tranfi (Node.js / WASM)

Streaming ETL in JavaScript, powered by a native C11 core via N-API (Node.js) or WASM (browsers). Process CSV, JSONL, and text data with composable pipelines that run in constant memory, no matter how large the input.

import { pipeline, codec, ops, expr } from 'tranfi'

const result = await pipeline([
  codec.csv(),
  ops.filter(expr("col('age') > 25")),
  ops.sort(['-age']),
  ops.derive({ label: expr("if(col('age')>30, 'senior', 'junior')") }),
  ops.select(['name', 'age', 'label']),
  codec.csvEncode(),
]).run({ input: 'name,age\nAlice,30\nBob,25\nCharlie,35\nDiana,28\n' })

console.log(result.outputText)
// name,age,label
// Charlie,35,senior
// Alice,30,junior
// Diana,28,junior

Or use the pipe DSL for one-liners:

const result = await pipeline('csv | filter "col(age) > 25" | sort -age | csv')
  .run({ inputFile: 'data.csv' })

Install

npm install tranfi

Uses N-API natively in Node.js, falls back to WASM in browsers automatically.

CLI

Installing the package also provides the tranfi command:

# Via npx (no install)
echo 'name,age\nAlice,30\nBob,25' | npx tranfi 'csv | filter "age > 25" | csv'

# Or install globally
npm i -g tranfi
tranfi 'csv | filter "age > 25" | sort -age | csv' < data.csv
tranfi profile < data.csv
tranfi -R  # list recipes

Run tranfi -h for all options.

Quick start

Two APIs

Builder API -- composable, type-safe, IDE-friendly:

const p = pipeline([
  codec.csv(),
  ops.filter(expr("col('score') >= 80")),
  ops.derive({ grade: expr("if(col('score')>=90, 'A', 'B')") }),
  ops.sort(['-score']),
  ops.head(10),
  codec.csvEncode(),
])
const result = await p.run({ inputFile: 'students.csv' })

DSL strings -- compact, suitable for CLI-like use:

const p = pipeline('csv | filter "col(score) >= 80" | sort -score | head 10 | csv')
const result = await p.run({ inputFile: 'students.csv' })

Both produce identical pipelines under the hood.

Running pipelines

// From string or Buffer
const result = await p.run({ input: 'name,age\nAlice,30\n' })

// From file (streamed in 64 KB chunks)
const result = await p.run({ inputFile: 'data.csv' })

// Access results
result.output         // Buffer
result.outputText     // string (UTF-8 decoded)
result.errors         // Buffer (error channel)
result.stats          // Buffer (pipeline stats)
result.statsText      // string
result.samples        // Buffer (sample channel)

Codecs

Codecs convert between raw bytes and columnar batches. Every pipeline starts with a decoder and ends with an encoder.

Method	Description
`codec.csv({ delimiter, header, batchSize, repair })`	CSV decoder. `repair: true` pads short / truncates long rows
`codec.csvEncode({ delimiter })`	CSV encoder
`codec.jsonl({ batchSize })`	JSON Lines decoder
`codec.jsonlEncode()`	JSON Lines encoder
`codec.text({ batchSize })`	Line-oriented text decoder (single `_line` column)
`codec.textEncode()`	Text encoder
`codec.tableEncode({ maxWidth, maxRows })`	Pretty-print Markdown table

Cross-codec pipelines work naturally:

// CSV in, JSONL out
pipeline([codec.csv(), ops.head(5), codec.jsonlEncode()])

// JSONL in, CSV out
pipeline([codec.jsonl(), ops.sort(['name']), codec.csvEncode()])

Operators

Row filtering

Method	Description
`ops.filter(expr)`	Keep rows matching expression
`ops.head(n)`	First N rows
`ops.tail(n)`	Last N rows
`ops.skip(n)`	Skip first N rows
`ops.top(n, column, desc?)`	Top N by column value
`ops.sample(n)`	Reservoir sampling (uniform random)
`ops.grep(pattern, { invert, column, regex })`	Substring/regex filter
`ops.validate(expr)`	Add `_valid` boolean column, keep all rows

Column operations

Method	Description
`ops.select(columns)`	Keep and reorder columns
`ops.rename(mapping)`	Rename columns: `rename({ name: 'full_name' })`
`ops.derive(columns)`	Computed columns: `derive({ total: expr("col('a')*col('b')") })`
`ops.cast(mapping)`	Type conversion: `cast({ age: 'int', score: 'float' })`
`ops.trim(columns?)`	Strip whitespace
`ops.fillNull(mapping)`	Replace nulls: `fillNull({ age: '0' })`
`ops.fillDown(columns?)`	Forward-fill nulls
`ops.clip(column, { min, max })`	Clamp numeric values
`ops.replace(column, pattern, replacement, { regex })`	String find/replace
`ops.hash(columns?)`	Add `_hash` column (DJB2)
`ops.bin(column, boundaries)`	Discretize into bins

Sorting and deduplication

Method	Description
`ops.sort(columns)`	Sort rows. Prefix `-` for descending: `sort(['-age', 'name'])`
`ops.unique(columns?)`	Deduplicate on specified columns

Aggregation

Method	Description
`ops.stats(statsList?)`	Column statistics. Stats: `count`, `min`, `max`, `sum`, `avg`, `stddev`, `variance`, `median`, `p25`, `p75`, `p90`, `p99`, `distinct`, `hist`, `sample`
`ops.frequency(columns?)`	Value counts (descending)
`ops.groupAgg(groupBy, aggs)`	Group by + aggregate

// Group aggregation
ops.groupAgg(['city'], [
  { column: 'price', func: 'sum', result: 'total' },
  { column: 'price', func: 'avg', result: 'avg_price' },
])

Sequential / window

Method	Description
`ops.step(column, func, result?)`	Running aggregation: `running-sum`, `running-avg`, `running-min`, `running-max`, `lag`
`ops.window(column, size, func, result?)`	Sliding window: `avg`, `sum`, `min`, `max`
`ops.lead(column, { offset, result })`	Lookahead N rows

Reshape

Method	Description
`ops.explode(column, delimiter?)`	Split delimited string into rows
`ops.split(column, names, delimiter?)`	Split column into multiple columns
`ops.unpivot(columns)`	Wide to long (melt)
`ops.stack(file, { tag, tagValue })`	Vertically concatenate another CSV file

Date/time

Method	Description
`ops.datetime(column, extract?)`	Extract parts: `year`, `month`, `day`, `hour`, `minute`, `second`, `weekday`
`ops.dateTrunc(column, trunc, { result })`	Truncate to: `year`, `month`, `day`, `hour`, `minute`, `second`

Other

Method	Description
`ops.flatten()`	Flatten nested columns
`ops.reorder(columns)`	Alias for `select`
`ops.dedup(columns?)`	Alias for `unique`

Expressions

Used in filter, derive, and validate. Reference columns with col('name').

ops.filter(expr("col('age') > 25 and contains(col('name'), 'A')"))
ops.derive({
  full:  expr("concat(col('first'), ' ', col('last'))"),
  grade: expr("if(col('score')>=90, 'A', if(col('score')>=80, 'B', 'C'))"),
})

Available functions

Category	Functions
Arithmetic	`+` `-` `*` `/`
Comparison	`>` `>=` `<` `<=` `==` `!=`
Logic	`and` `or` `not`
String	`upper(s)` `lower(s)` `initcap(s)` `len(s)` `trim(s)` `left(s,n)` `right(s,n)` `concat(a,b,...)` `replace(s,old,new)` `slice(s,start,len)` `pad_left(s,w)` `pad_right(s,w)`
Predicates	`starts_with(s,prefix)` `ends_with(s,suffix)` `contains(s,sub)`
Conditional	`if(cond,then,else)` `coalesce(a,b,...)` `nullif(a,b)`
Math	`abs(x)` `round(x)` `floor(x)` `ceil(x)` `sign(x)` `pow(x,y)` `sqrt(x)` `log(x)` `exp(x)` `mod(a,b)` `greatest(a,b,...)` `least(a,b,...)`

Aliases: substr=slice, length=len, lpad=pad_left, rpad=pad_right, min=least, max=greatest.

Recipes

Built-in named pipelines for common tasks. Use by name:

const result = await pipeline('preview').run({ inputFile: 'data.csv' })
const result = await pipeline('freq').run({ inputFile: 'data.csv' })

Recipe	Pipeline	Description
`profile`	`csv \| stats \| csv`	Full data profiling
`preview`	`csv \| head 10 \| csv`	First 10 rows
`schema`	`csv \| head 0 \| csv`	Column names only
`summary`	`csv \| stats count,min,max,avg,stddev \| csv`	Summary statistics
`count`	`csv \| stats count \| csv`	Row count
`cardinality`	`csv \| stats count,distinct \| csv`	Unique value counts
`distro`	`csv \| stats min,p25,median,p75,max \| csv`	Five-number summary
`freq`	`csv \| frequency \| csv`	Value frequency
`dedup`	`csv \| dedup \| csv`	Remove duplicates
`clean`	`csv \| trim \| csv`	Trim whitespace
`sample`	`csv \| sample 100 \| csv`	Random 100 rows
`head`	`csv \| head 20 \| csv`	First 20 rows
`tail`	`csv \| tail 20 \| csv`	Last 20 rows
`csv2json`	`csv \| jsonl`	CSV to JSONL
`json2csv`	`jsonl \| csv`	JSONL to CSV
`tsv2csv`	`csv delimiter="\t" \| csv`	TSV to CSV
`csv2tsv`	`csv \| csv delimiter="\t"`	CSV to TSV
`look`	`csv \| table`	Pretty-print table
`histogram`	`csv \| stats hist \| csv`	Distribution histograms
`hash`	`csv \| hash \| csv`	Row hash for change detection
`samples`	`csv \| stats sample \| csv`	Sample values per column

List all recipes programmatically:

import { recipes } from 'tranfi'

for (const r of await recipes()) {
  console.log(`${r.name.padEnd(15)} ${r.description}`)
}

DuckDB engine

Run pipelines on DuckDB instead of the native C streaming core. The DSL is transpiled to SQL in C, then executed by DuckDB.

npm install duckdb

import { pipeline, compileToSql } from 'tranfi'

// Run a pipeline via DuckDB
const result = await pipeline('csv | filter "age > 25" | sort -age | csv', { engine: 'duckdb' })
  .run({ inputFile: 'data.csv' })

// Or with string/Buffer input
const result2 = await pipeline('csv | head 10 | csv', { engine: 'duckdb' })
  .run({ input: csvString })

SQL transpilation

Generate SQL directly from DSL strings:

const sql = await compileToSql('csv | filter "col(age) > 25" | sort -age | head 10 | csv')
console.log(sql)
// WITH
//   step_1 AS (SELECT * FROM input_data WHERE ("age" > 25)),
//   step_2 AS (SELECT * FROM step_1 ORDER BY "age" DESC LIMIT 10)
// SELECT * FROM step_2

Browser (WASM + DuckDB-WASM)

In the browser, use @duckdb/duckdb-wasm with the tranfi WASM module:

import createTranfi from 'tranfi/wasm'
import * as duckdb from '@duckdb/duckdb-wasm'

const tf = await createTranfi()

// SQL generation (synchronous, no DuckDB needed)
const sql = tf.compileToSql('csv | filter "age > 25" | csv')

// Full execution with DuckDB-WASM
const db = new duckdb.AsyncDuckDB(...)
await db.instantiate(...)

const result = await tf.runDuckDB(db, 'csv | filter "age > 25" | csv', csvData)
console.log(result.outputText)  // CSV output
console.log(result.rows)        // Array of row objects

runDuckDB accepts string, Uint8Array, or File objects as data input.

Advanced

DSL compilation

import { compileDsl, saveRecipe, loadRecipe } from 'tranfi'

// Compile DSL to JSON plan
const json = await compileDsl('csv | filter "col(age) > 25" | sort -age | csv')

// Save / load recipes
await saveRecipe([codec.csv(), ops.head(10), codec.csvEncode()], 'preview.tranfi')
const p = await loadRecipe('preview.tranfi')
const result = await p.run({ inputFile: 'data.csv' })

Side channels

Every pipeline produces four output channels:

output -- main pipeline result
errors -- rows that failed processing
stats -- pipeline execution statistics (rows in/out, timing)
samples -- reserved for sampling operators

const result = await p.run({ inputFile: 'data.csv' })
console.log(result.statsText)   // {"rows_in": 1000, "rows_out": 42, ...}

Pipeline from JSON

const p = await loadRecipe({
  steps: [
    { op: 'codec.csv.decode', args: {} },
    { op: 'head', args: { n: 5 } },
    { op: 'codec.csv.encode', args: {} },
  ]
})

Backend selection

The package automatically selects the best backend:

N-API (Node.js) -- native C addon, fastest, used when available
WASM (browsers/fallback) -- same C core compiled to WebAssembly, ~313 KB single-file
DuckDB (opt-in) -- SQL execution via { engine: 'duckdb' }, requires npm install duckdb

// Force WASM backend (e.g., for testing)
import createTranfi from 'tranfi/wasm'
const tf = await createTranfi()

Architecture

The Node.js package wraps the same C11 core used by the CLI, Python, and WASM targets. Data flows through columnar batches with typed columns (bool, int64, float64, string, date, timestamp) and per-cell null bitmaps. All operators are streaming with bounded memory, except those that require full input (sort, unique, stats, tail, top, group-agg, frequency, pivot).