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https://issues.apache.org/jira/browse/ARROW-10585?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andy Grove updated ARROW-10585:
-------------------------------
Description:
Add join support to DataFrame and LogicalPlan.
h2. Logical Plan
My initial thoughts on the design of the LogicalPlan struct would be:
{code:java}
struct InnerJoin {
left: Box<LogicalPlan>,
right: Box<LogicalPlan>,
left_keys: Vec<Expr>,
right_keys: Vec<Expr>
} {code}
The left_keys and right_keys vectors must have the same length. Example
pseudo-code:
{code:java}
let join = InnerJoin {
left: read_parquet("customers"),
right: read_parquer("orders"),
left_keys: vec![col("id")],
right_keys: vec![col("customer_id")]
}; {code}
h2. DataFrame
{code:java}
let customer = ctx.read_parquet("customers").alias("c");
let orders = ctx.read_parquet("orders").alias("o");
// generic join method that can support all types of join
let join = customer.join(orders, col("c.id").eq("o.customer_id"))
// or we could start with a more specific equijoin method
let join = customer.inner_join(orders, vec![col("id")],
vec![col("customer_id")]);{code}
was:Add join support to DataFrame and LogicalPlan
> [Rust] [DataFusion] Add join support to DataFrame and LogicalPlan
> -----------------------------------------------------------------
>
> Key: ARROW-10585
> URL: https://issues.apache.org/jira/browse/ARROW-10585
> Project: Apache Arrow
> Issue Type: Sub-task
> Components: Rust - DataFusion
> Reporter: Andy Grove
> Priority: Major
>
> Add join support to DataFrame and LogicalPlan.
> h2. Logical Plan
> My initial thoughts on the design of the LogicalPlan struct would be:
> {code:java}
> struct InnerJoin {
> left: Box<LogicalPlan>,
> right: Box<LogicalPlan>,
> left_keys: Vec<Expr>,
> right_keys: Vec<Expr>
> } {code}
> The left_keys and right_keys vectors must have the same length. Example
> pseudo-code:
> {code:java}
> let join = InnerJoin {
> left: read_parquet("customers"),
> right: read_parquer("orders"),
> left_keys: vec![col("id")],
> right_keys: vec![col("customer_id")]
> }; {code}
> h2. DataFrame
> {code:java}
> let customer = ctx.read_parquet("customers").alias("c");
> let orders = ctx.read_parquet("orders").alias("o");
> // generic join method that can support all types of join
> let join = customer.join(orders, col("c.id").eq("o.customer_id"))
> // or we could start with a more specific equijoin method
> let join = customer.inner_join(orders, vec![col("id")],
> vec![col("customer_id")]);{code}
>
>
>
>
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