Deduplicate README.md (#79)

* Deduplicate README.md

* Remove CONTRIBUTING.md as it is no longer relevant

Co-authored-by: Sathis Kumar <sathis.kumar@udemy.com>

datafusion/DEVELOPERS.md → DEVELOPERS.md

@@ -35,40 +35,40 @@ DataFusion is written in Rust and it uses a standard rust toolkit:
 Below is a checklist of what you need to do to add a new scalar function to DataFusion:
 
 * Add the actual implementation of the function:
-  * [here](src/physical_plan/string_expressions.rs) for string functions
-  * [here](src/physical_plan/math_expressions.rs) for math functions
-  * [here](src/physical_plan/datetime_expressions.rs) for datetime functions
-  * create a new module [here](src/physical_plan) for other functions
-* In [src/physical_plan/functions](src/physical_plan/functions.rs), add:
+  * [here](datafusion/src/physical_plan/string_expressions.rs) for string functions
+  * [here](datafusion/src/physical_plan/math_expressions.rs) for math functions
+  * [here](datafusion/src/physical_plan/datetime_expressions.rs) for datetime functions
+  * create a new module [here](datafusion/src/physical_plan) for other functions
+* In [src/physical_plan/functions](datafusion/src/physical_plan/functions.rs), add:
   * a new variant to `BuiltinScalarFunction`
   * a new entry to `FromStr` with the name of the function as called by SQL
   * a new line in `return_type` with the expected return type of the function, given an incoming type
   * a new line in `signature` with the signature of the function (number and types of its arguments)
   * a new line in `create_physical_expr` mapping the built-in to the implementation
   * tests to the function.
-* In [tests/sql.rs](tests/sql.rs), add a new test where the function is called through SQL against well known data and returns the expected result.
-* In [src/logical_plan/expr](src/logical_plan/expr.rs), add:
+* In [tests/sql.rs](datafusion/tests/sql.rs), add a new test where the function is called through SQL against well known data and returns the expected result.
+* In [src/logical_plan/expr](datafusion/src/logical_plan/expr.rs), add:
   * a new entry of the `unary_scalar_expr!` macro for the new function.
-* In [src/logical_plan/mod](src/logical_plan/mod.rs), add:
+* In [src/logical_plan/mod](datafusion/src/logical_plan/mod.rs), add:
   * a new entry in the `pub use expr::{}` set.
 
 ## How to add a new aggregate function
 
 Below is a checklist of what you need to do to add a new aggregate function to DataFusion:
 
 * Add the actual implementation of an `Accumulator` and `AggregateExpr`:
-  * [here](src/physical_plan/string_expressions.rs) for string functions
-  * [here](src/physical_plan/math_expressions.rs) for math functions
-  * [here](src/physical_plan/datetime_expressions.rs) for datetime functions
-  * create a new module [here](src/physical_plan) for other functions
-* In [src/physical_plan/aggregates](src/physical_plan/aggregates.rs), add:
+  * [here](datafusion/src/physical_plan/string_expressions.rs) for string functions
+  * [here](datafusion/src/physical_plan/math_expressions.rs) for math functions
+  * [here](datafusion/src/physical_plan/datetime_expressions.rs) for datetime functions
+  * create a new module [here](datafusion/src/physical_plan) for other functions
+* In [src/physical_plan/aggregates](datafusion/src/physical_plan/aggregates.rs), add:
   * a new variant to `BuiltinAggregateFunction`
   * a new entry to `FromStr` with the name of the function as called by SQL
   * a new line in `return_type` with the expected return type of the function, given an incoming type
   * a new line in `signature` with the signature of the function (number and types of its arguments)
   * a new line in `create_aggregate_expr` mapping the built-in to the implementation
   * tests to the function.
-* In [tests/sql.rs](tests/sql.rs), add a new test where the function is called through SQL against well known data and returns the expected result.
+* In [tests/sql.rs](datafusion/tests/sql.rs), add a new test where the function is called through SQL against well known data and returns the expected result.
 
 ## How to display plans graphically
 

README.md

@@ -97,8 +97,8 @@ async fn main() -> datafusion::error::Result<()> {
   let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
 
   let df = df.filter(col("a").lt_eq(col("b")))?
-           .aggregate(vec![col("a")], vec![min(col("b"))])?
-           .limit(100)?;
+          .aggregate(vec![col("a")], vec![min(col("b"))])?
+          .limit(100)?;
 
   // execute and print results
   let results: Vec<RecordBatch> = df.collect().await?;
@@ -141,11 +141,11 @@ DataFusion also includes a simple command-line interactive SQL utility. See the
 - [x] SQL Parser
 - [x] SQL Query Planner
 - [x] Query Optimizer
- - [x] Constant folding
- - [x] Join Reordering
- - [x] Limit Pushdown
- - [x] Projection push down
- - [x] Predicate push down
+- [x] Constant folding
+- [x] Join Reordering
+- [x] Limit Pushdown
+- [x] Projection push down
+- [x] Predicate push down
 - [x] Type coercion
 - [x] Parallel query execution
 
@@ -213,7 +213,9 @@ DataFusion also includes a simple command-line interactive SQL utility. See the
   - [ ] MINUS
 - [x] Joins
   - [x] INNER JOIN
-  - [ ] CROSS JOIN
+  - [x] LEFT JOIN
+  - [x] RIGHT JOIN
+  - [x] CROSS JOIN
   - [ ] OUTER JOIN
 - [ ] Window
 

datafusion/Cargo.toml

@@ -21,6 +21,7 @@ description = "DataFusion is an in-memory query engine that uses Apache Arrow as
 version = "4.0.0-SNAPSHOT"
 homepage = "https://github.com/apache/arrow"
 repository = "https://github.com/apache/arrow"
+readme = "../README.md"
 authors = ["Apache Arrow <dev@arrow.apache.org>"]
 license = "Apache-2.0"
 keywords = [ "arrow", "query", "sql" ]