* chore: Update arrow + tokio deps
* chore: Use bleeding edge azure
* chore: Update aws + other deps
* fix: fmt
* fix: Switch to in-house version of routerify
* fix: Upgrade to hyper 0.14
The hyper::error module is now private; hyper::Error is the public
re-export
* fix: Upgrade cloud storage to get tokio upgrade
* fix: Upgrade open_telemetry
* fix: Do not call `panic::set_hook` during another panic
Doing so leads to a double panic which aborts the process.
* fix: new h2 error who dis
Co-authored-by: Carol (Nichols || Goulding) <carol.nichols@integer32.com>
Co-authored-by: Jake Goulding <jake.goulding@integer32.com>
This makes it easier to see what's being tested and zero in on what
you're interested in rather than needing to scroll and scan through a
lot of code in the main test.
Restarting the test server and testing for WAL restoration on startup
(and possible error scenarios) was disabled in 70ff426, which also noted
that significant refactoring will likely be needed when loading info on
startup is re-enabled. With that in mind, remove this code creating an
invalid dir in the WAL dir.
* feat: implement schema merging
* docs: Update data_types/src/schema.rs
Co-authored-by: Carol (Nichols || Goulding) <193874+carols10cents@users.noreply.github.com>
* test: add test for merging schemas with no measurement names
* test: add test for merging and different measurements
* docs: clarify comments about merging
* fix: fmt
* fix: update comments to retrigger semantic pull request
Co-authored-by: Carol (Nichols || Goulding) <193874+carols10cents@users.noreply.github.com>
* feat: Add selection interface to mutable buffer and query interface
* docs: Update mutable_buffer/src/table.rs
* refactor: rename for consistency
* refactor: use map and filter_map rather than fold
This commit adds some benchmarks for `table_names` against the read
buffer's Database implementation. On my laptop these look like:
database_table_names_all_tables
time: [2.2104 us 2.2242 us 2.2381 us]
Found 2 outliers among 100 measurements (2.00%)
1 (1.00%) high mild
1 (1.00%) high severe
database_table_names_meta_pred_no_match
time: [1.8389 us 1.8488 us 1.8593 us]
Found 3 outliers among 100 measurements (3.00%)
1 (1.00%) high mild
2 (2.00%) high severe
database_table_names_single_pred_match
time: [5.5457 us 5.5694 us 5.5919 us]
Found 5 outliers among 100 measurements (5.00%)
3 (3.00%) high mild
2 (2.00%) high severe
database_table_names_multi_pred_match
time: [478.85 us 480.32 us 481.83 us]
Found 4 outliers among 100 measurements (4.00%)
2 (2.00%) high mild
2 (2.00%) high severe
database_table_names_multi_pred_match_multi_tables
time: [476.47 us 478.93 us 482.25 us]
Found 11 outliers among 100 measurements (11.00%)
4 (4.00%) high mild
7 (7.00%) high severe