* Inline cast to date
* Update docs/changelog/123460.yaml
* New capability for `::date` casting
* More tests
* Update tests
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Co-authored-by: Fang Xing <155562079+fang-xing-esql@users.noreply.github.com>
Resolves#123053
This adds the thread name to the driver sleep profile output.
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Co-authored-by: elasticsearchmachine <infra-root+elasticsearchmachine@elastic.co>
Adds options to QSTR function.
#118619 added named function parameters. This PR uses this mechanism for allowing query string function parameters, so query string parameters can be used in ES|QL.
Closes#120933
The original work at https://github.com/elastic/elasticsearch/pull/106065 did not support geospatial types with this comment:
> I made this work for everything but geo_point and cartesian_point because I'm not 100% sure how to integrate with those. We can grab those in a follow up.
The geospatial types should be possible to collect using the VALUES aggregation with similar behavior to the `ST_COLLECT` OGC function, based on the Elasticsearch convention that treats multi-value geospatial fields as behaving similarly to any geometry collection. So this implementation is a trivial addition to the existing values types support.
This updates the kibana signature json files in two ways:
* Renames `eval` to `scalar` - that's the name we use inside of ESQL and
we may as well make the name the same.
* Calls the `CATEGORIZE` and `BUCKET` function `grouping` because they
can only be used in the "grouping" positions of the `STATS` command.
Closes#113411
Resolves#109999
This adds support for date nanos in the date diff function, as well as mixed nanos/millis use cases.
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Co-authored-by: elasticsearchmachine <infra-root+elasticsearchmachine@elastic.co>
* ESQL: Signatures for `NOT IN` et al
This generates signatures for `NOT IN`, `NOT LIKE`, and `NOT RLIKE`
using a small hack on top of the process used to generate the signatures
for `IN`, `LIKE`, and `RLIKE`. This is a very perl-worth hack, replacing
`LIKE` with `NOT LIKE` in the description. But it's useful for our
kibana friends and if we need to make it nicer we can do so later.
* Zap
There were different error cases with `ROUND(number, decimals)`:
- Decimals accepted unsigned longs, but threw a 500 with a `can't process [unsigned_long -> long]` in the cast evaluator
- Fixed by improving the `resolveType()`
- If the number was a BigInteger unsigned long, there were 2 cases throwing an exception:
1. Negative decimals outside the range of integer: Error
2. Negative decimals insie the range of integer, but "big enough" for `BigInteger.TEN.pow(...)` to throw a `BigInteger would overflow supported range`
3. -19 decimals with big unsigned longs like `18446744073709551615` was throwing an `unsigned_long overflow`
Also, when the number is a BigInteger and the decimals is a big negative (but not big enough to throw), it may be **very** slow. Taking _many_ seconds for a single computation (It tries to calculate a `10^(big number)`. I didn't do anything here, but I wonder if we should limit it.
To solve most of the cases, a warnExceptions was added for the overflow case, and a guard clause to return 0 for <-19 decimals on unsigned longs.
Another issue is that rounding to a number like 7 to -1 returns 0 instead of 10, which may be considered an error. But it's consistent, so I'm leaving it to another PR
This reduces the number of test cases in ESQL a little more ala #119678.
It migrates a few random tests and all of the multivalue functions:
```
92775 -> 43760
3m45 -> 4m04
```
This adds a few more error test cases that were missing to make sure it all
lines up well. And it fixes a few error messages in a few functions. That's
*likely* where the extra time goes.
This adds support for passing Date Nanos into the Date Format function. It works for both the single argument and two argument versions. Format strings are unchanged, as the same formatting logic works for both resolutions.
resolves#109994
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Co-authored-by: elasticsearchmachine <infra-root+elasticsearchmachine@elastic.co>
I forgot to link the ToDateNanos docs when I merged that function.
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Co-authored-by: elasticsearchmachine <infra-root+elasticsearchmachine@elastic.co>
`fold` can be surprisingly heavy! The maximally efficient/paranoid thing
would be to fold each expression one time, in the constant folding rule,
and then store the result as a `Literal`. But this PR doesn't do that
because it's a big change. Instead, it creates the infrastructure for
tracking memory usage for folding as plugs it into as many places as
possible. That's not perfect, but it's better.
This infrastructure limit the allocations of fold similar to the
`CircuitBreaker` infrastructure we use for values, but it's different
in a critical way: you don't manually free any of the values. This is
important because the plan itself isn't `Releasable`, which is required
when using a real CircuitBreaker. We could have tried to make the plan
releasable, but that'd be a huge change.
Right now there's a single limit of 5% of heap per query. We create the
limit at the start of query planning and use it throughout planning.
There are about 40 places that don't yet use it. We should get them
plugged in as quick as we can manage. After that, we should look to the
maximally efficient/paranoid thing that I mentioned about waiting for
constant folding. That's an even bigger change, one I'm not equipped
to make on my own.
This wires up the randomized testing for DateFormat. Prior to this PR, none of the randomized testing was hitting the one parameter version of the function, so I wired that up as well. This required some compromises on the type signatures, see comments in line.less
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Co-authored-by: elasticsearchmachine <infra-root+elasticsearchmachine@elastic.co>
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
