Elusion is a high-performance DataFrame / Data Engineering library designed for in-memory data formats such as CSV, EXCEL, JSON, PARQUET, DELTA, as well as for SharePoint Connection, Azure Blob Storage Connections, Postgres Database Connection, MySql Database Connection, and REST API's for creating JSON files which can be forwarded to DataFrame, with advanced query results caching abilities with Redis and Native cashing.
This Library is designed to be used for Business Data Engineering with reasonable file sizes, with focus on accuracy, user experience by auto-creating schema and simplified query usage (which is very CPU intensive). Elusion is not made for Data Science nor Machine Learning 1TB and 500 columns datasets.
All of the DataFrame operations can be placed in PipelineScheduler for automated Data Engineering Pipelines.
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, as well as 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 ANY ORDER or 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.
- 🔃 Loading:
CustomDataFrame::new(), auto shema recognition, auto file extension recognition. Just make sure that data can fit within your RAM.
-
- 🔃 Regular Processing:
.elusion()loads all results into memory.
- 🔃 Regular Processing:
-
- 💾 Cached Processing: Smart result caching for repeated queries
.elusion_with_cache()- locally saves query results to disk.elusion_with_redis_cache()- uses Redis to store query results for distributed access- ✅ Perfect for: Repeated queries, dashboard applications, multi-user scenarios
-
- 🔃 In-Memory Export:
.write_to_csv(),.write_to_json(),.write_to_parquet(),.write_to_delta_table(),.write_to_excel()- Loads results into memory first
-
- ☁️ Cloud Export:
write_parquet_to_azure_with_sas(),write_json_to_azure_with_sas()
- ☁️ Cloud Export:
-
- 🚀 Streaming Export:
.elusion_streaming_write()streams results directly to files- Formats:
.json,.csv,.parquet
-
- 📊 Console Display:
.elusion()needs.display()to display dataframe result.elusion_streaming()displays results automatically (.display()not allowed)
| Approach | Source Data | Query Processing | Caching | Memory Usage | Best For |
|---|---|---|---|---|---|
new() + elusion() |
In Memory | In Memory | 🕣 | High | Small datasets, interactive analysis |
new() + elusion_streaming() |
In Memory | Streaming | 🚀 | Medium | Medium datasets, large result sets |
new() + elusion_with_cache() |
In Memory | In Memory | 💾 Local | Medium | Repeated queries, development |
new() + elusion_with_redis_cache() |
In Memory | In Memory | 🔄 Redis | Medium | Multi-user dashboards, 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.
- REST API's: Create JSON files from REST API endpoints with Customizable Headers, Params, Date Ranges, Pagination...
- SharePoint: Elusion provides seamless integration with Microsoft SharePoint Online, allowing you to load data directly from SharePoint document libraries into DataFrames.
- Seamless Data Loading: Easily load and process data from CSV, EXCEL, 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:
Reduced Computation Time, Memory Management, Query Optimization, Interactive Analysis, Multiple visualizations for Dashboards and Reports, Resource Utilization, Concurrency
High-performance distributed caching for production environments, multi-server deployments, and large-scale data processing. Redis caching provides:
- Persistent cache across application restarts
- Distributed caching for multiple application instances
- Production-ready performance and reliability
- Automatic TTL management and expiration
- 6-10x performance improvements for repeated queries
- Native Cache: Development, single-instance apps, temporary caching
- Redis Cache: Production, distributed systems, persistent caching, large datasets
- 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 operations such as APPEND, UNION, UNION ALL, INTERSECT, and EXCEPT. For multiple Dataframea operations: APPEND_MANY, UNION_MANY, UNION_ALL_MANY.
Write data transformations that read like natural language:
sales_df
.join_many([
(customers_df, ["s.CustomerKey = c.CustomerKey"], "INNER"),
(products_df, ["s.ProductKey = p.ProductKey"], "INNER"),
])
.select(["c.name", "p.category", "s.amount"])
.filter("s.amount > 1000")
.agg(["SUM(s.amount) AS total_revenue"])
.group_by(["c.region", "p.category"])
.order_by(["total_revenue"], ["DESC"])
.elusion("quarterly_report")
.await?Ready to transform your data engineering workflow? Elusion combines the performance of Rust, the flexibility of modern DataFrames, and the reliability of enterprise software into one powerful library.
To add 🚀 Latest and the Greatest 🚀 version of Elusion to your Rust project, include the following lines in your Cargo.toml under [dependencies]:
elusion = "6.0.0"
tokio = { version = "1.45.0", features = ["rt-multi-thread"] }>= 1.89.0Elusion uses Cargo feature flags to keep the library lightweight and modular. You can enable only the features you need, which helps reduce dependencies and compile time.
["postgres"]Enables Postgres Database connectivity.
["mysql"]Enables MySql Database connectivity
["azure"]Enables Azure BLOB storage connectivity.
["sharepoint"]Enables SharePoint connectivity.
["api"]Enables HTTP API integration for fetching data from web services.
["dashboard"]Enables data visualization and dashboard creation capabilities.
["excel"]Enables writing DataFrame to Excel file.
["all"]Enables all available features.
Usage:
-
In your Cargo.toml, specify which features you want to enable:
-
Add the POSTGRES feature when specifying the dependency:
[dependencies]
elusion = { version = "6.0.0", features = ["postgres"] }- Using NO Features (minimal dependencies):
[dependencies]
elusion = "6.0.0"- Using multiple specific features:
[dependencies]
elusion = { version = "6.0.0", features = ["dashboard", "api", "mysql"] }- Using all features:
[dependencies]
elusion = { version = "6.0.0", features = ["all"] }All DataFrame query expresions, functions, aliases and column names will be normalized to LOWERCASE(), TRIM() and REPLACE(" ","_")
If your column names have special characters like: / * + - ...or any special characters that can be part of sql operation keywords, group_by_all() can brake as I am unable to handle special characters in column names, during automatical expansion from select([""]) or select(["alias."]). For best usage and performance use snake_case style column names.
// Import everything needed
use elusion::prelude::*;
#[tokio::main]
async fn main() -> ElusionResult<()> {
Ok(())
}Delimiters are auto-detected: b'\t' => "tab (TSV)", b',' => "comma (CSV)", b';' => "semicolon", b'|' => "pipe"
let csv_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\csv_data.csv";
let df = CustomDataFrame::new(csv_path, "csv_data").await?; let excel_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\excel_data.xlsx";
let df = CustomDataFrame::new(excel_path, "xlsx_data").await?;let parquet_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\prod_data.parquet";
let df = CustomDataFrame::new(parquet_path, "parq_data").await?;let json_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\mongo_data.json";
let df = CustomDataFrame::new(json_path, "json_data").await?;let delta_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\agg_sales"; // for DELTA you just specify folder name without extension
let df = CustomDataFrame::new(delta_path, "delta_data").await?;let big_file_path = "C:\\Borivoj\\RUST\\Elusion\\bigdata\\customers-2000000.csv";
let big_file_path_df = CustomDataFrame::new(big_file_path, "raw22").await?;
big_file_path_df
.select(["first_name", "last_name","company", "city" ,"country"])
.string_functions(["CAST(subscription_date AS DATE) as date"])
.limit(10)
.elusion_streaming("logentries1").await?;let big_file_path = "C:\\Borivoj\\RUST\\Elusion\\bigdata\\customers-2000000.csv";
let big_file_path_df = CustomDataFrame::new_with_stream(big_file_path, "raw22").await?;
big_file_path_df
.select(["first_name", "last_name","company", "city" ,"country"])
.string_functions(["CAST(subscription_date AS DATE) as date"])
.limit(10)
.elusion_streaming_write("data", "C:\\output\\results.csv", "overwrite").await?; // you can also use "append"
SAME USAGE IS FOR .json and .parquet
.elusion_streaming_write("data", "C:\\output\\results.json", "overwrite").await?; // you can also use "append"
.elusion_streaming_write("data", "C:\\output\\results.parquet", "overwrite").await?; // you can also use "append"// Load all supported files from folder
let combined_data = CustomDataFrame::load_folder(
"C:\\BorivojGrujicic\\RUST\\Elusion\\SalesReports",
None, // Load all supported file types (csv, xlsx, json, parquet)
"combined_sales_data"
).await?;
// Load only specific file types
let csv_excel_data = CustomDataFrame::load_folder(
"C:\\BorivojGrujicic\\RUST\\Elusion\\SalesReports",
Some(vec!["csv", "xlsx"]), // Only load CSV and Excel files
"filtered_data"
).await?;// Load files with filename tracking
let data_with_source = CustomDataFrame::load_folder_with_filename_column(
"C:\\BorivojGrujicic\\RUST\\Elusion\\DailyReports",
None, // Load all supported file types
"daily_data_with_source"
).await?;
// Load only specific file types with filename tracking
let excel_files_with_source = CustomDataFrame::load_folder_with_filename_column(
"C:\\BorivojGrujicic\\RUST\\Elusion\\MonthlySales",
Some(vec!["xlsx", "xls"]), // Only Excel files
"monthly_excel_data"
).await?;You can load single EXCEL, CSV, JSON and PARQUET files OR All files from a FOLDER into Single DataFrame
- Download and install Azure CLI from: https://docs.microsoft.com/en-us/cli/azure/install-azure-cli
- Microsoft users can download here: https://learn.microsoft.com/en-us/cli/azure/install-azure-cli-windows?view=azure-cli-latest&pivots=msi
- 🍎 macOS: brew install azure-cli
- 🐧 Linux:
curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash
sudo rpm --import https://packages.microsoft.com/keys/microsoft.asc sudo dnf install azure-cli
sudo pacman -S azure-cli
Open Command Prompt and write:
"az login"This will open a browser window for authentication. Sign in with your Microsoft account that has access to your SharePoint site.
"az account show"This should display your account information and confirm you're logged in.
- Sites.Read.All or Sites.ReadWrite.All
- Files.Read.All or Files.ReadWrite.All
//Example:
let df = CustomDataFrame::load_from_sharepoint(
"your-tenant-id", //tenant id
"your-client-id", //clientid
"https://contoso.sharepoint.com/sites/MySite", //siteid
"Shared Documents/Data/customer_data.csv", //file path
"combined_data" //dataframe alias
).await?;
let sales_data = df
.select(["Column_1","Column_2","Column_3"])
.elusion("my_sales_data").await?;
sales_data.display().await?;let dataframes = CustomDataFrame::load_folder_from_sharepoint(
"your-tenant-id",//tenant id
"your-client-id", //client id
"http://companyname.sharepoint.com/sites/SiteName", //site id
"Shared Documents/MainFolder/SubFolder",//folder path
None, // None will read any file type, or you can filter by extension: Some(vec!["xlsx", "csv"])
"combined_data" //dataframe alias
).await?;
dataframes.display().await?;let dataframes = CustomDataFrame::load_folder_from_sharepoint_with_filename_column(
"your-tenant-id",
"your-client-id",
"http://companyname.sharepoint.com/sites/SiteName",
"Shared Documents/MainFolder/SubFolder",
None, // None will read any file type, or you can filter by extension: Some(vec!["xlsx", "csv"])
"combined_data" //dataframe alias
).await?;
dataframes.display().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?;LOADING data from POSTGRES into CustomDataFrame (scroll till the end for FULL example with config, conn and query)
let df = CustomDataFrame::from_postgres(&conn, query, "df_alias").await?;LOADING data from MySQL into CustomDataFrame (scroll till the end for FULL example with config, conn and query)
let df = CustomDataFrame::from_mysql(&conn, query, "df_alias").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"let csv_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\sales_data.csv";
let df = CustomDataFrame::new(csv_path, "sales").await?;
// Show first 5 rows
df.show_head(5).await?;
// Show last 10 rows
df.show_tail(10).await?;
// Show first 3 and last 3 rows
df.peek(3).await?;
// Show Column names and their types
df_arhiva.df_schema();let complex_result = df_arhiva
.filter_many([("mesec = 'Januar'"), ("neto_vrednost > 1000")])
.select([
"veledrogerija as pharm",
"region AS refionale" ,
"kolicina",
"neto_vrednost",
"mesto"
])
.window("ROW_NUMBER() OVER (PARTITION BY region ORDER BY mesto DESC) as region_rank")
.agg([
"COUNT(*) as broj_transakcija",
"SUM(kolicina) AS ukupna_kolicina",
"SUM(neto_vrednost) AS ukupna_vrednost"
])
.group_by(["pharm",
"regionale" ,
"kolicina",
"neto_vrednost",
"mesto"])
.order_by(["ukupna_vrednost"], ["DESC"])
.limit(10);
complex_result.display_query();
complex_result.display_query_with_info();
let res = complex_result.elusion("analysis1").await?;
res.display().await?;============================================================
SELECT count( * ) as "broj_transakcija", sum("analysis"."kolicina") as "ukupna_kolicina", sum("analysis"."neto_vrednost") as "ukupna_vrednost", "veledrogerija" AS "pharm", "region" AS "regionale", "kolicina", "neto_vrednost", "mesto", row_number() over (partition by region order by mesto desc) as region_rank
FROM "analysis" AS analysis
WHERE "mesec" = 'Januar' AND "neto_vrednost" > 1000
GROUP BY "veledrogerija", "region", "kolicina", "neto_vrednost", "mesto"
ORDER BY "ukupna_vrednost" DESC
LIMIT 10SELECT count( * ) as "broj_transakcija", sum("analysis"."kolicina") as "ukupna_kolicina", sum("analysis"."neto_vrednost") as "ukupna_vrednost", "veledrogerija" AS "pharm", "region" AS "regionale", "kolicina", "neto_vrednost", "mesto", row_number() over (partition by region order by mesto desc) as region_rank
FROM "analysis" AS analysis
WHERE "mesec" = 'Januar' AND "neto_vrednost" > 1000
GROUP BY "veledrogerija", "region", "kolicina", "neto_vrednost", "mesto"
ORDER BY "ukupna_vrednost" DESC
LIMIT 10df.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 |
-------------------------------------------------------------------------------------------These functions detect: NULL, empty strings (''), 'null'/'NULL', 'na'/'NA', 'n/a'/'N/A', 'none'/'NONE', '-', '?', 'NaN'/'nan'
let csv_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\customer_data.csv";
let df = CustomDataFrame::new(csv_path, "customers").await?;
// Fill nulls in single column
let cleaned_df = df
.fill_null(["age"], "0")
.elusion("cleaned_customers").await?;
// Fill nulls in multiple columns
let cleaned_df = df
.fill_null(["age", "salary", "phone"], "Unknown")
.elusion("cleaned_customers").await?;
// Chain with other operations
let processed_df = df
.fill_null(["age"], "0")
.fill_null(["name"], "Anonymous")
.filter("age > 18")
.select(["name", "age", "salary"])
.elusion("processed_data").await?;let csv_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\customer_data.csv";
let df = CustomDataFrame::new(csv_path, "customers").await?;
// Drop rows with nulls in single column
let cleaned_df = df
.drop_null(["email"])
.elusion("customers_with_email").await?;
// Drop rows with nulls in multiple columns
let cleaned_df = df
.drop_null(["email", "phone", "address"])
.elusion("complete_customers").await?;
// Chain with other operations
let processed_df = df
.drop_null(["customer_id"])
.fill_null(["age"], "0")
.filter("age > 21")
.elusion("adult_customers").await?;FILL_DOWN function - fill_down() - that fills down null values in column with firs non null values above
+---------------------+---------------+----------------+----------+----------+
| site | location | centre | net | gross |
+---------------------+---------------+----------------+----------+----------+
| null | null | null | null | null |
| null | null | null | null | null |
| | | | Dinner | null |
| Site Name | Location Name | Revenue Centre | Net | Gross |
| Babaluga | Bar | Beer | 95.24 | 110 |
| null | null | Food | 1080.04 | 1247.4 |
| null | null | Liquor | 0 | 0 |
| null | null | Non Alc. Bev | 51.08 | 59 |
| null | null | Wine | 64.94 | 75 |
| null | Terrace | Beer | 2642.89 | 3052.5 |
| null | null | Champagne | 450.2 | 520 |
| null | null | Food | 77974.82 | 90060.93 |
| null | null | Liquor | 21258.71 | 24554 |
| null | null | Non Alc. Bev | 15560.95 | 17973.5 |
| null | null | Tobacco | 19939.11 | 23030 |
| null | null | Wine | 18774.9 | 21685 |
+---------------------+---------------+----------------+----------+----------+let sales_data = df
.select(["Site","Location","Centre","Net","Gross"])
.filter("Centre != 'Revenue Centre'")
.drop_null(["gross"])
.fill_down(["Site", "Location"])
.elusion("my_sales_data").await?;
sales_data.display().await?;
//THEN WE GET THIS RESULT
+---------------------+----------+--------------+----------+----------+
| site | location | centre | net | gross |
+---------------------+----------+--------------+----------+----------+
| Babaluga | Bar | Beer | 95.24 | 110 |
| Babaluga | Bar | Food | 1080.04 | 1247.4 |
| Babaluga | Bar | Liquor | 0 | 0 |
| Babaluga | Bar | Non Alc. Bev | 51.08 | 59 |
| Babaluga | Bar | Wine | 64.94 | 75 |
| Babaluga | Terrace | Beer | 2642.89 | 3052.5 |
| Babaluga | Terrace | Champagne | 450.2 | 520 |
| Babaluga | Terrace | Food | 77974.82 | 90060.93 |
| Babaluga | Terrace | Liquor | 21258.71 | 24554 |
| Babaluga | Terrace | Non Alc. Bev | 15560.95 | 17973.5 |
| Babaluga | Terrace | Tobacco | 19939.11 | 23030 |
| Babaluga | Terrace | Wine | 18774.9 | 21685 |
+---------------------+----------+--------------+----------+----------+let excel_path = "C:\\BorivojGrujicic\\RUST\\Elusion\\report.xlsx";
let df = CustomDataFrame::new(excel_path, "report").await?;
// Skip first 3 rows (common for Excel reports with titles)
let data_df = df
.skip_rows(3)
.elusion("clean_report").await?;
// Chain with other operations
let processed_df = df
.skip_rows(2) // Skip title and empty row
.filter("amount > 0") // Filter valid amounts
.fill_null(["category"], "Other") // Fill missing categories
.elusion("processed_report").await?;let 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"]);
// example usage
let join_result = sales_df
.join_many([
(customers_df, ["s.CustomerKey = c.CustomerKey"], "RIGHT"),
(products_df, ["s.ProductKey = p.ProductKey"], "LEFT OUTER"),
])
.select(["c.*","p.*"])
.elusion("sales_join") .await?;
join_result.display().await?;
// example usage 2
aggregate_result
.filter_many([("mesec = 'Januar'"), ("neto_vrednost > 1000")])
.select(["*"]) // Full star selection on large dataset
.agg([
"COUNT(*) AS transaction_count",
"SUM(neto_vrednost) AS total_value",
"AVG(kolicina) AS avg_quantity"
])
.group_by_all()
.order_by(["total_value"], ["DESC"])
.limit(200)
.elusion("archive_star_full").await?;
aggregate_result.display().await?When using star selections
select(["*"]) or select(["alias.*"])with joined tables, duplicate column names are automatically removed to prevent SQL errors and schema conflicts. This behavior ensures your queries work reliably while following intuitive rules.
// When you use star selections:
.select(["s.*", "c.*", "p.*"])What happens:
s.*expands to:s.customerkey,s.productkey,s.orderdate, etc.c.*expands to:c.customerkey,c.firstname,c.lastname, etc.p.*expands to:p.productkey,p.productname,p.productcolor, etc.
Duplicate Detection:
- ✅ KEEPS:
s.customerkey(first occurrence - main table priority) - ❌ REMOVES:
c.customerkey(duplicate of customerkey) - ✅ KEEPS:
s.productkey(first occurrence - main table priority) - ❌ REMOVES:
p.productkey(duplicate of productkey)
Priority Order: Main table → Joined tables (in join order)
// When you explicitly specify columns:
.select(["s.CustomerKey", "c.CustomerKey", "p.ProductName"])What happens:
- ✅ KEEPS:
s.CustomerKey(explicitly requested) - ✅ KEEPS:
c.CustomerKey(explicitly requested - different qualified name) - ✅ KEEPS:
p.ProductName(explicitly requested)
No duplicate removal - you get exactly what you specify.
// Mix star and explicit columns:
.select(["c.*", "s.OrderDate", "p.ProductName as product"])Behavior:
c.*expands with duplicate removal applied- Explicit columns (
s.OrderDate,p.ProductName as product) are always preserved - Final result combines both approaches
.select(["s.CustomerKey AS sales_customer", "c.*", "p.*"])
// Result: sales_customer + all c.* columns + all p.* columns (duplicates removed).select([
"s.CustomerKey AS sales_key",
"c.CustomerKey AS master_key",
"p.ProductName AS product"
])
// Result: All three columns preserved with their aliases.select([
"c.CustomerKey as customer_master",
"s.CustomerKey as sales_fk",
"p.ProductName"
])
// Result: Both customerkey columns kept with different aliases- You want "all relevant columns" without conflicts
- You don't need to see duplicate foreign key values
- You want simple, predictable behavior
- You're doing exploratory data analysis
// Simple approach - no conflicts, works reliably
.select(["s.*", "c.*", "p.*"])
.group_by_all() // Just works!- You need both foreign key values for comparison
- You want specific control over which columns appear
- You need different aliases for duplicate column names
- You're building production reports with exact specifications
// Advanced approach - full control
.select([
"s.CustomerKey AS sales_fk",
"c.CustomerKey AS customer_pk",
"c.FirstName",
"p.ProductName"
])
.group_by_all() // Will include both customerkey columnsWhen using .elusion() to register query results, the system automatically handles duplicate column scenarios:
.select([
"s.CustomerKey AS sales_key",
"c.CustomerKey AS customer_key", // Different aliases
"p.ProductName"
])
.group_by_all()
.elusion("my_result") // ✅ Works - unique aliases- Start with star selections for quick analysis and exploration
- Use explicit columns when you need duplicate keys or precise control
- Use descriptive aliases to rename duplicate columns when needed
- Test your queries to ensure you get expected columns
- Mix approaches when appropriate (star + explicit)
Only columns with identical base names are considered duplicates:
s.customerkeyvsc.customerkey→ Duplicate (same base: "customerkey")s.orderdatevsc.birthdate→ Not duplicate (different base names)s.productkeyvsp.productkey→ Duplicate (same base: "productkey")s.CustomerIdvss.CustomerKey→ Not duplicate (different column names)
let star_query = sales_df
.join_many([
(customers_df, ["s.CustomerKey = c.CustomerKey"], "LEFT"),
(products_df, ["s.ProductKey = p.ProductKey"], "LEFT"),
])
.select(["s.*", "c.*", "p.*"]) // Duplicates removed automatically
.agg(["SUM(s.OrderQuantity) AS total_qty"])
.group_by_all() // Just works!
.limit(100);let explicit_query = sales_df
.join_many([
(customers_df, ["s.CustomerKey = c.CustomerKey"], "LEFT"),
(products_df, ["s.ProductKey = p.ProductKey"], "LEFT"),
])
.select([
"s.CustomerKey AS sales_customer_key",
"c.CustomerKey AS customer_master_key", // Both kept
"c.FirstName",
"p.ProductName",
"s.OrderQuantity"
])
.agg(["SUM(s.OrderQuantity) AS total_qty"])
.group_by_all() // Handles both customerkey columns
.limit(100);let mixed_query = sales_df
.join_many([
(customers_df, ["s.CustomerKey = c.CustomerKey"], "LEFT"),
(products_df, ["s.ProductKey = p.ProductKey"], "LEFT"),
])
.select([
"c.*", // All customer columns (deduplicated)
"s.OrderDate", // Specific sales column
"s.OrderQuantity", // Another specific column
"p.ProductName AS product", // Aliased product column
"p.ProductPrice" // Product price
])
.agg(["COUNT(*) AS order_count"])
.group_by_all()
.limit(100);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"], ["ASC"])
.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"], ["ASC"])
.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", "DESC"),
("max_abs_billable", "ASC"),
])//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", "ASC"),
("p.ProductName", "DESC")
]);
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"], ["ASC"])
.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"], ["ASC"])
.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"], ["ASC"])
.limit(10);
let scalar_res = scalar_df.elusion("scalar_df").await?;
scalar_res.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", "DESC"),
("max_abs_billable", "ASC"),
]);
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 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",
"CONCAT(region, ' - Rank ', CAST(region_rank AS TEXT)) AS region_rank_label",
"CASE WHEN region_rank <= 5 THEN 'TOP_5' ELSE 'OTHER' END AS performance_tier"
]);
let result_df = df.elusion("df").await?;
result_df.display().await?;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 as custmer_code"
"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"
])
.agg([
"COUNT(*) AS total_records",
"STRING_AGG(p.ProductName, ', ') AS all_products"
])
.filter("c.emailaddress IS NOT NULL")
//.group_by_all() YOU CAN USE GROUP BY ALL to group on all non-aggregated columns
.group_by([
"c.CustomerKey",
"c.FirstName",
"c.LastName",
"c.EmailAddress",
"p.ProductName"
])
.having("COUNT(*) > 1")
.order_by(["c.CustomerKey"], ["ASC"]);
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",
// Extract Day
"DATE_PART('day', CAST(delivery_date AS DATE) - CAST(order_date AS DATE)) AS delivery_days",
"DATE_PART('day', CAST(CURRENT_DATE() AS DATE) - CAST(order_date AS 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 - FIX: Cast for arithmetic
"CASE
WHEN CAST(order_date AS DATE) = CAST(CURRENT_DATE() AS DATE) THEN 'Today'
WHEN DATE_PART('day', CAST(CURRENT_DATE() AS DATE) - CAST(order_date AS DATE)) <= 7 THEN 'Last Week'
WHEN DATE_PART('day', CAST(CURRENT_DATE() AS DATE) - CAST(order_date AS 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 -
"CASE
WHEN CAST(order_date AS DATE) < CAST(MAKE_DATE(2024, 1, 1) AS DATE) 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",
])
.agg([
"SUM(order_value) AS total_order"
])
.group_by([
"customer_name",
"order_date",
"delivery_date"
])
// .group_by_all() OR YOU CAN USE grouping by all columns
.order_by(["order_date"], ["DESC"]);
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()When using .string_functions() or .datetime_functions() with aggregations, you have two options:
df.select([
"customer_name", // ← Include all columns your functions will use
"email", // ← TRIM(email) needs this
"first_name", // ← UPPER(first_name) needs this
"order_date" // ← DATE_PART('month', order_date) needs this
])
.string_functions([
"TRIM(email) AS clean_email",
"UPPER(first_name) AS upper_name"
])
.datetime_functions([
"DATE_PART('month', order_date) AS month"
])
.agg(["COUNT(*) AS total"])
.group_by_all() // ✅ Automatically groups by all SELECT columnsdf.select(["customer_name"])
.string_functions(["TRIM(email) AS clean_email"])
.agg(["COUNT(*) AS total"])
.group_by([
"customer_name",
"email" // ← Manually specify function dependencies
])df.select(["customer_name"]) // ← Only customer_name
.string_functions(["TRIM(email)"]) // ← Function uses 'email' but it's not in SELECT
.group_by_all() // ❌ Error: email missing from GROUP BYlet 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"], ["DESC"])
.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", "ASC"),
("p.ProductName", "DESC")
])
.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"], ["DESC"]);
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", "ASC"),
("p.ProductName", "DESC")
]);
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 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?;📝 Note: Window functions require any columns used in
PARTITION BYorORDER BYclauses to be included in your.select([...])statement. For example, if your window function usesPARTITION BY region, make sure"region"is in your select list.
[{"Key1":"Value1","Key2":"Value2","Key3":"Value3"}]let path = "C:\\Borivoj\\RUST\\Elusion\\jsonFile.csv";
let json_df = CustomDataFrame::new(path, "j").await?;
let df_extracted = json_df.json([
"ColumnName.'$Key1' AS column_name_1",
"ColumnName.'$Key2' AS column_name_2",
"ColumnName.'$Key3' AS column_name_3"
])
.select(["some_column1", "some_column2"])
.elusion("json_extract").await?;
df_extracted.display().await?;RESULT:
+---------------+---------------+---------------+---------------+---------------+
| column_name_1 | column_name_2 | column_name_3 | some_column1 | some_column2 |
+---------------+---------------+---------------+---------------+---------------+
| registrations | 2022-09-15 | CustomerCode | 779-0009E3370 | 646443D134762 |
| registrations | 2023-09-11 | CustomerCode | 770-00009ED61 | 463497C334762 |
| registrations | 2017-10-01 | CustomerCode | 889-000049C9E | 634697C134762 |
| registrations | 2019-03-26 | CustomerCode | 000-00006C4D5 | 446397D134762 |
| registrations | 2021-08-31 | CustomerCode | 779-0009E3370 | 463643D134762 |
| registrations | 2019-05-09 | CustomerCode | 770-00009ED61 | 634697C934762 |
| registrations | 2005-10-24 | CustomerCode | 889-000049C9E | 123397C334762 |
| registrations | 2023-02-14 | CustomerCode | 000-00006C4D5 | 932393D134762 |
| registrations | 2021-01-20 | CustomerCode | 779-0009E3370 | 323297C334762 |
| registrations | 2018-07-17 | CustomerCode | 000-00006C4D5 | 322097C921462 |
+---------------+---------------+---------------+---------------+---------------+function works with Columns that has Array of objects with pathern "column.'$ValueField:IdField=IdValue' AS column_alias"
The function parameters: column: The column containing the JSON array ValueField: The field to extract from matching objects IdField: The field to use as identifier IdValue: The value to match on the identifier field column_alias: The output column name
[
{"Id":"Date","Value":"2022-09-15","ValueKind":"Date"},
{"Id":"MadeBy","Value":"Borivoj Grujicic","ValueKind":"Text"},
{"Id":"Timeline","Value":1.0,"ValueKind":"Number"},
{"Id":"ETR_1","Value":1.0,"ValueKind":"Number"}
]let multiple_values = df_json.json_array([
"Value.'$Value:Id=Date' AS date",
"Value.'$Value:Id=MadeBy' AS made_by",
"Value.'$Value:Id=Timeline' AS timeline",
"Value.'$Value:Id=ETR_1' AS etr_1",
"Value.'$Value:Id=ETR_2' AS etr_2",
"Value.'$Value:Id=ETR_3' AS etr_3"
])
.select(["Id"])
.elusion("multiple_values")
.await?;
multiple_values.display().await?;
RESULT:
+-----------------+-------------------+----------+-------+-------+-------+--------+
| date | made_by | timeline | etr_1 | etr_2 | etr_3 | id |
+-----------------+-------------------+----------+-------+-------+-------+--------+
| 2022-09-15 | Borivoj Grujicic | 1.0 | 1.0 | 1.0 | 1.0 | 77E10C |
| 2023-09-11 | | 5.0 | | | | 770C24 |
| 2017-10-01 | | | | | | 7795FA |
| 2019-03-26 | | 1.0 | | | | 77F2E6 |
| 2021-08-31 | | 5.0 | | | | 77926E |
| 2019-05-09 | | | | | | 77CC0F |
| 2005-10-24 | | | | | | 7728BA |
| 2023-02-14 | | | | | | 77F7F8 |
| 2021-01-20 | | | | | | 7731F6 |
| 2018-07-17 | | 3.0 | | | | 77FB18 |
+-----------------+-------------------+----------+-------+-------+-------+--------+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
.join(
customers_df, ["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
.join(
customers_df, ["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"], ["ASC"])
.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?;//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"], ["ASC"])
.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"], ["ASC"])
.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?;
// Using materialized view for customer count
// The TTL parameter (3600) specifies how long the view remains valid in seconds (1 hour)
customers_df
.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
.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", "ASC"),
("p.ProductName", "TRUE")
])
.elusion_with_cache("sales_join") // caching query with DataFrame alias
.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 views# Install Redis (Windows)
# Download from: https://github.com/tporadowski/redis/releases
# Install Redis (macOS)
brew install redis
brew services start redis
# Install Redis (Linux)
sudo apt install redis-server
sudo systemctl start redis
# Docker (All platforms)
docker run --name redis-cache -p 6379:6379 -d redis:latest
# Test connection
redis-cli ping # Should return: PONGlet 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?;
// Connect to Redis (requires Redis server running)
let redis_conn = CustomDataFrame::create_redis_cache_connection().await?;
// Use Redis caching for high-performance distributed caching
let redis_cached_result = sales_df
.join_many([
(customers_df, ["s.CustomerKey = c.CustomerKey"], "RIGHT"),
(products_df, ["s.ProductKey = p.ProductKey"], "LEFT OUTER"),
])
.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", "ASC"),
("p.ProductName", "DESC")
])
.elusion_with_redis_cache(&redis_conn, "sales_join_redis", Some(3600)) // Redis caching with 1-hour TTL
.await?;
redis_cached_result.display().await?;// Custom Redis connection with authentication
let redis_conn = CustomDataFrame::create_redis_cache_connection_with_config(
"localhost", // host
6379, // port
Some("password"), // password (optional)
Some(1) // database (optional)
).await?;// Clear Redis cache
CustomDataFrame::clear_redis_cache(&redis_conn, None).await?;
// Invalidate cache for specific tables
CustomDataFrame::invalidate_redis_cache(&redis_conn, &["sales", "customers"]).await?; println!("📊 Getting Redis cache statistics...");
let stats = CustomDataFrame::redis_cache_stats(&redis_conn).await?;
println!("🔹 Cache Statistics:");
println!(" 📈 Total cached keys: {}", stats.total_keys);
println!(" ✅ Cache hits: {}", stats.cache_hits);
println!(" ❌ Cache misses: {}", stats.cache_misses);
println!(" 📊 Hit rate: {:.2}%", stats.hit_rate);
println!(" 💾 Memory used: {}", stats.total_memory_used);
println!(" ⏱️ Average query time: {:.2}ms", stats.avg_query_time_ms);
println!(); let pg_config = PostgresConfig {
host: "localhost".to_string(),
port: 5432,
user: "borivoj".to_string(),
password: "pass123".to_string(),
database: "db_test".to_string(),
pool_size: Some(5),
};
let conn = PostgresConnection::new(pg_config).await?;
Option2: You can use map_err()
let conn = PostgresConnection::new(pg_config).await
.map_err(|e| ElusionError::Custom(format!("PostgreSQL connection error: {}", e)))?;
let query = "
SELECT
c.id,
c.name,
s.product_name,
SUM(s.quantity * s.price) as total_revenue
FROM customers c
LEFT JOIN sales s ON c.id = s.customer_id
GROUP BY c.id, c.name, s.product_name
ORDER BY total_revenue DESC
";
let sales_by_customer_df = CustomDataFrame::from_postgres(&conn, query, "postgres_df").await?;
sales_by_customer_df.display().await?;let mysql_config = MySqlConfig {
host: "localhost".to_string(),
port: 3306,
user: "borivoj".to_string(),
password: "pass123".to_string(),
database: "db_test".to_string(),
pool_size: Some(5),
};
let conn = MySqlConnection::new(mysql_config).await?;
let mysql_query = "
WITH ranked_sales AS (
SELECT
c.color AS brew_color,
bd.beer_style,
bd.location,
SUM(bd.total_sales) AS total_sales
FROM
brewery_data bd
JOIN
colors c ON bd.Color = c.color_number
WHERE
bd.brew_date >= '2020-01-01' AND bd.brew_date <= '2020-03-01'
GROUP BY
c.color, bd.beer_style, bd.location
)
SELECT
brew_color,
beer_style,
location,
total_sales,
ROW_NUMBER() OVER (PARTITION BY brew_color ORDER BY total_sales DESC) AS ranked
FROM
ranked_sales
ORDER BY
brew_color, total_sales DESC";
let df = CustomDataFrame::from_mysql(&conn, mysql_query, "mysql_df").await?;
df.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.csv"), // 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"], ["ASC"]);
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?; df.write_to_excel(
"C:\\Borivoj\\RUST\\Elusion\\Excel\\sales2.xlsx", //path
Some("string_interop") // Optional sheet name. Can be None
).await?;// overwrite existing file
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
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
df.write_to_csv(
"overwrite",
"C:\\Borivoj\\RUST\\Elusion\\agg_sales.csv",
custom_csv_options
)
.await?;
// append to exisiting file
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
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
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_and_file_name = "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_and_file_name,
sas_write_token
).await?;
// append version
data.write_parquet_to_azure_with_sas(
"append",
url_to_folder_and_file_name,
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_and_file_name = "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_and_file_name,
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
.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", "DESC"),
("max_abs_billable", "ASC"),
]);
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 for multiple usages
.select([
"customer_name",
"total_billable",
"avg_abs_billable",
"max_abs_billable",
"percentage_total_billable"
])
.order_by_many([
("total_billable", "DESC")
])
.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", "DESC"),
("abs_billable_value", "DESC")
])
.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?;
I appreciate the interest in contributing to Elusion! However, I'm not currently accepting contributions.
- Feature requests: Feel free to message me if you need any new features - if possible, I'll be happy to implement them
- Modifications: You're welcome to fork the repository for your own changes
- Issues: Bug reports are always appreciated
Thanks for understanding!
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, Tokio Cron Scheduler, Tokio... for efficient query processing. Special thanks to the open-source community for making this project possible.