This adds support for running the bucket function over a date nanos field. Code wise, this just delegates to DateTrunc, which already supports date nanos, so most of the PR is just tests and the auto-generated docs.
Resolves#118031
This PR adds support for ST_EXTENT_AGG aggregation, i.e., computing a bounding box over a set of points/shapes (Cartesian or geo). Note the difference between this aggregation and the already implemented scalar function ST_EXTENT.
This isn't a very efficient implementation, and future PRs will attempt to read these extents directly from the doc values.
We currently always use longitude wrapping, i.e., we may wrap around the dateline for a smaller bounding box. Future PRs will let the user control this behavior.
Fixes#104659.
Make the dependency checker for query plans take into account binary plans and make sure that fields required from the left hand side are actually obtained from there (and analogously for the right).
Resolves#116281
Introduces support for comparing millisecond dates with nanosecond dates, without the need for casting. Millisecond dates outside of the nanosecond date range are handled correctly.
Resolves#109995
This adds support and tests for addition and subtraction of date nanos with periods and durations. It does not include support for date_diff, which is a separate ticket (#109999). The bulk of the PR is testing, the actual date math is all handled by library functions.
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Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Re-implement `CATEGORIZE` in a way that works for multi-node clusters.
This requires that data is first categorized on each data node in a first pass, then the categorizers from each data node are merged on the coordinator node and previously categorized rows are re-categorized.
BlockHashes, used in HashAggregations, already work in a very similar way. E.g. for queries like `... | STATS ... BY field1, field2` they map values for `field1` and `field2` to unique integer ids that are then passed to the actual aggregate functions to identify which "bucket" a row belongs to. When passed from the data nodes to the coordinator, the BlockHashes are also merged to obtain unique ids for every value in `field1, field2` that is seen on the coordinator (not only on the local data nodes).
Therefore, we re-implement `CATEGORIZE` as a special BlockHash.
To choose the correct BlockHash when a query plan is mapped to physical operations, the `AggregateExec` query plan node needs to know that we will be categorizing the field `message` in a query containing `... | STATS ... BY c = CATEGORIZE(message)`. For this reason, _we do not extract the expression_ `c = CATEGORIZE(message)` into an `EVAL` node, in contrast to e.g. `STATS ... BY b = BUCKET(field, 10)`. The expression `c = CATEGORIZE(message)` simply remains inside the `AggregateExec`'s groupings.
**Important limitation:** For now, to use `CATEGORIZE` in a `STATS` command, there can be only 1 grouping (the `CATEGORIZE`) overall.
This enables date nanos support as tech preview. Basic operations, like reading values, binary comparisons, and functions that don't care about type should work, but some functions are not yet supported. Most notably, Bucket is not yet supported, although Date_Trunc is and can be used for grouping. See the docs for the full list of limitations.
relates to #109352
While working on #110008 I discovered that the Date Trunc tests were only running in folding mode, because the interval types are marked as not representable. The correct way to test this is to set the forceLiteral flag for those fields, which will (as the name suggests) force them to be literals even in non-folding tests.
Doing that turned up errors in the evaluatorToString tests, which I fixed. There are two big changes here. First, the second parameter to the evaluator is a Rounding instance, not the actual interval. Since Rounding includes some information about the specific rounding in the toString results, I am just using a starts with matcher to validate the majority of the string, rather than trying to reconstruct the expected rounding string. Second, passing in a literal null for the interval parameter folds the whole expression to null, and thus a completely different toString. I added a clause in AnyNullIsNull to account for this.
While I was in there, I moved some specific test cases to a different file. I know moving code is something we're trying to minimize right now, but this seemed worth it. The tests in question do not depend on the parameters of the test case, but all methods in the class get run for every set of parameters. This was causing these tests to be run many times with the same values, which bloats our test run time and test count. Moving them to a distinct class means they'll only be executed once per test run. I feel like this benefit outweighs the cost of git history complexity.
Now that the match and qstr functions are Tech Previewing, we should add them to the top-level functions doc page.
Co-authored-by: Craig Taverner <craig@amanzi.com>
Always return `KEYWORD` for functions that previously returned `TEXT`, because any change to the value, no matter how small, is enough to render meaningless the original analyzer associated with the `TEXT` field value. In principle, if the attribute is no longer the original `FieldAttribute`, it can no longer claim to have the type `TEXT`.
This has been done for all functions: conversion functions, aggregating functions, multi-value functions. There were several that already produced `KEYWORD` for `TEXT` input (eg. ToString, FromBase64 and ToBase64, MvZip, ToLower, ToUpper, DateFormat, Concat, Left, Repeat, Replace, Right, Split, Substring), but many others that incorrectly claimed to produce `TEXT`, while this was really a false claim. This PR makes that now strict, and includes changes to the functions' units tests to disallow the tests to expect any functions output to be `TEXT`.
One side effect of this change is that methods that take multiple parameters that require all of them to have the same type, will now treat TEXT and KEYWORD the same. This was already the case for functions like `Concat`, but is now also the case for `Greatest`, `Least`, `Case`, `Coalesce` and `MvAppend`.
An associated change is that the type casting operator `::text` has been entirely removed. It used to map onto the `ToString` function which returned type KEYWORD, and so `::text` really produced a `KEYWORD`, which is a lie, or at least a `bug`, which is now fixed. Should we ever wish to actually produce real `TEXT`, we might love the fact that this operator has been freed up for future use (although it seems likely that function will require parameters to specify the analyzer, so might never be an operator again).
### Backwards compatibility issues:
This is a change that will fail BWC tests, since we have many tests that assert on TEXT output to functions. For this reason we needed to block two scenarios:
* We used the capability `functions_never_emit_text` to prevent 7 csv-spec tests and 2 yaml tests from being run against older versions that still emit text.
* We used `skipTest` to also block those two yaml tests from being run against the latest build, but using older yaml files downloaded (as far back as 8.14).
In all cases the change observed in these tests was simply the results columns no longer having `text` type, and instead being `keyword`.
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Co-authored-by: Luigi Dell'Aquila <luigi.dellaquila@gmail.com>
`MV_SLICE` is useful, but loading values from lucene frequently sorts
them so `MV_SLICE` is not as useful as you think it is. It's mostly for
after, say, a `SPLIT`. This documents that and adds a link to the
section on multivalues.
It also moves similar docs to a separate paragraph in the docs for
easier reading.
While working on Date Nanos, I noticed that Least and Greatest didn't have support for datetime. This PR corrects that and adds tests for it.
It seems to me that resolveType() is doing the wrong thing for these functions, as it accepts types that then do not have evaluator mappings, but refactoring that seems out of scope right now.
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Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Resolves#111842
This adds a conversion function that yields DATE_NANOS. Mostly this is straight forward.
It is worth noting that when converting a millisecond date into a nanosecond date, the conversion function truncates it to 0 nanoseconds (i.e. first nanosecond of that millisecond). This is, of course, a bit of an assumption, but I don't have a better assumption we can make. I'd thought about adding a second, optional, parameter to control this behavior, but it's important that TO_DATE_NANOS extend AbstractConvertFunction, which itself extends UnaryScalarFunction, so that it will work correctly with union types. Also, it's unlikely the user will have any better guess than we do for filling in the nanoseconds.
Making that assumption does, however, create some weirdness. Consider two comparisons:
TO_DATETIME("2023-03-23T12:15:03.360103847") == TO_DATETIME("2023-03-23T12:15:03.360") will return true while TO_DATE_NANOS("2023-03-23T12:15:03.360103847") == TO_DATE_NANOS("2023-03-23T12:15:03.360") will return false. This is akin to casting between longs and doubles, where things may compare equal in one type that are not equal in the other. This seems fine, and I can't think of a better way to do it, but it's worth being aware of.
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Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
This will correct/switch "year" unit diffing from the current integer
subtraction to a crono subtraction. Consequently, two dates are (at
least) one year apart now if (at least) a full calendar year separates
them. The previous implementation simply subtracted the year part of the
dates.
Note: this parts with ES SQL's implementation of the same function,
which itself is aligned with MS SQL's implementation, which works
equivalent to an integer subtraction.
Fixes#112482.
