Using the DataFrame API¶

What is a DataFrame¶

DataFrame in DataFrame is modeled after the Pandas DataFrame interface, and is a thin wrapper over LogicalPlan that adds functionality for building and executing those plans.

pub struct DataFrame {
    session_state: SessionState,
    plan: LogicalPlan,
}

You can build up DataFrames using its methods, similarly to building LogicalPlans using LogicalPlanBuilder:

let df = ctx.table("users").await?;

// Create a new DataFrame sorted by  `id`, `bank_account`
let new_df = df.select(vec![col("id"), col("bank_account")])?
    .sort(vec![col("id")])?;

// Build the same plan using the LogicalPlanBuilder
let plan = LogicalPlanBuilder::from(&df.to_logical_plan())
    .project(vec![col("id"), col("bank_account")])?
    .sort(vec![col("id")])?
    .build()?;

You can use collect or execute_stream to execute the query.

How to generate a DataFrame¶

You can directly use the DataFrame API or generate a DataFrame from a SQL query.

For example, to use sql to construct DataFrame:

let ctx = SessionContext::new();
// Register the in-memory table containing the data
ctx.register_table("users", Arc::new(create_memtable()?))?;
let dataframe = ctx.sql("SELECT * FROM users;").await?;

To construct DataFrame using the API:

let ctx = SessionContext::new();
// Register the in-memory table containing the data
ctx.register_table("users", Arc::new(create_memtable()?))?;
let dataframe = ctx
  .table("users")
  .filter(col("a").lt_eq(col("b")))?
  .sort(vec![col("a").sort(true, true), col("b").sort(false, false)])?;

Collect / Streaming Exec¶

DataFusion DataFrames are “lazy”, meaning they do not do any processing until they are executed, which allows for additional optimizations.

When you have a DataFrame, you can run it in one of three ways:

collect which executes the query and buffers all the output into a Vec<RecordBatch>
streaming_exec, which begins executions and returns a SendableRecordBatchStream which incrementally computes output on each call to next()
cache which executes the query and buffers the output into a new in memory DataFrame.

You can just collect all outputs once like:

let ctx = SessionContext::new();
let df = ctx.read_csv("tests/data/example.csv", CsvReadOptions::new()).await?;
let batches = df.collect().await?;

You can also use stream output to incrementally generate output one RecordBatch at a time

let ctx = SessionContext::new();
let df = ctx.read_csv("tests/data/example.csv", CsvReadOptions::new()).await?;
let mut stream = df.execute_stream().await?;
while let Some(rb) = stream.next().await {
    println!("{rb:?}");
}

Write DataFrame to Files¶

You can also serialize DataFrame to a file. For now, Datafusion supports write DataFrame to csv, json and parquet.

When writing a file, DataFusion will execute the DataFrame and stream the results to a file.

For example, to write a csv_file

let ctx = SessionContext::new();
// Register the in-memory table containing the data
ctx.register_table("users", Arc::new(mem_table))?;
let dataframe = ctx.sql("SELECT * FROM users;").await?;

dataframe
    .write_csv("user_dataframe.csv", DataFrameWriteOptions::default(), None)
    .await;

and the file will look like (Example Output):

id,bank_account
1,9000

Transform between LogicalPlan and DataFrame¶

As shown above, DataFrame is just a very thin wrapper of LogicalPlan, so you can easily go back and forth between them.

// Just combine LogicalPlan with SessionContext and you get a DataFrame
let ctx = SessionContext::new();
// Register the in-memory table containing the data
ctx.register_table("users", Arc::new(mem_table))?;
let dataframe = ctx.sql("SELECT * FROM users;").await?;

// get LogicalPlan in dataframe
let plan = dataframe.logical_plan().clone();

// construct a DataFrame with LogicalPlan
let new_df = DataFrame::new(ctx.state(), plan);

Working with Exprs

Building Logical Plans