tranfi

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).