Codex Task Logs

Task ID: task_e_682fd50fbab4832389420699355efed1

Environment setup
Configuring language runtimes... + rm -- /tmp/FHwm68-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 playwright (43.1MiB) Downloading ruff (11.0MiB) Downloading duckdb (19.3MiB) Downloading pyright (5.3MiB) Downloading ruff Built scubaduck @ file:///workspace/scubaduck Downloading duckdb Downloading playwright Downloading pyright Prepared 33 packages in 2.58s Installed 33 packages in 104ms + 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/FHwm68-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% 29.7s167.7 MiB [] 0% 29.1s167.7 MiB [] 0% 20.7s167.7 MiB [] 0% 13.2s167.7 MiB [] 0% 9.9s167.7 MiB [] 1% 6.7s167.7 MiB [] 2% 5.5s167.7 MiB [] 2% 4.3s167.7 MiB [] 3% 3.6s167.7 MiB [] 4% 3.1s167.7 MiB [] 5% 3.0s167.7 MiB [] 6% 3.0s167.7 MiB [] 6% 2.9s167.7 MiB [] 7% 2.6s167.7 MiB [] 9% 2.4s167.7 MiB [] 10% 2.3s167.7 MiB [] 11% 2.2s167.7 MiB [] 12% 2.1s167.7 MiB [] 13% 2.0s167.7 MiB [] 15% 1.9s167.7 MiB [] 17% 1.8s167.7 MiB [] 19% 1.6s167.7 MiB [] 20% 1.6s167.7 MiB [] 22% 1.5s167.7 MiB [] 23% 1.4s167.7 MiB [] 24% 1.4s167.7 MiB [] 26% 1.3s167.7 MiB [] 27% 1.3s167.7 MiB [] 28% 1.2s167.7 MiB [] 30% 1.2s167.7 MiB [] 32% 1.1s167.7 MiB [] 34% 1.1s167.7 MiB [] 36% 1.0s167.7 MiB [] 37% 1.0s167.7 MiB [] 39% 0.9s167.7 MiB [] 40% 0.9s167.7 MiB [] 42% 0.8s167.7 MiB [] 44% 0.8s167.7 MiB [] 46% 0.8s167.7 MiB [] 47% 0.8s167.7 MiB [] 48% 0.7s167.7 MiB [] 49% 0.7s167.7 MiB [] 51% 0.7s167.7 MiB [] 52% 0.7s167.7 MiB [] 54% 0.6s167.7 MiB [] 55% 0.6s167.7 MiB [] 57% 0.6s167.7 MiB [] 58% 0.6s167.7 MiB [] 59% 0.6s167.7 MiB [] 61% 0.5s167.7 MiB [] 63% 0.5s167.7 MiB [] 64% 0.5s167.7 MiB [] 65% 0.5s167.7 MiB [] 66% 0.4s167.7 MiB [] 68% 0.4s167.7 MiB [] 69% 0.4s167.7 MiB [] 70% 0.4s167.7 MiB [] 72% 0.4s167.7 MiB [] 73% 0.4s167.7 MiB [] 74% 0.3s167.7 MiB [] 75% 0.3s167.7 MiB [] 77% 0.3s167.7 MiB [] 79% 0.3s167.7 MiB [] 80% 0.3s167.7 MiB [] 82% 0.2s167.7 MiB [] 84% 0.2s167.7 MiB [] 85% 0.2s167.7 MiB [] 87% 0.2s167.7 MiB [] 89% 0.1s167.7 MiB [] 90% 0.1s167.7 MiB [] 92% 0.1s167.7 MiB [] 94% 0.1s167.7 MiB [] 95% 0.1s167.7 MiB [] 97% 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 [] 3% 0.5s2.3 MiB [] 8% 0.4s2.3 MiB [] 17% 0.2s2.3 MiB [] 36% 0.1s2.3 MiB [] 52% 0.1s2.3 MiB [] 99% 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% 21.7s101.4 MiB [] 0% 21.2s101.4 MiB [] 0% 14.3s101.4 MiB [] 0% 9.6s101.4 MiB [] 1% 5.6s101.4 MiB [] 2% 4.2s101.4 MiB [] 2% 3.9s101.4 MiB [] 3% 3.4s101.4 MiB [] 4% 3.2s101.4 MiB [] 6% 2.4s101.4 MiB [] 8% 1.9s101.4 MiB [] 10% 1.7s101.4 MiB [] 11% 1.6s101.4 MiB [] 12% 1.6s101.4 MiB [] 14% 1.4s101.4 MiB [] 16% 1.4s101.4 MiB [] 17% 1.4s101.4 MiB [] 19% 1.2s101.4 MiB [] 22% 1.1s101.4 MiB [] 24% 1.0s101.4 MiB [] 26% 0.9s101.4 MiB [] 28% 0.9s101.4 MiB [] 31% 0.8s101.4 MiB [] 33% 0.8s101.4 MiB [] 36% 0.7s101.4 MiB [] 37% 0.7s101.4 MiB [] 39% 0.7s101.4 MiB [] 42% 0.6s101.4 MiB [] 44% 0.6s101.4 MiB [] 46% 0.6s101.4 MiB [] 49% 0.5s101.4 MiB [] 53% 0.5s101.4 MiB [] 57% 0.4s101.4 MiB [] 60% 0.4s101.4 MiB [] 65% 0.3s101.4 MiB [] 68% 0.3s101.4 MiB [] 71% 0.2s101.4 MiB [] 74% 0.2s101.4 MiB [] 76% 0.2s101.4 MiB [] 79% 0.2s101.4 MiB [] 82% 0.1s101.4 MiB [] 84% 0.1s101.4 MiB [] 86% 0.1s101.4 MiB [] 88% 0.1s101.4 MiB [] 90% 0.1s101.4 MiB [] 91% 0.1s101.4 MiB [] 94% 0.0s101.4 MiB [] 97% 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'
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Bug: Aggregate avg cannot be applied to column flag Repro: Use TEST db. Events table, table view, start = -100000 day, end = now, group by id, aggregate avg. It fails with Aggregate avg cannot be applied to column flag. But a bool column should be treated as 0/1 integer when computing averages. Don't overzealously error out here, do the conversion!

I’ll start by opening the AGENTS.md to see if there are any useful instructions.

shell
ls bash: .venv/bin/activate: No such file or directory root@17549eec6c89:/workspace# ls scubaduck
root@17549eec6c89:/workspace# cd scubaduck root@17549eec6c89:/workspace/scubaduck# ls AGENTS.md codex_setup.sh scubaduck tests README.md pyproject.toml stubs uv.lock
root@17549eec6c89:/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

I'll check the repo layout and confirm where to add tests based on the instructions.

shell
root@17549eec6c89:/workspace/scubaduck# ls -R .: AGENTS.md codex_setup.sh scubaduck tests README.md pyproject.toml stubs uv.lock ./scubaduck: __init__.py sample.csv server.py static ./scubaduck/static: index.html js ./scubaduck/static/js: chip_input.js table_view.js timeseries_chart.js sidebar.js time_column.js view_settings.js ./stubs: duckdb ./stubs/duckdb: __init__.pyi ./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 open the README file to get some context on the project.

shell
root@17549eec6c89:/workspace/scubaduck# cat README.md | head -n 20 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) as well a s specifying an absolute date. The way this field is rendered is there is a fre e form text box, a drop down arrow (for the relative selectors), and then a cale ndar 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.

I’ll scroll a bit more to see if there’s any mention of aggregator.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "aggregate" -n README.md | head 5:* Time series by default. In the dedicated "time series" view, there are many features specifically oriented towards working towards tables that represent eve nts that occurred over time: the start, end, compare, aggregate and granularity fields all specially privilege the timestamp field. In fact, you can't log event s to Scuba's backing data store without a timestamp, they always come with one. (Scuba also supports other views that don't presuppose a time series, but the ti me series is the most beloved and well used view.) This is in contrast to typica l software which tries to generalize to arbitrary data first, with time series b eing added on later. 34:ORDER BY aggregate(column)
root@17549eec6c89:/workspace/scubaduck# sed -n '30,80p' README.md 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 need to look for the aggregator or server code next.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "avg" -R scubaduck | head scubaduck/server.py:245: if agg == "avg" and column_types is not None : scubaduck/server.py:249: f"CAST(avg(epoch({_quote(col)})) AS BIGINT)" scubaduck/server.py:534: elif agg == "avg" or agg in {"min", "max"}:
root@17549eec6c89:/workspace/scubaduck# sed -n '200,260p' scubaduck/server.py return qcol def build_query(params: QueryParams, column_types: Dict[str, str] | None = None) -> str: select_parts: list[str] = [] group_cols = params.group_by[:] selected_for_order = set(params.columns) | set(params.derived_columns.keys() ) if params.graph_type == "timeseries": sec = _granularity_seconds(params.granularity, params.start, params.end) x_axis = params.x_axis or params.time_column if x_axis is None: raise ValueError("x_axis required for timeseries") xexpr = _time_expr(x_axis, column_types, params.time_unit) if params.start: bucket_expr = ( f"TIMESTAMP '{params.start}' + INTERVAL '{sec} second' * " f"CAST(floor((epoch({xexpr}) - epoch(TIMESTAMP '{params.start}') )/{sec}) AS BIGINT)" ) else: bucket_expr = ( f"TIMESTAMP 'epoch' + INTERVAL '{sec} second' * " f"CAST(floor(epoch({xexpr})/{sec}) AS BIGINT)" ) select_parts.append(f"{bucket_expr} AS bucket") group_cols = ["bucket"] + group_cols selected_for_order.add("bucket") has_agg = bool(group_cols) or params.aggregate is not None if has_agg: select_cols = ( group_cols[1:] if params.graph_type == "timeseries" else group_cols ) select_parts.extend(_quote(c) for c in select_cols) agg = (params.aggregate or "count").lower() selected_for_order.update(group_cols) def agg_expr(col: str) -> str: expr = _quote(col) ctype = column_types.get(col, "").upper() if column_types else "" if "BOOL" in ctype: expr = f"CAST({_quote(col)} AS BIGINT)" if agg.startswith("p"): quant = float(agg[1:]) / 100 return f"quantile({expr}, {quant})" if agg == "count distinct": return f"count(DISTINCT {expr})" if agg == "avg" and column_types is not None: if "TIMESTAMP" in ctype or "DATE" in ctype or "TIME" in ctype: return ( "TIMESTAMP 'epoch' + INTERVAL '1 second' * " f"CAST(avg(epoch({_quote(col)})) AS BIGINT)" ) return f"{agg}({expr})" if agg == "count": select_parts.append("count(*) AS Count") selected_for_order.add("Count") else: for col in params.columns: if col in group_cols: continue select_parts.append(f"{agg_expr(col)} AS {_quote(col)}")

I need to check more lines to find the second piece of avg-related code.

shell
root@17549eec6c89:/workspace/scubaduck# sed -n '260,560p' scubaduck/server.py select_parts.append(f"{agg_expr(col)} AS {_quote(col)}") selected_for_order.add(col) if params.show_hits: select_parts.insert(len(group_cols), "count(*) AS Hits") selected_for_order.add("Hits") else: select_parts.extend(_quote(c) for c in params.columns) selected_for_order.update(params.columns) order_by = params.order_by if params.order_by in selected_for_order else Non e if has_agg and params.derived_columns: inner_params = replace( params, derived_columns={}, order_by=None, limit=None, ) inner_sql = build_query(inner_params, column_types) outer_select = ["t.*"] + [ f"{expr} AS {name}" for name, expr in params.derived_columns.items() ] indented_inner = "\n".join(" " + line for line in inner_sql.splitline s()) lines = [ f"SELECT {', '.join(outer_select)}", "FROM (", indented_inner, ") t", ] if order_by: lines.append(f"ORDER BY {_quote(order_by)} {params.order_dir}") elif params.graph_type == "timeseries": lines.append("ORDER BY bucket") if params.limit is not None: lines.append(f"LIMIT {params.limit}") return "\n".join(lines) for name, expr in params.derived_columns.items(): select_parts.append(f"{expr} AS {name}") selected_for_order.add(name) select_clause = ", ".join(select_parts) if select_parts else "*" lines = [f"SELECT {select_clause}", f'FROM "{params.table}"'] where_parts: list[str] = [] if params.time_column: time_expr = _time_expr(params.time_column, column_types, params.time_uni t) else: time_expr = None if time_expr and params.start: where_parts.append(f"{time_expr} >= '{params.start}'") if time_expr and params.end: where_parts.append(f"{time_expr} <= '{params.end}'") for f in params.filters: op = f.op if op in {"empty", "!empty"}: val = "''" else: if f.value is None: continue if isinstance(f.value, list): if not f.value: continue if op == "=": qcol = _quote(f.column) vals = " OR ".join(f"{qcol} = '{v}'" for v in f.value) where_parts.append(f"({vals})") continue val = f"'{f.value}'" if isinstance(f.value, str) else str(f.value) qcol = _quote(f.column) if op == "contains": where_parts.append(f"{qcol} ILIKE '%' || {val} || '%'") elif op == "!contains": where_parts.append(f"{qcol} NOT ILIKE '%' || {val} || '%'") elif op == "empty": where_parts.append(f"{qcol} = {val}") elif op == "!empty": where_parts.append(f"{qcol} != {val}") else: where_parts.append(f"{qcol} {op} {val}") if where_parts: lines.append("WHERE " + " AND ".join(where_parts)) if group_cols: lines.append("GROUP BY " + ", ".join(_quote(c) for c in group_cols)) if order_by: lines.append(f"ORDER BY {_quote(order_by)} {params.order_dir}") elif params.graph_type == "timeseries": lines.append("ORDER BY bucket") if params.limit is not None: lines.append(f"LIMIT {params.limit}") return "\n".join(lines) def create_app(db_file: str | Path | None = None) -> Flask: app = Flask(__name__, static_folder="static") if db_file is None: env_db = os.environ.get("SCUBADUCK_DB") if env_db: db_file = env_db if isinstance(db_file, str) and db_file.upper() == "TEST": con = _create_test_database() else: db_path = Path(db_file or Path(__file__).with_name("sample.csv")).resolv e() con = _load_database(db_path) tables = [r[0] for r in con.execute("SHOW TABLES").fetchall()] if not tables: raise ValueError("No tables found in database") default_table = tables[0] columns_cache: Dict[str, Dict[str, str]] = {} def get_columns(table: str) -> Dict[str, str]: if table not in columns_cache: rows = con.execute(f'PRAGMA table_info("{table}")').fetchall() if not rows: raise ValueError(f"Unknown table: {table}") columns_cache[table] = {r[1]: r[2] for r in rows} return columns_cache[table] sample_cache: Dict[Tuple[str, str, str], Tuple[List[str], float]] = {} CACHE_TTL = 60.0 ... values = [r[0] for r in rows] _cache_set(key, values) return jsonify(values) @app.route("/api/query", methods=["POST"]) def query() -> Any: # pyright: ignore[reportUnusedFunction] payload = request.get_json(force=True) try: start = parse_time(payload.get("start")) end = parse_time(payload.get("end")) except Exception as exc: return jsonify({"error": str(exc)}), 400 params = QueryParams( start=start, end=end, order_by=payload.get("order_by"), order_dir=payload.get("order_dir", "ASC"), limit=payload.get("limit"), columns=payload.get("columns", []), derived_columns=payload.get("derived_columns", {}), graph_type=payload.get("graph_type", "samples"), group_by=payload.get("group_by", []), aggregate=payload.get("aggregate"), show_hits=payload.get("show_hits", False), x_axis=payload.get("x_axis"), granularity=payload.get("granularity", "Auto"), fill=payload.get("fill", "0"), table=payload.get("table", default_table), time_column=payload.get("time_column", "timestamp"), time_unit=payload.get("time_unit", "s"), ) 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 ): return ( jsonify( { "error": "group_by, aggregate and show_hits are only val id for table or timeseries view" } ), 400, ) valid_cols = set(column_types.keys()) valid_cols.update(params.derived_columns.keys()) if params.graph_type == "timeseries": if params.x_axis is None: params.x_axis = params.time_column if params.x_axis is None or params.x_axis not in valid_cols: return jsonify({"error": "Invalid x_axis"}), 400 ctype = column_types.get(params.x_axis, "").upper() is_time = any(t in ctype for t in ["TIMESTAMP", "DATE", "TIME"]) is_numeric = any( t in ctype for t in [ "INT", "DECIMAL", "REAL", "DOUBLE", "FLOAT", "NUMERIC", "HUGEINT", ] ) if not (is_time or is_numeric): return jsonify({"error": "x_axis must be a time column"}), 400 for col in params.columns: if col not in valid_cols: return jsonify({"error": f"Unknown column: {col}"}), 400 for col in params.group_by: if col not in valid_cols: return jsonify({"error": f"Unknown column: {col}"}), 400 if params.order_by and params.order_by not in valid_cols: return jsonify({"error": f"Unknown column: {params.order_by}"}), 400 if params.group_by or params.graph_type == "timeseries": agg = (params.aggregate or "count").lower() if agg.startswith("p") or agg == "sum": need_numeric = True allow_time = False elif agg == "avg" or agg in {"min", "max"}: need_numeric = False allow_time = True else: need_numeric = False allow_time = False if need_numeric or allow_time: for c in params.columns: if c in params.group_by or c == params.x_axis: continue if c not in column_types: continue ctype = column_types.get(c, "").upper() is_numeric = any( t in ctype 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:
root@17549eec6c89:/workspace/scubaduck# sed -n '560,660p' scubaduck/server.py 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 divisor = { "s": 1, "ms": 1000, "us": 1_000_000, "ns": 1_000_000_000, }.get(params.time_unit, 1) if isinstance(mn, (int, float)): try: mn = datetime.fromtimestamp(int(mn) / divisor, tz=timezone.u tc) 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 = datetime.fromtimestamp(int(mx) / divisor, tz=timezone.u tc) 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: 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()

I'm checking code near lines 245-250 in build_query for avg and time columns. The bug might be related to Bool columns being incorrectly handled in an aggregate function as avg. I'll track down code for validating bool column types and see if it's cast to 0/1.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "BOOL" -n scubaduck/server.py 85: "CREATE TABLE events (id INTEGER PRIMARY KEY, ts INTEGER, val REAL, n ame TEXT, flag BOOLEAN)" 238: if "BOOL" in ctype:

I’ll take a look at the area near line 230 for more details.

shell
root@17549eec6c89:/workspace/scubaduck# sed -n '230,250p' scubaduck/server.py ) select_parts.extend(_quote(c) for c in select_cols) agg = (params.aggregate or "count").lower() selected_for_order.update(group_cols) def agg_expr(col: str) -> str: expr = _quote(col) ctype = column_types.get(col, "").upper() if column_types else "" if "BOOL" in ctype: expr = f"CAST({_quote(col)} AS BIGINT)" if agg.startswith("p"): quant = float(agg[1:]) / 100 return f"quantile({expr}, {quant})" if agg == "count distinct": return f"count(DISTINCT {expr})" if agg == "avg" and column_types is not None: if "TIMESTAMP" in ctype or "DATE" in ctype or "TIME" in ctype: return ( "TIMESTAMP 'epoch' + INTERVAL '1 second' * " f"CAST(avg(epoch({_quote(col)})) AS BIGINT)" )

It seems the issue comes from the validation step, where booleans aren't treated as numeric. I'll adjust the checks to treat bool as numeric for aggregates. Let's take a closer look at lines 520-555 in server.py.

shell
root@17549eec6c89:/workspace/scubaduck# sed -n '520,560p' scubaduck/server.py for col in params.columns: if col not in valid_cols: return jsonify({"error": f"Unknown column: {col}"}), 400 for col in params.group_by: if col not in valid_cols: return jsonify({"error": f"Unknown column: {col}"}), 400 if params.order_by and params.order_by not in valid_cols: return jsonify({"error": f"Unknown column: {params.order_by}"}), 400 if params.group_by or params.graph_type == "timeseries": agg = (params.aggregate or "count").lower() if agg.startswith("p") or agg == "sum": need_numeric = True allow_time = False elif agg == "avg" or agg in {"min", "max"}: need_numeric = False allow_time = True else: need_numeric = False allow_time = False if need_numeric or allow_time: for c in params.columns: if c in params.group_by or c == params.x_axis: continue if c not in column_types: continue ctype = column_types.get(c, "").upper() is_numeric = any( t in ctype 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:
root@17549eec6c89:/workspace/scubaduck# sed -n '560,620p' scubaduck/server.py 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 divisor = { "s": 1, "ms": 1000, "us": 1_000_000, "ns": 1_000_000_000, }.get(params.time_unit, 1) if isinstance(mn, (int, float)): try: mn = datetime.fromtimestamp(int(mn) / divisor, tz=timezone.u tc) 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 = datetime.fromtimestamp(int(mx) / divisor, tz=timezone.u tc) except Exception: return ( jsonify( { "error": ( f"Invalid time value {mx} for column {axis}" f" with time_unit {params.time_unit}"

The aggregator fails on booleans. I'll search for "flag" in tests or dataset.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "flag" -R grep: .venv/lib64/python3.12/site-packages/markupsafe/_speedups.cpython-312-x86_ 64-linux-gnu.so: binary file matches .venv/lib64/python3.12/site-packages/werkzeug/sansio/http.py:108: flags=re.AS CII | re.VERBOSE, .venv/lib64/python3.12/site-packages/werkzeug/http.py:402:_parameter_key_re = re .compile(r"([\w!#$%&'*+\-.^`|~]+)=", flags=re.ASCII) .venv/lib64/python3.12/site-packages/werkzeug/http.py:403:_parameter_token_value _re = re.compile(r"[\w!#$%&'*+\-.^`|~]+", flags=re.ASCII) .venv/lib64/python3.12/site-packages/duckdb/query_graph/__main__.py:126:def open _utf8(fpath: str, flags: str) -> object: .venv/lib64/python3.12/site-packages/duckdb/query_graph/__main__.py:127: retu rn open(fpath, flags, encoding="utf8") grep: .venv/lib64/python3.12/site-packages/duckdb/duckdb.cpython-312-x86_64-linu x-gnu.so: binary file matches .venv/lib64/python3.12/site-packages/jinja2/environment.py:268: #: have a loo k at jinja2.sandbox. This flag alone controls the code .venv/lib64/python3.12/site-packages/jinja2/runtime.py:712: # time autoes cape flag. .venv/lib64/python3.12/site-packages/jinja2/ext.py:795: The `newstyle_gett ext` flag can be set to `True` to enable newstyle .venv/lib64/python3.12/site-packages/jinja2/debug.py:129: code.co_fla gs, .venv/lib64/python3.12/site-packages/jinja2/filters.py:256:_attr_key_re = re.com pile(r"[\s/>=]", flags=re.ASCII) .venv/lib64/python3.12/site-packages/charset_normalizer-3.4.2.dist-info/METADATA :158: flag with caution. .venv/lib64/python3.12/site-packages/requests-2.32.3.dist-info/METADATA:104:fetc h.fsck.badTimezone=ignore` flag to avoid an error about a bad commit (see .venv/lib64/python3.12/site-packages/click/parser.py:46:# Sentinel value that in dicates an option was passed as a flag without a .venv/lib64/python3.12/site-packages/click/parser.py:47:# value but is not a fla g option. Option.consume_value uses this to .venv/lib64/python3.12/site-packages/click/parser.py:48:# prompt or use the flag _value. .venv/lib64/python3.12/site-packages/click/parser.py:49:_flag_needs_value = obje ct() .venv/lib64/python3.12/site-packages/click/parser.py:437: if option.o bj._flag_needs_value: .venv/lib64/python3.12/site-packages/click/parser.py:439: value = _flag_needs_value .venv/lib64/python3.12/site-packages/click/parser.py:453: option. obj._flag_needs_value .venv/lib64/python3.12/site-packages/click/parser.py:460: value = _flag_needs_value .venv/lib64/python3.12/site-packages/click/termui.py:263: Added the `color ` flag. .venv/lib64/python3.12/site-packages/click/globals.py:55: """Internal helper to get the default value of the color flag. If a .venv/lib64/python3.12/site-packages/click/_winconsole.py:107: flags: int = PyBUF_WRITABLE if writable else PyBUF_SIMPLE .venv/lib64/python3.12/site-packages/click/_winconsole.py:108: PyObject_G etBuffer(py_object(obj), byref(buf), flags) .venv/lib64/python3.12/site-packages/click/decorators.py:396: kwargs.setdefau lt("is_flag", True) .venv/lib64/python3.12/site-packages/click/decorators.py:519: kwargs.setdefau lt("is_flag", True) .venv/lib64/python3.12/site-packages/click/decorators.py:545: kwargs.setdefau lt("is_flag", True) .venv/lib64/python3.12/site-packages/click/core.py:33:from .parser import _flag_ needs_value .venv/lib64/python3.12/site-packages/click/core.py:207: :param resilient_pars ing: if this flag is enabled then Click will .venv/lib64/python3.12/site-packages/click/core.py:328: #: This flag indi cates if a subcommand is going to be executed. A .venv/lib64/python3.12/site-packages/click/core.py:896: #: the default for th e :attr:`Context.allow_extra_args` flag. .venv/lib64/python3.12/site-packages/click/core.py:899: #: the default for th e :attr:`Context.allow_interspersed_args` flag. .venv/lib64/python3.12/site-packages/click/core.py:902: #: the default for th e :attr:`Context.ignore_unknown_options` flag. .venv/lib64/python3.12/site-packages/click/core.py:1993: argument. This is a list of flags or argument .venv/lib64/python3.12/site-packages/click/core.py:2476: For single optio n boolean flags, the default remains hidden if .venv/lib64/python3.12/site-packages/click/core.py:2489: prompted for inp ut only when the option was specified as a flag .venv/lib64/python3.12/site-packages/click/core.py:2493: :param is_flag: forc es this option to act as a flag. The default is .venv/lib64/python3.12/site-packages/click/core.py:2495: :param flag_value: w hich value should be used for this flag if it's .venv/lib64/python3.12/site-packages/click/core.py:2502: :param count: this f lag makes an option increment an integer. .venv/lib64/python3.12/site-packages/click/core.py:2512: ``envvar`` used with ``flag_value`` will always use the ``flag_value``, .venv/lib64/python3.12/site-packages/click/core.py:2524: The default of a single option boolean flag is not shown if the .venv/lib64/python3.12/site-packages/click/core.py:2528: ``type`` is dete cted from ``flag_value`` if given. .venv/lib64/python3.12/site-packages/click/core.py:2541: is_flag: bool | None = None, .venv/lib64/python3.12/site-packages/click/core.py:2542: flag_value: t.An y | None = None, .venv/lib64/python3.12/site-packages/click/core.py:2587: # used as a flag to indicate using prompt or flag_value. .venv/lib64/python3.12/site-packages/click/core.py:2588: self._flag_needs _value = self.prompt is not None and not self.prompt_required .venv/lib64/python3.12/site-packages/click/core.py:2590: if is_flag is No ne: .venv/lib64/python3.12/site-packages/click/core.py:2591: if flag_valu e is not None: .venv/lib64/python3.12/site-packages/click/core.py:2592: # Implic itly a flag because flag_value was set. .venv/lib64/python3.12/site-packages/click/core.py:2593: is_flag = True .venv/lib64/python3.12/site-packages/click/core.py:2594: elif self._f lag_needs_value: .venv/lib64/python3.12/site-packages/click/core.py:2595: # Not a flag, but when used as a flag it shows a prompt. .venv/lib64/python3.12/site-packages/click/core.py:2596: is_flag = False .venv/lib64/python3.12/site-packages/click/core.py:2598: # Implic itly a flag because flag options were given. .venv/lib64/python3.12/site-packages/click/core.py:2599: is_flag = bool(self.secondary_opts) .venv/lib64/python3.12/site-packages/click/core.py:2600: elif is_flag is False and not self._flag_needs_value: .venv/lib64/python3.12/site-packages/click/core.py:2601: # Not a flag , and prompt is not enabled, can be used as a .venv/lib64/python3.12/site-packages/click/core.py:2602: # flag if fl ag_value is set. .venv/lib64/python3.12/site-packages/click/core.py:2603: self._flag_n eeds_value = flag_value is not None .venv/lib64/python3.12/site-packages/click/core.py:2607: if is_flag and d ... and not self.is_bool_flag: .venv/lib64/python3.12/site-packages/click/core.py:2921: retu rn t.cast(Option, param).flag_value .venv/lib64/python3.12/site-packages/click/core.py:2938: # If this is a p rompt for a flag we need to handle this .venv/lib64/python3.12/site-packages/click/core.py:2940: if self.is_bool_ flag: .venv/lib64/python3.12/site-packages/click/core.py:2964: if self.is_f lag and self.flag_value: .venv/lib64/python3.12/site-packages/click/core.py:2965: return s tr(self.flag_value) .venv/lib64/python3.12/site-packages/click/core.py:3003: # given as a fla g without a value. This is different from None .venv/lib64/python3.12/site-packages/click/core.py:3004: # to distinguish from the flag not being given at all. .venv/lib64/python3.12/site-packages/click/core.py:3005: if value is _fla g_needs_value: .venv/lib64/python3.12/site-packages/click/core.py:3010: value = self.flag_value .venv/lib64/python3.12/site-packages/click/core.py:3016: and any(v is _flag_needs_value for v in value) .venv/lib64/python3.12/site-packages/click/core.py:3018: value = [sel f.flag_value if v is _flag_needs_value else v for v in value] .venv/lib64/python3.12/site-packages/click/shell_completion.py:235: safe_ name = re.sub(r"\W*", "", self.prog_name.replace("-", "_"), flags=re.ASCII) .venv/lib64/python3.12/site-packages/click/shell_completion.py:524: if param. is_flag or param.count: .venv/lib64/python3.12/site-packages/click/_termui_impl.py:440: # them by def ault we find the -R flag in the command line arguments. .venv/lib64/python3.12/site-packages/click/_termui_impl.py:442: less_flag s = f"{os.environ.get('LESS', '')}{' '.join(cmd_params)}" .venv/lib64/python3.12/site-packages/click/_termui_impl.py:443: if not le ss_flags: .venv/lib64/python3.12/site-packages/click/_termui_impl.py:446: elif "r" in less_flags or "R" in less_flags: .venv/lib64/python3.12/site-packages/click/_compat.py:87: The forcing of read able and writable flags are there because some tools .venv/lib64/python3.12/site-packages/click/_compat.py:382: # atomic flag. Use fsdecode to handle Path("-"). .venv/lib64/python3.12/site-packages/click/_compat.py:421: flags = os.O_RDWR | os.O_CREAT | os.O_EXCL .venv/lib64/python3.12/site-packages/click/_compat.py:424: flags |= getat tr(os, "O_BINARY", 0) .venv/lib64/python3.12/site-packages/click/_compat.py:432: fd = os.op en(tmp_filename, flags, 0o666 if perm is None else perm) .venv/lib64/python3.12/site-packages/click/types.py:722: The `lazy` flag cont rols if the file should be opened immediately or upon .venv/lib64/python3.12/site-packages/click/types.py:762: def resolve_lazy_fla g(self, value: str | os.PathLike[str]) -> bool: .venv/lib64/python3.12/site-packages/click/types.py:783: lazy = self. resolve_lazy_flag(value) .venv/lib64/python3.12/site-packages/click/types.py:1153:#: A boolean parameter. This is the default for boolean flags. This can .venv/lib64/python3.12/site-packages/click/formatting.py:53: :param preserve_ paragraphs: if this flag is set then the wrapping will .venv/lib64/python3.12/site-packages/click/formatting.py:286: any_prefix_is_s lash)`` where the second item in the tuple is a flag that .venv/lib64/python3.12/site-packages/requests/utils.py:167: "flag in the mode). This may lead to an incorrect " .venv/lib64/python3.12/site-packages/requests/utils.py:503: charset_re = re.c ompile(r'<meta.*?charset=["\']*(.+?)["\'>]', flags=re.I) .venv/lib64/python3.12/site-packages/requests/utils.py:504: pragma_re = re.co mpile(r'<meta.*?content=["\']*;?charset=(.+?)["\'>]', flags=re.I) .venv/lib64/python3.12/site-packages/requests/auth.py:262: pat = re.c ompile(r"digest ", flags=re.IGNORECASE) .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:164:mod_enable_option al_cleanup(PyObject* UNUSED(module), PyObject* flag) .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:166: int is_true = PyObject_IsTrue(flag); .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:212: .ml_flags=M ETH_NOARGS, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:218: .ml_flags=M ETH_VARARGS, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:224: .ml_flags=M ETH_NOARGS, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:230: .ml_flags=M ETH_VARARGS, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:236: .ml_flags=M ETH_NOARGS, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:242: .ml_flags=M ETH_NOARGS, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:248: .ml_flags=M ETH_NOARGS, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:254: .ml_flags=M ETH_O, .venv/lib64/python3.12/site-packages/greenlet/PyModule.cpp:261: .ml_flags=M ETH_NOARGS, .venv/lib64/python3.12/site-packages/greenlet/PyGreenletUnswitchable.cpp:132: .tp_flags=G_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */ .venv/lib64/python3.12/site-packages/greenlet/PyGreenlet.cpp:672: .ml_flags =METH_VARARGS | METH_KEYWORDS, .venv/lib64/python3.12/site-packages/greenlet/PyGreenlet.cpp:675: {.ml_name=" throw", .ml_meth=(PyCFunction)green_throw, .ml_flags=METH_VARARGS, .ml_doc=green _throw_doc}, .venv/lib64/python3.12/site-packages/greenlet/PyGreenlet.cpp:676: {.ml_name=" __getstate__", .ml_meth=(PyCFunction)green_getstate, .ml_flags=METH_NOARGS, .ml_ doc=NULL}, .venv/lib64/python3.12/site-packages/greenlet/PyGreenlet.cpp:713: .tp_flags=G _TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */ .venv/lib64/python3.12/site-packages/greenlet/greenlet_cpython_compat.hpp:92:// Define these flags like Cython does if we're on an old version. .venv/lib64/python3.12/site-packages/greenlet/TPythonState.cpp:57: meaning that its ``use_tracing`` flag is never updated. We don't grep: .venv/lib64/python3.12/site-packages/greenlet/tests/_test_extension_cpp.cp ython-312-x86_64-linux-gnu.so: binary file matches grep: .venv/lib64/python3.12/site-packages/greenlet/_greenlet.cpython-312-x86_64 -linux-gnu.so: binary file matches .venv/lib64/python3.12/site-packages/dateutil/parser/_parser.py:1067: may erroneously trigger the AM/PM flag. Deal with that .venv/lib64/python3.12/site-packages/dateutil/parser/_parser.py:1072: # I f there's already an AM/PM flag, this one isn't one. .venv/lib64/python3.12/site-packages/dateutil/parser/_parser.py:1081: raise ValueError('No hour specified with AM or PM flag.') .venv/lib64/python3.12/site-packages/charset_normalizer/cli/__main__.py:171: help="Replace file without asking if you are sure, use this flag with cautio n.", .venv/lib64/python3.12/site-packages/charset_normalizer/api.py:87: "c p_isolation is set. use this flag for debugging purpose. " .venv/lib64/python3.12/site-packages/charset_normalizer/api.py:98: "c p_exclusion is set. use this flag for debugging purpose. " .venv/lib64/python3.12/site-packages/pyright/_utils.py:118: # This flag i s set by the language server as the output must always be machine parseable .venv/lib64/python3.12/site-packages/pyright/_utils.py:122: # If this fla g is set then the output must be machine parseable
Error: Output for session 'shell' contained a line exceeding the max of 1600 bytes (observed at least 233415 bytes). The byte sequence which exceeded the limit started with: b'\x1b[35m\x1b[K.venv/lib64/' The exec session has been deleted. Please start a new session. Tip - rerun the command and extract only what you need, e.g.: * grep -nE 'PATTERN' FILE | cut -c1-200 * grep -o 'PATTERN' FILE * jq -r '.json.path' FILE * grep -a PATTERN FILE

I’ll exclude the .venv folder from the search to avoid scanning unnecessary lines.

shell
bash: .venv/bin/activate: No such file or directory root@17549eec6c89:/workspace# grep -n "flag" -R --exclude-dir=.venv | head scubaduck/.git/hooks/fsmonitor-watchman.sample:141: # return the fas t "everything is dirty" flag to git and do the scubaduck/scubaduck/server.py:85: "CREATE TABLE events (id INTEGER PRIMAR Y KEY, ts INTEGER, val REAL, name TEXT, flag BOOLEAN)" scubaduck/tests/test_server_db_types.py:129: conn.execute("CREATE TABLE event s (timestamp TEXT, flag BOOLEAN)") scubaduck/tests/test_server_db_types.py:143: "columns": ["flag"],

I'll check how boolean values are managed in test_server_db_types.py.

shell
root@17549eec6c89:/workspace# cat tests/test_server_db_types.py | sed -n '1,200p ' cat: tests/test_server_db_types.py: No such file or directory

I got sidetracked from the path. Need to switch back to the repo root.

shell
root@17549eec6c89:/workspace# cd scubaduck root@17549eec6c89:/workspace/scubaduck# ls AGENTS.md codex_setup.sh scubaduck tests README.md pyproject.toml stubs uv.lock
root@17549eec6c89:/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
root@17549eec6c89:/workspace/scubaduck# sed -n '1,220p' tests/test_server_db_typ es.py from __future__ import annotations import json from pathlib import Path import duckdb import pytest from scubaduck import server def _make_payload() -> dict[str, object]: return { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-02 00:00:00", "order_by": "timestamp", "order_dir": "ASC", "limit": 10, "columns": ["timestamp", "event", "value", "user"], "filters": [], } def test_database_types(tmp_path: Path) -> None: csv_file = tmp_path / "events.csv" csv_file.write_text(Path("scubaduck/sample.csv").read_text()) sqlite_file = tmp_path / "events.sqlite" import sqlite3 conn = sqlite3.connect(sqlite_file) conn.execute( "CREATE TABLE events (timestamp TEXT, event TEXT, value INTEGER, user TE XT)" ) with open(csv_file) as f: next(f) for line in f: ts, ev, val, user = line.strip().split(",") conn.execute( "INSERT INTO events VALUES (?, ?, ?, ?)", (ts, ev, int(val), use r) ) conn.commit() conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce ssIssue] duckdb_file = tmp_path / "events.duckdb" con = duckdb.connect(duckdb_file) con.execute( f"CREATE TABLE events AS SELECT * FROM read_csv_auto('{csv_file.as_posix ()}')" ) con.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcces sIssue] for db in (csv_file, sqlite_file, duckdb_file): app = server.create_app(db) client = app.test_client() payload = _make_payload() rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/js on" ) rows = rv.get_json()["rows"] assert len(rows) == 3 def test_sqlite_longvarchar(tmp_path: Path) -> None: sqlite_file = tmp_path / "events.sqlite" import sqlite3 conn = sqlite3.connect(sqlite_file) conn.execute( "CREATE TABLE events (timestamp TEXT, url LONGVARCHAR, title VARCHAR(10) )" ) conn.execute( "INSERT INTO events VALUES ('2024-01-01 00:00:00', 'https://a.com', 'Hom e')" ) conn.commit() conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce ssIssue] app = server.create_app(sqlite_file) client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-01 01:00:00", "order_by": "timestamp", "columns": ["timestamp", "url", "title"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["rows"][0][1] == "https://a.com" def test_sqlite_bigint(tmp_path: Path) -> None: sqlite_file = tmp_path / "big.sqlite" import sqlite3 conn = sqlite3.connect(sqlite_file) conn.execute("CREATE TABLE events (timestamp TEXT, value INTEGER)") big_value = 13385262862605259 conn.execute( "INSERT INTO events VALUES ('2024-01-01 00:00:00', ?)", (big_value,), ) conn.commit() conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce ssIssue] app = server.create_app(sqlite_file) client = app.test_client() payload = { "table": "events", "order_by": "timestamp", "columns": ["timestamp", "value"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["rows"][0][1] == big_value def test_sqlite_boolean_aggregation(tmp_path: Path) -> None: sqlite_file = tmp_path / "bool.sqlite" import sqlite3 conn = sqlite3.connect(sqlite_file) conn.execute("CREATE TABLE events (timestamp TEXT, flag BOOLEAN)") conn.execute("INSERT INTO events VALUES ('2024-01-01 00:00:00', 1)") conn.execute("INSERT INTO events VALUES ('2024-01-01 00:30:00', 0)") conn.commit() conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce ssIssue] app = server.create_app(sqlite_file) client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-02 00:00:00", "graph_type": "table", "aggregate": "Avg", "columns": ["flag"], "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 assert data["rows"][0][0] == 2 assert data["rows"][0][1] == 0.5 def test_envvar_db(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: csv_file = tmp_path / "custom.csv" csv_file.write_text("timestamp,event,value,user\n2024-01-01 00:00:00,login,5 ,bob\n") monkeypatch.setenv("SCUBADUCK_DB", str(csv_file)) app = server.create_app() client = app.test_client() payload = _make_payload() rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) rows = rv.get_json()["rows"] assert len(rows) == 1 def test_envvar_db_missing(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> N one: missing = tmp_path / "missing.sqlite" monkeypatch.setenv("SCUBADUCK_DB", str(missing)) with pytest.raises(FileNotFoundError): server.create_app()

The test named test_sqlite_boolean_aggregation covers averaging of boolean values, but doesn't use group_by. I'll review other tests to see if any cover boolean aggregation with group_by.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "boolean" -R tests | head tests/test_server_db_types.py:124:def test_sqlite_boolean_aggregation(tmp_path: Path) -> None:

The issue involves group_by. I'll try replicating with id and the bool column.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "_create_test_database" -n scuba duck/server.py 81:def _create_test_database() -> duckdb.DuckDBPyConnection: 359: con = _create_test_database()
root@17549eec6c89:/workspace/scubaduck# sed -n '70,110p' scubaduck/server.py for r in con.execute( "SELECT name FROM sqlite_master WHERE type='table'" ).fetchall() ] for t in tables: con.execute(f'CREATE VIEW "{t}" AS SELECT * FROM db."{t}"') else: con = duckdb.connect(path) return con def _create_test_database() -> duckdb.DuckDBPyConnection: """Return a DuckDB connection with a small multi-table dataset.""" con = duckdb.connect() con.execute( "CREATE TABLE events (id INTEGER PRIMARY KEY, ts INTEGER, val REAL, name TEXT, flag BOOLEAN)" ) con.execute("INSERT INTO events VALUES (1, 1704067200, 1.5, 'alice', 1)") con.execute("INSERT INTO events VALUES (2, 1704070800, 2.0, 'bob', 0)") con.execute('CREATE TABLE extra (ts INTEGER, "desc" TEXT, num INTEGER)') con.execute("INSERT INTO extra VALUES (1704067200, 'x', 1)") con.execute("INSERT INTO extra VALUES (1704070800, 'y', 2)") return con _REL_RE = re.compile( r"([+-]?\d+(?:\.\d*)?)\s*(hour|hours|day|days|week|weeks|fortnight|fortnight s)", re.IGNORECASE, ) def parse_time(val: str | None) -> str | None: """Parse an absolute or relative time string into ``YYYY-MM-DD HH:MM:SS``."" " if val is None or val == "": return None s = val.strip() if s.lower() == "now": dt = datetime.now(timezone.utc) return dt.replace(microsecond=0).strftime("%Y-%m-%d %H:%M:%S") m = _REL_RE.fullmatch(s)

I see the events table has 'flag BOOLEAN'. I'll check tests/test_server.py for API usage.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "TEST" -n tests/test_server.py

I should check if any tests are using the 'TEST' dataset.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "TEST" -R tests | head tests/test_server_timeseries.py:253: app = server.create_app("TEST") tests/test_multi_table_web.py:14: app = create_app("TEST")

I'll review the lines around 253 in test_server_timeseries.py to explore the other tests.

shell
root@17549eec6c89:/workspace/scubaduck# sed -n '240,300p' tests/test_server_time series.py "derived_columns": {"derived_1": "value * 2"}, "aggregate": "Avg", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 rows = data["rows"] assert all(r[2] == r[1] * 2 for r in rows) def test_reserved_word_column() -> None: app = server.create_app("TEST") client = app.test_client() payload = { "table": "extra", "columns": ["ts", "desc"], "order_by": "ts", "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 len(data["rows"]) == 2 assert data["rows"][0][1] == "x"

I’ll continue looking around line 253 for more context.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "group_by" -n tests/test_server_ timeseries.py | head -n 20 9:def test_group_by_table() -> None: 20: "group_by": ["user"], 44: "group_by": ["user"], 70: "group_by": ["user"], 141: "group_by": ["user"],
root@17549eec6c89:/workspace/scubaduck# sed -n '1,120p' tests/test_server_timese ries.py from __future__ import annotations import json from typing import Any, cast from scubaduck import server def test_group_by_table() -> 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": "user", "limit": 10, "columns": ["value"], "group_by": ["user"], "aggregate": "Sum", "show_hits": True, } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) rows = rv.get_json()["rows"] assert rows[0][0] == "alice" assert rows[0][1] == 2 assert rows[0][2] == 40 def test_table_avg_with_timestamp() -> 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": "user", "limit": 100, "columns": ["user", "timestamp", "value"], "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 == 200 assert "error" not in data rows = data["rows"] assert rows[0][0] == "alice" from dateutil import parser ts = parser.parse(rows[0][1]).replace(tzinfo=None) assert ts == parser.parse("2024-01-01 12:00:00") def test_timeseries_basic() -> 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": "timeseries", "limit": 100, "group_by": ["user"], "aggregate": "Count", "columns": ["value"], "x_axis": "timestamp", "granularity": "1 day", } 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"]) == 4 def test_timeseries_orders_by_xaxis() -> 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": "timeseries", "limit": 100, "columns": ["value"], "x_axis": "timestamp", "granularity": "1 day", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 rows = data["rows"] from dateutil import parser timestamps = [parser.parse(r[0]).replace(tzinfo=None) for r in rows] assert timestamps == sorted(timestamps) def test_timeseries_count_no_columns() -> None: app = server.app client = app.test_client() payload: dict[str, Any] = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "graph_type": "timeseries", "granularity": "1 day", "columns": [], } rv = client.post(

I'm scanning for mentions of group_by and related details.

shell
root@17549eec6c89:/workspace/scubaduck# sed -n '120,200p' tests/test_server_time series.py rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 rows = data["rows"] assert len(rows) == 2 assert rows[0][1] == 2 assert rows[1][1] == 2 def test_timeseries_limit_applies_to_series() -> 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": "timeseries", "limit": 1, "order_by": "user", "group_by": ["user"], "aggregate": "Count", "columns": ["value"], "x_axis": "timestamp", "granularity": "1 day", } 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 assert all(r[1] == "alice" for r in data["rows"]) def test_timeseries_auto_and_fine_buckets() -> None: app = server.app client = app.test_client() def run(gran: str) -> None: payload = { "start": "2024-01-01 00:00:00", "end": "2024-01-02 03:00:00", "graph_type": "timeseries", "columns": ["value"], "x_axis": "timestamp", "granularity": gran, } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/js on" ) data = rv.get_json() assert rv.status_code == 200 from dateutil import parser start = parser.parse(cast(str, payload["start"])).replace(tzinfo=None) buckets = [ parser.parse(cast(str, r[0])).replace(tzinfo=None) for r in data["ro ws"] ] assert buckets[0] == start if len(buckets) > 1: step = (buckets[1] - buckets[0]).total_seconds() assert step % data["bucket_size"] == 0 assert any(r[1] != 0 for r in data["rows"]) run("Auto") run("Fine") def test_timeseries_string_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": "timeseries", "limit": 7, "columns": ["timestamp", "event", "value", "user"], "x_axis": "timestamp",

I'm checking near the bottom for timeseries, deciding if I need to group by.

shell
root@17549eec6c89:/workspace/scubaduck# sed -n '200,260p' tests/test_server_time series.py "x_axis": "timestamp", "granularity": "1 hour", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) assert rv.status_code == 200 def test_derived_column_basic() -> 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", "limit": 10, "columns": ["timestamp"], "derived_columns": {"val2": "value * 2"}, "filters": [], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["rows"][0][1] == 20 def test_timeseries_derived_column() -> 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": "timeseries", "granularity": "1 hour", "limit": 7, "columns": ["value"], "derived_columns": {"derived_1": "value * 2"}, "aggregate": "Avg", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 rows = data["rows"] assert all(r[2] == r[1] * 2 for r in rows) def test_reserved_word_column() -> None: app = server.create_app("TEST") client = app.test_client() payload = { "table": "extra", "columns": ["ts", "desc"], "order_by": "ts", "time_column": "", }

It looks like the tests don't cover boolean group by. I'll check test_server.py next.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "boolean" -R tests | head tests/test_server_db_types.py:124:def test_sqlite_boolean_aggregation(tmp_path: Path) -> None:

Time to explore the test files and see how the bool aggregator fails with group_by.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "Aggregate" tests -n | head grep: tests: Is a directory

I mistakenly wrote "tests -n," but it's actually grep -n Aggregate -R tests | head.

shell
root@17549eec6c89:/workspace/scubaduck# grep -n "Aggregate" -R tests | head tests/test_web_timeseries.py:378: assert page.is_visible("#legend .drill-link s a:text('Aggregate')")

I'll recreate the bug to confirm, then proceed with implementing the fix.

shell
root@17549eec6c89:/workspace/scubaduck# source .venv/bin/activate (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I’m going to run the Python script now to replicate the task.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# python - <<'PY' > from scubaduck import server > import json > > app = server.create_app('TEST') > client = app.test_client() > > payload = { > 'table': 'events', > 'start': '-100000 day', > 'end': 'now', > 'graph_type': 'table', > 'aggregate': 'Avg', > 'group_by': ['id'], > 'columns': ['flag'], > } > rv = client.post('/api/query', data=json.dumps(payload), content_type='applica tion/json') > print('status', rv.status_code) > print(rv.get_json()) > PY status 400 {'error': 'Invalid time_column'} (scubaduck) root@17549eec6c89:/workspace/scubaduck#

The test fails due to using an incorrect "timestamp" column instead of "ts." I'll specify "ts."

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# python - <<'PY' > from scubaduck import server > import json > > app = server.create_app('TEST') > client = app.test_client() > > payload = { > 'table': 'events', > 'start': '-100000 day', > 'end': 'now', > 'graph_type': 'table', > 'time_column': 'ts', > 'aggregate': 'Avg', > 'group_by': ['id'], > 'columns': ['flag'], > } > rv = client.post('/api/query', data=json.dumps(payload), content_type='applica tion/json') > print('status', rv.status_code) > print(rv.get_json()) > PY status 400 {'error': 'Aggregate avg cannot be applied to column flag'} (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I’ll patch the is_numeric check to treat booleans as numeric by adding "BOOL".

I'll create a test to check if boolean aggregation with group_by works correctly.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# grep -n "create_app" -n scub aduck/server.py 352:def create_app(db_file: str | Path | None = None) -> Flask: 681:app = create_app() (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll scroll to the bottom to see what’s there.

shell

(scubaduck) root@17549eec6c89:/workspace/scubaduck# sed -n '660,710p' scubaduck/ server.py 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, ) result: Dict[str, Any] = {"sql": sql, "rows": rows} if params.start is not None: result["start"] = str(params.start) if params.end is not None: result["end"] = str(params.end) if bucket_size is not None: result["bucket_size"] = bucket_size return jsonify(result) return app app = create_app() if __name__ == "__main__": app.run(debug=True) (scubaduck) root@17549eec6c89:/workspace/scubaduck# sed -n '680,710p' scubaduck/ server.py app = create_app() if __name__ == "__main__": app.run(debug=True) (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I’ll check tests/test_server.py to investigate how sample.csv is used in the tests.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# head tests/test_server.py """Legacy test module. Tests have been split into multiple files: - test_server_basic.py - test_server_db_types.py - test_server_time.py - test_server_errors.py - test_server_timeseries.py """ (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll review tests/test_server_basic to verify the dataset.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# grep -n "create_app(" -R tes ts | head tests/test_server_time.py:15: app = server.create_app(csv_file) tests/test_server_time.py:36: app = server.create_app(csv_file) tests/test_server_time.py:58: app = server.create_app(csv_file) tests/test_server_time.py:84: app = server.create_app(csv_file) tests/test_server_time.py:114: app = server.create_app(sqlite_file) tests/test_server_timeseries.py:253: app = server.create_app("TEST") tests/test_multi_table_web.py:14: app = create_app("TEST") tests/test_server_db_types.py:54: app = server.create_app(db) tests/test_server_db_types.py:78: app = server.create_app(sqlite_file) tests/test_server_db_types.py:109: app = server.create_app(sqlite_file) (scubaduck) root@17549eec6c89:/workspace/scubaduck#

The bug relates to treating bool as numeric—adding tests to test_server_db_types.py.

I need to check the ORDER BY in build_query to ensure correct sorting.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# grep -n "ORDER BY" -n scubad uck/server.py | head -n 20 290: lines.append(f"ORDER BY {_quote(order_by)} {params.order_dir}") 292: lines.append("ORDER BY bucket") 344: lines.append(f"ORDER BY {_quote(order_by)} {params.order_dir}") 346: lines.append("ORDER BY bucket") (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll check lines 276–351 to verify the details.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# sed -n '268,350p' scubaduck/ server.py order_by = params.order_by if params.order_by in selected_for_order else Non e if has_agg and params.derived_columns: inner_params = replace( params, derived_columns={}, order_by=None, limit=None, ) inner_sql = build_query(inner_params, column_types) outer_select = ["t.*"] + [ f"{expr} AS {name}" for name, expr in params.derived_columns.items() ] indented_inner = "\n".join(" " + line for line in inner_sql.splitline s()) lines = [ f"SELECT {', '.join(outer_select)}", "FROM (", indented_inner, ") t", ] if order_by: lines.append(f"ORDER BY {_quote(order_by)} {params.order_dir}") elif params.graph_type == "timeseries": lines.append("ORDER BY bucket") if params.limit is not None: lines.append(f"LIMIT {params.limit}") return "\n".join(lines) for name, expr in params.derived_columns.items(): select_parts.append(f"{expr} AS {name}") selected_for_order.add(name) select_clause = ", ".join(select_parts) if select_parts else "*" lines = [f"SELECT {select_clause}", f'FROM "{params.table}"'] where_parts: list[str] = [] if params.time_column: time_expr = _time_expr(params.time_column, column_types, params.time_uni t) else: time_expr = None if time_expr and params.start: where_parts.append(f"{time_expr} >= '{params.start}'") if time_expr and params.end: where_parts.append(f"{time_expr} <= '{params.end}'") for f in params.filters: op = f.op if op in {"empty", "!empty"}: val = "''" else: if f.value is None: continue if isinstance(f.value, list): if not f.value: continue if op == "=": qcol = _quote(f.column) vals = " OR ".join(f"{qcol} = '{v}'" for v in f.value) where_parts.append(f"({vals})") continue val = f"'{f.value}'" if isinstance(f.value, str) else str(f.value) qcol = _quote(f.column) if op == "contains": where_parts.append(f"{qcol} ILIKE '%' || {val} || '%'") elif op == "!contains": where_parts.append(f"{qcol} NOT ILIKE '%' || {val} || '%'") elif op == "empty": where_parts.append(f"{qcol} = {val}") elif op == "!empty": where_parts.append(f"{qcol} != {val}") else: where_parts.append(f"{qcol} {op} {val}") if where_parts: lines.append("WHERE " + " AND ".join(where_parts)) if group_cols: lines.append("GROUP BY " + ", ".join(_quote(c) for c in group_cols)) if order_by: lines.append(f"ORDER BY {_quote(order_by)} {params.order_dir}") elif params.graph_type == "timeseries": lines.append("ORDER BY bucket") if params.limit is not None: lines.append(f"LIMIT {params.limit}") return "\n".join(lines) (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I’m thinking about where to add the test, maybe in test_server_timeseries.py.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# sed -n '232,272p' tests/test _server_timeseries.py payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-03 00:00:00", "graph_type": "timeseries", "granularity": "1 hour", "limit": 7, "columns": ["value"], "derived_columns": {"derived_1": "value * 2"}, "aggregate": "Avg", } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 rows = data["rows"] assert all(r[2] == r[1] * 2 for r in rows) def test_reserved_word_column() -> None: app = server.create_app("TEST") client = app.test_client() payload = { "table": "extra", "columns": ["ts", "desc"], "order_by": "ts", "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 len(data["rows"]) == 2 assert data["rows"][0][1] == "x" (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll add the new test to tests/test_server_timeseries.py or test_server_db_types.py, checking both.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# tail -n 20 tests/test_server _timeseries.py rows = data["rows"] assert all(r[2] == r[1] * 2 for r in rows) def test_reserved_word_column() -> None: app = server.create_app("TEST") client = app.test_client() payload = { "table": "extra", "columns": ["ts", "desc"], "order_by": "ts", "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 len(data["rows"]) == 2 assert data["rows"][0][1] == "x" (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'm thinking of placing the test in test_server_db_types.py, but need to check its end.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# tail -n 20 tests/test_server _db_types.py def test_envvar_db(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: csv_file = tmp_path / "custom.csv" csv_file.write_text("timestamp,event,value,user\n2024-01-01 00:00:00,login,5 ,bob\n") monkeypatch.setenv("SCUBADUCK_DB", str(csv_file)) app = server.create_app() client = app.test_client() payload = _make_payload() rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) rows = rv.get_json()["rows"] assert len(rows) == 1 def test_envvar_db_missing(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> N one: missing = tmp_path / "missing.sqlite" monkeypatch.setenv("SCUBADUCK_DB", str(missing)) with pytest.raises(FileNotFoundError): server.create_app() (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I recall seeing _make_payload at the top of tests/test_server_db_types.py; let’s scroll more.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# sed -n '90,160p' tests/test_ server_db_types.py data = rv.get_json() assert rv.status_code == 200 assert data["rows"][0][1] == "https://a.com" def test_sqlite_bigint(tmp_path: Path) -> None: sqlite_file = tmp_path / "big.sqlite" import sqlite3 conn = sqlite3.connect(sqlite_file) conn.execute("CREATE TABLE events (timestamp TEXT, value INTEGER)") big_value = 13385262862605259 conn.execute( "INSERT INTO events VALUES ('2024-01-01 00:00:00', ?)", (big_value,), ) conn.commit() conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce ssIssue] app = server.create_app(sqlite_file) client = app.test_client() payload = { "table": "events", "order_by": "timestamp", "columns": ["timestamp", "value"], } rv = client.post( "/api/query", data=json.dumps(payload), content_type="application/json" ) data = rv.get_json() assert rv.status_code == 200 assert data["rows"][0][1] == big_value def test_sqlite_boolean_aggregation(tmp_path: Path) -> None: sqlite_file = tmp_path / "bool.sqlite" import sqlite3 conn = sqlite3.connect(sqlite_file) conn.execute("CREATE TABLE events (timestamp TEXT, flag BOOLEAN)") conn.execute("INSERT INTO events VALUES ('2024-01-01 00:00:00', 1)") conn.execute("INSERT INTO events VALUES ('2024-01-01 00:30:00', 0)") conn.commit() conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce ssIssue] app = server.create_app(sqlite_file) client = app.test_client() payload = { "table": "events", "start": "2024-01-01 00:00:00", "end": "2024-01-02 00:00:00", "graph_type": "table", "aggregate": "Avg", "columns": ["flag"], "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 assert data["rows"][0][0] == 2 assert data["rows"][0][1] == 0.5 def test_envvar_db(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: csv_file = tmp_path / "custom.csv" csv_file.write_text("timestamp,event,value,user\n2024-01-01 00:00:00,login,5 ,bob\n") monkeypatch.setenv("SCUBADUCK_DB", str(csv_file)) app = server.create_app() client = app.test_client() (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I’ll create the test_sqlite_boolean_group_by to check avg aggregation by ID, and sort results.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# apply_patch<<'PATCH' > *** Begin Patch > *** Update File: scubaduck/server.py > @@ > - is_numeric = any( > - t in ctype > - for t in [ > - "INT", > - "DECIMAL", > - "REAL", > - "DOUBLE", > - "FLOAT", > - "NUMERIC", > - "HUGEINT", > - ] > - ) > + is_numeric = "BOOL" in ctype or any( > + t in ctype > + for t in [ > + "INT", > + "DECIMAL", > + "REAL", > + "DOUBLE", > + "FLOAT", > + "NUMERIC", > + "HUGEINT", > + ] > + ) > *** End Patch > PATCH Success. Updated the following files: M scubaduck/server.py (scubaduck) root@17549eec6c89:/workspace/scubaduck#

Time to add a test for the changes I've made!

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# apply_patch<<'PATCH' > *** Begin Patch > *** Update File: tests/test_server_db_types.py > @@ > def test_sqlite_boolean_aggregation(tmp_path: Path) -> None: > @@ > assert data["rows"][0][1] == 0.5 > + > + > +def test_sqlite_boolean_group_by(tmp_path: Path) -> None: > + sqlite_file = tmp_path / "bool.sqlite" > + import sqlite3 > + > + conn = sqlite3.connect(sqlite_file) > + conn.execute("CREATE TABLE events (id INTEGER, ts TEXT, flag BOOLEAN)") > + conn.execute("INSERT INTO events VALUES (1, '2024-01-01 00:00:00', 1)") > + conn.execute("INSERT INTO events VALUES (1, '2024-01-01 00:30:00', 0)") > + conn.execute("INSERT INTO events VALUES (2, '2024-01-01 01:00:00', 1)") > + conn.commit() > + conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeA ccessIssue] > + > + app = server.create_app(sqlite_file) > + client = app.test_client() > + payload = { > + "table": "events", > + "start": "2024-01-01 00:00:00", > + "end": "2024-01-02 00:00:00", > + "graph_type": "table", > + "time_column": "ts", > + "aggregate": "Avg", > + "group_by": ["id"], > + "columns": ["flag"], > + } > + rv = client.post( > + "/api/query", data=json.dumps(payload), content_type="application/jso n" > + ) > + data = rv.get_json() > + assert rv.status_code == 200 > + rows = sorted(data["rows"]) # order can vary > + assert rows == [[1, 0.5], [2, 1.0]] > *** End Patch > PATCH Success. Updated the following files: M tests/test_server_db_types.py (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll run ruff format, ruff check, pyright, and the tests now.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# ruff format scubaduck/server .py tests/test_server_db_types.py 2 files left unchanged
(scubaduck) root@17549eec6c89:/workspace/scubaduck# ruff check scubaduck/server. py tests/test_server_db_types.py All checks passed!
(scubaduck) root@17549eec6c89:/workspace/scubaduck# pyright scubaduck/server.py tests/test_server_db_types.py > /tmp/pyright.log && tail -n 20 /tmp/pyright.log
0 errors, 0 warnings, 0 informations
(scubaduck) root@17549eec6c89:/workspace/scubaduck# cat /tmp/pyright.log 0 errors, 0 warnings, 0 informations
(scubaduck) root@17549eec6c89:/workspace/scubaduck# pytest -q > /tmp/pytest.log && tail -n 20 /tmp/pytest.log










bringing up nodes... bringing up nodes... ........................................................................ [ 64%] ........................................ [100%] 112 passed in 18.51s
(scubaduck) root@17549eec6c89:/workspace/scubaduck# tail -n 20 /tmp/pytest.log bringing up nodes... bringing up nodes... ........................................................................ [ 64%] ........................................ [100%] 112 passed in 18.51s (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'm confirming the current git status after all tests passed.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# git status --short M scubaduck/server.py M tests/test_server_db_types.py (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll check around the modifications in scubaduck/server.py to add citations.

shell
540 if need_numeric or allow_time: 541 for c in params.columns: 542 if c in params.group_by or c == params.x_axis: 543 continue 544 if c not in column_types: 545 continue 546 ctype = column_types.get(c, "").upper() 547 is_numeric = "BOOL" in ctype or any( 548 t in ctype 549 for t in [ 550 "INT", 551 "DECIMAL", 552 "REAL", 553 "DOUBLE", 554 "FLOAT", 555 "NUMERIC", 556 "HUGEINT", 557 ] 558 ) 559 is_time = "TIMESTAMP" in ctype or "DATE" in ctype or "TIME" in ctype 560 if need_numeric and not is_numeric: 561 return ( 562 jsonify( 563 { 564 "error": f"Aggregate {agg} cannot be applied to column {c}", 565 } 566 ), 567 400, 568 ) 569 if allow_time and not (is_numeric or is_time): 570 return ( 571 jsonify( 572 { 573 "error": f"Aggregate {agg} cannot be applied to column {c}", 574 } 575 ), 576 400, 577 ) 578 if (params.start is None or params.end is None) and ( 579 params.x_axis or params.time_column 580 ): 581 axis = params.x_axis or params.time_column 582 assert axis is not None 583 row = cast( 584 tuple[datetime | None, datetime | None], 585 con.execute( (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll locate the test lines around line 130 or so.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# nl -ba tests/test_server_db_ types.py | sed -n '120,190p' 120 assert rv.status_code == 200 121 assert data["rows"][0][1] == big_value 122 123 124 def test_sqlite_boolean_aggregation(tmp_path: Path) -> None: 125 sqlite_file = tmp_path / "bool.sqlite" 126 import sqlite3 127 128 conn = sqlite3.connect(sqlite_file) 129 conn.execute("CREATE TABLE events (timestamp TEXT, flag BOOLEAN)") 130 conn.execute("INSERT INTO events VALUES ('2024-01-01 00:00:00', 1)") 131 conn.execute("INSERT INTO events VALUES ('2024-01-01 00:30:00', 0)") 132 conn.commit() 133 conn.close() # pyright: ignore[reportUnknownMemberType, reportAttri buteAccessIssue] 134 135 app = server.create_app(sqlite_file) 136 client = app.test_client() 137 payload = { 138 "table": "events", 139 "start": "2024-01-01 00:00:00", 140 "end": "2024-01-02 00:00:00", 141 "graph_type": "table", 142 "aggregate": "Avg", 143 "columns": ["flag"], 144 "show_hits": True, 145 } 146 rv = client.post( 147 "/api/query", data=json.dumps(payload), content_type="applicatio n/json" 148 ) 149 data = rv.get_json() 150 assert rv.status_code == 200 151 assert data["rows"][0][0] == 2 152 assert data["rows"][0][1] == 0.5 153 154 155 def test_sqlite_boolean_group_by(tmp_path: Path) -> None: 156 sqlite_file = tmp_path / "bool.sqlite" 157 import sqlite3 158 159 conn = sqlite3.connect(sqlite_file) 160 conn.execute("CREATE TABLE events (id INTEGER, ts TEXT, flag BOOLEAN )") 161 conn.execute("INSERT INTO events VALUES (1, '2024-01-01 00:00:00', 1 )") 162 conn.execute("INSERT INTO events VALUES (1, '2024-01-01 00:30:00', 0 )") 163 conn.execute("INSERT INTO events VALUES (2, '2024-01-01 01:00:00', 1 )") 164 conn.commit() 165 conn.close() # pyright: ignore[reportUnknownMemberType, reportAttri buteAccessIssue] 166 167 app = server.create_app(sqlite_file) 168 client = app.test_client() 169 payload = { 170 "table": "events", 171 "start": "2024-01-01 00:00:00", 172 "end": "2024-01-02 00:00:00", 173 "graph_type": "table", 174 "time_column": "ts", 175 "aggregate": "Avg", 176 "group_by": ["id"], 177 "columns": ["flag"], 178 } 179 rv = client.post( 180 "/api/query", data=json.dumps(payload), content_type="applicatio n/json" 181 ) 182 data = rv.get_json() 183 assert rv.status_code == 200 184 rows = sorted(data["rows"]) # order can vary 185 assert rows == [[1, 0.5], [2, 1.0]] 186 187 188 def test_envvar_db(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> N one: 189 csv_file = tmp_path / "custom.csv" 190 csv_file.write_text("timestamp,event,value,user\n2024-01-01 00:00:00 ,login,5,bob\n")
(scubaduck) root@17549eec6c89:/workspace/scubaduck# git add scubaduck/server.py tests/test_server_db_types.py
(scubaduck) root@17549eec6c89:/workspace/scubaduck# git commit -m "fix(server): allow avg on bool columns" && git status --short [work 5b6409d] fix(server): allow avg on bool columns 2 files changed, 34 insertions(+), 1 deletion(-)
(scubaduck) root@17549eec6c89:/workspace/scubaduck# git status --short (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I’ll draft a PR referencing the bug fix and modifying aggregator to allow bool.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# nl -ba scubaduck/server.py | sed -n '544,559p' 544 if c not in column_types: 545 continue 546 ctype = column_types.get(c, "").upper() 547 is_numeric = "BOOL" in ctype or any( 548 t in ctype 549 for t in [ 550 "INT", 551 "DECIMAL", 552 "REAL", 553 "DOUBLE", 554 "FLOAT", 555 "NUMERIC", 556 "HUGEINT", 557 ] 558 ) 559 is_time = "TIMESTAMP" in ctype or "DATE" in ctype or "TIME" in ctype (scubaduck) root@17549eec6c89:/workspace/scubaduck#

The PR will include a fix for adding bool as numeric and new test lines from 155-185.

shell
(scubaduck) root@17549eec6c89:/workspace/scubaduck# nl -ba tests/test_server_db_ types.py | sed -n '154,186p' 154 155 def test_sqlite_boolean_group_by(tmp_path: Path) -> None: 156 sqlite_file = tmp_path / "bool.sqlite" 157 import sqlite3 158 159 conn = sqlite3.connect(sqlite_file) 160 conn.execute("CREATE TABLE events (id INTEGER, ts TEXT, flag BOOLEAN )") 161 conn.execute("INSERT INTO events VALUES (1, '2024-01-01 00:00:00', 1 )") 162 conn.execute("INSERT INTO events VALUES (1, '2024-01-01 00:30:00', 0 )") 163 conn.execute("INSERT INTO events VALUES (2, '2024-01-01 01:00:00', 1 )") 164 conn.commit() 165 conn.close() # pyright: ignore[reportUnknownMemberType, reportAttri buteAccessIssue] 166 167 app = server.create_app(sqlite_file) 168 client = app.test_client() 169 payload = { 170 "table": "events", 171 "start": "2024-01-01 00:00:00", 172 "end": "2024-01-02 00:00:00", 173 "graph_type": "table", 174 "time_column": "ts", 175 "aggregate": "Avg", 176 "group_by": ["id"], 177 "columns": ["flag"], 178 } 179 rv = client.post( 180 "/api/query", data=json.dumps(payload), content_type="applicatio n/json" 181 ) 182 data = rv.get_json() 183 assert rv.status_code == 200 184 rows = sorted(data["rows"]) # order can vary 185 assert rows == [[1, 0.5], [2, 1.0]] 186 (scubaduck) root@17549eec6c89:/workspace/scubaduck#

I'll create the PR, summarizing the bug fix, referencing changes and test results.