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RFC: lambda expression #69

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---
feature: my_excited_feature
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authors:
- "st1page"
start_date: "2023/08/01"
---

# Lambda expression

## Background

To better support user's semi-structure data, We have supported `Array` and `Struct` types compatible with PG. But in some user's requirement, We found we can not support them easily.

### Induction
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```SQL
CREATE TABLE t(
arr_i int[],
arr_json jsonb[],
k int primary key
);
```

Consider we have the table definition and want to get the sum of the `arr_i` and sum of `arr_json`'s field `x`. In PG user can write the correlated subquery like this.

```SQL
dev=# explain (verbose)
select *,
(select sum(i) from unnest(arr_i) i) as sum_i,
(select sum((i->'x')::int) from unnest(arr_json) i) as sum_x
from t;
QUERY PLAN
------------------------------------------------------------------------------------------
Seq Scan on public.t (cost=0.00..294.75 rows=850 width=84)
Output: t.arr_i, t.arr_json, t.k, (SubPlan 1), (SubPlan 2)
SubPlan 1
-> Aggregate (cost=0.13..0.14 rows=1 width=8)
Output: sum(i.i)
-> Function Scan on pg_catalog.unnest i (cost=0.00..0.10 rows=10 width=4)
Output: i.i
Function Call: unnest(t.arr_i)
SubPlan 2
-> Aggregate (cost=0.18..0.19 rows=1 width=8)
Output: sum(((i_1.i -> 'x'::text))::integer)
-> Function Scan on pg_catalog.unnest i_1 (cost=0.00..0.10 rows=10 width=32)
Output: i_1.i
Function Call: unnest(t.arr_json)
```

The correlated column is in the table function `Unnest` and it is hard to do the sub-query decorrelation. Unfortunately, we do not implement `Apply` operator in streaming execution.
Their are some equivalent query that I think could be decorrelated plan. But they all are complex and with high cost.

```SQL
select * from t t1
join (
select k, sum(i)
from (select k, unnest(arr_i) i from t) u group by k
) t2 on t1.k = t2.k
join (
select k, sum((i->'x')::int)
from (select k, unnest(arr_json) i from t) u group by k
) t3 on t1.k = t3.k;
/* Or */
select distinct on(k)
k, arr_i, arr_json,
sum(i) over (partition by k),
sum((j->'x')::int) over (partition by k)
from ( select *, unnest(arr_i) i, unnest(arr_json) j from t) foo;
```

In investigation of other SQL systems, there are 3 levels' enhancement on this kind of query, this RFC will be concerned with the three levels.

### level 1: array aggregate expression

It is a kind of SQL **scalar expression** which accept a array type and return the array's aggregation result, such as `array_sum`, `array_max`. It can solve some part of the issue but can not handle the complex nested datatype in the array.

```SQL
select *, array_sum(arr_i) from t;
```

### level 2: scalar lambda expression

Implement lambda expression function to make user can define there `transform` logic for array's each elements.
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risingwavelabs/risingwave#11123
Another part of this solution is to implement transform and reduce functions, whose inputs contain both data column and the lambda expression.

It can be used with the array aggregate expression together.

```SQL
select *,
array_sum(arr_i),
array_sum(transform(arr_json, v -> (v->'x')::int))
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It'll be a little confused that use -> for two purposes, and increase the complexity of parser and binder.

I'd propose using => for lambda function.

from t;
```

### level 3: aggregate lambda expression

Based on the level 2, these system support the `reduce` to make user use lambda function implement the aggregation logic.

```SQL
select *,
reduce(arr_i, 0, (s, v) -> s + v),
reduce(arr_json, 0, (s, v) -> s + (v->'x')::int)),
from t;
```

Btw, It often can be used to express UDAF too.

## Investigation

Array expressions support. (All of them supports the `array_size/cardinality`, `unnest/flatten/explode` which is not listed in the table.)

| | array_cnt | array_sum | array_cum_sum | array_avg | array_max/min | array_sort | array_distinct | array_zip | Unnest<br>with idx |
| ---------- | :-------: | :-------: | :-----------: | :-------: | :--------------: | :--------: | :------------: | :-------: | :----------------: |
| Databricks | | | | | Y | Y | Y | Y | Y |
| Trino | | | | | Y | Y | Y | Y | Y |
| PrestoDB | Y | Y | Y | Y | Y | Y | Y | Y | Y |
| Starrocks | Y | Y | Y | Y | Y | Y | Y | Y | |
| Doris | Y | Y | Y | Y | Y | Y | Y | Y | |
| ClickHouse | Y | Y | Y | Y | Y | Y | Y | Y | |
| DuckDB | Y | Y | Y | Y | Y | Y | Y | | |
| Vertica | Y | Y | | Y | Y | | | | Y |
| FlinkSQL | | | | Y | only MAX, no MIN | | | | |
| Snowflake | | | | | | | Y | | Y |



Array expressions with lambda expression paramter

| | transform | transform<br>with idx | filter | filter<br>with idx | sort cmp | reduce | UDAF | Lambda Limitations |
| ---------- | --------- | --------------------- | ------ | ------------------ | -------- | ------ | :---: | ---------------------------------------------------------------------------------------------------------------------- |
| Databricks | Y | Y | Y | Y | Y | Y | Y | No subquery, No SQL UDF |
| Trino | Y | | Y | | Y | Y | Y | No subquery, No Aggregations |
| PrestoDB | Y | | Y | | Y | Y | Y | No subquery, No Aggregations |
| Starrocks | Y | | Y | | Y | | | |
| Doris | Y | | Y | | Y | | | |
| ClickHouse | Y | Y | Y | Y | Y | | | |
| DuckDB | Y | | Y | | | | | |
| Vertica | Y | Y | Y | Y | Y | | | arg name [limitations](https://docs.vertica.com/23.3.x/en/sql-reference/language-elements/lambda-functions/#arguments) |
| FlinkSQL | | | | | | | | |
| Snowflake | | | | | | | | |

- No capture in lambda function, I think No system support refer column or sub query in the lambda function
- Snowflake do not anything about it and fall back to the `unnest/flatten/explode` https://stackoverflow.com/questions/62807878/how-to-get-maximum-value-from-an-array-column-in-snowflake
- [to confirm] Accroding to the doc, FlinkSQL only support flatten the array in join clause https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#array-expansion
- There are some Syntactic sugar.
- Trino/PrestoDB/Starrocks: support pass multiple arrays in `unnest/flatten/explode` so that the arrays will do zip first.
- ClickHousr/Starrocks: `array_sum(lambda_function, arr1,arr2...) = array_sum(array_map(lambda_function, arr1,arr2...))`
- ClickHouse support any aggregation function on array directly, e.g. `SELECT arrayReduce('max', [1, 2, 3]);`

### Refers
- Databricks
- https://docs.databricks.com/sql/language-manual/sql-ref-lambda-functions.html
- https://docs.databricks.com/sql/language-manual/sql-ref-functions-builtin-alpha.html
- Trino
- https://trino.io/docs/current/functions/lambda.html
- https://trino.io/docs/current/functions/array.html?highlight=array#reduce
- https://trino.io/docs/current/functions/aggregate.html?highlight=lambda+expressions#lambda-aggregate-functions
- https://trino.io/docs/current/functions/array.html?highlight=array#array_max
- PrestoDB
- https://prestodb.io/blog/2020/03/02/presto-lambda
- https://prestodb.io/docs/current/functions/lambda.html
- https://prestodb.io/docs/current/functions/aggregate.html#reduce_agg
- Starrocks
- https://docs.starrocks.io/en-us/3.0/sql-reference/sql-functions/Lambda_expression
- Doris
- https://doris.apache.org/docs/dev/sql-manual/sql-functions/array-functions/array_max
- https://doris.apache.org/search?q=lambda
- ClickHouse
- https://clickhouse.com/docs/en/sql-reference/functions/array-functions
- https://clickhouse.com/docs/en/sql-reference/functions#higher-order-functions---operator-and-lambdaparams-expr-function
- DuckDB
- https://duckdb.org/docs/sql/functions/nested#list-aggregates
- https://duckdb.org/docs/sql/functions/nested#lambda-functions
- Vertica
- https://docs.vertica.com/23.3.x/en/sql-reference/language-elements/lambda-functions/
- FlinkSQL
- https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/functions/systemfunctions/

## Design
[TODO]
We can support missing array function's at first and they has been implemented for aggregators so we can reuse the code.
For the lambda expression, it is like a anonymous SQL UDF. We can implement the SQL UDF first and design lambda expression later.

## Unresolved questions
[TODO]

- Are there some questions that haven't been resolved in the RFC?
- Can they be resolved in some future RFCs?
- Move some meaningful comments to here.

## Alternatives
[TODO]
What other designs have been considered and what is the rationale for not choosing them?

## Future possibilities
[TODO]
Some potential extensions or optimizations can be done in the future based on the RFC.