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Elusion is a high-performance DataFrame / Data Engineering / Data Analysis library designed for in-memory data formats such as CSV, JSON, PARQUET, DELTA, as well as for ODBC Database Connections for MySQL and PostgreSQL, as well as for Azure Blob Storage Connections, as well as for creating JSON files from REST API's which can be forwarded to DataFrame.
All of the DataFrame operations, Reading and Writing can be placed in PipelineScheduler for automated Data Engineering Pipelines.
DataFrame operations are built atop the DataFusion SQL query engine, Database operations are built atop Arrow ODBC, Azure BLOB HTTPS operations are built atop Azure Storage with BLOB and DFS (Data Lake Storage Gen2) endpoints available, Pipeline Scheduling is built atop Tokio Cron Scheduler, REST API is build atop Reqwest. Report Creation is built atop Plotly and AG GRID. (scroll down for examples)
Tailored for Data Engineers and Data Analysts seeking a powerful abstraction over data transformations. Elusion streamlines complex operations like filtering, joining, aggregating, and more with its intuitive, chainable DataFrame API, and provides a robust interface for managing and querying data efficiently. It also has Integrated Plotting and Interactive Dashboard features.
Elusion wants you to be you!
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.
Tested for MacOS, Linux and Windows
Codebase has Undergone Rigorous Auditing and Security Testing, ensuring that it is fully prepared for Production.
Flexible Intervals: From 1 minute to 30 days scheduling intervals. Graceful Shutdown: Built-in Ctrl+C signal handling for clean termination. Async Support: Built on tokio for non-blocking operations.
- Azure Blob Storage: Direct integration with Azure Blob Storage for Reading and Writing data files.
- Database Connectors: ODBC support for seamless data access from MySQL and PostgreSQL databases.
- REST API's: Create JSON files from REST API endpoints with Customizable Headers, Params, Date Ranges, Pagination...
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, HAVING, GROUP BY, ORDER BY, DATETIME and WINDOW with ease.
The caching and views functionality offer several significant advantages over regular querying:
Complex queries (especially with joins, aggregations, and string functions) only need to be computed once Subsequent requests use pre-computed results, which can be 10-100x faster
Large intermediate results are managed better in cached form (prevents stack overflow) The query execution plan doesn't need to be rebuilt each time Memory allocation patterns become more predictable
Results are stored in an optimized format (Arrow RecordBatches) Repeated access doesn't require re-parsing SQL or rebuilding execution plans
Data scientists can explore data interactively without waiting for the same queries to execute repeatedly Makes iterative analysis practical even with large datasets
Multiple visualizations can share the same underlying data without redundant computation Refresh only when needed (TTL-based expiration)
Reduced CPU usage for repeated queries Less I/O pressure for file-based data sources More efficient use of memory (prevent re-allocations)
Multiple users/processes can access the same cached results Reduces contention for system resources
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.
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.
Analytical Capabilities: Implement window functions like RANK, DENSE_RANK, ROW_NUMBER, and custom partition-based calculations to perform advanced analytics.
Data Reshaping: Transform your data structure using PIVOT and UNPIVOT functions to suit your analytical needs.
Create HTML files with Interactive Dashboards with multiple interactive Plots and Tables. Plots Available: TimeSeries, Bar, Pie, Donut, Histogram, Scatter, Box... Tables can Paginate pages, Filter, Resize, Reorder columns... Export Tables data to EXCEL and CSV
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 APPEND, UNION, UNION ALL, INTERSECT, and EXCEPT. For multiple Dataframea operations: APPEND_MANY, UNION_MANY, UNION_ALL_MANY.
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.
To add Elusion to your Rust project, include the following lines in your Cargo.toml under [dependencies]:
elusion = "3.7.1"
tokio = { version = "1.42.0", features = ["rt-multi-thread"] }>= 1.81Elusion now provides ODBC functionality behind an optional feature flag to keep the core library lightweight and provide flexibility for users.
To use ODBC-related features, you need to:
- Add the ODBC feature when specifying the dependency:
[dependencies]
elusion = { version = "3.7.1", features = ["odbc"] }- Make sure to install ODBC Driver(unixodbc) on Ubuntu and macOS Ubuntu/Debian:
sudo apt-get install unixodbc-devmacOS:
brew install unixodbccargo build --features odbccargo run --features odbc All DataFrame query expresions, functions, aliases and column names will be normalized to LOWERCASE(), TRIM() and REPLACE(" ","_")
// Import everything needed
use elusion::prelude::*;
#[tokio::main]
async fn main() -> ElusionResult<()> {
Ok(())
}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 extensionlet df_sales = CustomDataFrame::new(csv_data, "sales").await?;
let df_customers = CustomDataFrame::new(parquet_path, "customers").await?;let pg_df = CustomDataFrame::from_db(pg_connection, sql_query).await?;let df = CustomDataFrame::from_azure_with_sas_token(
blob_url,
sas_token,
Some("folder-name/file-name"), // FILTERING is optional. Can be None if you want to take everything from Container
"data" // alias for registering table
).await?; let temp_df = CustomDataFrame::empty().await?;
let date_table = temp_df
.datetime_functions([
"CURRENT_DATE() as current_date",
"DATE_TRUNC('week', CURRENT_DATE()) AS week_start",
"DATE_TRUNC('week', CURRENT_DATE()) + INTERVAL '1 week' AS next_week_start",
"DATE_PART('year', CURRENT_DATE()) AS current_year",
"DATE_PART('week', CURRENT_DATE()) AS current_week_num",
])
.elusion("date_table").await?;
date_table.display().await?;
RESULT:
+--------------+---------------------+---------------------+--------------+------------------+
| current_date | week_start | next_week_start | current_year | current_week_num |
+--------------+---------------------+---------------------+--------------+------------------+
| 2025-03-07 | 2025-03-03T00:00:00 | 2025-03-10T00:00:00 | 2025.0 | 10.0 |
+--------------+---------------------+---------------------+--------------+------------------+let date_table = CustomDataFrame::create_date_range_table(
"2025-01-01", // start date
"2025-12-31", // end date
"calendar_2025" // table alias
).await?;
date_table.display().await?;
RESULT:
+------------+------+-------+-----+---------+----------+-------------+------------------+-------------+------------+-------------+---------------+------------+------------+
| date | year | month | day | quarter | week_num | day_of_week | day_of_week_name | day_of_year | week_start | month_start | quarter_start | year_start | is_weekend |
+------------+------+-------+-----+---------+----------+-------------+------------------+-------------+------------+-------------+---------------+------------+------------+
| 2025-01-01 | 2025 | 1 | 1 | 1 | 1 | 3 | Wednesday | 1 | 2024-12-29 | 2025-01-01 | 2025-01-01 | 2025-01-01 | false |
| 2025-01-02 | 2025 | 1 | 2 | 1 | 1 | 4 | Thursday | 2 | 2024-12-29 | 2025-01-01 | 2025-01-01 | 2025-01-01 | false |
| 2025-01-03 | 2025 | 1 | 3 | 1 | 1 | 5 | Friday | 3 | 2024-12-29 | 2025-01-01 | 2025-01-01 | 2025-01-01 | false |
| 2025-01-04 | 2025 | 1 | 4 | 1 | 1 | 6 | Saturday | 4 | 2024-12-29 | 2025-01-01 | 2025-01-01 | 2025-01-01 | true |
| 2025-01-05 | 2025 | 1 | 5 | 1 | 1 | 0 | Sunday | 5 | 2025-01-05 | 2025-01-01 | 2025-01-01 | 2025-01-01 | true |
| 2025-01-06 | 2025 | 1 | 6 | 1 | 2 | 1 | Monday | 6 | 2025-01-05 | 2025-01-01 | 2025-01-01 | 2025-01-01 | false |
| 2025-01-07 | 2025 | 1 | 7 | 1 | 2 | 2 | Tuesday | 7 | 2025-01-05 | 2025-01-01 | 2025-01-01 | 2025-01-01 | false |
| 2025-01-08 | 2025 | 1 | 8 | 1 | 2 | 3 | Wednesday | 8 | 2025-01-05 | 2025-01-01 | 2025-01-01 | 2025-01-01 | false |
| 2025-01-09 | 2025 | 1 | 9 | 1 | 2 | 4 | Thursday | 9 | 2025-01-05 | 2025-01-01 | 2025-01-01 | 2025-01-01 | false |
| .......... | .... | . | . | . | . | . | ................ | .......... | .......... | .......... | ............. | ...........| .......... |
+------------+------+-------+-----+---------+----------+-------------+------------------+-------------+------------+-------------+---------------+------------+------------+You can create Date Table with Custom formats (ISO, Compact, Human Readable...) and week, month, quarter, year Ranges (start-end)
let date_table = CustomDataFrame::create_formatted_date_range_table(
"2025-01-01", // date start
"2025-12-31", // date end
"calendar_2025", // table alias
"date".to_string(), // first column name
DateFormat::HumanReadable, // 1 Jan 2025
true, // Include period ranges (start - end)
Weekday::Mon // Week starts on Monday
).await?;
date_table.display().await?;
RESULT:
+-------------+------+-------+-----+---------+----------+-------------+------------------+-------------+------------+-------------+-------------+-------------+-------------+---------------+-------------+-------------+-------------+
| date | year | month | day | quarter | week_num | day_of_week | day_of_week_name | day_of_year | is_weekend | week_start | week_end | month_start | month_end | quarter_start | quarter_end | year_start | year_end |
+-------------+------+-------+-----+---------+----------+-------------+------------------+-------------+------------+-------------+-------------+-------------+-------------+---------------+-------------+-------------+-------------+
| 1 Jan 2025 | 2025 | 1 | 1 | 1 | 1 | 2 | Wednesday | 1 | false | 30 Dec 2024 | 5 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 2 Jan 2025 | 2025 | 1 | 2 | 1 | 1 | 3 | Thursday | 2 | false | 30 Dec 2024 | 5 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 3 Jan 2025 | 2025 | 1 | 3 | 1 | 1 | 4 | Friday | 3 | false | 30 Dec 2024 | 5 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 4 Jan 2025 | 2025 | 1 | 4 | 1 | 1 | 5 | Saturday | 4 | true | 30 Dec 2024 | 5 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 5 Jan 2025 | 2025 | 1 | 5 | 1 | 1 | 6 | Sunday | 5 | true | 30 Dec 2024 | 5 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 6 Jan 2025 | 2025 | 1 | 6 | 1 | 2 | 0 | Monday | 6 | false | 6 Jan 2025 | 12 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 7 Jan 2025 | 2025 | 1 | 7 | 1 | 2 | 1 | Tuesday | 7 | false | 6 Jan 2025 | 12 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 8 Jan 2025 | 2025 | 1 | 8 | 1 | 2 | 2 | Wednesday | 8 | false | 6 Jan 2025 | 12 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| 9 Jan 2025 | 2025 | 1 | 9 | 1 | 2 | 3 | Thursday | 9 | false | 6 Jan 2025 | 12 Jan 2025 | 1 Jan 2025 | 31 Jan 2025 | 1 Jan 2025 | 31 Mar 2025 | 1 Jan 2025 | 31 Dec 2025 |
| ........... | .... | .. | .. | . | . | . | ......... | ... | ..... | ........... | .......... | .......... | ........... | .......... | ........... | .......... | ........... |
+-------------+------+-------+-----+---------+----------+-------------+------------------+-------------+------------+-------------+-------------+-------------+-------------+---------------+-------------+-------------+-------------+IsoDate, // YYYY-MM-DD
IsoDateTime, // YYYY-MM-DD HH:MM:SS
UsDate, // MM/DD/YYYY
EuropeanDate, // DD.MM.YYYY
EuropeanDateDash, // DD-MM-YYYY
BritishDate, // DD/MM/YYYY
HumanReadable, // 1 Jan 2025
HumanReadableTime, // 1 Jan 2025 00:00
SlashYMD, // YYYY/MM/DD
DotYMD, // YYYY.MM.DD
CompactDate, // YYYYMMDD
YearMonth, // YYYY-MM
MonthYear, // MM-YYYY
MonthNameYear, // January 2025
Custom(String) // Custom format string
For Custom Date formats some of the common format specifiers:
%Y - Full year (2025)
%y - Short year (25)
%m - Month as number (01-12)
%b - Abbreviated month name (Jan)
%B - Full month name (January)
%d - Day of month (01-31)
%e - Day of month, space-padded ( 1-31)
%a - Abbreviated weekday name (Mon)
%A - Full weekday name (Monday)
%H - Hour (00-23)
%I - Hour (01-12)
%M - Minute (00-59)
%S - Second (00-59)
%p - AM/PM
EXAMPLES:
DateFormat::Custom("%d %b %Y %H:%M".to_string()), // "01 Jan 2025 00:00"
// ISO 8601 with T separator and timezone
DateFormat::Custom("%Y-%m-%dT%H:%M:%S%z".to_string())
// US date with 12-hour time
DateFormat::Custom("%m/%d/%Y %I:%M %p".to_string())
// Custom format with weekday
DateFormat::Custom("%A, %B %e, %Y".to_string()) // "Monday, January 1, 2025"//create calendar dataframe
let date_calendar = CustomDataFrame::create_formatted_date_range_table(
"2025-01-01",
"2025-12-31",
"dt",
"date".to_string(),
DateFormat::HumanReadableTime,
true,
Weekday::Mon
).await?;
// take columns from Calendar
let week_range_2025 = date_calendar
.select(["DISTINCT(week_start)","week_end", "week_num"])
.order_by(["week_num"], [true])
.elusion("wr")
.await?;
// create empty dataframe
let temp_df = CustomDataFrame::empty().await?;
//populate empty dataframe with current week number
let current_week = temp_df
.datetime_functions([
"CAST(DATE_PART('week', CURRENT_DATE()) as INT) AS current_week_num",
])
.elusion("cd").await?;
// join data frames to get range for current week
let week_for_api = week_range_2025
.join(current_week,["wr.week_num == cd.current_week_num"], "INNER")
.select(["TRIM(wr.week_start) AS datefrom", "TRIM(wr.week_end) AS dateto"])
.elusion("api_week")
.await?;
// Extract Date Value from DataFrame based on column name and Row Index
let date_from = extract_value_from_df(&week_for_api, "datefrom", 0).await?;
let date_to = extract_value_from_df(&week_for_api, "dateto", 0).await?;
//PRINT results for preview
week_for_api.display().await?;
println!("Date from: {}", date_from);
println!("Date to: {}", date_to);
RESULT:
+------------------+------------------+
| datefrom | dateto |
+------------------+------------------+
| 3 Mar 2025 00:00 | 9 Mar 2025 00:00 |
+------------------+------------------+
Date from: 3 Mar 2025 00:00
Date to: 9 Mar 2025 00:00
NOW WE CAN USE THESE EXTRACTED VALUES:
let post_df = ElusionApi::new();
post_df.from_api_with_dates(
"https://jsonplaceholder.typicode.com/posts", // url
&date_from, // date from
&date_to, // date to
"C:\\Borivoj\\RUST\\Elusion\\JSON\\rest_api_data.json", // path where json will be stored
).await?;//create calendar dataframe
let date_calendar = CustomDataFrame::create_formatted_date_range_table(
"2025-01-01",
"2025-12-31",
"dt",
"date".to_string(),
DateFormat::IsoDate,
true,
Weekday::Mon
).await?;
//take columns from calendar
let week_range_2025 = date_calendar
.select(["DISTINCT(week_start)","week_end", "week_num"])
.order_by(["week_num"], [true])
.elusion("wr")
.await?;
// create empty dataframe
let temp_df = CustomDataFrame::empty().await?;
//populate empty dataframe with current week number
let current_week = temp_df
.datetime_functions([
"CAST(DATE_PART('week', CURRENT_DATE()) as INT) AS current_week_num",
])
.elusion("cd").await?;
// join data frames to ge range for current week
let week_for_api = week_range_2025
.join(current_week,["wr.week_num == cd.current_week_num"], "INNER")
.select(["TRIM(wr.week_start) AS datefrom", "TRIM(wr.week_end) AS dateto"])
.elusion("api_week")
.await?;
// Extract Row Values from DataFrame based on Row Index
let row_values = extract_row_from_df(&week_for_api, 0).await?;
// PRINT row for preview
println!("DataFrame row: {:?}", row_values);
RESULT:
DataFrame row: {"datefrom": "2025-03-03", "dateto": "2025-03-09"}
NOW WE CAN USE THESE EXTRACTED ROW:
let post_df = ElusionApi::new();
post_df.from_api_with_dates(
"https://jsonplaceholder.typicode.com/posts", // url
row_values.get("datefrom").unwrap_or(&String::new()), // date from
row_values.get("dateto").unwrap_or(&String::new()), // date to
"C:\\Borivoj\\RUST\\Elusion\\JSON\\extraction_df2.json", // path where json will be stored
).await?;For long-term storage of complex query results. When results need to be referenced by name. For data that changes infrequently. Example: Monthly sales summaries, customer metrics, product analytics
For transparent performance optimization. When the same query might be run multiple times in a session. For interactive analysis scenarios. Example: Dashboard queries, repeated data exploration.
let sales = "C:\\Borivoj\\RUST\\Elusion\\SalesData2022.csv";
let products = "C:\\Borivoj\\RUST\\Elusion\\Products.csv";
let customers = "C:\\Borivoj\\RUST\\Elusion\\Customers.csv";
let sales_df = CustomDataFrame::new(sales, "s").await?;
let customers_df = CustomDataFrame::new(customers, "c").await?;
let products_df = CustomDataFrame::new(products, "p").await?;
// Example 1: Using materialized view for customer count
// The TTL parameter (3600) specifies how long the view remains valid in seconds (1 hour)
customers_df.clone()
.select(["COUNT(*) as count"])
.limit(10)
.create_view("customer_count_view", Some(3600))
.await?;
// Access the view by name - no recomputation needed
let customer_count = CustomDataFrame::from_view("customer_count_view").await?;
customer_count.display().await?;
// Example 2: Using query caching with complex joins and aggregations
// First execution computes and stores the result
let join_result = sales_df.clone()
.join_many([
(customers_df.clone(), ["s.CustomerKey = c.CustomerKey"], "INNER"),
(products_df.clone(), ["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),
("p.ProductName", false)
])
.elusion_with_cache("sales_join")
.await?;
join_result.display().await?;
// Other useful cache/view management functions:
CustomDataFrame::invalidate_cache(&["table_name".to_string()]); // Clear cache for specific tables
CustomDataFrame::clear_cache(); // Clear entire cache
CustomDataFrame::refresh_view("view_name").await?; // Refresh a materialized view
CustomDataFrame::drop_view("view_name").await?; // Remove a materialized view
CustomDataFrame::list_views().await; // Get info about all viewslet df_AS = select_df
.select(["CustomerKey AS customerkey_alias", "FirstName as first_name", "LastName", "EmailAddress"]);
let df_select_all = select_df.select(["*"]);
let df_count_all = select_df.select(["COUNT(*)"]);
let df_distinct = select_df.select(["DISTINCT(column_name) as distinct_values"]);let num_ops_sales = sales_order_df
.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?;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?;
// exmple 2
const FILTER_CUSTOMER: &str = "customer_name == 'Customer IRRVL'";
let filter_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(FILTER_CUSTOMER)
.group_by_all()
.order_by_many([
("total_billable", false), // Order by total_billable descending
("max_abs_billable", true), // Then by max_abs_billable ascending
])//Example 1 with aggregatied column names
let example1 = sales_df
.join_many([
(customers_df, ["s.CustomerKey = c.CustomerKey"], "INNER"),
(products_df, ["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 ),
("p.ProductName", false)
]);
let result = example1.elusion("sales_res").await?;
result.display().await?;
//Example 2 with aggregation in having
let df_having= sales_df
.join(customers_df, ["s.CustomerKey = c.CustomerKey"],
"INNER"
)
.select(["c.CustomerKey", "c.FirstName", "c.LastName"])
.agg([
"SUM(s.OrderQuantity) AS total_quantity",
"AVG(s.OrderQuantity) AS avg_quantity"
])
.group_by(["c.CustomerKey", "c.FirstName", "c.LastName"])
.having_many([
("SUM(s.OrderQuantity) > 10"),
("AVG(s.OrderQuantity) < 100")
])
.order_by(["total_quantity"], [true])
.limit(5);
let result = df_having.elusion("sales_res").await?;
result.display().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);
let scalar_res = scalar_df.elusion("scalar_df").await?;
scalar_res.display().await?;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?;let df = sales_df
.select(["FirstName", "LastName"])
.string_functions([
"'New' AS new_old_customer",
"TRIM(c.EmailAddress) AS trimmed_email",
"CONCAT(TRIM(c.FirstName), ' ', TRIM(c.LastName)) AS full_name",
]);
let result_df = df.elusion("df").await?;
result_df.display().await?;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?;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 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, SIGNUMlet 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?;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?;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?;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",
"CASE c.CreditLimit
WHEN 1000 THEN 'Basic'
WHEN 2000 THEN 'Premium'
ELSE 'Standard'
END AS credit_tier",
"CASE
WHEN c.CreditLimit > 2000 THEN 'High'
WHEN c.CreditLimit > 1000 THEN 'Medium'
ELSE 'Low'
END AS credit_status",
"CASE
WHEN o.Amount > 1000 AND c.Status = 'active' THEN 'Priority'
WHEN o.Amount > 500 THEN 'Regular'
ELSE 'Standard'
END AS order_priority",
"CASE r.RegionName
WHEN 'East Coast' THEN 'Eastern'
WHEN 'West Coast' THEN 'Western'
ELSE 'Other'
END AS region_category",
"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?;"INNER", "LEFT", "RIGHT", "FULL",
"LEFT SEMI", "RIGHT SEMI",
"LEFT ANTI", "RIGHT ANTI", "LEFT MARK" 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?; 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()let dt_query = sales_order_df
.select([
"customer_name",
"order_date",
"delivery_date"
])
.datetime_functions([
// Current date/time comparisons
"CURRENT_DATE() AS today",
"CURRENT_TIME() AS current_time",
"CURRENT_TIMESTAMP() AS now",
"NOW() AS now_timestamp",
"TODAY() AS today_timestamp",
// Date binning (for time-series analysis)
"DATE_BIN('1 week', order_date, MAKE_DATE(2020, 1, 1)) AS weekly_bin",
"DATE_BIN('1 month', order_date, MAKE_DATE(2020, 1, 1)) AS monthly_bin",
// Date formatting
"DATE_FORMAT(order_date, '%Y-%m-%d') AS formatted_date",
"DATE_FORMAT(order_date, '%Y/%m/%d') AS formatted_date_alt",
// Basic date components
"DATE_PART('year', order_date) AS year",
"DATE_PART('month', order_date) AS month",
"DATE_PART('day', order_date) AS day",
// Quarters and weeks
"DATE_PART('quarter', order_date) AS order_quarter",
"DATE_PART('week', order_date) AS order_week",
// Day of week/year
"DATE_PART('dow', order_date) AS day_of_week",
"DATE_PART('doy', order_date) AS day_of_year",
// Analysis
"DATE_PART('day', delivery_date - order_date) AS delivery_days",
"DATE_PART('day', CURRENT_DATE() - order_date) AS days_since_order",
// Date truncation (alternative syntax)
"DATE_TRUNC('week', order_date) AS week_start",
"DATE_TRUNC('quarter', order_date) AS quarter_start",
"DATE_TRUNC('month', order_date) AS month_start",
"DATE_TRUNC('year', order_date) AS year_start",
// Complex date calculations
"CASE
WHEN DATE_PART('month', order_date) <= 3 THEN 'Q1'
WHEN DATE_PART('month', order_date) <= 6 THEN 'Q2'
WHEN DATE_PART('month', order_date) <= 9 THEN 'Q3'
ELSE 'Q4'
END AS fiscal_quarter",
// Date comparisons with current date
"CASE
WHEN order_date = CURRENT_DATE() THEN 'Today'
WHEN DATE_PART('day', CURRENT_DATE() - order_date) <= 7 THEN 'Last Week'
WHEN DATE_PART('day', CURRENT_DATE() - order_date) <= 30 THEN 'Last Month'
ELSE 'Older'
END AS order_recency",
// Time windows
"CASE
WHEN DATE_BIN('1 week', order_date, CURRENT_DATE()) = DATE_BIN('1 week', CURRENT_DATE(), CURRENT_DATE())
THEN 'This Week'
ELSE 'Previous Weeks'
END AS week_window",
// Fiscal year calculations
"CASE
WHEN DATE_PART('month', order_date) >= 7
THEN DATE_PART('year', order_date) + 1
ELSE DATE_PART('year', order_date)
END AS fiscal_year",
// Complex date logic - modified to work with Date32
"CASE
WHEN order_date < MAKE_DATE(2024, 1, 1) THEN 'Past'
ELSE 'Present'
END AS temporal_status",
"CASE
WHEN DATE_PART('hour', CURRENT_TIMESTAMP()) < 12 THEN 'Morning'
ELSE 'Afternoon'
END AS time_of_day"
])
.order_by(["order_date"], [false])
let dt_res = dt_query.elusion("datetime_df").await?;
dt_res.display().await?;CURRENT_DATE()
CURRENT_TIME()
CURRENT_TIMESTAMP()
NOW()
TODAY()
DATE_PART()
DATE_TRUNC()
DATE_BIN()
MAKE_DATE()
DATE_FORMAT()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 (ORDER BY c.CustomerKey) AS customer_index")
.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?;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?;let df1 = "C:\\Borivoj\\RUST\\Elusion\\API\\df1.json";
let df2 = "C:\\Borivoj\\RUST\\Elusion\\API\\df2.json";
let df3 = "C:\\Borivoj\\RUST\\Elusion\\API\\df3.json";
let df4 = "C:\\Borivoj\\RUST\\Elusion\\API\\df4.json";
let df5 = "C:\\Borivoj\\RUST\\Elusion\\API\\df5.json";
let df1 = CustomDataFrame::new(df1, "msales1").await?;
let df2 = CustomDataFrame::new(df2, "msales2").await?;
let df3 = CustomDataFrame::new(df3, "msales3").await?;
let df4 = CustomDataFrame::new(df4, "msales4").await?;
let df5 = CustomDataFrame::new(df5, "msales5").await?;
let res_df1 = df1.select(["Month", "TotalSales"]).string_functions(["'site1' AS Restaurant"]);
let result_df1 = res_df1.elusion("el1").await?;
let res_df2 = df2.select(["Month", "TotalSales"]).string_functions(["'site2' AS Restaurant"]);
let result_df2 = res_df2.elusion("el2").await?;
let res_df3 = df3.select(["Month", "TotalSales"]).string_functions(["'site3' AS Restaurant"]);
let result_df3 = res_df3.elusion("el3").await?;
let res_df4 = df4.select(["Month", "TotalSales"]).string_functions(["'site4' AS Restaurant"]);
let result_df4 = res_df4.elusion("el4").await?;
let res_df5 = df5.select(["Month", "TotalSales"]).string_functions(["'site5' AS Restaurant"]);
let resuld_df5 = res_df5.elusion("el5").await?;
//APPEND
let append_df = result_df1.append(result_df2).await?;
//APPEND_MANY
let append_many_df = result_df1.append_many([result_df2, result_df3, result_df4, resuld_df5]).await?;//UNION
let df1 = sales_df.clone()
.join(
customers_df.clone(), ["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 = sales_df.clone()
.join(
customers_df.clone(), ["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 result_df1 = df1.elusion("df1").await?;
let result_df2 = df2.elusion("df2").await?;
let union_df = result_df1.union(result_df2).await?;
let union_df_final = union_df.limit(100).elusion("union_df").await?;
union_df_final.display().await?;
//UNION ALL
let union_all_df = result_df1.union_all(result_df2).await?;
//EXCEPT
let except_df = result_df1.except(result_df2).await?;
//INTERSECT
let intersect_df = result_df1.intersect(result_df2).await?;let df1 = "C:\\Borivoj\\RUST\\Elusion\\API\\df1.json";
let df2 = "C:\\Borivoj\\RUST\\Elusion\\API\\df2.json";
let df3 = "C:\\Borivoj\\RUST\\Elusion\\API\\df3.json";
let df4 = "C:\\Borivoj\\RUST\\Elusion\\API\\df4.json";
let df5 = "C:\\Borivoj\\RUST\\Elusion\\API\\df5.json";
let df1 = CustomDataFrame::new(df1, "msales").await?;
let df2 = CustomDataFrame::new(df2, "msales").await?;
let df3 = CustomDataFrame::new(df3, "msales").await?;
let df4 = CustomDataFrame::new(df4, "msales").await?;
let df5 = CustomDataFrame::new(df5, "msales").await?;
let res_df1 = df1.select(["Month", "TotalSales"]).string_functions(["'df1' AS Sitename"]);
let result_df1 = res_df1.elusion("el1").await?;
let res_df2 = df2.select(["Month", "TotalSales"]).string_functions(["'df2' AS Sitename"]);
let result_df2 = res_df2.elusion("el2").await?;
let res_df3 = df3.select(["Month", "TotalSales"]).string_functions(["'df3' AS Sitename"]);
let result_df3 = res_df3.elusion("el3").await?;
let res_df4 = df4.select(["Month", "TotalSales"]).string_functions(["'df4' AS Sitename"]);
let result_df4 = res_df4.elusion("el4").await?;
let res_df5 = df5.select(["Month", "TotalSales"]).string_functions(["'df5' AS Sitename"]);
let resuld_df5 = res_df5.elusion("el5").await?;
//UNION_MANY
let union_all_df = result_df1.union_many([result_df2, result_df3, result_df4, resuld_df5]).await?;
//UNION_ALL_MANY
let union_all_many_df = result_df1.union_all_many([result_df2, result_df3, result_df4, resuld_df5]).await?;They should be used separately from other functions: 1. directly on initial CustomDataFrame, 2. after .elusion() evaluation.
// 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?;
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?;df.display_stats(&[
"abs_billable_value",
"sqrt_billable_value",
"double_billable_value",
"percentage_billable"
]).await?;
=== Column Statistics ===
--------------------------------------------------------------------------------
Column: abs_billable_value
------------------------------------------------------------------------------
| Metric | Value | Min | Max |
------------------------------------------------------------------------------
| Records | 10 | - | - |
| Non-null Records | 10 | - | - |
| Mean | 1025.71 | - | - |
| Standard Dev | 761.34 | - | - |
| Value Range | - | 67.4 | 2505.23 |
------------------------------------------------------------------------------
Column: sqrt_billable_value
------------------------------------------------------------------------------
| Metric | Value | Min | Max |
------------------------------------------------------------------------------
| Records | 10 | - | - |
| Non-null Records | 10 | - | - |
| Mean | 29.48 | - | - |
| Standard Dev | 13.20 | - | - |
| Value Range | - | 8.21 | 50.05 |
------------------------------------------------------------------------------
// Display null analysis
// Keep None if you want all columns to be analized
df.display_null_analysis(None).await?;
----------------------------------------------------------------------------------------
| Column | Total Rows | Null Count | Null Percentage |
----------------------------------------------------------------------------------------
| total_billable | 10 | 0 | 0.00% |
| order_count | 10 | 0 | 0.00% |
| customer_name | 10 | 0 | 0.00% |
| order_date | 10 | 0 | 0.00% |
| abs_billable_value | 10 | 0 | 0.00% |
----------------------------------------------------------------------------------------
// Display correlation matrix
df.display_correlation_matrix(&[
"abs_billable_value",
"sqrt_billable_value",
"double_billable_value",
"percentage_billable"
]).await?;
=== Correlation Matrix ===
-------------------------------------------------------------------------------------------
| | abs_billable_va | sqrt_billable_v | double_billable | percentage_bill |
-------------------------------------------------------------------------------------------
| abs_billable_va | 1.00 | 0.98 | 1.00 | 1.00 |
| sqrt_billable_v | 0.98 | 1.00 | 0.98 | 0.98 |
| double_billable | 1.00 | 0.98 | 1.00 | 1.00 |
| percentage_bill | 1.00 | 0.98 | 1.00 | 1.00 |
-------------------------------------------------------------------------------------------Don't forget that you can always load tables from Database into DataFrames and work with DataFrame API, but for better performance you should aggregate data in SQL server than push it into dataframe.
let connection_string = "
Driver={MySQL ODBC 9.1 Unicode Driver};\
Server=127.0.0.1;\
Port=3306;\
Database=your_database_name;\
User=your_user_name;\
Password=your_password";
let sql_query = "
SELECT
b.beer_style,
b.location,
c.color,
AVG(b.fermentation_time) AS avg_fermentation_time,
ROUND(AVG(b.temperature), 2) AS avg_temperature,
ROUND(AVG(b.quality_score), 2) AS avg_quality,
ROUND(AVG(b.brewhouse_efficiency), 2) AS avg_efficiency,
SUM(b.volume_produced) AS total_volume,
ROUND(AVG(b.loss_during_brewing), 2) AS avg_brewing_loss,
ROUND(AVG(b.loss_during_fermentation), 2) AS avg_fermentation_loss,
ROUND(SUM(b.total_sales), 2) AS total_sales,
ROUND(AVG(b.brewhouse_efficiency - (b.loss_during_brewing + b.loss_during_fermentation)), 2) AS net_efficiency
FROM brewery_data b
JOIN colors c ON b.color = c.color_number
WHERE volume_produced > 1000
GROUP BY b.beer_style, b.location, c.color
HAVING avg_quality > 8
ORDER BY total_sales DESC, avg_quality DESC
LIMIT 20
";
let mysql_df = CustomDataFrame::from_db(
connection_string,
sql_query
).await?;
let analysis_df = mysql_df.elusion("brewing_analysis").await?;
analysis_df.display().await?;let pg_connection = "\
Driver={PostgreSQL UNICODE};\
Servername=127.0.0.1;\
Port=5433;\
Database=your_database_name;\
UID=your_user_name;\
PWD=your_password;\
";
let sql_query = "
SELECT
c.name,
c.email,
SUM(s.quantity * s.price) as total_sales,
COUNT(*) as number_of_purchases
FROM sales s
JOIN customers c ON s.customer_id = c.id
GROUP BY c.id, c.name, c.email
ORDER BY total_sales DESC
";
let pg_df = CustomDataFrame::from_db(pg_connection, sql_query).await?;
let pg_res = pg_df.elusion("pg_res").await?;
pg_res.display().await?;DFS endpoint is “Data Lake Storage Gen2” and behave more like a real file system. This makes reading operations more efficient—especially at large scale.
let blob_url= "https://your_storage_account_name.blob.core.windows.net/your-container-name";
let sas_token = "your_sas_token";
let df = CustomDataFrame::from_azure_with_sas_token(
blob_url,
sas_token,
Some("folder-name/file-name"), // FILTERING is optional. Can be None if you want to take everything from Container
"data" // alias for registering table
).await?;
let data_df = df.select(["*"]);
let test_data = data_df.elusion("data_df").await?;
test_data.display().await?;let dfs_url= "https://your_storage_account_name.dfs.core.windows.net/your-container-name";
let sas_token = "your_sas_token";
let df = CustomDataFrame::from_azure_with_sas_token(
dfs_url,
sas_token,
Some("folder-name/file-name"), // FILTERING is optional. Can be None if you want to take everything from Container
"data" // alias for registering table
).await?;
let data_df = df.select(["*"]);
let test_data = data_df.elusion("data_df").await?;
test_data.display().await?;"1min","2min","5min","10min","15min","30min" ,
"1h","2h","3h","4h","5h","6h","7h","8h","9h","10h","11h","12h","24h"
"2days","3days","4days","5days","6days","7days","14days","30days" PipelineScheduler Example (parsing data from Azure BLOB Stoarge, DataFrame operation and Writing to Parquet)
use elusion::prelude::*;
#[tokio::main]
async fn main() -> ElusionResult<()>{
// Create Pipeline Scheduler
let scheduler = PipelineScheduler::new("5min", || async {
let dfs_url= "https://your_storage_account_name.dfs.core.windows.net/your-container-name";
let sas_token = "your_sas_token";
// Read from Azure
let header_df = CustomDataFrame::from_azure_with_sas_token(
dfs_url,
dfs_sas_token,
Some("folder_name/"), // Optional: FILTERING can filter any part of string: file path, file name...
"head"
).await?;
// DataFrame operation
let headers_payments = header_df
.select(["Brand", "Id", "Name", "Item", "Bill", "Tax",
"ServCharge", "Percentage", "Discount", "Date"])
.agg([
"SUM(Bill) AS total_bill",
"SUM(Tax) AS total_tax",
"SUM(ServCharge) AS total_service",
"AVG(Percentage) AS avg_percentage",
"COUNT(*) AS transaction_count",
"SUM(ServCharge) / SUM(Bill) * 100 AS service_ratio"
])
.group_by(["Brand", "Date"])
.filter("Bill > 0")
.order_by(["total_bill"], [true])
let headers_data = headers_payments.elusion("headers_df").await?;
// Write output
headers_data
.write_to_parquet(
"overwrite",
"C:\\Borivoj\\RUST\\Elusion\\Scheduler\\sales_data.parquet",
None
)
.await?;
Ok(())
}).await?;
scheduler.shutdown().await?;
Ok(())
}// example json structure with key:value pairs
{
"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?;
let df = json_df.select(["*"]).limit(10);
let result = df.elusion("df").await?;
result.display().await?;
// example json structure with Fields and Arrays
[
{
"id": "1",
"name": "Form 1",
"fields": [
{"key": "first_name", "type": "text", "required": true},
{"key": "age", "type": "number", "required": false},
{"key": "email", "type": "email", "required": true}
]
},
{
"id": "2",
"name": "Form 2",
"fields": [
{"key": "address", "type": "text", "required": false},
{"key": "phone", "type": "tel", "required": true}
]
},
{
"id": "3",
"name": "Form 3",
"fields": [
{"key": "notes", "type": "textarea", "required": false},
{"key": "date", "type": "date", "required": true},
{"key": "status", "type": "select", "required": true}
]
}
]
let json_path = "C:\\Borivoj\\RUST\\Elusion\\test2.json";
let json_df = CustomDataFrame::new(json_path, "test2").await?;// example 1
let posts_df = ElusionApi::new();
posts_df
.from_api(
"https://jsonplaceholder.typicode.com/posts", // url
"C:\\Borivoj\\RUST\\Elusion\\JSON\\posts_data.json" // path where json will be stored
).await?;
// example 2
let users_df = ElusionApi::new();
users_df.from_api(
"https://jsonplaceholder.typicode.com/users",
"C:\\Borivoj\\RUST\\Elusion\\JSON\\users_data.json",
).await?;
// example 3
let ceo = ElusionApi::new();
ceo.from_api(
"https://dog.ceo/api/breeds/image/random/3",
"C:\\Borivoj\\RUST\\Elusion\\JSON\\ceo_data.json"
).await?;// example 1
let mut headers = HashMap::new();
headers.insert("Custom-Header".to_string(), "test-value".to_string());
let bin_df = ElusionApi::new();
bin_df.from_api_with_headers(
"https://httpbin.org/headers", // url
headers, // headers
"C:\\Borivoj\\RUST\\Elusion\\JSON\\bin_data.json", // path where json will be stored
).await?;
// example 2
let mut headers = HashMap::new();
headers.insert("Accept".to_string(), "application/vnd.github.v3+json".to_string());
headers.insert("User-Agent".to_string(), "elusion-dataframe-test".to_string());
let git_hub = ElusionApi::new();
git_hub.from_api_with_headers(
"https://api.github.com/search/repositories?q=rust+language:rust&sort=stars&order=desc",
headers,
"C:\\Borivoj\\RUST\\Elusion\\JSON\\git_hub_data.json"
).await?;
// example 3
let mut headers = HashMap::new();
headers.insert("Accept".to_string(), "application/json".to_string());
headers.insert("X-Version".to_string(), "1".to_string());
let pokemon_df = ElusionApi::new();
pokemon_df.from_api_with_headers(
"https://pokeapi.co/api/v2/pokemon",
headers,
"C:\\Borivoj\\RUST\\Elusion\\JSON\\pokemon_data.json"
).await?;// Using OpenLibrary API with params
let mut params = HashMap::new();
params.insert("q", "rust programming");
params.insert("limit", "10");
let open_lib = ElusionApi::new();
open_lib.from_api_with_params(
"https://openlibrary.org/search.json", // url
params, // params
"C:\\Borivoj\\RUST\\Elusion\\JSON\\open_lib_data.json", // path where json will be stored
).await?;
// Random User Generator API with params
let mut params = HashMap::new();
params.insert("results", "10");
params.insert("nat", "us,gb");
let generator = ElusionApi::new();
generator.from_api_with_params(
"https://randomuser.me/api",
params,
"C:\\Borivoj\\RUST\\Elusion\\JSON\\generator_data.json"
).await?;
// JSON Placeholder with multiple endpoints
let mut params = HashMap::new();
params.insert("userId", "1");
params.insert("_limit", "5");
let multi = ElusionApi::new();
multi.from_api_with_params(
"https://jsonplaceholder.typicode.com/posts",
params,
"C:\\Borivoj\\RUST\\Elusion\\JSON\\multi_data.json"
).await?;
// NASA Astronomy Picture of the Day
let mut params = HashMap::new();
params.insert("count", "5");
params.insert("thumbs", "true");
let nasa = ElusionApi::new();
nasa.from_api_with_params(
"https://api.nasa.gov/planetary/apod",
params,
"C:\\Borivoj\\RUST\\Elusion\\JSON\\nasa_pics_data.json"
).await?;
// example 5
let mut params = HashMap::new();
params.insert("brand", "elusion");
params.insert("password", "some_password");
params.insert("siteid", "993");
params.insert("Datefrom", "01 jan 2025 06:00");
params.insert("Dateto", "31 jan 2025 06:00");
params.insert("user", "borivoj");
let api = ElusionApi::new();
api.from_api_with_params(
"https://salesapi.net.co.rs/SSPAPI/api/data",
params,
"C:\\Borivoj\\RUST\\Elusion\\JSON\\sales_jan_2025.json"
).await?;let mut params = HashMap::new();
params.insert("since", "2024-01-01T00:00:00Z");
params.insert("until", "2024-01-07T23:59:59Z");
let mut headers = HashMap::new();
headers.insert("Accept".to_string(), "application/vnd.github.v3+json".to_string());
headers.insert("User-Agent".to_string(), "elusion-dataframe-test".to_string());
let commits_df = ElusionApi::new();
commits_df.from_api_with_params_and_headers(
"https://api.github.com/repos/rust-lang/rust/commits", // url
params, // params
headers, // headers
"C:\\Borivoj\\RUST\\Elusion\\JSON\\commits_data.json", // path where json will be stored
).await?;// example 1
let post_df = ElusionApi::new();
post_df.from_api_with_dates(
"https://jsonplaceholder.typicode.com/posts", // url
"2024-01-01", // date from
"2024-01-07", // date to
"C:\\Borivoj\\RUST\\Elusion\\JSON\\post_data.json", // path where json will be stored
).await?;
// Example 2: COVID-19 historical data
let covid_df = ElusionApi::new();
covid_df.from_api_with_dates(
"https://disease.sh/v3/covid-19/historical/all",
"2024-01-01",
"2024-01-07",
"C:\\Borivoj\\RUST\\Elusion\\JSON\\covid_data.json"
).await?;// example 1
let reqres = ElusionApi::new();
reqres.from_api_with_pagination(
"https://reqres.in/api/users",
1, // page
10, // per_page
"C:\\Borivoj\\RUST\\Elusion\\JSON\\reqres_data.json"
).await?;let movie_db = ElusionApi::new();
movie_db.from_api_with_sort(
"https://api.themoviedb.org/3/discover/movie", // base url
"popularity", // sort field
"desc", // order
"C:\\Borivoj\\RUST\\Elusion\\JSON\\popular_movies.json"
).await?;let mut headers = HashMap::new();
headers.insert("Authorization".to_string(), "Bearer YOUR_TMDB_API_KEY".to_string());
headers.insert("accept".to_string(), "application/json".to_string());
let movie_db = ElusionApi::new();
movie_db.from_api_with_headers_and_sort(
"https://api.themoviedb.org/3/discover/movie", // base url
headers, // headers
"popularity", // sort field
"desc", // order
"C:\\Borivoj\\RUST\\Elusion\\JSON\\popular_movies1.json"
).await?;// 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?;
// 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?;let custom_csv_options = CsvWriteOptions {
delimiter: b',',
escape: b'\\',
quote: b'"',
double_quote: false,
null_value: "NULL".to_string(),
};// overwrite existing file
result_df
.write_to_csv(
"overwrite",
"C:\\Borivoj\\RUST\\Elusion\\agg_sales.csv",
custom_csv_options
)
.await?;
// append to exisiting file
result_df
.write_to_csv(
"append",
"C:\\Borivoj\\RUST\\Elusion\\agg_sales.csv",
custom_csv_options
)
.await?;df.write_to_json(
"C:\\Borivoj\\RUST\\Elusion\\date_table.json", // path
true // pretty-printed JSON, false for compact JSON
).await?;Partitioning column is OPTIONAL and if you decide to use column for partitioning, make sure that you don't need that column as you won't be able to read it back to dataframe
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");let df = CustomDataFrame::new(csv_data, "sales").await?;
let query = df.select(["*"]);
let data = query.elusion("df_sales").await?;
let url_to_folder = "https://your_storage_account_name.dfs.core.windows.net/your-container-name/folder/sales.parquet";
let sas_write_token = "your_sas_token"; // make sure SAS token has writing permissions
data.write_parquet_to_azure_with_sas(
"overwrite",
url_to_folder,
sas_write_token
).await?;
// append version
data.write_parquet_to_azure_with_sas(
"append",
url_to_folder,
sas_write_token
).await?;let df = CustomDataFrame::new(csv_data, "sales").await?;
let query = df.select(["*"]);
let data = query.elusion("df_sales").await?;
let url_to_folder = "https://your_storage_account_name.dfs.core.windows.net/your-container-name/folder/data.json";
let sas_write_token = "your_sas_token"; // make sure SAS token has writing permissions
data.write_json_to_azure_with_sas(
url_to_folder,
sas_write_token,
true // Set to true for pretty-printed JSON, false for compact JSON
).await?;let ord = "C:\\Borivoj\\RUST\\Elusion\\sales_order_report.csv";
let sales_order_df = CustomDataFrame::new(ord, "ord").await?;
let mix_query = sales_order_df.clone()
.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?;
//INTERACTIVE PLOTS
// Line plot showing sales over time
let line = mix_res.plot_line(
"order_date", // - x_col: column name for x-axis (can be date or numeric)
"double_billable_value", // - y_col: column name for y-axis
true, // - show_markers: true to show points, false for line only
Some("Sales over time") // - title: optional custom title (can be None)
).await?;
// Bar plot showing aggregated values
let bars = mix_res
.plot_bar(
"customer_name", // X-axis: Customer names
"total_billable", // Y-axis: Total billable amount
Some("Customer Total Sales") // Title of the plot
).await?;
// Time series showing sales trend
let time_series = mix_res
.plot_time_series(
"order_date", // X-axis: Date column (must be Date32 type)
"total_billable", // Y-axis: Total billable amount
true, // Show markers on the line
Some("Sales Trend Over Time") // Title of the plot
).await?;
// Histogram showing distribution of abs billable values
let histogram = mix_res
.plot_histogram(
"abs_billable_value", // Data column for distribution analysis
Some("Distribution of Sale Values") // Title of the plot
).await?;
// Box plot showing abs billable value distribution
let box_plot = mix_res
.plot_box(
"abs_billable_value", // Value column for box plot
Some("customer_name"), // Optional grouping column
Some("Sales Distribution by Customer") // Title of the plot
).await?;
// Scatter plot showing relationship between original and doubled values
let scatter = mix_res
.plot_scatter(
"abs_billable_value", // X-axis: Original values
"double_billable_value", // Y-axis: Doubled values
Some(8) // Optional marker size
).await?;
// Pie chart showing sales distribution
let pie = mix_res
.plot_pie(
"customer_name", // Labels for pie segments
"total_billable", // Values for pie segments
Some("Sales Share by Customer") // Title of the plot
).await?;
// Donut chart alternative view
let donut = mix_res
.plot_donut(
"customer_name", // Labels for donut segments
"percentage_total_billable", // Values as percentages
Some("Percentage Distribution") // Title of the plot
).await?;
// Create Tables to add to report
let summary_table = mix_res.clone() //Clone for multiple usages
.select([
"customer_name",
"total_billable",
"avg_abs_billable",
"max_abs_billable",
"percentage_total_billable"
])
.order_by_many([
("total_billable", false)
])
.elusion("summary")
.await?;
let transactions_table = mix_res
.select([
"customer_name",
"order_date",
"abs_billable_value",
"double_billable_value",
"percentage_billable"
])
.order_by_many([
("order_date", false),
("abs_billable_value", false)
])
.elusion("transactions")
.await?;
// Create comprehensive dashboard with all plots
let plots = [
(&line, "Sales Line"), // Line based analysis
(&time_series, "Sales Timeline"), // Time-based analysis
(&bars, "Customer Sales"), // Customer comparison
(&histogram, "Sales Distribution"), // Value distribution
(&scatter, "Value Comparison"), // Value relationships
(&box_plot, "Customer Distributions"), // Statistical distribution
(&pie, "Sales Share"), // Share analysis
(&donut, "Percentage View"), // Percentage breakdown
];
// Add tables array
let tables = [
(&summary_table, "Customer Summary"),
(&transactions_table, "Transaction Details")
];
let layout = ReportLayout {
grid_columns: 2, // Arrange plots in 2 columns
grid_gap: 30, // 30px gap between plots
max_width: 1600, // Maximum width of 1600px
plot_height: 450, // Each plot 450px high
table_height: 500, // Height for tables
};
let table_options = TableOptions {
pagination: true, // Enable pagination for tables
page_size: 15, // Show 15 rows per page
enable_sorting: true, // Allow column sorting
enable_filtering: true, // Allow column filtering
enable_column_menu: true, // Show column menu (sort/filter/hide options)
theme: "ag-theme-alpine".to_string(), // Use Alpine theme for modern look
};
// Generate the enhanced interactive report with all plots and tables
CustomDataFrame::create_report(
Some(&plots), // plots (Optional)
Some(&tables), // tables (Optional)
"Interactive Sales Analysis Dashboard", // report_title
"C:\\Borivoj\\RUST\\Elusion\\Plots\\interactive_aggrid_dashboard.html", // filename
Some(layout), // layout_config (Optional)
Some(table_options) // table_options (Optional)
).await?;
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.
This library leverages the power of Rust's type system and libraries like DataFusion , Appache Arrow, Arrow ODBC, Tokio Cron Scheduler, Tokio... for efficient query processing. Special thanks to the open-source community for making this project possible.