tranfi (Node.js / WASM)

Tranfi transforms CSV, JSON Lines and plain text as streams. In Node.js it runs on a native C core; in the browser it runs on WebAssembly. Data flows through in chunks, so most pipelines use a small, fixed amount of memory however large the input is. Operations that need the whole input, such as sort, must be allowed explicitly. Tranfi works on byte streams; it is not an in-memory DataFrame library.

Save this example as quickstart.mjs:

import tranfi from 'tranfi'

const { pipeline, codec, ops, expr } = tranfi

const result = await pipeline([
  codec.csv(),
  ops.filter(expr("col('age') > 25")),
  ops.top(100, '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" | top-k 100 age | csv')
  .run({ inputFile: 'data.csv' })

Install

npm install tranfi

No Python or compiler is required to install the npm package. Installation tries an optional native build on Linux/macOS and uses packaged WebAssembly if that build fails. Windows uses WebAssembly. npm run build:native explicitly builds the addon and reports compilation errors.

The root tranfi entry automatically falls back for ordinary pipelines. Its synchronous prepared-transform classes still need native support; check hasNativePreparedTransforms() before using them. For portable prepared transforms, use await require('tranfi/wasm')(). The same WASM entry works in browsers. TRANFI_SKIP_NATIVE_BUILD=1 explicitly skips the optional build.

| | Linux | Windows | macOS | |---|---|---|---| | Native addon | Tested | Not supported; use WASM | Not tested | | WebAssembly (tranfi/wasm) | Tested in Node.js and Chromium | Tested in Node.js | Not tested |

Use pipeline(...) for byte-stream ETL. The separate TransformRecipe -> TransformAnalyzer -> TransformPlan -> TransformApply lifecycle is for typed batches whose learned state must be frozen and reused. Calling .run() collects output for convenience; use writeTo() or toReadable() for large outputs.

CLI

Installing the package also provides the tranfi command:

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

# Or install globally
npm i -g tranfi
tranfi -q 'csv | filter "age > 25" | top-k 100 age | csv' < data.csv
tranfi profile < data.csv
tranfi -R  # list recipes

Run tranfi -h for the npm CLI options. Use --allow-blocking for known-small full-input operations, --memory max:64MB for a memory policy, and --spill-dir DIR for an existing private spill directory. --stats-json FILE writes the stats channel; --target json|sql compiles without executing. The detailed --explain report requires the standalone C CLI built from source.

Quick start

Two APIs

Builder API -- composable, structured, and IDE-friendly:

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

Both produce identical pipelines under the hood.

Dataframe-style DSL aliases are accepted and normalize to canonical ops: mutate -> derive, summarise/summarize -> group-agg, distinct -> unique, and arrange -> sort.

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 fileResult = 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)

For large outputs, drain chunks instead of collecting result.output:

import { createReadStream, createWriteStream } from 'node:fs'

// Write to a Node Writable and wait for `drain` when the sink applies backpressure.
const out = createWriteStream("out.csv")
const result = await p.writeTo(out, { inputFile: "data.csv" })

// Or consume Tranfi output as a Node Readable.
for await (const chunk of p.toReadable({ inputFile: "data.csv" })) {
  // process each output chunk
}

// Existing input streams can feed the native pipeline without readFile() materialization.
const result2 = await p.run({ inputStream: createReadStream("data.csv") })

// .gz input files are decompressed as streaming sources by default.
const gz = await p.run({ inputFile: "events.jsonl.gz" })
const raw = await p.run({ inputFile: "events.jsonl.gz", compression: "none" })
const streamGz = await p.run({ inputStream: createReadStream("events.jsonl.gz"), compression: "gzip" })

// Multiple files stream sequentially through one pipeline.
const inputFiles = ["part-a.csv", "part-b.csv"]
const combined = await p.run({ inputFiles, sourceColumn: "src" })

// Low-level async iteration is still available.
for await (const chunk of p.iterChunks({ inputFiles, sourceColumn: "src" })) {
  // process each output chunk
}

sourceColumn appends the path for each input row without preloading file contents. Tranfi flushes decoder input at each file boundary so an unterminated final record belongs to the correct source file. Repeated headers are kept by default. Use codec.csv({ skipRepeatedHeader: true }) to skip a later file's first non-comment record when it exactly matches the original header; matching rows inside a file remain data.

Standalone WASM exposes the same non-collecting shape for in-memory data: tf.run(dsl, data, { onOutput, collectOutput: false }) and tf.iterChunks(dsl, data).

For browser UI work, keep Worker placement outside the core pipeline and use the optional WASM Worker adapter:

// main thread
import { createWorkerClient } from 'tranfi/wasm/worker'

const client = createWorkerClient(new Worker(new URL('./tranfi-worker.js', import.meta.url), { type: 'module' }))
const result = await client.runFile('csv | filter "col(age) >= 18" | csv', file, {
  chunkSize: 64 * 1024,
  collectOutput: false,
  onOutput: chunk => downloadSink.write(chunk),
  onProgress: p => updateProgress(p),
  signal: abortController.signal
})

// tranfi-worker.js, bundled by the app
import { runWorkerServer } from 'tranfi/wasm/worker'
runWorkerServer()

The Worker protocol streams chunks with transferable buffers and sends progress, stats, errors, and cancellation messages around the same WASM create/push/pull/finish/free API; it is not a separate IR target.

The bundled Tranfi app runner is also preview-bounded by default: file chunks are streamed into WASM, main output is drained incrementally into a table preview capped by hidden preview_rows (default 200), and full output text is only materialized when collect_output is explicitly true in the schema.

Prepared reusable transforms

Learn imputation, scaling or category mappings from reference data, then apply the same plan to new batches. Analyze the reference batches, finalize the plan, and apply it. Replaying the reference data through that plan gives a second-pass fit_transform workflow.

Inputs are typed float32/float64 columns. This example learns mean imputation and standard scaling, applies them to the reference batch, and exports the plan:

import tf from 'tranfi'

const recipeSpec = {
  format: 'tranfi.transform-recipe',
  version: 1,
  policyVersion: 1,
  outputDtype: 'float64',
  semanticLimits: {
    maxOutputColumns: 65536,
    maxOutputElementsPerApply: 134217728
  },
  columns: [{
    sourceId: 'x0',
    kind: { op: 'declared', value: 'numeric', rule: null, maxCategories: null },
    numeric: {
      impute: { op: 'mean', constant: null, allMissing: 'zero' },
      normalize: { op: 'standard', ddof: 0 }
    },
    categorical: null
  }]
}
const schema = [{ id: 'x0', dtype: 'float64' }]

const recipe = tf.TransformRecipe.fromJSON(recipeSpec)
const analyzer = recipe.analyzer(schema)
analyzer.push({ rows: 3, columns: [new Float64Array([1, NaN, 3])] })
const plan = analyzer.finalize()

const apply = plan.apply(schema)
const fittedReference = apply.run({
  rows: 3,
  columns: [new Float64Array([1, NaN, 3])]
})
const planBytes = plan.toBytes() // canonical TFTR artifact
const recipeSha256 = plan.recipeSha256() // canonical recipe + input-schema identity

apply.close()
plan.close()
analyzer.close()
recipe.close()

Recipe fields use the same names in all bindings.

| Recipe field | Choices / behavior | |--------------|--------------------| | Numeric impute.op | none, zero, constant, mean, exact median | | Numeric normalize.op | none, standard, minmax | | Categorical impute.op | none or mode; mode supports allMissing of error or zero | | Categorical encode.op | none, label, onehot | | Label unknown policy | error, sentinel, other | | One-hot unknown policy | error, all_zero, other |

No imputation or encoding: finite values pass through; missing input becomes canonical qNaN. This combination needs no learned category dictionary. Mode imputation with no encoding retains a dictionary and rejects unseen finite values with error 108.

Encoding: label ordinals are zero-based and sorted; one-hot blocks follow source order. A label sentinel must be a safe integer outside the known ordinal range. other appends an ordinal or field after known categories. Without imputation, missing input follows the encoder's unknown policy.

Generated IDs, names and category metadata are deterministic. Collisions fail with error 102.

Kind inference: configure both numeric and categorical branches, set kind.op to infer, kind.rule to finite-integer-cardinality-v1, and kind.maxCategories to at least 2. Missing/NaN values are ignored.

| Observed reference values | Selected branch | |---------------------------|-----------------| | 2..maxCategories distinct finite integers | Categorical | | Zero or one distinct value, any noninteger, or more than maxCategories | Numeric |

Set encode.categories to a nonempty, sorted, unique array of finite typed tags. For example, { "t": "f64", "v": "4000000000000000" } represents 2. Tags must match the input dtype; represent negative zero as positive zero.

• Unobserved categories stay in the dictionary.
• Unknown training values follow the encoder policy and never vote for mode.
• An all-missing zero fallback must exist in the dictionary.
• Fixed encoding without imputation can finalize without training rows.

String categories, categorical constant imputation and fixed dictionaries combined with kind inference are not supported.

Exact median obeys allocation and resident-state limits. Inference, category discovery and output expansion also enforce category/output-width limits. Resource-limit failures use error 104.

Host-policy and spill settings are reserved. A non-null host policy or nonempty spill path fails with unsupported-runtime error 113.

Runtime options reject unknown fields:

| Runtime | Options | |---------|---------| | Native Node | limits, cancelFlag, hostPolicy, spillDir | | Standalone WASM | limits, cancelToken, hostPolicy, spillDir |

Cancellation spellings are not interchangeable. WASM tokens created with createTransformCancelToken() expose a read-only requested boolean for host conversion loops; reading a closed token fails.

tranfi/wasm exposes the same classes on the initialized module. The Worker adapter adds analyzeTransform() and applyTransform(). With SharedArrayBuffer, an AbortSignal interrupts a synchronous C call through an atomic poll cell. Without it, cancellation terminates the whole worker and reclaims its WASM heap. Pass a worker URL directly so the client can recreate it, or supply an owned worker plus workerFactory:

const workerUrl = new URL('./tranfi-worker.js', import.meta.url)
const makeWorker = () => new Worker(workerUrl, { type: 'module' })
const client = createWorkerClient(makeWorker(), {
  workerFactory: makeWorker,
  terminateOnDispose: true
})

All prepared-transform failures use TranfiTransformError; its numeric code is stable across native Node and WASM. Native Node accepts a SharedArrayBuffer-backed Int32Array as cancelFlag: cell 0 is the cancellation request, and an optional cell 1 is incremented modulo 2^32 at every native poll so another realm can observe operation progress without a timer.

Codecs

Start with a decoder and finish with an encoder. Input and output formats can differ.

| Format | Decoder | Encoder | |--------|---------|---------| | CSV | codec.csv(options) | codec.csvEncode({ delimiter }) | | JSON Lines | codec.jsonl({ batchSize, onError, maxErrorBytes, maxRecordBytes }) | codec.jsonlEncode() | | Text (one _line column) | codec.text({ batchSize, maxErrorBytes, maxRecordBytes }) | codec.textEncode() | | Markdown table | — | codec.tableEncode({ maxWidth, maxRows }) |

CSV defaults to permissive field-count handling. Choose mode: 'strict' (or strict: true) to reject width mismatches. Choose repair: true (or mode: 'repair') to pad/truncate rows and collect diagnostics in result.errors.

Malformed JSONL is skipped by default. Set onError to fail to stop, warn to keep valid rows with diagnostics, or quarantine to route bad records to error diagnostics. Read those diagnostics from result.errors.

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()])

| Need | Option | Behavior | |------|--------|----------| | Field separator / header | delimiter, header | Configure CSV decoding; encoder accepts delimiter | | Add null markers | nulls: ['NA', 'NULL'] | Adds to default unquoted-empty-field null handling | | Preserve quoted markers | quotedNulls: false | Keeps quoted "NA" and "" as strings | | Skip a preamble | skip: 2 | Before schema/header discovery; comments are handled afterward | | Limit rows | nMax: 100 or maxRows: 100 | One shared counter after skip/comment/header handling | | Keep only the schema | nMax: 0 | Preserves a header-only batch | | Comments | comment: '#' | Removes text after unquoted markers and skips comment-only rows; quoted markers remain | | Preserve spaces/tabs | trimWs: false | Disables default trimming of unquoted values | | Drop blank rows | skipEmptyRows: true | Otherwise blank physical rows after the header become all-null rows | | Read CSV shards | skipRepeatedHeader: true | Skips matching headers only at explicit file boundaries |

All decoder size options are checked integers:

| Option | Range | Behavior | |--------|-------|----------| | batchSize | 1..65536 | Rows per batch | | maxErrorBytes | 0..67108864 | Bounds raw diagnostic previews | | maxRecordBytes | 0..1073741824 | Default 67108864 bytes (64 MiB); 0 disables the record guard | | maxColumns (CSV) | 1..65536 | Default 8192; overflow fails with csv_too_many_columns |

Record limits apply before a newline is seen, including CSV, JSONL and text. CSV record overflow fails with csv_record_too_large.

Repair audit/raw payloads accept auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes. The raw preview is exposed to those controls as pseudo-column raw. CSV repair also accepts audit, auditLimit.

Each record must be one complete UTF-8 JSON object. Invalid number syntax, trailing content, embedded NULs and numbers outside finite float64 range follow the configured malformed-record policy. The encoder rejects nonfinite values.

The first valid record determines schema order. Repeated keys use their first value. maxErrorBytes bounds diagnostic previews, and maxRecordBytes caps the current line.

Operators

Row filtering

| Method | Description | |--------|-------------| | ops.filter(expr, { audit, auditLimit, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }?) | Keep rows matching expression; optional dropped-row audit records support row omission, column allowlists, redaction, hashes, and payload caps | | 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, { seed }) | Deterministic bounded reservoir sampling; use seed: 'random' for nondeterministic mode | | ops.grep(pattern, { invert, column, regex }) | Substring/regex filter | | ops.validate(expr, { rules, rulesFile, audit, auditLimit, maxFailures, warnFailureRate, maxFailureRate, name, message, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }?) | Add _valid boolean column, keep all rows; supports inline rules or local JSON rule-suite files; bounded failure audit records support count/rate thresholds plus privacy controls | | ops.assert(expr, { action, name, message, result, aggregate, op, value, column, tolerance, rel, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }?) | Row-local data-quality rule or finish-time O(1) aggregate assertion; row-local failure side-channel records support privacy controls | | ops.quarantine(expr, { name, message, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }?) | Route rows matching expression to errors and drop them from main output; row payloads support privacy controls | | ops.schema({ columns, required, nonNull, nullable, values, min, max, regex, mode='fail', result='_schema', maxRegexPatternBytes, maxRegexCellBytes, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }) | Row-local table schema contract; fail, warn, filter, quarantine, or annotate; regex budgets default to 4096-byte patterns and 65536-byte cells; schema audit/error records support row omission, column allowlists, redaction, stable non-cryptographic hashes, and row/cell payload caps | | ops.schemaInfer({ rows }) | Bounded decoded-type/nullability schema report; defaults to 10000 sampled rows | | ops.tee({ expr, channel, columns, limit, every, name, includeRow, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }) | Preserve main rows and write bounded JSONL row snapshots to a side channel; row payloads support privacy controls |

Column operations

| Method | Description | |--------|-------------| | ops.select(columns) | Keep and reorder columns | | ops.relocate(columns, { before, after }) | Move columns while preserving all columns | | ops.rename(mapping) | Rename columns: rename({ name: 'full_name' }) | | ops.derive(columns) | Computed columns: derive({ total: expr("col('a')*col('b')") }) | | ops.sourceName({ result, defaultValue }) | Append the current host source path/name as a row-local string column | | ops.across(columns, { fn, functions, names, replace }) | Apply row-local functions over selected columns | | ops.cast(mapping, { audit, auditLimit, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }) | Type conversion; optional bounded value/coercion audit records support privacy controls | | ops.trim(columns?) | Strip whitespace | | ops.fillNull(mapping, { audit, auditLimit, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }) | Replace nulls; optional bounded audit records support privacy controls | | ops.fillDown(columns?) | Forward-fill nulls | | ops.clip(column, { min, max }) | Clamp numeric values | | ops.replace(column, pattern, replacement, { regex, audit, auditLimit, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }) | String find/replace; optional bounded audit records support privacy controls | | ops.hash(columns?) | Add _hash column (DJB2) | | ops.bin(column, boundaries, { missing, onTypeError }) | Discretize into bins with strict numeric-source defaults | | ops.ewma(column, alpha, { result, missing, onTypeError }) | Exponentially weighted moving average | | ops.anomaly(column, { threshold, result, missing, onTypeError }) | Streaming z-score anomaly flag | | ops.normalize(columns, { method, audit, auditLimit, missing, onTypeError, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }) | Blocking minmax/zscore normalization; optional audit records support privacy controls | | ops.acf(column, { lags, missing, onTypeError }) | Blocking autocorrelation table |

columns may contain exact names or selector strings: id:score, starts_with(score_), ends_with(_id), contains(temp), matches(^score_), where(numeric), strict all_of(score,name), lenient any_of(optional,score), exclusions with !name or -name, and boolean selector algebra such as starts_with(score_)&where(numeric), starts_with(score_)&!ends_with(raw), or !(id:score). Ranges use input schema order and can be reversed. Helper matching is case-insensitive; exact names are case-sensitive. These selectors are resolved by the native schema-aware select, relocate, and across ops in O(columns) without retaining rows; SQL lowering rejects selector helpers without a known schema.

Example: ops.across(['starts_with(score_)'], { fn: 'round' }) replaces selected numeric columns per row; ops.across(['name'], { functions: ['lower'], replace: false, names: '{col}_{fn}' }) appends templated columns. This is native/WASM row-local execution, not arbitrary lambdas or grouped dplyr evaluation.

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?, { maxValues, maxStateBytes, overflow, other, audit, auditLimit, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }?) | Value counts; overflow: "other" can emit bounded category-overflow audit records with privacy controls | | ops.groupAgg(groupBy, aggs) | Group by + aggregate. count on a column counts non-null values; column: '*' counts rows. |

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

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, resultOrOptions?) | Sliding window: avg, sum, min, max; options include result, missing, onTypeError | | ops.rollingSum/rollingMean/rollingMin/rollingMax(column, size, { result, missing, onTypeError }) | Named trailing fixed-row numeric windows; default missing/non-numeric source fails | | ops.rollingAny/rollingAll(column, size, { result, nulls }) | Boolean trailing windows; nulls: ignore, false, true, propagate | | ops.lead(column, { offset, result }) | Lookahead N rows | | ops.lag(column, { offset, result }) | Previous-row shift | | ops.shift(column, { offset, result, type }) | Shift alias; type='lag' default or type='lead' | | ops.rowid(columns, { result, sorted, maxKeys }) | Global or per-key 1-based row ids | | ops.rleid(columns, { result }) | Consecutive run id by selected column(s) |

Reshape

| Method | Description | |--------|-------------| | ops.explode(column, delimiter?, { maxTokensPerRow, maxOutputRowsPerInputRow, maxOutputRowsPerBatch, maxTokenBytes }) | Split delimited string into rows with optional expansion caps | | ops.split(column, names, delimiter?) | Split column into multiple columns | | ops.unpivot(columns, { maxOutputRowsPerInputRow, maxOutputRowsPerBatch }) | Wide to long (melt) with optional expansion caps | | ops.stack(file, { tag, tagValue }) | Vertically concatenate another CSV file |

explode caps fail fast with maxTokensPerRow, maxOutputRowsPerInputRow, maxOutputRowsPerBatch, or maxTokenBytes when a single row or batch would expand beyond the configured limit. unpivot supports maxOutputRowsPerInputRow and maxOutputRowsPerBatch.

Date/time

| Method | Description | |--------|-------------| | ops.datetime(column, extractOrOptions?) | Extract parts: year, month, day, hour, minute, second, weekday; accepts { extract, missing, onTypeError } | | ops.dateTrunc(column, trunc, { result, missing, onTypeError }) | Truncate to: year, month, day, hour, minute, second |

Other

| Method | Description | |--------|-------------| | ops.flatten() | Flatten nested columns | | ops.interpolate(column, { method, missing, onTypeError }) | Fill nulls in a numeric column; default missing/non-numeric source fails | | ops.jsonExtract(path, result, { column, type }) | Extract JSON Pointer/simple JSONPath value into a new column | | ops.jsonFilter(path, { op, value, column, type }) | Filter rows by JSON Pointer/simple JSONPath predicate | | ops.jsonSchema(schema, { column, mode, result, audit, auditLimit, auditIncludeRow, auditColumns, auditRedact, auditHashColumns, auditMaxBytes, auditMaxCellBytes }) | Validate JSON text with a supported JSON Schema subset; filter-mode audit records support privacy controls | | ops.jsonFlatten(fields, { column }) | Append declared JSON Pointer/simple JSONPath fields as bounded output columns | | ops.reorder(columns) | Alias for select | | ops.dedup(columns?) | Alias for unique |

Expressions

Used in filter, derive, validate, and assert. 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) between(x,left,right) inrange(x,left,right) | | Date/time | year(x) month(x) day(x) hour(x) minute(x) second(x) weekday(x) epoch(x) date_trunc(x,unit) | | Conditional | if(cond,then,else) case_when(cond,value,...,default) case_match(value,key,result,...,default) if_any(pred,...) if_all(pred,...) 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. Date/time functions are row-local and accept date/timestamp values plus parseable date/timestamp strings; weekday() returns 0=Sunday through 6=Saturday.

ops.derive({
  year: expr("year(col('date'))"),
  monthStart: expr("date_trunc(col('date'), 'month')"),
})

Recipes

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

const result = await pipeline('preview').run({ inputFile: 'data.csv' })
const frequencies = 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 | | sniff | csv \| schema infer rows=1000 \| csv | Bounded-memory schema/type/nullability sniff | | 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}`)
}

Memory policy

Native Node/WASM execution rejects full-input blocking steps such as sort, pivot, normalize, acf, and table encoding unless you opt in for known-small data:

await pipeline('csv | sort age | csv').run({ inputFile: 'small.csv', allowBlocking: true })

For capped key-state operators, pass memory to validate the conservative native state estimate before execution:

await pipeline('csv | unique city max_keys=10000 | csv').run({ inputFile: 'data.csv', memory: '64MB' })

Native Node execution supports spill-backed sort, capped unsorted pivot, unsorted unique/dedup, unsorted group-agg, capped unsorted join inner/left, unsorted semi-join/anti-join, unsorted set operations, and duplicate-eliminating union when spillDir is provided. run(), iterChunks(), toReadable(), and writeTo() drain finish-time merge output through N-API finishStep() instead of waiting for one whole finish(). Standalone WASM supports allowBlocking and memory, but rejects spillDir because browser/WASM spill storage would occupy WASM memory. Use the Node native addon, CLI/direct C, or { engine: 'duckdb' } for external spill; with DuckDB, memory maps to memory_limit and spillDir maps to temp_directory.

Plan-internal file reads are denied by default in Node and WASM. Pass allowFs: true for trusted local lookup files used by join, set operations, union, or stack; pass both allowFs: true and allowRulesFile: true for validate rules_file=...; pass workspaceRoot to pin resolved core plan paths inside a trusted directory. Supplying spillDir opts into local filesystem spill for Node native execution; standalone WASM still rejects spill. inputFile and inputFiles are host source adapters and are not controlled by allowFs.

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',
  { dialect: 'duckdb' }
)
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

DuckDB is the only implemented SQL dialect today. dialect: 'sqlite' and dialect: 'postgres' are recognized but rejected until those dialects have their own compatibility tests and SQL-generation rules.

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', { dialect: 'duckdb' })

// 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, codec, ops } 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 -- newline-delimited execution statistics: run summary plus per-step counters, state estimates, and warnings
• samples -- reserved for sampling operators

const result = await p.run({ inputFile: 'data.csv' })
console.log(result.statsText)   // newline-delimited JSON: summary plus step_stats/state_bytes_estimate/warnings

Pipeline from JSON

import { loadRecipe } from 'tranfi'

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:

1. N-API (Node.js) -- native C addon, fastest, used when available
2. WASM (browsers/fallback) -- same C core compiled to WebAssembly, single-file module
3. 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

Node, Python, CLI and WASM use the same C11 engine. It processes columnar batches with typed columns (bool, int64, float64, string, date, timestamp) and per-cell null bitmaps.

| Need | Node API behavior | |------|-------------------| | Stream input | run({ inputFile }) uses createReadStream() and drains output after each push and incremental finish boundary | | Avoid collecting all output | Use inputStream, toReadable(), writeTo(), iterChunks(), or onOutput with collectOutput: false | | Read gzip | .gz files use zlib.createGunzip(); override with compression: "none" or "gzip" for inputFile/inputStream | | Permit a blocking operation | Set allowBlocking: true or use a supported spillDir path | | Cap retained key state | Supply caps and check the plan with memory: "64MB" | | Allow plan file reads | Supply explicit host-policy options |

Input streaming still collects the final output by default. Choose a streaming sink for large outputs; row-local and bounded-state operators stream under the default strict native memory policy.