shows the view. The sidebar is always visible and defaults to the query parameters of the current view. After you make changes to the query, clicking the "Dive" button updates the view. The URL of the page encodes all of the values of the query (and gets updated when you Dive), so the browser's back button lets you view previous queries. The query editor's job is to generate a SQL query, which then is applied on the database, and then the result visualized according to the view. Here are the settings you can apply to the query. The help text should show up w hen you mouse over the field name: * Start/End - Help text: "Sets the start/end of the time range to query. Can be any kind of datetime string. For example: 'April 23, 2014' or 'yesterday'." The UI for this selector supports both relative selections (now, -1 hour, -3 hours, -12 hours, -1 day, -3 days, -1 week, -1 fortnight, -30 days, -90 days, -1 month, -1 year) as well as specifying an absolute date. The way this field is rendere d is there is a free form text box, a drop down arrow (for the relative selector s), and then a calendar button (for date selection). * Order By - Help text: "Choose a column to sort results by." There is an ASC/D ESC toggle next to it. * Limit - Help text: "Choose the maximum number of results to show in the chart after any aggregations have been applied. For example, a limit of 10 will show no more than 10 rows for a table, etc." * Filters - You can create as many filters as you want. You can either write a f ilter using a UI or manual SQL. In the UI, filter consists of a column name, a r elation (e.g., =, !=, <, >) and then a text field. The text field is a token inp ut. It accepts multiple tokens for = relation, in which case we match using an O R for all options. There is also a "Columns" tab which lets you view all fields in the table, organ ized by their type. You can also define derived columns, by specifying a column name and SQL expression. Derived columns can be used for all parts of the UI, in cluding filters/group by/etc. Columns have checkboxes indicating if we should SE LECT them or not. Each selected column shows up in the graph. There is an All/N one link which can be used to select/deselect all checkboxes. The query UI constructs a SQL query that intuitively has this form: ``` SELECT column, column, ..., FROM table WHERE time >= min-timestamp AND time <= max-timestamp [AND condition ...] ORDER BY aggregate(column) LIMIT number ``` You should write tests for the server backend, demonstrating that at specific qu ery values we get back the correct rows of data. ## Running the server Activate the virtual environment and run the Flask development server: ```bash flask --app scubaduck.server run --debug ``` By default the server loads `sample.csv`. Set the `SCUBADUCK_DB` environment variable to point at a different database file (CSV, SQLite or DuckDB) if you want to use another dataset. The special value `TEST` starts the server with a small in-memory SQLite dataset used by the automated tests. If the file does not exist, the server will raise a `FileNotFoundError` during startup.

I'll check out the scubaduck directory to explore how to implement the heuristics.

shell
root@141de59f5186:/workspace/scubaduck# ls scubaduck __init__.py sample.csv server.py static
root@141de59f5186:/workspace/scubaduck# ls scubaduck/static index.html js
root@141de59f5186:/workspace/scubaduck# ls scubaduck/static/js chip_input.js table_view.js timeseries_chart.js sidebar.js time_column.js view_settings.js
root@141de59f5186:/workspace/scubaduck# ls tests conftest.py test_server_time.py test_web_graph.py test_multi_table_web.py test_server_timeseries.py test_web_table.py test_server.py test_web.py test_web_timeseries.py test_server_basic.py test_web_basics.py web_utils.py test_server_db_types.py test_web_chips.py test_server_errors.py test_web_columns.py

I'll search the repo for any existing time parsing code to help with heuristics.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "time_unit" -r
grep: .venv/bin/ruff: binary file matches scubaduck/static/index.html:205: <select id="time_unit" style="margin-l eft:4px"> scubaduck/static/js/table_view.js:59: const unit = document.getElementB yId("time_unit").value; scubaduck/static/js/view_settings.js:186: document.getElementById('time_unit'). style.display = show ? '' : 'none'; scubaduck/static/js/view_settings.js:696: time_unit: document.getElementById( 'time_unit').value, scubaduck/static/js/view_settings.js:744: if (params.time_unit) sp.set('time_un it', params.time_unit); scubaduck/static/js/view_settings.js:774: if (params.time_unit) document.getEle mentById('time_unit').value = params.time_unit; scubaduck/static/js/view_settings.js:845: document.getElementById('time_unit'). value = 's'; scubaduck/static/js/view_settings.js:877: if (sp.has('time_unit')) params.time_ unit = sp.get('time_unit'); scubaduck/server.py:52: time_unit: str = "s" scubaduck/server.py:257: xexpr = _time_expr(x_axis, column_types, params. time_unit) scubaduck/server.py:352: time_expr = _time_expr(params.time_column, colum n_types, params.time_unit) scubaduck/server.py:517: time_unit=payload.get("time_unit", "s"), scubaduck/server.py:532: if params.time_unit not in {"s", "ms", "us", "ns "}: scubaduck/server.py:533: return jsonify({"error": "Invalid time_unit" }), 400 scubaduck/server.py:643: mn = _numeric_to_datetime(mn, params .time_unit) scubaduck/server.py:650: f" with time_unit {p arams.time_unit}" scubaduck/server.py:658: mx = _numeric_to_datetime(mx, params .time_unit) scubaduck/server.py:665: f" with time_unit {p arams.time_unit}" tests/test_web_timeseries.py:410: select_value(page, "#time_unit", "s") tests/test_server_time.py:33:def test_integer_time_unit_ms(tmp_path: Path) -> No ne: tests/test_server_time.py:45: "time_unit": "ms", tests/test_server_time.py:69: "time_unit": "ms", tests/test_server_time.py:79:def test_integer_time_unit_us_default_start_end(tmp _path: Path) -> None: tests/test_server_time.py:91: "time_unit": "us", tests/test_server_time.py:103:def test_sqlite_integer_time_unit_us(tmp_path: Pat h) -> None: tests/test_server_time.py:123: "time_unit": "us", tests/test_server_time.py:239: "time_unit": "s", tests/test_server_time.py:257: "time_unit": "us", tests/test_web_basics.py:77:def test_time_unit_dropdown(page: Any, server_url: s tr) -> None: tests/test_web_basics.py:79: page.wait_for_selector("#time_unit", state="atta ched") tests/test_web_basics.py:80: opts = page.locator("#time_unit option").all_inn er_texts() tests/test_web_basics.py:82: assert page.input_value("#time_unit") == "s" tests/test_web_basics.py:85:def test_time_unit_hidden_when_no_time_column(page: Any, server_url: str) -> None: tests/test_web_basics.py:89: assert page.is_hidden("#time_unit")

I'll check lines around 650 for time unit conversions in the server.py.

shell
root@141de59f5186:/workspace/scubaduck# sed -n '640,675p' scubaduck/server.py mn, mx = row if isinstance(mn, (int, float)): try: mn = _numeric_to_datetime(mn, params.time_unit) except Exception: return ( jsonify( { "error": ( f"Invalid time value {mn} for column {axis}" f" with time_unit {params.time_unit}" ) } ), 400, ) if isinstance(mx, (int, float)): try: mx = _numeric_to_datetime(mx, params.time_unit) except Exception: return ( jsonify( { "error": ( f"Invalid time value {mx} for column {axis}" f" with time_unit {params.time_unit}" ) } ), 400, ) if params.start is None and mn is not None: params.start = ( mn.strftime("%Y-%m-%d %H:%M:%S") if not isinstance(mn, str) else mn ) if params.end is None and mx is not None:

I'm reviewing the _numeric_to_datetime function to understand the current date conversion logic.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "_numeric_to_datetime" -n scubad uck/server.py 143:def _numeric_to_datetime(value: int | float, unit: str) -> datetime: 643: mn = _numeric_to_datetime(mn, params.time_unit) 658: mx = _numeric_to_datetime(mx, params.time_unit)
root@141de59f5186:/workspace/scubaduck# sed -n '140,155p' scubaduck/server.py return dt.replace(microsecond=0, tzinfo=None).strftime("%Y-%m-%d %H:%M:%S") def _numeric_to_datetime(value: int | float, unit: str) -> datetime: """Convert a numeric timestamp ``value`` with unit ``unit`` to ``datetime``. Heuristically fall back to seconds when the converted value is before 1990 but the seconds interpretation is in a reasonable range. This handles integer columns stored in seconds even when ``unit`` is mistakenly set to a finer granularity. """ divisor = { "s": 1, "ms": 1000, "us": 1_000_000,
root@141de59f5186:/workspace/scubaduck# sed -n '155,175p' scubaduck/server.py "us": 1_000_000, "ns": 1_000_000_000, }.get(unit, 1) dt = datetime.fromtimestamp(int(value) / divisor, tz=timezone.utc) if unit != "s" and dt.year < 1990: alt = datetime.fromtimestamp(int(value), tz=timezone.utc) if alt.year >= 1990: dt = alt return dt def _granularity_seconds(granularity: str, start: str | None, end: str | None) - > int: gran = granularity.lower() mapping = { "1 second": 1, "5 seconds": 5, "10 seconds": 10, "30 seconds": 30, "1 minute": 60, "4 minutes": 240,

I think there might be an issue with interpreting integers as time. I'll check _time_expr.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "_time_expr" -n scubaduck/server .py 201:def _time_expr(col: str, column_types: Dict[str, str] | None, unit: str) -> str: 257: xexpr = _time_expr(x_axis, column_types, params.time_unit) 352: time_expr = _time_expr(params.time_column, column_types, params.time _unit)
root@141de59f5186:/workspace/scubaduck# sed -n '190,215p' scubaduck/server.py try: s = dtparser.parse(start) e = dtparser.parse(end) except Exception: return 3600 total = max((e - s).total_seconds(), 1) buckets = 100 if gran == "auto" else 500 return max(int(total // buckets), 1) return 3600 def _time_expr(col: str, column_types: Dict[str, str] | None, unit: str) -> str: """Return SQL expression for column interpreted as timestamp.""" qcol = _quote(col) if column_types is None: return qcol ctype = column_types.get(col, "").upper() if not any(t in ctype for t in ["TIMESTAMP", "DATE", "TIME"]): if any( t in ctype for t in [ "INT", "DECIMAL", "REAL", "DOUBLE", "FLOAT",
root@141de59f5186:/workspace/scubaduck# sed -n '215,245p' scubaduck/server.py "FLOAT", "NUMERIC", "HUGEINT", ] ): if unit == "ns": # Use nanosecond helper unless column cannot represent such larg e values if "INT" in ctype and "BIGINT" not in ctype and "HUGEINT" not in ctype: unit = "s" else: expr = f"CAST({qcol} AS BIGINT)" return f"make_timestamp_ns({expr})" if ( unit != "s" and "INT" in ctype and "BIGINT" not in ctype and "HUGEINT" not in ctype ): # 32-bit integers cannot store sub-second precision for modern d ates unit = "s" multiplier = { "s": 1_000_000, "ms": 1_000, "us": 1, }.get(unit, 1_000_000) base = f"CAST({qcol} AS BIGINT)" expr = f"CAST({base} * {multiplier} AS BIGINT)" if multiplier != 1 e lse base return f"make_timestamp({expr})" return qcol

I’ll check the tests in test_server_time.py to see what's already being covered.

shell
root@141de59f5186:/workspace/scubaduck# sed -n '1,160p' tests/test_server_time.p y from __future__ import annotations import json from pathlib import Path import pytest from scubaduck import server def test_integer_time_column(tmp_path: Path) -> None: csv_file = tmp_path / "events.csv" csv_file.write_text("created,event\n1704067200,login\n1704070800,logout\n") app = server.create_app(csv_file) client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-01 01:00:00", "order_by": "created", "columns": ["created", "event"], "time_column": "created", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert len(data["rows"]) == 2 def test_integer_time_unit_ms(tmp_path: Path) -> None: csv_file = tmp_path / "events.csv" csv_file.write_text("created,event\n1704067200000,login\n1704070800000,logou t\n") app = server.create_app(csv_file) client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-01 01:00:00", "order_by": "created", "columns": ["created", "event"], "time_column": "created", "time_unit": "ms", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert len(data["rows"]) == 2 def test_timeseries_default_xaxis_uses_time_column(tmp_path: Path) -> None: csv_file = tmp_path / "events.csv" csv_file.write_text("created,event\n1704067200000,login\n1704070800000,logou t\n") app = server.create_app(csv_file) client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-01 01:00:00", "graph_type": "timeseries", "granularity": "1 hour", "columns": ["event"], "aggregate": "Count", "time_column": "created", "time_unit": "ms", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert len(data["rows"]) == 2 def test_integer_time_unit_us_default_start_end(tmp_path: Path) -> None: csv_file = tmp_path / "events.csv" csv_file.write_text( "created,event\n1704067200000000,login\n1704070800000000,logout\n" ) app = server.create_app(csv_file) client = app.test_client() payload = { "table": "events", "order_by": "created", "columns": ["created", "event"], "time_column": "created", "time_unit": "us", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["start"] == "2024-01-01 00:00:00" assert data["end"] == "2024-01-01 01:00:00" assert len(data["rows"]) == 2 def test_sqlite_integer_time_unit_us(tmp_path: Path) -> None: sqlite_file = tmp_path / "events.sqlite" import sqlite3 conn = sqlite3.connect(sqlite_file) conn.execute("CREATE TABLE visits (visit_time INTEGER, event TEXT)") big_ts = 13384551652000000 conn.execute("INSERT INTO visits VALUES (?, ?)", (big_ts, "foo")) conn.commit() conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce ssIssue] app = server.create_app(sqlite_file) client = app.test_client() payload = { "table": "visits", "start": "2394-02-20 00:00:00", "end": "2394-02-21 00:00:00", "order_by": "visit_time", "columns": ["visit_time", "event"], "time_column": "visit_time", "time_unit": "us", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert len(data["rows"]) == 1 def test_relative_time_query(monkeypatch: pytest.MonkeyPatch) -> None: app = server.app client = app.test_client() from datetime import datetime fixed_now = datetime(2024, 1, 2, 4, 0, 0) class FixedDateTime(datetime): @classmethod def now(cls, tz=None): # type: ignore[override] return fixed_now if tz is None else fixed_now.astimezone(tz) monkeypatch.setattr(server, "datetime", FixedDateTime) payload = { "table": "events", "start": "-1 hour", "end": "now", "order_by": "timestamp", "limit": 100, "columns": ["timestamp", "event", "value", "user"], "filters": [], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json()
root@141de59f5186:/workspace/scubaduck# sed -n '160,320p' tests/test_server_time .py data = rv.get_json() assert rv.status_code == 200 assert len(data["rows"]) == 1 assert data["rows"][0][3] == "charlie" def test_relative_month_year(monkeypatch: pytest.MonkeyPatch) -> None: app = server.app client = app.test_client() from datetime import datetime fixed_now = datetime(2024, 1, 2, 0, 0, 0) class FixedDateTime(datetime): @classmethod def now(cls, tz=None): # type: ignore[override] return fixed_now if tz is None else fixed_now.astimezone(tz) monkeypatch.setattr(server, "datetime", FixedDateTime) payload = { "table": "events", "start": "-1 year", "end": "-1 month", "order_by": "timestamp", "limit": 10, "columns": ["timestamp"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["start"] == "2023-01-02 00:00:00" assert data["end"] == "2023-12-02 00:00:00" def test_default_start_end_returned() -> None: app = server.app client = app.test_client() payload = { "table": "events", "order_by": "timestamp", "limit": 5, "columns": ["timestamp"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["start"] == "2024-01-01 00:00:00" assert data["end"] == "2024-01-02 03:00:00" def test_time_column_none_no_time_filter() -> None: app = server.app client = app.test_client() payload = { "table": "events", "columns": ["timestamp", "event"], "time_column": "", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert "start" not in data and "end" not in data assert len(data["rows"]) == 4 def test_test_dataset_int32_time_s() -> None: app = server.create_app("TEST") client = app.test_client() payload = { "table": "events", "time_column": "ts", "time_unit": "s", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["start"] == "2024-01-01 00:00:00" assert data["end"] == "2024-01-01 01:00:00" assert len(data["rows"]) == 2 def test_test_dataset_int32_time_us() -> None: app = server.create_app("TEST") client = app.test_client() payload = { "table": "events", "time_column": "ts", "time_unit": "us", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["start"] == "2024-01-01 00:00:00" assert data["end"] == "2024-01-01 01:00:00" assert len(data["rows"]) == 2

I’m scrolling down to check additional tests.

shell
root@141de59f5186:/workspace/scubaduck# sed -n '320,480p' tests/test_server_time .py

I'll look at tests/test_server_errors.py for time parsing and invalid values.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "Invalid time value" -r tests

I'll search for tests related to invalid time errors now.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "invalid time" -r tests

I don't see a test for invalid time values, so I'll check the server tests for errors.

shell
root@141de59f5186:/workspace/scubaduck# sed -n '1,160p' tests/test_server_errors .py from __future__ import annotations import json from scubaduck import server def test_invalid_time_error() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "nonsense", "end": "now", "order_by": "timestamp", "limit": 10, "columns": ["timestamp"], "filters": [], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "error" in data def test_query_error_returns_sql_and_traceback() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "columns": ["event"], "group_by": ["user"], "aggregate": "avg", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "error" in data def test_table_unknown_column_error() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "graph_type": "table", "order_by": "timestamp", "limit": 100, "columns": ["user", "Hits", "value"], "group_by": ["user"], "aggregate": "Count", "show_hits": True, } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) _data = rv.get_json() assert rv.status_code == 200 def test_samples_view_rejects_group_by() -> None: app = server.app client = app.test_client() payload = { "table": "events", "graph_type": "samples", "group_by": ["user"], "columns": ["timestamp"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "only valid" in data["error"]
root@141de59f5186:/workspace/scubaduck# sed -n '160,320p' tests/test_server_erro rs.py

I'll check server tests for timeseries to see if heuristics are involved with time_unit.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "Invalid time" -r scubaduck scubaduck/server.py:530: return jsonify({"error": "Invalid time_colum n"}), 400 scubaduck/server.py:533: return jsonify({"error": "Invalid time_unit" }), 400 scubaduck/server.py:649: f"Invalid time value {mn} for column {axis}" scubaduck/server.py:664: f"Invalid time value {mx} for column {axis}"

I'll search earlier in the file for a previous invalid time_column check.

shell
root@141de59f5186:/workspace/scubaduck# sed -n '520,536p' scubaduck/server.py params.order_by = "Hits" for f in payload.get("filters", []): params.filters.append(Filter(f["column"], f["op"], f.get("value"))) if params.table not in tables: return jsonify({"error": "Invalid table"}), 400 column_types = get_columns(params.table) if params.time_column and params.time_column not in column_types: return jsonify({"error": "Invalid time_column"}), 400 if params.time_unit not in {"s", "ms", "us", "ns"}: return jsonify({"error": "Invalid time_unit"}), 400 if params.graph_type not in {"table", "timeseries"} and ( params.group_by or params.aggregate or params.show_hits

I'm trying to figure out how to handle large numeric values as time in different units.

shell
root@141de59f5186:/workspace/scubaduck# python - <<'EOF' > from datetime import datetime > print(datetime.utcfromtimestamp(1338441480)) > print(datetime.utcfromtimestamp(1338441480/1000)) > print(datetime.utcfromtimestamp(1338441480/1000000)) > EOF 2012-05-31 05:18:00 1970-01-16 11:47:21.480000 1970-01-01 00:22:18.441480

I think I calculated incorrectly—let me double-check the conversion on that.

shell
root@141de59f5186:/workspace/scubaduck# python - <<'EOF' > import datetime > print(datetime.datetime.utcfromtimestamp(13384414800000000/1e6)) > EOF 2394-02-19 05:00:00

If given microseconds as seconds, this would point to a year far in the future.

shell
root@141de59f5186:/workspace/scubaduck# python - <<'EOF' > import datetime > print(datetime.datetime.utcfromtimestamp(13384414800000000)) > EOF Traceback (most recent call last): File "<stdin>", line 2, in <module> ValueError: year 424137190 is out of range

I'll create a function to heuristically guess the time unit if _numeric_to_datetime fails.

I’ll integrate _guess_time_unit when calculating default start/end in _numeric_to_datetime code.

shell
root@141de59f5186:/workspace/scubaduck# sed -n '600,660p' scubaduck/server.py for t in [ "INT", "DECIMAL", "REAL", "DOUBLE", "FLOAT", "NUMERIC", "HUGEINT", ] ) is_time = "TIMESTAMP" in ctype or "DATE" in ctype or "TIME" in ctype if need_numeric and not is_numeric: return ( jsonify( { "error": f"Aggregate {agg} cannot be applied to column {c}", } ), 400, ) if allow_time and not (is_numeric or is_time): return ( jsonify( { "error": f"Aggregate {agg} cannot be applied to column {c}", } ), 400, ) if (params.start is None or params.end is None) and ( params.x_axis or params.time_column ): axis = params.x_axis or params.time_column assert axis is not None row = cast( tuple[datetime | None, datetime | None], con.execute( f'SELECT min({_quote(axis)}), max({_quote(axis)}) FROM "{par ams.table}"' ).fetchall()[0], ) mn, mx = row if isinstance(mn, (int, float)): try: mn = _numeric_to_datetime(mn, params.time_unit) except Exception: return ( jsonify( { "error": ( f"Invalid time value {mn} for column {axis}" f" with time_unit {params.time_unit}" ) } ), 400, ) if isinstance(mx, (int, float)): try: mx = _numeric_to_datetime(mx, params.time_unit) except Exception: return (
root@141de59f5186:/workspace/scubaduck# sed -n '660,720p' scubaduck/server.py return ( jsonify( { "error": ( f"Invalid time value {mx} for column {axis}" f" with time_unit {params.time_unit}" ) } ), 400, ) if params.start is None and mn is not None: params.start = ( mn.strftime("%Y-%m-%d %H:%M:%S") if not isinstance(mn, str) else mn ) if params.end is None and mx is not None: params.end = ( mx.strftime("%Y-%m-%d %H:%M:%S") if not isinstance(mx, str) else mx ) bucket_size: int | None = None if params.graph_type == "timeseries": bucket_size = _granularity_seconds( params.granularity, params.start if isinstance(params.start, str) else None, params.end if isinstance(params.end, str) else None, ) if ( params.limit is not None and params.start is not None and params.end is not None ): try: start_dt = dtparser.parse(params.start) end_dt = dtparser.parse(params.end) buckets = math.ceil( (end_dt - start_dt).total_seconds() / bucket_size ) if buckets > 1: params.limit *= buckets except Exception: pass sql = build_query(params, column_types) try: rows = con.execute(sql).fetchall() except Exception as exc: tb = traceback.format_exc() print(f"Query failed:\n{sql}\n{tb}") return ( jsonify({"sql": sql, "error": str(exc), "traceback": tb}), 400, ) def _serialize(value: Any) -> Any: if isinstance(value, bytes): return repr(value) return value rows = [[_serialize(v) for v in r] for r in rows] result: Dict[str, Any] = {"sql": sql, "rows": rows}

I want to modify the error messaging to guess the correct time unit based on the value. I'll create a function to check units like seconds, milliseconds, microseconds, and nanoseconds. I'll test them by parsing the timestamp and ensure the year is within a reasonable range. This helps avoid unrealistic guesses, while not snooping data.

I’ll implement a function to suggest a time unit based on date validity, avoiding unrealistic years.

I’ll add a function to suggest a time unit upon error and update exception handling accordingly.

I’ll write a test to check if the server suggests the correct time unit in errors.

shell
root@141de59f5186:/workspace/scubaduck# python - <<'EOF' > from datetime import datetime > value = 1704067200000000 > for u,d in ["s",1],["ms",1000],["us",1_000_000],["ns",1_000_000_000]: > try: > dt=datetime.utcfromtimestamp(value/d) > print(u, dt) > except Exception as e: > print(u, 'err') > EOF s err ms err us 2024-01-01 00:00:00 ns 1970-01-20 17:21:07.200000

I'm reviewing the timestamp 1704067200000000 and confirming it's close to January 2024.

shell
root@141de59f5186:/workspace/scubaduck# python - <<'EOF' > import datetime > print(datetime.datetime.utcfromtimestamp(1704067200)) > EOF 2024-01-01 00:00:00

I realized I made a mistake by not properly creating tuples in the zip.

shell
root@141de59f5186:/workspace/scubaduck# python - <<'EOF' > from datetime import datetime > value=1704067200000000 > for u,d in [("s",1),("ms",1000),("us",1_000_000),("ns",1_000_000_000)]: > try: > dt=datetime.utcfromtimestamp(value/d) > print(u, dt) > except Exception: > print(u, 'err') > EOF s err ms err us 2024-01-01 00:00:00 ns 1970-01-20 17:21:07.200000

The conversion seems valid, but I’ll replicate the step to verify the issue.

shell
root@141de59f5186:/workspace/scubaduck# python - <<'EOF' > import datetime > print(1704067200000000/1000) > print(datetime.datetime.utcfromtimestamp(1704067200000000/1000)) > EOF 1704067200000.0 Traceback (most recent call last): File "<stdin>", line 3, in <module> ValueError: year 55969 is out of range

I'm determining that the proper unit is microseconds, so I'll adjust my heuristic accordingly.

I will add a test for incorrect time units and implement a helper function to guess.

I will modify the exception block to suggest a corrected time unit when an error occurs.

I’ll check the README for relevant documentation and add details about the new behavior.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "maybe" -r README.md scubaduck

I'll search for "time_unit" in the README to see if it covers new time units.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "time_unit" README.md

I'll open the README to see if the "Heuristically fall back to seconds" section is mentioned.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "heuristic" -r
grep: .venv/lib/python3.12/site-packages/playwright/driver/node: binary file mat ches .venv/lib/python3.12/site-packages/playwright/driver/package/types/protocol.d.ts :1364: * Specified whether a filled field was done so by using the html auto complete attribute or autofill heuristics. .venv/lib/python3.12/site-packages/playwright/driver/package/types/protocol.d.ts :11020: * Whether 3pc heuristics exceptions should be enabled; false by de fault. .venv/lib/python3.12/site-packages/playwright/driver/package/types/protocol.d.ts :13825:produced upon backend discretion, based on internal heuristics. .venv/lib/python3.12/site-packages/xdist/scheduler/load.py:185: heuristic to influence how many tests the node is assigned. .venv/lib/python3.12/site-packages/xdist/scheduler/load.py:193: # if our node goes below a heuristic minimum, fill it out to .venv/lib/python3.12/site-packages/xdist/scheduler/load.py:194: # heu ristic maximum .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/net workx/networkx/algorithms/shortest_paths/astar.pyi:13: heuristic: Callable[.. ., Incomplete] | None = None, .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/net workx/networkx/algorithms/shortest_paths/astar.pyi:23: heuristic: Callable[.. ., Incomplete] | None = None, .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/net workx/networkx/algorithms/shortest_paths/weighted.pyi:97: heuristic: bool = F alse, .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/net workx/networkx/algorithms/shortest_paths/weighted.pyi:144: heuristic: bool = True, .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/net workx/networkx/utils/rcm.pyi:4:def cuthill_mckee_ordering(G, heuristic: Incomple te | None = None) -> Generator[Incomplete, Incomplete, None]: ... .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/net workx/networkx/utils/rcm.pyi:5:def reverse_cuthill_mckee_ordering(G, heuristic: Incomplete | None = None): ... .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/pyw in32/_win32typing.pyi:1381: def srvheuristics(self) -> str: ... .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/pyw in32/_win32typing.pyi:1443: def srvheuristics(self) -> str: ... .venv/lib/python3.12/site-packages/pyright/dist/dist/typeshed-fallback/stubs/pyw in32/_win32typing.pyi:2183: def wrk_heuristics(self) -> str: ... .venv/lib/python3.12/site-packages/charset_normalizer/api.py:619: Based on th e same main heuristic algorithms and default kwargs at the sole exception that f allbacks match grep: .venv/bin/ruff: binary file matches

I'll check the README for data errors and time/start details before adding the new heuristic.

shell
root@141de59f5186:/workspace/scubaduck# grep -n "Invalid" README.md

I'll add a note about the server guessing the probable time unit for invalid inputs.

shell
root@141de59f5186:/workspace/scubaduck# sed -n '1,80p' README.md I really like Scuba (Meta's internal real-time database system). The distributed , real-time database part of Scuba is quite difficult (and expensive) to replica te, but I also really like Scuba's UI for doing queries, and I have found myself wishing that I have access to it even for "small" databases, e.g., I have a sql ite dataset I want to explore. Pivotal ideas: * Time series by default. In the dedicated "time series" view, there are many fe atures specifically oriented towards working towards tables that represent event s that occurred over time: the start, end, compare, aggregate and granularity fi elds all specially privilege the timestamp field. In fact, you can't log events to Scuba's backing data store without a timestamp, they always come with one. (S cuba also supports other views that don't presuppose a time series, but the time series is the most beloved and well used view.) This is in contrast to typical software which tries to generalize to arbitrary data first, with time series bei ng added on later. * It's all about exploration. Scuba is predicated on the idea that you don't kno w what you're looking for, that you are going to spend time tweaking queries and changing filters/grouping as part of an investigation to figure out why a syste m behaves the way it is. So the filters/comparisons/groupings you want to edit a re always visible on the left sidebar, with the expectation that you're going to tweak the query to look at something else. Similarly, all the parameters of you r query get saved into your URL, so your browser history can double up as a quer y history / you can easily share a query with someone else. This is contrast to typical software which is often oriented to making pretty dashboards and reports . (This function is important too, but it's not what I want in exploration mode! ) * You can fix data problems in the query editor. It's pretty common to have mess ed up and ended up with a database that doesn't have exactly the columns you nee d, or some columns that are corrupted in some way. Scuba has pretty robust suppo rt for defining custom columns with arbitrary SQL functions, grouping over them as if they were native functions, and doing so with minimal runtime cost (Scuba aims to turn around your query in milliseconds!) Having to go and run a huge dat a pipeline to fix your data is a big impediment to exploration; quick and easy c ustom columns means you can patch over problems when you're investigating and fi x them for real later. We're going to build a exploratory data analysis tool like Scuba for time series database (i.e., a database with a mandatory timestamp representing the time an event occurred). We'll use DuckDB as the underlying SQL engine served from a Py thon server, and render the GUI/results as a webpage with vanilla HTML and JS. W e'll use choices.js to support token inputs. We define a token input to mean a text input element where as you type a dropdown displays with valid values, and if you select one or press enter, the selection turns into a token/chip that can only be deleted as one unit. To start, we are going to support one views: samples. The samples view only all ows you to view individual samples from the database, subject to a filter. Our m ain UI concept is that there is a left sidebar that is the query editor, and the right side that shows the view. The sidebar is always visible and defaults to the query parameters of the current view. After you make changes to the query, clicking the "Dive" button updates the view. The URL of the page encodes all of the values of the query (and gets updated when you Dive), so the browser's back button lets you view previous queries. The query editor's job is to generate a SQL query, which then is applied on the database, and then the result visualized according to the view. Here are the settings you can apply to the query. The help text should show up w hen you mouse over the field name: * Start/End - Help text: "Sets the start/end of the time range to query. Can be any kind of datetime string. For example: 'April 23, 2014' or 'yesterday'." The UI for this selector supports both relative selections (now, -1 hour, -3 hours, -12 hours, -1 day, -3 days, -1 week, -1 fortnight, -30 days, -90 days, -1 month, -1 year) as well as specifying an absolute date. The way this field is rendere d is there is a free form text box, a drop down arrow (for the relative selector s), and then a calendar button (for date selection). * Order By - Help text: "Choose a column to sort results by." There is an ASC/D ESC toggle next to it. * Limit - Help text: "Choose the maximum number of results to show in the chart after any aggregations have been applied. For example, a limit of 10 will show no more than 10 rows for a table, etc." * Filters - You can create as many filters as you want. You can either write a f ilter using a UI or manual SQL. In the UI, filter consists of a column name, a r elation (e.g., =, !=, <, >) and then a text field. The text field is a token inp ut. It accepts multiple tokens for = relation, in which case we match using an O R for all options. There is also a "Columns" tab which lets you view all fields in the table, organ ized by their type. You can also define derived columns, by specifying a column name and SQL expression. Derived columns can be used for all parts of the UI, in cluding filters/group by/etc. Columns have checkboxes indicating if we should SE LECT them or not. Each selected column shows up in the graph. There is an All/N one link which can be used to select/deselect all checkboxes. The query UI constructs a SQL query that intuitively has this form: ``` SELECT column, column, ..., FROM table WHERE time >= min-timestamp AND time <= max-timestamp [AND condition ...] ORDER BY aggregate(column) LIMIT number ``` You should write tests for the server backend, demonstrating that at specific qu ery values we get back the correct rows of data. ## Running the server Activate the virtual environment and run the Flask development server: ```bash flask --app scubaduck.server run --debug ``` By default the server loads `sample.csv`. Set the `SCUBADUCK_DB` environment variable to point at a different database file (CSV, SQLite or DuckDB) if you want to use another dataset. The special value `TEST` starts the server with a small in-memory SQLite dataset used by the automated tests. If the file does not exist, the server will raise a `FileNotFoundError` during startup.

I'll add a test for invalid time values in test_server_errors.py using the existing format.

shell
root@141de59f5186:/workspace/scubaduck# cat pyproject.toml [project] name = "scubaduck" version = "0.1.0" description = "Scuba on DuckDB" readme = "README.md" requires-python = ">=3.12" license = {text = "MIT"} dependencies = [ "duckdb", "flask", "python-dateutil>=2.9.0.post0", ] [build-system] requires = ["hatchling"] build-backend = "hatchling.build" [tool.pytest.ini_options] addopts = "-n auto --tb=native" [tool.pyright] # Pyright configuration with strict settings include = ["scubaduck", "tests"] exclude = ["**/__pycache__", "dist"] stubPath = "stubs" venvPath = "." venv = ".venv" reportMissingImports = true reportMissingTypeStubs = true pythonVersion = "3.12" pythonPlatform = "All" typeCheckingMode = "strict" reportUnknownMemberType = true reportUnknownParameterType = true reportUnknownVariableType = true reportUnknownArgumentType = true reportPrivateImportUsage = true reportUntypedFunctionDecorator = true reportFunctionMemberAccess = true reportIncompatibleMethodOverride = true [tool.ruff] target-version = "py312" [tool.uv] dev-dependencies = [ "pytest>=8.3.5", "ruff>=0.11.2", "pytest-xdist>=3.6.1", "playwright>=1.52.0", "pyright>=1.1.350", "pytest-playwright>=0.7.0", ]

I'll check the tests in test_server_errors to match their style with mine.

shell
from __future__ import annotations import json from scubaduck import server def test_invalid_time_error() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "nonsense", "end": "now", "order_by": "timestamp", "limit": 10, "columns": ["timestamp"], "filters": [], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "error" in data def test_query_error_returns_sql_and_traceback() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "columns": ["event"], "group_by": ["user"], "aggregate": "avg", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "error" in data def test_table_unknown_column_error() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "graph_type": "table", "order_by": "timestamp", "limit": 100, "columns": ["user", "Hits", "value"], "group_by": ["user"], "aggregate": "Count", "show_hits": True,

I'll open the remaining sections to examine the style further.

shell
s.py "show_hits": True, } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) _data = rv.get_json() assert rv.status_code == 200 def test_samples_view_rejects_group_by() -> None: app = server.app client = app.test_client() payload = { "table": "events", "graph_type": "samples", "group_by": ["user"], "columns": ["timestamp"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "only valid" in data["error"]

I’m checking if the new test should go in test_server_time or test_server_errors.

shell
"table": "events", "time_column": "ts", "time_unit": "s", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["start"] == "2024-01-01 00:00:00" assert data["end"] == "2024-01-01 01:00:00" assert len(data["rows"]) == 2 def test_test_dataset_int32_time_us() -> None: app = server.create_app("TEST") client = app.test_client() payload = { "table": "events", "time_column": "ts", "time_unit": "us", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["start"] == "2024-01-01 00:00:00" assert data["end"] == "2024-01-01 01:00:00" assert len(data["rows"]) == 2

I’ll add the new test in test_server_errors.py and then review server.__init__ for specifics.

shell
root@141de59f5186:/workspace/scubaduck# ls scubaduck __init__.py sample.csv server.py static

I'll check init.py to review the exports and related details.

shell
root@141de59f5186:/workspace/scubaduck# cat scubaduck/__init__.py def hello() -> str: return "Hello from scubaduck!"

I'll open server.py and add the _suggest_unit function, integrating it with _numeric_to_datetime.

shell
dt = dtparser.parse(s) return dt.replace(microsecond=0, tzinfo=None).strftime("%Y-%m-%d %H:%M:%S") def _numeric_to_datetime(value: int | float, unit: str) -> datetime: """Convert a numeric timestamp ``value`` with unit ``unit`` to ``datetime``. Heuristically fall back to seconds when the converted value is before 1990 but the seconds interpretation is in a reasonable range. This handles integer columns stored in seconds even when ``unit`` is mistakenly set to a finer granularity. """ divisor = { "s": 1, "ms": 1000, "us": 1_000_000, "ns": 1_000_000_000, }.get(unit, 1) dt = datetime.fromtimestamp(int(value) / divisor, tz=timezone.utc) if unit != "s" and dt.year < 1990: alt = datetime.fromtimestamp(int(value), tz=timezone.utc) if alt.year >= 1990: dt = alt return dt def _granularity_seconds(granularity: str, start: str | None, end: str | None) - > int: gran = granularity.lower() mapping = { "1 second": 1, "5 seconds": 5, "10 seconds": 10, "30 seconds": 30, "1 minute": 60, "4 minutes": 240, "5 minutes": 300, "10 minutes": 600, "15 minutes": 900, "30 minutes": 1800,

I’ll add the function after _numeric_to_datetime for clarity, checking different time units.

shell
> from datetime import datetime > value = 13384414800000000 > for u, d in [('s', 1), ('ms', 1000), ('us', 1_000_000), ('ns', 1_000_000_000)] : > try: > dt = datetime.utcfromtimestamp(value / d) > print(u, dt) > except Exception as e: > print(u, 'error') > EOF s error ms error us 2394-02-19 05:00:00 ns 1970-06-04 21:53:34.800000

I'll add the test, handle positive values, and run format, check, and tests after.

shell
dt = dtparser.parse(s) return dt.replace(microsecond=0, tzinfo=None).strftime("%Y-%m-%d %H:%M:%S") def _numeric_to_datetime(value: int | float, unit: str) -> datetime: """Convert a numeric timestamp ``value`` with unit ``unit`` to ``datetime``. Heuristically fall back to seconds when the converted value is before 1990 but the seconds interpretation is in a reasonable range. This handles integer columns stored in seconds even when ``unit`` is mistakenly set to a finer granularity. """ divisor = { "s": 1, "ms": 1000, "us": 1_000_000, "ns": 1_000_000_000, }.get(unit, 1) dt = datetime.fromtimestamp(int(value) / divisor, tz=timezone.utc) if unit != "s" and dt.year < 1990: alt = datetime.fromtimestamp(int(value), tz=timezone.utc) if alt.year >= 1990: dt = alt return dt def _granularity_seconds(granularity: str, start: str | None, end: str | None) - > int: gran = granularity.lower() mapping = { "1 second": 1, "5 seconds": 5, "10 seconds": 10, "30 seconds": 30, "1 minute": 60, "4 minutes": 240, "5 minutes": 300, "10 minutes": 600, "15 minutes": 900, "30 minutes": 1800, "1 hour": 3600, "3 hours": 10800, "6 hours": 21600, "1 day": 86400, "1 week": 604800, "30 days": 2592000,

I’ll add the _suggest_time_unit function right after _numeric_to_datetime.

shell
> *** Begin Patch > *** Update File: scubaduck/server.py > @@ > def _numeric_to_datetime(value: int | float, unit: str) -> datetime: > @@ > if unit != "s" and dt.year < 1990: > alt = datetime.fromtimestamp(int(value), tz=timezone.utc) > if alt.year >= 1990: > dt = alt > return dt > + > + > +def _suggest_time_unit(value: int | float, given: str) -> str | None: > + """Return a plausible time unit for ``value`` not equal to ``given``.""" > + > + for unit in ("s", "ms", "us", "ns"): > + if unit == given: > + continue > + try: > + dt = _numeric_to_datetime(value, unit) > + except Exception: > + continue > + if 1990 <= dt.year <= 2500: > + return unit > + return None > *** End Patch > PATCH Success. Updated the following files: M scubaduck/server.py

I'll include suggestions in the error messages to make them more helpful.

shell
} ), 400, ) if (params.start is None or params.end is None) and ( params.x_axis or params.time_column ): axis = params.x_axis or params.time_column assert axis is not None row = cast( tuple[datetime | None, datetime | None], con.execute( f'SELECT min({_quote(axis)}), max({_quote(axis)}) FROM "{par ams.table}"' ).fetchall()[0], ) mn, mx = row if isinstance(mn, (int, float)): try: mn = _numeric_to_datetime(mn, params.time_unit) except Exception: return ( jsonify( { "error": ( f"Invalid time value {mn} for column {axis}" f" with time_unit {params.time_unit}" ) } ),

I'll improve the error message to include a helpful suggestion. Time to patch it!

shell
root@141de59f5186:/workspace/scubaduck# apply_patch<<'PATCH' > *** Begin Patch > *** Update File: scubaduck/server.py > @@ > if isinstance(mn, (int, float)): > try: > mn = _numeric_to_datetime(mn, params.time_unit) > except Exception: > - return ( > - jsonify( > - { > - "error": ( > - f"Invalid time value {mn} for column {axi s}" > - f" with time_unit {params.time_unit}" > - ) > - } > - ), > - 400, > - ) > + suggestion = _suggest_time_unit(mn, params.time_unit) > + msg = ( > + f"Invalid time value {mn} for column {axis} with time _unit {params.time_unit}" > + ) > + if suggestion: > + msg += f"; maybe try time_unit {suggestion}" > + return jsonify({"error": msg}), 400 > if isinstance(mx, (int, float)): > try: > mx = _numeric_to_datetime(mx, params.time_unit) > except Exception: > - return ( > - jsonify( > - { > - "error": ( > - f"Invalid time value {mx} for column {axi s}" > - f" with time_unit {params.time_unit}" > - ) > - } > - ), > - 400, > - ) > + suggestion = _suggest_time_unit(mx, params.time_unit) > + msg = ( > + f"Invalid time value {mx} for column {axis} with time _unit {params.time_unit}" > + ) > + if suggestion: > + msg += f"; maybe try time_unit {suggestion}" > + return jsonify({"error": msg}), 400 > *** End Patch > PATCH Success. Updated the following files: M scubaduck/server.py

I need to activate the environment before running ruff format.

shell
root@141de59f5186:/workspace/scubaduck# source .venv/bin/activate (scubaduck) root@141de59f5186:/workspace/scubaduck# ruff format scubaduck/server .py tests/test_server_errors.py 1 file reformatted, 1 file left unchanged (scubaduck) root@141de59f5186:/workspace/scubaduck#

I’ll add a new test to tests/test_server_errors.py and open the file to verify.

shell
import json from scubaduck import server def test_invalid_time_error() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "nonsense", "end": "now", "order_by": "timestamp", "limit": 10, "columns": ["timestamp"], "filters": [], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "error" in data def test_query_error_returns_sql_and_traceback() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "columns": ["event"], "group_by": ["user"], "aggregate": "avg", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "error" in data def test_table_unknown_column_error() -> None: app = server.app client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "graph_type": "table", "order_by": "timestamp", "limit": 100, "columns": ["user", "Hits", "value"], "group_by": ["user"], "aggregate": "Count", "show_hits": True, } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) _data = rv.get_json() assert rv.status_code == 200 def test_samples_view_rejects_group_by() -> None: app = server.app client = app.test_client() payload = { "table": "events", "graph_type": "samples", "group_by": ["user"], "columns": ["timestamp"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "only valid" in data["error"] (scubaduck) root@141de59f5186:/workspace/scubaduck#

The file's around 100 lines. I’ll add the new test and update it.

shell
> *** Begin Patch > *** Update File: tests/test_server_errors.py > @@ > def test_samples_view_rejects_group_by() -> None: > @@ > assert page.is_hidden("#time_unit") > *** End Patch > PATCH Failed to find expected lines [" assert page.is_hidden(\"#time_unit\")"] in t ests/test_server_errors.py (scubaduck) root@141de59f5186:/workspace/scubaduck#

I made a mistake; I’ll open the file at the bottom to fix it.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# tail -n 20 tests/test_server _errors.py ) _data = rv.get_json() assert rv.status_code == 200 def test_samples_view_rejects_group_by() -> None: app = server.app client = app.test_client() payload = { "table": "events", "graph_type": "samples", "group_by": ["user"], "columns": ["timestamp"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 400 assert "only valid" in data["error"] (scubaduck) root@141de59f5186:/workspace/scubaduck#

I'll add a new test at the end of this file. Time to patch!

shell
> *** Begin Patch > *** Update File: tests/test_server_errors.py > @@ > def test_samples_view_rejects_group_by() -> None: > @@ > assert "only valid" in data["error"] > + > + > +def test_invalid_integer_time_value_suggests_unit(tmp_path: Path) -> None: > + csv_file = tmp_path / "events.csv" > + csv_file.write_text("created,event\n1704067200000000,login\n") > + app = server.create_app(csv_file) > + client = app.test_client() > + payload = { > + "table": "events", > + "columns": ["created"], > + "time_column": "created", > + "time_unit": "s", > + } > + rv = client.post( > + "/api/query", data=json.dumps(payload), content_type="application/jso n" > + ) > + data = rv.get_json() > + assert rv.status_code == 400 > + assert "maybe try time_unit us" in data["error"] > *** End Patch > PATCH Success. Updated the following files: M tests/test_server_errors.py (scubaduck) root@141de59f5186:/workspace/scubaduck#

I need to import Path from pathlib at the top of the file. Let's do that.

shell
rver_errors.py from __future__ import annotations import json from scubaduck import server def test_invalid_time_error() -> None: app = server.app client = app.test_client() (scubaduck) root@141de59f5186:/workspace/scubaduck#

The file’s truncated; I'll open the first 10 lines to inspect the content.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# head -n 10 tests/test_server _errors.py from __future__ import annotations import json from scubaduck import server def test_invalid_time_error() -> None: app = server.app client = app.test_client() (scubaduck) root@141de59f5186:/workspace/scubaduck#

I'll add 'from pathlib import Path' after 'import json' and patch the new test.

shell
> *** Begin Patch > *** Update File: tests/test_server_errors.py > @@ > -from scubaduck import server > +from pathlib import Path > + > +from scubaduck import server > *** End Patch > PATCH Success. Updated the following files: M tests/test_server_errors.py (scubaduck) root@141de59f5186:/workspace/scubaduck#

I'll format the tests and server files using ruff.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# ruff format scubaduck/server .py tests/test_server_errors.py 2 files left unchanged
(scubaduck) root@141de59f5186:/workspace/scubaduck# ruff check scubaduck/server. py tests/test_server_errors.py All checks passed!
tests/test_server_errors.py > /tmp/pyright.log && tail -n 20 /tmp/pyright.log
0 errors, 0 warnings, 0 informations (scubaduck) root@141de59f5186:/workspace/scubaduck#

I'm going to go ahead and run the tests now.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# pytest -q tests/test_server_ errors.py::test_invalid_integer_time_value_suggests_unit > /tmp/pytest.log && ta il -n 20 /tmp/pytest.log

bringing up nodes... bringing up nodes... . [100%] 1 passed in 0.99s
(scubaduck) root@141de59f5186:/workspace/scubaduck# pytest -q > /tmp/pytest.log && tail -n 20 /tmp/pytest.log











bringing up nodes... bringing up nodes... ........................................................................ [ 58%] .................................................... [100%] 124 passed in 18.83s (scubaduck) root@141de59f5186:/workspace/scubaduck#

All tests pass, now let's check the git status to see my changes.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# git status --short M scubaduck/server.py M tests/test_server_errors.py
(scubaduck) root@141de59f5186:/workspace/scubaduck# git add scubaduck/server.py tests/test_server_errors.py
(scubaduck) root@141de59f5186:/workspace/scubaduck# git commit -m "feat(server): suggest time unit in errors" && git status --short [work a79afa1] feat(server): suggest time unit in errors 2 files changed, 46 insertions(+), 22 deletions(-) (scubaduck) root@141de59f5186:/workspace/scubaduck#

I’ll check the git status to confirm the current state of the repository.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# git status --short (scubaduck) root@141de59f5186:/workspace/scubaduck#

I'll summarize the changes and include relevant lines from server.py with the updates.

shell
140 return dt.replace(microsecond=0, tzinfo=None).strftime("%Y-%m-%d %H: %M:%S") 141 142 143 def _numeric_to_datetime(value: int | float, unit: str) -> datetime: 144 """Convert a numeric timestamp ``value`` with unit ``unit`` to ``dat etime``. 145 146 Heuristically fall back to seconds when the converted value is befor e 1990 147 but the seconds interpretation is in a reasonable range. This handl es 148 integer columns stored in seconds even when ``unit`` is mistakenly s et to a 149 finer granularity. 150 """ 151 152 divisor = { 153 "s": 1, 154 "ms": 1000, 155 "us": 1_000_000, 156 "ns": 1_000_000_000, 157 }.get(unit, 1) 158 159 dt = datetime.fromtimestamp(int(value) / divisor, tz=timezone.utc) 160 if unit != "s" and dt.year < 1990: 161 alt = datetime.fromtimestamp(int(value), tz=timezone.utc) 162 if alt.year >= 1990: 163 dt = alt 164 return dt 165 166 167 def _suggest_time_unit(value: int | float, given: str) -> str | None: 168 """Return a plausible time unit for ``value`` not equal to ``given`` .""" 169 170 for unit in ("s", "ms", "us", "ns"): 171 if unit == given: 172 continue 173 try: 174 dt = _numeric_to_datetime(value, unit) 175 except Exception: 176 continue 177 if 1990 <= dt.year <= 2500: 178 return unit 179 return None 180 181 182 def _granularity_seconds(granularity: str, start: str | None, end: str | None) -> int: 183 gran = granularity.lower()
(scubaduck) root@141de59f5186:/workspace/scubaduck# nl -ba scubaduck/server.py | sed -n '660,707p' 660 suggestion = _suggest_time_unit(mn, params.time_unit ) 661 msg = f"Invalid time value {mn} for column {axis} wi th time_unit {params.time_unit}" 662 if suggestion: 663 msg += f"; maybe try time_unit {suggestion}" 664 return jsonify({"error": msg}), 400 665 if isinstance(mx, (int, float)): 666 try: 667 mx = _numeric_to_datetime(mx, params.time_unit) 668 except Exception: 669 suggestion = _suggest_time_unit(mx, params.time_unit ) 670 msg = f"Invalid time value {mx} for column {axis} wi th time_unit {params.time_unit}" 671 if suggestion: 672 msg += f"; maybe try time_unit {suggestion}" 673 return jsonify({"error": msg}), 400 674 if params.start is None and mn is not None: 675 params.start = ( 676 mn.strftime("%Y-%m-%d %H:%M:%S") if not isinstance(m n, str) else mn 677 ) 678 if params.end is None and mx is not None: 679 params.end = ( 680 mx.strftime("%Y-%m-%d %H:%M:%S") if not isinstance(m x, str) else mx 681 ) 682 683 bucket_size: int | None = None 684 if params.graph_type == "timeseries": 685 bucket_size = _granularity_seconds( 686 params.granularity, 687 params.start if isinstance(params.start, str) else None, 688 params.end if isinstance(params.end, str) else None, 689 ) 690 if ( 691 params.limit is not None 692 and params.start is not None 693 and params.end is not None 694 ): 695 try: 696 start_dt = dtparser.parse(params.start) 697 end_dt = dtparser.parse(params.end) 698 buckets = math.ceil( 699 (end_dt - start_dt).total_seconds() / bucket_siz e 700 ) 701 if buckets > 1: 702 params.limit *= buckets 703 except Exception: 704 pass 705 706 sql = build_query(params, column_types) 707 try:
(scubaduck) root@141de59f5186:/workspace/scubaduck# nl -ba tests/test_server_err ors.py | sed -n '70,120p' 70 71 def test_samples_view_rejects_group_by() -> None: 72 app = server.app 73 client = app.test_client() 74 payload = { 75 "table": "events", 76 "graph_type": "samples", 77 "group_by": ["user"], 78 "columns": ["timestamp"], 79 } 80 rv = client.post( 81 "/api/query", data=json.dumps(payload), content_type="applicatio n/json" 82 ) 83 data = rv.get_json() 84 assert rv.status_code == 400 85 assert "only valid" in data["error"] 86 87 88 def test_invalid_integer_time_value_suggests_unit(tmp_path: Path) -> Non e: 89 csv_file = tmp_path / "events.csv" 90 csv_file.write_text("created,event\n1704067200000000,login\n") 91 app = server.create_app(csv_file) 92 client = app.test_client() 93 payload = { 94 "table": "events", 95 "columns": ["created"], 96 "time_column": "created", 97 "time_unit": "s", 98 } 99 rv = client.post( 100 "/api/query", data=json.dumps(payload), content_type="applicatio n/json" 101 ) 102 data = rv.get_json() 103 assert rv.status_code == 400 104 assert "maybe try time_unit us" in data["error"] (scubaduck) root@141de59f5186:/workspace/scubaduck#

I'll grab the lines near the beginning to highlight the import change.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# nl -ba tests/test_server_err ors.py | sed -n '1,10p' 1 from __future__ import annotations 2 3 import json 4 5 from pathlib import Path 6 7 from scubaduck import server 8 9 10 def test_invalid_time_error() -> None: (scubaduck) root@141de59f5186:/workspace/scubaduck#

I’ll summarize the PR body and make sure the commit is correct before finalizing it.

shell
(scubaduck) root@141de59f5186:/workspace/scubaduck# git log -1 --stat commit a79afa1ec0163f97df24e2f1a9b501eba95cc07b (HEAD -> work) Author: Codex <codex@openai.com> Date: Fri May 23 02:44:42 2025 +0000 feat(server): suggest time unit in errors scubaduck/server.py | 47 ++++++++++++++++++++++++--------------------- tests/test_server_errors.py | 21 ++++++++++++++++++++ 2 files changed, 46 insertions(+), 22 deletions(-) (scubaduck) root@141de59f5186:/workspace/scubaduck#

I'll summarize the feature and reference the relevant lines for the final PR.