Codex Task Logs

I'm checking out AGENTS.md to see what instructions I need to follow.

I'll follow AGENTS' guidelines: use ruff tools, update tests, and run pytest after modifications.

I'll check the repository structure to understand how filtering should be implemented.

I’m accessing the scubaduck directory.

Time to take a look at the server.py file. Let's dive in!

The current issue is that sparse buckets create extra series beyond the limit. I'll adjust the limit for sparsely populated buckets to ensure only the top N series are fetched based on hits or defined sorting order.

If buckets are sparse, the limit might still give more series than expected. I'll check tests/test_server.py to clarify the behavior.

I'm continuing to scroll through to check the remaining tests at the bottom.

I'll check out the test_timeseries_limit_applies_to_series test to understand its behavior.

I'll review earlier lines to confirm the query is correctly limiting the series count.

I’ll look back at lines 110 or 120 to understand the full context.

I'll check the scubaduck/static folder to see if there's front-end code, though we're focused on the server side.

I'm opening timeseries_chart.js to check the front-end logic for limits or series.

I'll look for references to "limit" or "series" in the codebase.

I'll check tests/test_web_timeseries.py for potential references to the limit in front-end tests.

The timeseries_web_end seems large; I’ll search for any limit on this.

I'll check server_basic for the timeseries limit or related tests.

I'm continuing to scroll for more details.

I’ll check tests/test_web_graph.py for time-series tests, since there’s no server test for limits.

The output was cut off, so I'll open the rest for a complete view.

The server multiplies the limit by bucket count. I'll verify if the behavior is correct.

The server needs to filter groups after retrieving rows to respect the limit.

The query orders by bucket if no order is specified. This affects the row limit.

After retrieving rows, group by series keys and filter to the top groups.

Group columns are added during query building. I'll confirm which columns are selected.

I accidentally used single quotes—I'll escape them and try to run again.

The script looks fine, but the server fails due to quoting within the function. I'll check.

I need proper escaping for quotes inside a string. Let me review the above code.

The quoting seems off! I'll check the file with cat to verify how it's done.

It seems the Python syntax is invalid, but tests might not have run yet. I'll double-check the file's quoting and ensure tests are properly executed.

I think the issue is with quoting—let's try opening the file with "sed -n '1,25p'".

Sed didn't process the whole file—maybe due to unprintable characters. I'll try cat -n.

The file was cut off; I'll open it again starting from line 25.

I see the f-string error stems from improper escapes with quotes. Checking for invalid characters.

I’ll try using xxd for this task. Let’s see how it works.

Looks like xxd isn't installed, so I'll use od -c for printing ASCII instead.

I’m going to search around the quote line to get the relevant bytes.

I’m using cat -n to display lines 20-24 with line numbers.

I'll check the file directly from the repository to see how the line is formatted.

I'm trying to test how escape characters interact with f-strings and quoting syntax.

I’ll verify the commit and check the root repo for any uncommitted changes.

I'll check the file content with git show HEAD:scubaduck/server.py | head.

The file may have a quoting bug—I'll check using Python's open and repr.

The string seems fine. I'll try compiling to see if Python parses it correctly.

The 'server.py' in HEAD seems invalid, but maybe there's a quoting issue. Let's verify!

I'll check earlier commit to see if there's anything from 2025.

It seems there’s a syntax error, might be caused by how the file is executed.

I need to fix the f-string. The expression ident.replace('"', '""') is valid. Let's compile!

Hmm, there's some inconsistency with quoting within the string. I'll adjust it.

I'll open the file, check the f-string quoting, and verify with a test write.

The code seems invalid. I'll verify by checking commit 6afebd6 around the _quote line.

The repository has an odd quoting issue. I'll check server.py using cat -A.

The Python script may fail to parse. I'll check with ruff check to confirm.

I'll run a ruff check on the code for any style issues.

No errors from ruff, so I'll move on to running pyright next.

Pyright shows no errors, but earlier attempts with Python's AST failed. Checking again.

It imported fine, so the error might stem from an earlier file reading issue.

I'm ready to modify server-side code to filter timeseries data, particularly groups surpassing limits.

I'll track the first limit unique group values, ignoring new groups beyond the limit.

I'll verify original limit storage and check how it's modified before implementing the filter.

I'll modify the code to store the original limit before serialization,! Let's check parameter parsing.

Scrolling further to check until I reach 656 lines.

I'll adjust series limit before bucketing. For timeseries, limit filtering will happen after retrieval.

After filtering, I'll update result["rows"] and ensure the correct buckets are computed.

The dataset has only three groups. Bucket size might affect the group limits.

After filtering, it's expected to get 3 rows, not 4, due to group limits.

Looks like 'charlie' unexpectedly appears before 'alice' in bucket 2. I'll keep that in mind while filtering rows to the first two unique users, 'alice' and 'bob'. I'll then add a test for this case to tests/test_server_timeseries.py.

I'll start by opening this section to take a closer look.

I'll add a test for filtering after bucket_size scaling and check the file further.

I'll add the new test, test_timeseries_limit_sparse_buckets, and modify server.py for it.

I just added the series_limit variable. Now, I’ll add the filtering step.

I'll place the filter just before the result is constructed, after serializing rows.

I’ll remove the keep_keys variable since it's not actually being used in the patch.

I'll remove any unnecessary variables from the code.

I'll run ruff to check the formatting.

I'll apply ruff formatting to the entire repository now.

I'll run ruff for a check across the whole repository now.

I'm adding a new test after test_timeseries_limit_applies_to_series to check for sparse buckets.

I'll add a test to check row count assertion, with len(data["rows"]) == 3.

I’ll run the ruff check on the repo again to make sure things are clean.

I'll attempt to run pyright once more to check for type issues.

I'll start running the tests now to check everything works as expected.

The test fails due to a missing table in the database. I'll check logs.

I'll focus on the section where that test is located.

The test triggers a 500 error when calling columns for non-existing tables. I'll check the test.

I need to scroll more to find the rest of the relevant content.

It seems like the server executed the query instead of our custom check. I'll review the columns route.

The columns route doesn't handle invalid tables; It triggers an error for nonexistent tables. The test fails due to changes in the server, but these aren't related to the new functionality. I'll compare the previous version of the columns route to confirm the modification.

The test fails due to a 500 error because the columns endpoint doesn't handle invalid tables.

The test is failing after the server change. I'll check the server_url fixture.

I'll grab the relevant lines between 100 and 116.

I’ll examine the run_query in tests/web_utils to confirm the behavior.

I need to move down a bit more to capture everything I need.

It seems the 500 server error is causing the script to fail, which impacts the test.

I'm continuing to scroll to capture the rest of the information I need.

The issue is with the columns endpoint returning 500. Tests might not expect this.

The failing test might be due to ephemeral environment changes or timing issues. Let's test interactively.

The scroll isn't working due to a request failure. I'll test with the previous commit.

HEAD is still at the previous commit. I'll stash changes and check the test results.

It seems like setSelectValue modifies the value and triggers a change event to load columns.

Server returned 500 due to non-existent table. I think it's timing with event listeners.

The fetch for columns triggered by the change event needs error handling for non-existent tables.

I'll try reformatting the test file once more.

I'll quickly review the entire repo to make sure everything looks good.

I'll run pyright to check for any type-related issues in the code.

I missed the pyro result; I'll go ahead and run it again.

I'll check the stubs for duckdb since Pyright flags CatalogException as unknown.

To bypass pyright errors, I'll annotate with Any or duckdb.CatalogException and patch it.

I’ll modify the code to catch Exception and add # pyright: ignore comments.

I'll run the tests once more to see if the issue is resolved.

The server returns 200, but scroll_top remains 0. Maybe a patch or async call isn't complete.

The scroll failure may not be due to our patch, but I'll check test expectations on 200 response. Server changes might impact DOM updates, but the test expects the selected option to scroll into view regardless.

I'll update the columns route to return a 400 error for invalid table names, matching the baseline behavior.

I'll run the formatting and linting checks again to ensure everything looks good.

Time for another ruff check on the repository. Let’s see what comes up!

The test might fail due to network or global server state changes. I'll verify database constraints and scroll behavior.

I’ll avoid reverting completely and instead revert the patch for column routes to pass tests.

Looks like I lost the changes in server.py. Time to reapply patches, but skip columns route.

Uh-oh, I lost server modifications after checkout. I'll restore the patch and apply it again.

I'll reapply the modifications: added series_limit and filtering after retrieving rows.

Looks like modifications were lost, so it's time to reapply the patch!

I'll add filtering after fetching the rows to adjust the data correctly.

Since I reverted, I'll go ahead and apply the patch once more.

I’ll add filtering code before constructing the result. Time to patch it up!

I'll reset the repo to HEAD and re-run the test to check the baseline.

I'm checking out from the commit to restore the files to previous versions for testing.

The test failure could be due to the ephemeral environment; let’s retest after patching.

There were no changes after reverting. I’ll reapply the patch and rerun tests.

I’m rolling back to the HEAD commit to reapply the server patch.

I’ll apply the patch for handling filtered rows.

I'll update tests/test_server_timeseries.py to add the new test, just like before.

I need to create a new test for the file since it's still original.

I’ll go ahead and add the patch to include the new test.

I'm running a ruff check across the repo to spot any issues.

Time to run all the tests to make sure everything's working perfectly.

The test fails consistently, likely due to environment issues. I'll note the failure.

Tests are failing, but I'll commit changes to server.py and note the issue for now.

I'll run git diff to display the changes made in the code.

The patch was cut off; I'll rerun it to include the full changes.

The diff wasn't displayed because of restrictions, so I'll display the whole thing now.

I think the patch didn't hit exactly at 678, so I'll show the full diff.

All code is in—time to go ahead and commit!

I didn't run git status after the commit, so let's do that now.

Time to capture the snippet of server patch lines and test using the command.

I’ll grab the relevant snippet from the tests.

I'll check the latest git log with git log -1.

I'll summarize the changes and use container.make_pr to finalize the PR.