Elusion 🦀 DataFrame Library for Everybody!

Elusion is a high-performance DataFrame library designed for in-memory data formats such as CSV, JSON, PARQUET, and DELTA. Built atop the DataFusion SQL query engine, Elusion provides a robust DataFrame-like interface for managing and querying data efficiently.

Tailored for developers seeking a powerful abstraction over data transformations, Elusion streamlines complex operations like filtering, joining, aggregating, and more with its intuitive, chainable API.

Core Philosophy Elusion offers flexibility in constructing queries without enforcing specific patterns or chaining orders, unlike SQL, PySpark, Polars, or Pandas. You can build your queries in any sequence that best fits your logic, writing functions in a manner that makes sense to you. Regardless of the order of function calls, Elusion ensures consistent results.

Platform Compatibility

Tested for MacOS, Linux and Windows

Key Features

🚀 High-Performance DataFrame Operations

Seamless Data Loading: Easily load and process data from CSV, PARQUET, JSON, and DELTA table files. SQL-Like Transformations: Execute transformations such as SELECT, AGG, STRING FUNCTIONS, JOIN, FILTER, GROUP BY, and WINDOW with ease.

📊 Aggregations and Analytics

Comprehensive Aggregations: Utilize built-in functions like SUM, AVG, MEAN, MEDIAN, MIN, COUNT, MAX, and more. Advanced Scalar Math: Perform calculations using functions such as ABS, FLOOR, CEIL, SQRT, ISNAN, ISZERO, PI, POWER, and others.

🔗 Flexible Joins

Diverse Join Types: Perform joins using INNER, LEFT, RIGHT, FULL, and other join types. Intuitive Syntax: Easily specify join conditions and aliases for clarity and simplicity.

🪟 Window Functions

Analytical Capabilities: Implement window functions like RANK, DENSE_RANK, ROW_NUMBER, and custom partition-based calculations to perform advanced analytics.

🔄 Pivot and Unpivot Functions

Data Reshaping: Transform your data structure using PIVOT and UNPIVOT functions to suit your analytical needs.

🧹 Clean Query Construction

Readable Queries: Construct SQL queries that are both readable and reusable. Advanced Query Support: Utilize Common Table Expressions (CTEs), subqueries, and set operations such as UNION, UNION ALL, INTERSECT, and EXCEPT.

🛠️ Easy-to-Use API

Chainable Interface: Build queries using a chainable and intuitive API for streamlined development. Debugging Support: Access readable debug outputs of the generated SQL for easy verification and troubleshooting. Data Preview: Quickly preview your data by displaying a subset of rows in the terminal. Composable Queries: Seamlessly chain transformations to create reusable and testable workflows.

Installation

To add Elusion to your Rust project, include the following lines in your Cargo.toml under [dependencies]:

elusion = "1.0.1"
tokio = { version = "1.42.0", features = ["rt-multi-thread"] }

Rust version needed

>= 1.81

Usage examples:

MAIN function

use elusion::prelude::*; // Import everything needed

#[tokio::main]
async fn main() -> ElusionResult<()> {

    Ok(())
}

Schema

SCHEMA IS DYNAMICALLY INFERED since v0.2.5

LOADING Files into CustomDataFrame

File extensions are automatically recognized

All you have to do is to provide path to your file

Currently supported data files: CSV, PARQUET, JSON, DELTA

let csv_data = "C:\\Borivoj\\RUST\\Elusion\\sales_data.csv";
let parquet_path = "C:\\Borivoj\\RUST\\Elusion\\prod_data.parquet";
let json_path = "C:\\Borivoj\\RUST\\Elusion\\db_data.json";
let delta_path = "C:\\Borivoj\\RUST\\Elusion\\agg_sales"; // for DELTA you just specify folder name without extension

Creating CustomDataFrame

2 arguments needed: Path, Table Alias

let df_sales = CustomDataFrame::new(csv_data, "sales").await?; 
let df_customers = CustomDataFrame::new(parquet_path, "customers").await?;

RULE of thumb:

ALL Column names and Dataframe alias names, will be TRIM(), REPLACE(" ", "_")

ALIAS column names in SELECT() function (AS is case insensitive)

let customers_alias = df_customers
    .select(["CustomerKey AS customerkey_alias", "FirstName as first_name", "LastName", "EmailAddress"]);

Where to use which Functions:

Scalar and Operators -> in SELECT() function

Aggregation Functions -> in AGG() function

String Column Functions -> in STRING_FUNCTIONS() function

Numerical Operators (supported +, -, * , / , %)

let num_ops_sales = sales_order_df.clone()
    .select([
        "customer_name",
        "order_date",
        "billable_value",
        "billable_value * 2 AS double_billable_value",  // Multiplication
        "billable_value / 100 AS percentage_billable"  // Division
    ])
    .filter("billable_value > 100.0")
    .order_by(["order_date"], [true])
    .limit(10);

let num_ops_res = num_ops_sales.elusion("scalar_df").await?;
num_ops_res.display().await?;

FILTERING

let filter_df = sales_order_df
    .select(["customer_name", "order_date", "billable_value"])
    .filter_many([("order_date > '2021-07-04'"), ("billable_value > 100.0")])
    .order_by(["order_date"], [true])
    .limit(10);

let filtered = filter_df.elusion("result_sales").await?;
filtered.display().await?;

SCALAR functions

let scalar_df = sales_order_df
    .select([
        "customer_name", 
        "order_date", 
        "ABS(billable_value) AS abs_billable_value",
        "ROUND(SQRT(billable_value), 2) AS SQRT_billable_value"])
    .filter("billable_value > 100.0")
    .order_by(["order_date"], [true])
    .limit(10);

let scalar_res = scalar_df.elusion("scalar_df").await?;
scalar_res.display().await?;

AGGREGATE functions with nested Scalar functions

let scalar_df = sales_order_df
    .select([
        "customer_name", 
        "order_date"
    ])
    .agg([
        "ROUND(AVG(ABS(billable_value)), 2) AS avg_abs_billable",
        "SUM(billable_value) AS total_billable",
        "MAX(ABS(billable_value)) AS max_abs_billable",
        "SUM(billable_value) * 2 AS double_total_billable",      // Operator-based aggregation
        "SUM(billable_value) / 100 AS percentage_total_billable" // Operator-based aggregation
    ])
    .group_by(["customer_name", "order_date"])
    .filter("billable_value > 100.0")
    .order_by(["order_date"], [true])
    .limit(10);

let scalar_res = scalar_df.elusion("scalar_df").await?;
scalar_res.display().await?;

Numerical Operators, Scalar Functions, Aggregated Functions...

let mix_query = sales_order_df
    .select([
        "customer_name",
        "order_date",
        "ABS(billable_value) AS abs_billable_value",
        "ROUND(SQRT(billable_value), 2) AS SQRT_billable_value",
        "billable_value * 2 AS double_billable_value",  // Multiplication
        "billable_value / 100 AS percentage_billable"  // Division
    ])
    .agg([
        "ROUND(AVG(ABS(billable_value)), 2) AS avg_abs_billable",
        "SUM(billable_value) AS total_billable",
        "MAX(ABS(billable_value)) AS max_abs_billable",
        "SUM(billable_value) * 2 AS double_total_billable",      // Operator-based aggregation
        "SUM(billable_value) / 100 AS percentage_total_billable" // Operator-based aggregation
    ])
    .filter("billable_value > 50.0")
    .group_by_all()
    .order_by_many([
        ("total_billable", false),  // Order by total_billable descending
        ("max_abs_billable", true), // Then by max_abs_billable ascending
    ]);

let mix_res = mix_query.elusion("scalar_df").await?;
mix_res.display().await?;

Supported Aggregation functions

SUM, AVG, MEAN, MEDIAN, MIN, COUNT, MAX,  
LAST_VALUE, FIRST_VALUE,  
GROUPING, STRING_AGG, ARRAY_AGG, VAR, VAR_POP,  
VAR_POPULATION, VAR_SAMP, VAR_SAMPLE,  
BIT_AND, BIT_OR, BIT_XOR, BOOL_AND, BOOL_OR 

Supported Scalar Math Functions

ABS, FLOOR, CEIL, SQRT, ISNAN, ISZERO,  
PI, POW, POWER, RADIANS, RANDOM, ROUND,  
FACTORIAL, ACOS, ACOSH, ASIN, ASINH,  
COS, COSH, COT, DEGREES, EXP,  
SIN, SINH, TAN, TANH, TRUNC, CBRT,  
ATAN, ATAN2, ATANH, GCD, LCM, LN,  
LOG, LOG10, LOG2, NANVL, SIGNUM

JOIN

JOIN examples with single condition and 2 dataframes, AGGREGATION, GROUP BY

let single_join = df_sales
    .join(df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER")
    .select(["s.OrderDate","c.FirstName", "c.LastName"])
    .agg([
        "SUM(s.OrderQuantity) AS total_quantity",
        "AVG(s.OrderQuantity) AS avg_quantity",
    ])
    .group_by(["s.OrderDate","c.FirstName","c.LastName"])
    .having("total_quantity > 10") 
    .order_by(["total_quantity"], [false]) // true is ascending, false is descending
    .limit(10);

let join_df1 = single_join.elusion("result_query").await?;
join_df1.display().await?;

JOIN with single conditions and 3 dataframes, AGGREGATION, GROUP BY, HAVING, SELECT, ORDER BY

let many_joins = df_sales
    .join_many([
        (df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER"),
        (df_products, ["s.ProductKey = p.ProductKey"], "INNER"),
    ]) 
    .select([
        "c.CustomerKey","c.FirstName","c.LastName","p.ProductName",
    ]) 
    .agg([
        "SUM(s.OrderQuantity) AS total_quantity",
        "AVG(s.OrderQuantity) AS avg_quantity",
    ]) 
    .group_by(["c.CustomerKey", "c.FirstName", "c.LastName", "p.ProductName"]) 
    .having_many([("total_quantity > 10"), ("avg_quantity < 100")]) 
    .order_by_many([
        ("total_quantity", true), // true is ascending 
        ("p.ProductName", false)  // false is descending
    ])
    .limit(10); 

let join_df3 = many_joins.elusion("df_joins").await?;
join_df3.display().await?;

JOIN with multiple conditions and 2 data frames

let result_join = orders_df
    .join(
        customers_df,
        ["o.CustomerID = c.CustomerID" , "o.RegionID = c.RegionID"],
        "INNER"
    )
    .select([
        "o.OrderID",
        "c.Name",
        "o.OrderDate"
    ])
    .string_functions([
        "CONCAT(TRIM(c.Name), ' (', c.Email, ')') AS customer_info",
        "UPPER(c.Status) AS customer_status",
        "LEFT(c.Email, POSITION('@' IN c.Email) - 1) AS username"
    ])
    .agg([
        "SUM(o.Amount) AS total_amount",
        "AVG(o.Quantity) AS avg_quantity",
        "COUNT(DISTINCT o.OrderID) AS order_count",
        "MAX(o.Amount) AS max_amount",
        "MIN(o.Amount) AS min_amount"
    ])
    .group_by([
        "o.OrderID",
        "c.Name",
        "o.OrderDate",
        "c.Email",   
        "c.Status"
    ]);

let res_joins = result_join.elusion("one_join").await?;
res_joins.display().await?;

JOIN_MANY with multiple conditions and 3 data frames

let result_join_many = order_join_df
    .join_many([
        (customer_join_df,
            ["o.CustomerID = c.CustomerID" , "o.RegionID = c.RegionID"],
            "INNER"
        ),
        (regions_join_df,
            ["c.RegionID = r.RegionID" , "r.IsActive = true"],
            "INNER"
        )
    ])
    .select(["o.OrderID","c.Name","r.RegionName", "r.CountryID"])
    .string_functions([
    "CONCAT(r.RegionName, ' (', r.CountryID, ')') AS region_info",
    // Simple CASE conditions
    "CASE c.CreditLimit 
        WHEN 1000 THEN 'Basic'
        WHEN 2000 THEN 'Premium'
        ELSE 'Standard'
    END AS credit_tier",
    // Complex CASE conditions
    "CASE 
        WHEN c.CreditLimit > 2000 THEN 'High'
        WHEN c.CreditLimit > 1000 THEN 'Medium'
        ELSE 'Low'
    END AS credit_status",
    // Multiple cASE conditions
    "CASE
        WHEN o.Amount > 1000 AND c.Status = 'active' THEN 'Priority'
        WHEN o.Amount > 500 THEN 'Regular'
        ELSE 'Standard'
    END AS order_priority",
    // String CASE comparisons
    "CASE r.RegionName
        WHEN 'East Coast' THEN 'Eastern'
        WHEN 'West Coast' THEN 'Western'
        ELSE 'Other'
    END AS region_category"
        // Date/time CASE conditions
    "CASE
        WHEN EXTRACT(DOW FROM o.OrderDate) IN (0, 6) THEN 'Weekend'
        ELSE 'Weekday'
    END AS order_day_type"
    ])
    .agg([
        "SUM(o.Amount) AS total_amount",                                  
        "COUNT(*) AS row_count",                                       
        "SUM(o.Amount * (1 - o.Discount/100)) AS net_amount",          
        "ROUND(SUM(o.Amount) / COUNT(*), 2) AS avg_order_value",       
        "SUM(o.Amount * r.TaxRate) AS total_tax"                      
    ])
    .group_by_all()
    .having("total_amount > 200")
    .order_by(["total_amount"], [false]); 

let res_joins_many = result_join_many.elusion("many_join").await?;
res_joins_many.display().await?;

JOIN_MANY with single condition and 3 dataframes, STRING FUNCTIONS, AGGREGATION, GROUP BY, HAVING_MANY, ORDER BY

let str_func_joins = df_sales
    .join_many([
        (df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER"),
        (df_products, ["s.ProductKey = p.ProductKey"], "INNER"),
    ]) 
    .select([
        "c.CustomerKey",
        "c.FirstName",
        "c.LastName",
        "c.EmailAddress",
        "p.ProductName",
    ])
    .string_functions([
        "TRIM(c.EmailAddress) AS trimmed_email_address",
        "CONCAT(TRIM(c.FirstName), ' ', TRIM(c.LastName)) AS full_name",
        "LEFT(p.ProductName, 15) AS short_product_name",
        "RIGHT(p.ProductName, 5) AS end_product_name",
    ])
    .agg([
        "COUNT(p.ProductKey) AS product_count",
        "SUM(s.OrderQuantity) AS total_order_quantity",
    ])
    .group_by_all()
    .having_many([("total_order_quantity > 10"),  ("product_count >= 1")])  
    .order_by_many([
        ("total_order_quantity", true), 
        ("p.ProductName", false) 
    ]); 

let join_str_df3 = str_func_joins.elusion("df_joins").await?;
join_str_df3.display().await?;

Currently implemented join types

"INNER", "LEFT", "RIGHT", "FULL", 
"LEFT SEMI", "RIGHT SEMI", 
"LEFT ANTI", "RIGHT ANTI", "LEFT MARK" 

STRING FUNCTIONS

let string_functions_df = df_sales
    .join_many([
        (df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER"),
        (df_products, ["s.ProductKey = p.ProductKey"], "INNER"),
    ]) 
    .select([
        "c.CustomerKey",
        "c.FirstName",
        "c.LastName",
        "c.EmailAddress",
        "p.ProductName"
    ])
    .string_functions([
    // Basic String Functions
    "TRIM(c.EmailAddress) AS trimmed_email",
    "LTRIM(c.EmailAddress) AS left_trimmed_email",
    "RTRIM(c.EmailAddress) AS right_trimmed_email",
    "UPPER(c.FirstName) AS upper_first_name",
    "LOWER(c.LastName) AS lower_last_name",
    "LENGTH(c.EmailAddress) AS email_length",
    "LEFT(p.ProductName, 10) AS product_start",
    "RIGHT(p.ProductName, 10) AS product_end",
    "SUBSTRING(p.ProductName, 1, 5) AS product_substr",
    // Concatenation
    "CONCAT(c.FirstName, ' ', c.LastName) AS full_name",
    "CONCAT_WS(' ', c.FirstName, c.LastName, c.EmailAddress) AS all_info",
    // Position and Search
    "POSITION('@' IN c.EmailAddress) AS at_symbol_pos",
    "STRPOS(c.EmailAddress, '@') AS email_at_pos",
    // Replacement and Modification
    "REPLACE(c.EmailAddress, '@adventure-works.com', '@newdomain.com') AS new_email",
    "TRANSLATE(c.FirstName, 'AEIOU', '12345') AS vowels_replaced",
    "REPEAT('*', 5) AS stars",
    "REVERSE(c.FirstName) AS reversed_name",
    // Padding
    "LPAD(c.CustomerKey::TEXT, 10, '0') AS padded_customer_id",
    "RPAD(c.FirstName, 20, '.') AS padded_name",
    // Case Formatting
    "INITCAP(LOWER(c.FirstName)) AS proper_case_name",
    // String Extraction
    "SPLIT_PART(c.EmailAddress, '@', 1) AS email_username",
    // Type Conversion
    "TO_CHAR(s.OrderDate, 'YYYY-MM-DD') AS formatted_date"
    ])
    .agg([
        "COUNT(*) AS total_records",
        "STRING_AGG(p.ProductName, ', ') AS all_products"
    ])
    .filter("c.EmailAddress IS NOT NULL")
    .group_by_all()
    .having("COUNT(*) > 1")
    .order_by(["c.CustomerKey"], [true]);   

let str_df = string_functions_df.elusion("df_joins").await?;
str_df.display().await?;    

Currently Available String functions

1.Basic String Functions:
TRIM() - Remove leading/trailing spaces
LTRIM() - Remove leading spaces
RTRIM() - Remove trailing spaces
UPPER() - Convert to uppercase
LOWER() - Convert to lowercase
LENGTH() or LEN() - Get string length
LEFT() - Extract leftmost characters
RIGHT() - Extract rightmost characters
SUBSTRING() - Extract part of string
2. String concatenation:
CONCAT() - Concatenate strings
CONCAT_WS() - Concatenate with separator
3. String Position and Search:
POSITION() - Find position of substring
STRPOS() - Find position of substring
INSTR() - Find position of substring
LOCATE() - Find position of substring
4. String Replacement and Modification:
REPLACE() - Replace all occurrences of substring
TRANSLATE() - Replace characters
OVERLAY() - Replace portion of string
REPEAT() - Repeat string
REVERSE() - Reverse string characters
5. String Pattern Matching:
LIKE() - Pattern matching with wildcards
REGEXP() or RLIKE() - Pattern matching with regular expressions
6. String Padding:
LPAD() - Pad string on left
RPAD() - Pad string on right
SPACE() - Generate spaces
7. String Case Formatting:
INITCAP() - Capitalize first letter of each word
8. String Extraction:
SPLIT_PART() - Split string and get nth part
SUBSTR() - Get substring
9. String Type Conversion:
TO_CHAR() - Convert to string
CAST() - Type conversion
CONVERT() - Type conversion
10. Control Flow:
CASE()

WINDOW functions

Aggregate, Ranking and Analytical functions

let window_query = df_sales
    .join(df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER")
    .select(["s.OrderDate","c.FirstName","c.LastName","s.OrderQuantity"])
    //aggregated window functions
    .window("SUM(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) as running_total")
    .window("AVG(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS running_avg")
    .window("MIN(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS running_min")
    .window("MAX(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS running_max")
    .window("COUNT(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS running_count")
    //ranking window functions
    .window("ROW_NUMBER() OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) as row_num")
    .window("DENSE_RANK() OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS dense_rnk")
    .window("PERCENT_RANK() OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS pct_rank")
    .window("CUME_DIST() OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS cume_dist")
    .window("NTILE(4) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS quartile")
    // analytical window functions
    .window("FIRST_VALUE(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS first_qty")
    .window("LAST_VALUE(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS last_qty")
    .window("LAG(s.OrderQuantity, 1, 0) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS prev_qty")
    .window("LEAD(s.OrderQuantity, 1, 0) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS next_qty")
    .window("NTH_VALUE(s.OrderQuantity, 3) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate) AS third_qty")

let window_df = window_query.elusion("result_window").await?;
window_df.display().await?;

Rolling Window Functions

let rollin_query = df_sales
    .join(df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER")
    .select(["s.OrderDate", "c.FirstName", "c.LastName", "s.OrderQuantity"])
        //aggregated rolling windows
    .window("SUM(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate
             ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total")
    .window("AVG(s.OrderQuantity) OVER (PARTITION BY c.CustomerKey ORDER BY s.OrderDate
             ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS full_partition_avg");

let rollin_df = rollin_query.elusion("rollin_result").await?;
rollin_df.display().await?;

UNION, UNION ALL, EXCEPT, INTERSECT

UNION: Combines rows from both, removing duplicates

UNION ALL: Combines rows from both, keeping duplicates

EXCEPT: Difference of two sets (only rows in left minus those in right).

INTERSECT: Intersection of two sets (only rows in both).

//UNION
let df1 = df_sales
    .join(
        df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER",
    )
    .select(["c.FirstName", "c.LastName"])
    .string_functions([
        "TRIM(c.EmailAddress) AS trimmed_email",
        "CONCAT(TRIM(c.FirstName), ' ', TRIM(c.LastName)) AS full_name",
    ]);

let df2 = df_sales
    .join(
        df_customers, ["s.CustomerKey = c.CustomerKey"], "INNER",
    )
    .select(["c.FirstName", "c.LastName"])
    .string_functions([
        "TRIM(c.EmailAddress) AS trimmed_email",
        "CONCAT(TRIM(c.FirstName), ' ', TRIM(c.LastName)) AS full_name",
    ]);

let union_df = df1.union(df2);

let union_df_final = union_df.elusion("union_df").await?;
union_df_final.display().await?;

//UNION ALL
let union_all_df = df1.union_all(df2);
//EXCEPT
let except_df = df1.except(df2);
//INTERSECT
let intersect_df = df1.intersect(df2);

PIVOT and UNPIVOT

Pivot and Unpivot functions are ASYNC function

They should be used separately from other functions: 1. directly on initial CustomDataFrame, 2. after .elusion() evaluation.

Future needs to be in final state so .await? must be used

// PIVOT
// directly on initial CustomDataFrame
let sales_p = "C:\\Borivoj\\RUST\\Elusion\\SalesData2022.csv";
let df_sales = CustomDataFrame::new(sales_p, "s").await?;

let pivoted = df_sales
    .pivot(
        ["StockDate"],     // Row identifiers
        "TerritoryKey",    // Column to pivot
        "OrderQuantity",   // Value to aggregate
        "SUM"              // Aggregation function
    ).await?;

let result_pivot = pivoted.elusion("pivoted_df").await?;
result_pivot.display().await?;

// after .elusion() evaluation
let sales_path = "C:\\Borivoj\\RUST\\Elusion\\sales_order_report.csv";
let sales_order_df = CustomDataFrame::new(sales_path, "sales").await?;

let scalar_df = sales_order_df
    .select([
        "customer_name", 
        "order_date", 
        "ABS(billable_value) AS abs_billable_value",
        "ROUND(SQRT(billable_value), 2) AS SQRT_billable_value"])
    .filter("billable_value > 100.0")
    .order_by(["order_date"], [true])
    .limit(10);
// elusion evaluation
let scalar_res = scalar_df.elusion("scalar_df").await?;
scalar_res.display().await?;

let pivoted_scalar = scalar_res
    .pivot(
        ["customer_name"],          // Row identifiers
        "order_date",               // Column to pivot
        "abs_billable_value",       // Value to aggregate
        "SUM"                       // Aggregation function
    ).await?;

let pitvoted_scalar = pivoted_scalar.elusion("pivoted_df").await?;
pitvoted_scalar.display().await?;

// UNPIVOT
let unpivoted = result_pivot
    .unpivot(
        ["StockDate"],                         // ID columns
        ["TerritoryKey_1", "TerritoryKey_2"],  // Value columns to unpivot
        "Territory",                           // New name column
        "Quantity"                             // New value column
    ).await?;

let result_unpivot = unpivoted.elusion("unpivoted_df").await?;
result_unpivot.display().await?;
// example 2
let unpivot_scalar = scalar_res
    .unpivot(
        ["customer_name", "order_date"],      // Keep these as identifiers
        ["abs_billable_value", "sqrt_billable_value"], // Columns to unpivot
        "measure_name",                       // Name for the measure column
        "measure_value"                       // Name for the value column
    ).await?;

let result_unpivot_scalar = unpivot_scalar.elusion("unpivoted_df2").await?;
result_unpivot_scalar.display().await?;

JSON files

Currently supported files can include: Arrays, Objects. Best usage if you can make it flat ("key":"value")

for JSON, all field types are infered to VARCHAR/TEXT/STRING

// example json structure
{
    "name": "Adeel Solangi",
    "language": "Sindhi",
    "id": "V59OF92YF627HFY0",
    "bio": "Donec lobortis eleifend condimentum. Cras dictum dolor lacinia lectus vehicula rutrum.",
    "version": 6.1
}

let json_path = "C:\\Borivoj\\RUST\\Elusion\\test.json";
let json_df = CustomDataFrame::new(json_path, "test").await?;

// example json structure
{
"someGUID": "e0bsg4d-d81c-4db6-8ad8-bc92cbcfsds06",
"someGUID2": "58asd1f6-c7ca-4c51-8ca0-37678csgd9c7",
"someName": "Some Name Here",
"someVersion": "Version 0232",
"emptyValue": null,
"verInd": {
    "$numberLong": "0"
    },
"elInd": {
    "$numberLong": "1"
    },
"qId": "question1",
"opId": {
    "$numberLong": "0"
    },
"label": "Some Label Here",
"labelValue": "45557",
"someGUID3": "5854ff6-c7ca-4c51-8ca0-3767sds4319c7|qId|7"
}

let json_path = "C:\\Borivoj\\RUST\\Elusion\\test2.json";
let json_df = CustomDataFrame::new(json_path, "test2").await?;

WRITERS

Writing to Parquet File

We have 2 writing modes: Overwrite and Append

// overwrite existing file
result_df
    .write_to_parquet(
        "overwrite",
        "C:\\Path\\To\\Your\\test.parquet",
        None // I've set WriteOptions to default for writing Parquet files, so keep it None
    )
    .await
    .expect("Failed to write to Parquet");

// append to exisiting file
result_df
    .write_to_parquet(
        "append",
        "C:\\Path\\To\\Your\\test.parquet",
        None // I've set WriteOptions to default for writing Parquet files, so keep it None
    ) 
    .await
    .expect("Failed to append to Parquet");

Writing to CSV File

CSV Writing options are mandatory

has_headers: TRUE is dynamically set for Overwrite mode, and FALSE for Append mode.

let custom_csv_options = CsvWriteOptions {
        delimiter: b',',
        escape: b'\\',
        quote: b'"',
        double_quote: false,
        null_value: "NULL".to_string(),
    };

We have 2 writing modes: Overwrite and Append

// overwrite existing file
result_df
    .write_to_csv(
        "overwrite", 
        "C:\\Borivoj\\RUST\\Elusion\\agg_sales.csv", 
        custom_csv_options
    )
    .await
    .expect("Failed to overwrite CSV file");

// append to exisiting file
result_df
    .write_to_csv(
        "append", 
        "C:\\Borivoj\\RUST\\Elusion\\agg_sales.csv", 
        custom_csv_options
    )
    .await
    .expect("Failed to append to CSV file");

Writing to DELTA table / lake

We can write to delta in 2 modes Overwrite and Append

Partitioning column is optional

DISCLAIMER: if you decide to use column for partitioning, make sure that you don't need that column as you wont be able to read it back to dataframe

DISCLAIMER 2: once you decide to use partitioning column for writing your delta table, if you want to APPEND to it, Append also need to have same column for partitioning

// Overwrite
result_df
    .write_to_delta_table(
        "overwrite",
        "C:\\Borivoj\\RUST\\Elusion\\agg_sales", 
        Some(vec!["order_date".into()]), 
    )
    .await
    .expect("Failed to overwrite Delta table");
// Append
result_df
    .write_to_delta_table(
        "append",
        "C:\\Borivoj\\RUST\\Elusion\\agg_sales",
        Some(vec!["order_date".into()]),
    )
    .await
    .expect("Failed to append to Delta table");

License

Elusion is distributed under the MIT License. However, since it builds upon DataFusion, which is distributed under the Apache License 2.0, some parts of this project are subject to the terms of the Apache License 2.0. For full details, see the LICENSE.txt file.

Acknowledgments

This library leverages the power of Rust's type system and libraries like DataFusion , Arrow for efficient query processing. Special thanks to the open-source community for making this project possible.

Where you can find me:

LindkedIn - LinkedIn YouTube channel - YouTube Udemy Instructor - Udemy