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Unmaintained aquamarine dependency removal #119

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1 change: 0 additions & 1 deletion Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,6 @@ keywords = ["arrow", "arrow-rs", "datafusion"]
rust-version = "1.85.1"

[dependencies]
aquamarine = "0.6.0"
arrow = "57.1.0"
arrow-schema = "57.1.0"
async-trait = "0.1.89"
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115 changes: 88 additions & 27 deletions src/materialized/dependencies.rs
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Expand Up @@ -276,7 +276,6 @@ fn get_table_name(args: &[Expr]) -> Result<&String> {
}
}

#[cfg_attr(doc, aquamarine::aquamarine)]
/// Returns a logical plan that, when executed, lists expected build targets
/// for this materialized view, together with the dependencies for each target.
///
Expand Down Expand Up @@ -304,26 +303,65 @@ fn get_table_name(args: &[Expr]) -> Result<&String> {
/// Assume that both tables are partitioned by `date` only. We desired a materialized view partitioned by `date` and stored at `s3://daily_close/`.
/// This query gives us the following logical plan:
///
/// ```mermaid
/// %%{init: { 'flowchart': { 'wrappingWidth': 1000 }}}%%
/// graph TD
/// A["Projection: <br>ticker, LAST_VALUE(trades.price) AS close, LAST_VALUE(daily_statistics.settlement_price) AS settlement_price, <mark>trades.date AS date</mark>"]
/// A --> B["Aggregate: <br>expr=[LAST_VALUE(trades.price), LAST_VALUE(daily_statistics.settlement_price)] <br>groupby=[ticker, <mark>trades.date</mark>]"]
/// B --> C["Inner Join: <br>trades.ticker = daily_statistics.ticker AND <br>trades.date = daily_statistics.reference_date AND <br><mark>daily_statistics.date BETWEEN trades.date AND trades.date + INTERVAL 2 WEEKS</mark>"]
/// C --> D["TableScan: trades <br>projection=[ticker, price, <mark>date</mark>]"]
/// C --> E["TableScan: daily_statistics <br>projection=[ticker, settlement_price, reference_date, <mark>date</mark>]"]
/// ```text
/// +--------------------------------------------------------------------+
/// | Projection: |
/// | ticker, LAST_VALUE(trades.price) AS close, |
/// | LAST_VALUE(daily_statistics.settlement_price) AS settlement_price, |
/// | **trades.date AS date** |
/// +--------------------------------------------------------------------+
/// |
/// v
/// +------------------------------------------------------+
/// | Aggregate: |
/// | expr=[LAST_VALUE(trades.price), |
/// | LAST_VALUE(daily_statistics.settlement_price)] |
/// | groupby=[ticker, **trades.date**] |
/// +------------------------------------------------------+
/// |
/// v
/// +---------------------------------------------------+
/// | Inner Join: |
/// | trades.ticker = daily_statistics.ticker AND |
/// | trades.date = daily_statistics.reference_date AND |
/// | **daily_statistics.date BETWEEN trades.date AND |
/// | trades.date + INTERVAL 2 WEEKS** |
/// +---------------------------------------------------+
/// |
/// +---------------------+--------------+
/// v v
/// +-----------------------------+ +---------------------------------------+
/// | TableScan: trades | | TableScan: daily_statistics |
/// | projection= | | projection=[ticker, settlement_price, |
/// | [ticker, price, **date**] | | reference_date, **date**] |
/// +-----------------------------+ +---------------------------------------+
/// ```
///
/// All partition-column-derived expressions are marked in yellow. We now proceed with **Inexact Projection Pushdown**, and prune all unmarked expressions, resulting in the following plan:
/// All partition-column-derived expressions are marked with double asterisks (`**`). We now proceed with **Inexact Projection Pushdown**, and prune all unmarked expressions, resulting in the following plan:
///
/// ```mermaid
/// %%{init: { 'flowchart': { 'wrappingWidth': 1000 }}}%%
/// graph TD
/// A["Projection: trades.date AS date"]
/// A --> B["Projection: trades.date"]
/// B --> C["Inner Join: <br>daily_statistics.date BETWEEN trades.date AND trades.date + INTERVAL 2 WEEKS"]
/// C --> D["TableScan: trades (projection=[date])"]
/// C --> E["TableScan: daily_statistics (projection=[date])"]
/// ```text
/// +---------------------------------+
/// | Projection: trades.date AS date |
/// +---------------------------------+
/// |
/// v
/// +-------------------------+
/// | Projection: trades.date |
/// +-------------------------+
/// |
/// v
/// +-------------------------------------------+
/// | Inner Join: |
/// | daily_statistics.date BETWEEN trades.date |
/// | AND trades.date + INTERVAL 2 WEEKS |
/// +-------------------------------------------+
/// |
/// +----------------+-----------+
/// v v
/// +---------------------+ +-----------------------------+
/// | TableScan: trades | | TableScan: daily_statistics |
/// | (projection=[date]) | | (projection=[date]) |
/// +---------------------+ +-----------------------------+
/// ```
///
/// Note that the `Aggregate` node was converted into a projection. This is valid because we do not need to preserve duplicate rows. However, it does imply that
Expand All @@ -332,15 +370,38 @@ fn get_table_name(args: &[Expr]) -> Result<&String> {
/// Now we substitute all scans with equivalent row metadata scans (up to addition or removal of duplicates), and push up the row metadata to the root of the plan,
/// together with the target path constructed from the (static) partition columns. This gives us the following plan:
///
/// ```mermaid
/// %%{init: { 'flowchart': { 'wrappingWidth': 1000 }}}%%
/// graph TD
/// A["Projection: concat('s3://daily_close/date=', date::string, '/') AS target, __meta"]
/// A --> B["Projection: __meta, trades.date AS date"]
/// B --> C["Projection: <br>concat(trades_meta.__meta, daily_statistics_meta.__meta) AS __meta, date"]
/// C --> D["Inner Join: <br><b>daily_statistics_meta</b>.date BETWEEN <b>trades_meta</b>.date AND <b>trades_meta</b>.date + INTERVAL 2 WEEKS"]
/// D --> E["TableScan: <b>trades_meta</b> (projection=[__meta, date])"]
/// D --> F["TableScan: <b>daily_statistics_meta</b> (projection=[__meta, date])"]
/// ```text
/// +----------------------------------------------------------------+
/// | Projection: |
/// | concat('s3://daily_close/date=', date::string, '/') AS target, |
/// | __meta |
/// +----------------------------------------------------------------+
/// |
/// v
/// +-----------------------------------------+
/// | Projection: __meta, trades.date AS date |
/// +-----------------------------------------+
/// |
/// v
/// +----------------------------------------------------------+
/// | Projection: |
/// | concat(trades_meta.__meta, daily_statistics_meta.__meta) |
/// | AS __meta, date |
/// +----------------------------------------------------------+
/// |
/// v
/// +-----------------------------------------------------+
/// | Inner Join: |
/// | daily_statistics_meta.date BETWEEN trades_meta.date |
/// | AND trades_meta.date + INTERVAL 2 WEEKS |
/// +-----------------------------------------------------+
/// |
/// +------------------+----------------+
/// v v
/// +-----------------------------+ +----------------------------------+
/// | TableScan: trades_meta | | TableScan: daily_statistics_meta |
/// | (projection=[__meta, date]) | | (projection=[__meta, date]) |
/// +-----------------------------+ +----------------------------------+
/// ```
///
/// Here, `__meta` is a column containing a list of structs with the row metadata for each source file. The final query has this struct column
Expand Down