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Docs: Update SQL status page #6736

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Jun 23, 2023
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Docs: Update SQL status page #6736

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Docs: Update SQL status page
alamb Jun 21, 2023
0048678
Add catalog and data inserts
alamb Jun 21, 2023
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Merge remote-tracking branch 'apache/main' into alamb/window_function…
alamb Jun 23, 2023
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Try to clarify that support for nested types is in progress, but not …
alamb Jun 23, 2023
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prettier
alamb Jun 23, 2023
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127 changes: 37 additions & 90 deletions docs/source/user-guide/sql/sql_status.md
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Expand Up @@ -34,106 +34,53 @@

## SQL Support

- [x] Projection
- [x] Filter (WHERE)
- [x] Filter post-aggregate (HAVING)
- [x] Limit
- [x] Aggregate
- [x] Common math functions
- [x] cast
- [x] try_cast
- [x] Projection (`SELECT`)
- [x] Filter (`WHERE`)
- [x] Filter post-aggregate (`HAVING`)
- [x] Sorting (`ORDER BY`)
- [x] Limit (`LIMIT`
- [x] Aggregate (`GROUP BY`)
- [x] cast /try_cast
- [x] [`VALUES` lists](https://www.postgresql.org/docs/current/queries-values.html)
- Postgres compatible String functions
- [x] ascii
- [x] bit_length
- [x] btrim
- [x] char_length
- [x] character_length
- [x] chr
- [x] concat
- [x] concat_ws
- [x] initcap
- [x] left
- [x] length
- [x] lpad
- [x] ltrim
- [x] octet_length
- [x] regexp_replace
- [x] repeat
- [x] replace
- [x] reverse
- [x] right
- [x] rpad
- [x] rtrim
- [x] split_part
- [x] starts_with
- [x] strpos
- [x] substr
- [x] to_hex
- [x] translate
- [x] trim
- Conditional functions
- [x] nullif
- [x] case
- [x] coalesce
- Approximation functions
- [x] approx_distinct
- [x] approx_median
- [x] approx_percentile_cont
- [x] approx_percentile_cont_with_weight
- Common date/time functions
- [ ] Basic date functions
- [ ] Basic time functions
- [x] Basic timestamp functions
- [x] [to_timestamp](./scalar_functions.md#to_timestamp)
- [x] [to_timestamp_millis](./scalar_functions.md#to_timestamp_millis)
- [x] [to_timestamp_micros](./scalar_functions.md#to_timestamp_micros)
- [x] [to_timestamp_seconds](./scalar_functions.md#to_timestamp_seconds)
- [x] [extract](./scalar_functions.md#extract)
- [x] [date_part](./scalar_functions.md#date_part)
- nested functions
- [x] Array of columns
- [x] [String Functions](./scalar_functions.md#string-functions)
- [x] [Conditional Functions](./scalar_functions.md#conditional-functions)
- [x] [Time and Date Functions](./scalar_functions.md#time-and-date-functions)
- [x] [Math Functions](./scalar_functions.md#math-functions)
- [x] [Aggregate Functions](./aggregate_functions.md) (`SUM`, `MEDIAN`, and many more)
- [x] Schema Queries
- [x] SHOW TABLES
- [x] SHOW COLUMNS FROM <table/view>
- [x] SHOW CREATE TABLE <view>
- [x] information_schema.{tables, columns, views}
- [ ] information_schema other views
- [x] Sorting
- [ ] Nested types
- [ ] Lists
- [x] `SHOW TABLES`
- [x] `SHOW COLUMNS FROM <table/view>`
- [x] `SHOW CREATE TABLE <view>`
- [x] Basic SQL [Information Schema](./information_schema.md) (`TABLES`, `VIEWS`, `COLUMNS`)
- [ ] Full SQL [Information Schema](./information_schema.md) support
- [x] Support for nested types (`ARRAY`/`LIST` and `STRUCT`)- see [Array Functions](./scalar_functions.md#array-functions)
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I'm not sure whether DF have fully supported it

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Yes, you are right that the support is only partial - I'll see if I can find some way to make this clearer

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I think this better reflects the current status:

Screenshot 2023-06-23 at 12 04 47 PM

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@ozankabak ozankabak Jun 23, 2023 •
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I think good JSON support is very important for Datafusion to get more traction in the general community. SQL2023 has some good stuff about this (see here for a good summary.).

While we are on the SQL support/ease-of-use topic, this DuckDB page is also a good list of desiderata for us (some of these we implemented already).

I plan to actively talk about/promote Datafusion in various venues once we get into a state where these things "just work".

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I think good JSON support is very important for Datafusion to get more traction in the general community. SQL2023 has some good stuff about this (see here for a good summary.).

I agree.

While we are on the SQL support/ease-of-use topic, this DuckDB page is also a good list of desiderata for us (some of these we implemented already).

It would be great to file tickets about these features -- I have found clearly written tickets with a "good first issue" often attracts contributions. If you have a chance to file the tickets that would be awesome, otherwise I will try and find time to do so

I plan to actively talk about/promote Datafusion in various venues once we get into a state where these things "just work".

I think it is a balance -- part of the way we grow the DataFusion community (to get the resources to make it better) is to talk about it publically.

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We will file tickets for these 👍

- [x] Nested types (`ARRAY`/`LIST` and `STRUCT`)- see [Array Functions](./scalar_functions.md#array-functions)
- [x] Subqueries
- [x] Common table expressions
- [x] Set Operations
- [x] UNION ALL
- [x] UNION
- [x] INTERSECT
- [x] INTERSECT ALL
- [x] EXCEPT
- [x] EXCEPT ALL
- [x] Joins
- [x] INNER JOIN
- [x] LEFT JOIN
- [x] RIGHT JOIN
- [x] FULL JOIN
- [x] CROSS JOIN
- [ ] Window
- [x] Empty window
- [x] Common window functions
- [x] Window with PARTITION BY clause
- [x] Window with ORDER BY clause
- [ ] Window with FILTER clause
- [ ] [Window with custom WINDOW FRAME](https://github.com/apache/arrow-datafusion/issues/361)
- [ ] UDF and UDAF for window functions
- [x] Common Table Expressions (CTE)
- [x] Set Operations (`UNION [ALL]`, `INTERSECT [ALL]`, `EXCEPT[ALL]`)
- [x] Joins (`INNER`, `LEFT`, `RIGHT`, `FULL`, `CROSS`)
- [x] Window Functions
- [x] Empty (`OVER()`)
- [x] Partitioning and ordering: (`OVER(PARTITION BY <..> ORDER BY <..>)`)
- [x] Custom Window (`ORDER BY time ROWS BETWEEN 2 PRECEDING AND 0 FOLLOWING)`)
- [x] User Defined Window and Aggregate Functions

## Runtime

- [x] Streaming Grouping
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I am not sure if there are other features to highlight in the RUNTIME

- [x] Streaming Window Evaluation
- [x] Memory limits enforced
- [x] Spilling (to disk) Sort
- [ ] Spilling (to disk) Grouping
- [ ] Spilling (to disk) Joins

## Data Sources

In addition to allowing arbitrary datasources via the `TableProvider`
trait, DataFusion includes built in support for the following formats:

- [x] CSV
- [x] Parquet primitive types
- [x] Parquet nested types
- [x] Parquet (for all primitive and nested types)
- [x] JSON
- [x] Avro
- [x] Arrow