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SELECT * FROM $satellites WHERE $PARTITION.username('green')

This would instruct the engine to try (it can ignore if it wants to) to prune the by_username partitioning to the username=green partition.

Work out how best to implement FLATTEN functionality, to flatten a struct/dict into separate columns.

https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.flatten

It appears to want to work on a table - perhaps get the struct column, remove from the source table, apply to filter the column (now a table) and then add the now struct column table to the original table?

• SQL HAVING
• SQL ORDER BY
• $DATE Filters - not ready to start

• Sample dataset with lists and structs

The current DAG allows for an arbitrary set of unordered in coming edges, which means the planner can't include planning order or left/right nodes. This should be rewritten to have an order to the incoming edges (but not all nodes need to care)

Where Query planners are currently passed to nodes, these should be relations to other nodes.

The Node execution start at the final node and pull chunks through the DAG.

The Planner should plan the reads, including which portions to read and which selections and projections to push down to the reader operator.

This should then be described in JSON (or similar), so it can be hashed and used to persist partial reads to enable caching of 'results'

SELECT *
FROM (VALUES (1,2),(3,4),(340,455)) AS t(a,b)

Allow tables to be defined in the SQL which don't need to be backed by a on disk dataset.

The will be useful for small look ups, e.g. to order by an ordinal field like HIGH, MEDIUM, LOW, or to unify nomenclature like TRUE, YES, ON

https://blog.christianperone.com/2020/01/gandiva-using-llvm-and-arrow-to-jit-and-evaluate-pandas-expressions/

• CROSS JOIN
• JOIN
• INNER JOIN _ ON
• INNER JOIN _ USING
• LEFT JOIN _ ON _

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The Selection node contains a chunk of code from the Evaluation node because the functions in the WHERE are not evaluated ahead of this node.

EXPLAIN 
SELECT * FROM (SELECT * FROM $astronauts)

fails with this error: __repr__ returned non-string (type QueryPlanner)

when aggregation nodes rebuild the metadata, they don't recreate declared aliases (AS) for columns, only the implied aliases (e.g. table_name.column_name)

Use an AST to parse SQL, color code the code.

Shift-Alt-F to format the text.

Test performance first, initial comparison testing had the existing code running faster.

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To replicate:

SELECT * FROM $astronauts
WHERE VARCHAR(Group) = '10'

Type mismatch, unable to compare OTHER (NoneType) with VARCHAR

Whilst NoneType is technically correct (the function column doesn't exist so is nulls), this is not expected or helpful to users.

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Filter by dates, use the virtual date filters

Allow functions to be called on the result of other functions

CROSS JOIN
LEFT JOIN
INNER JOIN

This currently works by assigning a random number to each row and then sorting by the new column, what would probably be faster is creating a 0..n list, where n is the number of rows, shuffling this list and then using .take() to get the shuffled rows (in chunks)

https://cylondata.org/docs/python/

This may have no benefit if not distributed, where a device has 2 or more CPUs, is it practical and is it useful?

https://github.com/axiomhq/hyperloglog
https://github.com/plar/go-adaptive-radix-tree

UNION wil likely require changes to the parser as it may need to dynamically get the statement being worked on from a list, rather than hard-coded to 0

https://www.sqlshack.com/temporal-tables-in-sql-server/

The FOR SYSTEM_TIME clause has many variations and options. It is further classified into four temporal sub-clauses. This provides a way to query the data across current and history tables.

AS OF
FROM TO
BETWEEN AND
CONTAINED IN ( , )
ALL

The AS OF clause is used when there is a need to rebuild the original state of the data and need to know the state it was at any specific time in the past. This is possible by specifying the date time as its input.

Support for table subqueries is a MUST

SELECT results.account
FROM (SELECT * FROM Players) AS results;

Support for SELECT is a could

SELECT account, (SELECT mascot FROM Guilds WHERE Players.guild = id) AS player_mascot
FROM Players;

support for IN is a should

SELECT account
FROM users
WHERE account IN (SELECT account FROM billing WHERE paid = true)

• FROM table AS name

need to be able to SELECT, WHERE, ORDER and GROUP on the columns using either the aliases or the original names.

projection should result in unambiguous names, but not be just adding the prefixes to the column names

This returns records:

SELECT * FROM $astronauts
WHERE Group = 10

but this returns no records:

SELECT * FROM $astronauts
WHERE Group = '10'

Rather than return zero records, it should report a type mismatch.

Move the scan planning to the init for the scanner.

This will allow us to access the expected rows and other statistics when optimizing.

We can then use the expected rows to determine which dataset to hold in memory when joining.

This should be the information returned by DESCRIBE dataset which means changes to the writer to record more information.

Per column

Name
Type
Nulls?
Unique
Min
Max
Description

The Planner should plan the reads, including which portions to read and which selections and projections to push down to the reader operator.

This should then be described in JSON (or similar), so it can be hashed and used to persist partial reads to enable caching of 'results'

Some documentation has typos, is incomplete or incorrect.

Documentation is in different formats and isn't in the desired structure

Allow for infix notation, e.g.

SELECT first name || last name AS name

And

WHERE age - 18 = 0

• column aliases
• date functions

Split the planner into functional parts:

• the planning
• the plan
• the execution

Each of these new components will be smaller and simpler.

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Is your feature request related to a problem? Please describe.
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Table information

Size
File type

https://dev.mysql.com/doc/refman/8.0/en/show-tables.html

SELECT Birth_Place['state'] FROM $astronauts

"projection": [
            {
              "UnnamedExpr": {
                "MapAccess": {
                  "column": {
                    "Identifier": {
                      "value": "Birth_Place",
                      "quote_style": null
                    }
                  },
                  "keys": [
                    {
                      "Value": {
                        "SingleQuotedString": "state"
                      }
                    }
                  ]
                }
              }
            }
          ]

SELECT Misions[0] FROM $astronauts

"MapAccess": {
                  "column": {
                    "Identifier": {
                      "value": "Misions",
                      "quote_style": null
                    }
                  },
                  "keys": [
                    {
                      "Value": {
                        "Number": [
                          "0",
                          false
                        ]
                      }
                    }

FOR clauses should be specific to the context, for example

SELECT event
FROM log
FOR TODAY
WHERE event NOT IN 
   (
    SELECT event
    FROM log
    FOR YESTERDAY
    )

This will need an alternate implementation to the RegEx based one.

WITH x AS (SELECT a, MAX(b) AS b FROM t GROUP BY a)
SELECT a, b FROM x;

Initial design

Execute first, then keep the result in memory. Include in the subsequent queries as a LITERAL_TABLE.

This would probably work with a new reader (literal_tabel_reader), which would also need to be added to the factory.

NOT DONE =>

    /// SUBSTRING(<expr> [FROM <expr>] [FOR <expr>])
    Substring {
        expr: Box<Expr>,
        substring_from: Option<Box<Expr>>,
        substring_for: Option<Box<Expr>>,
    },

SELECT TRIM('#! ' FROM '    #SQL Tutorial!    ') AS TrimmedString;
    /// TRIM([BOTH | LEADING | TRAILING] <expr> [FROM <expr>])\
    /// Or\
    /// TRIM(<expr>)
    Trim {
        expr: Box<Expr>,
        // ([BOTH | LEADING | TRAILING], <expr>)
        trim_where: Option<(TrimWhereField, Box<Expr>)>,
    },

DONE =>

    /// EXTRACT(DateTimeField FROM <expr>)
    Extract {
        field: DateTimeField,
        expr: Box<Expr>,
    },

    /// TRY_CAST an expression to a different data type e.g. `TRY_CAST(foo AS VARCHAR(123))`
    //  this differs from CAST in the choice of how to implement invalid conversions
    TryCast {
        expr: Box<Expr>,
        data_type: DataType,
    },

Having cardinality information will help with query planning - for example deciding between a hash or tree for the accumulation as each perform better at the different ends of the spectrum.

SELECT * FROM employees PARTITION (p1);

https://dev.mysql.com/doc/refman/5.7/en/partitioning-selection.html

This isn't supported by the AST, so will need a custom interpreter like the temporal queries.

In order to have actual utility, the SQL engine needs some code constructs, CASE statements should be convertible to a UDF function which we can run in the eval step.

This allows columns that are arrays, like a list of references, to be filtered on like this:

SELECT * FROM findings CROSS JOIN UNNEST(cves) as cve WHERE cve = 'CVE-2017-0144

or implied CROSS JOIN like this:

SELECT * FROM findings, UNNEST(cves) as cve WHERE cve = 'CVE-2017-0144

see:
https://medium.com/firebase-developers/using-the-unnest-function-in-bigquery-to-analyze-event-parameters-in-analytics-fb828f890b42

SELECT * FROM UNNEST(['foo', 'bar', 'baz', 'qux', 'corge', 'garply', 'waldo', 'fred']) AS element

include hit/miss counts to the statistics

test client: https://github.com/trinodb/trino-python-client
API documentation: https://trino.io/docs/current/develop/client-protocol.html