tranfi (Python)

Streaming ETL in Python, powered by a native C11 core. Process CSV, JSONL, and text data with composable pipelines that run in constant memory, no matter how large the input.

import tranfi as tf

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

print(result.output_text)
# name,age,label
# Charlie,35,senior
# Alice,30,junior
# Diana,28,junior

Or use the pipe DSL for one-liners:

result = tf.pipeline('csv | filter "col(age) > 25" | top-k 100 age | csv').run(input_file='data.csv')

Install

pip install tranfi

Or from source:

cd build && cmake .. && make
pip install -e py/

CLI

Installing the package also installs the tranfi command:

# Filter and sort
tranfi 'csv | filter "age > 25" | top-k 100 age | csv' < data.csv

# Built-in recipe
tranfi profile < data.csv

# File I/O
tranfi -i input.csv -o output.csv 'csv | select name,age | csv'

# List recipes
tranfi -R

Run tranfi -h for all options.

Quick start

Two APIs

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

p = tf.pipeline([
    tf.codec.csv(),
    tf.ops.filter(tf.expr("col('score') >= 80")),
    tf.ops.derive({'grade': tf.expr("if(col('score')>=90, 'A', 'B')")}),
    tf.ops.top(10, 'score'),
    tf.codec.csv_encode(),
])
result = p.run(input_file='students.csv')

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

p = tf.pipeline('csv | filter "col(score) >= 80" | top-k 10 score | csv')
result = p.run(input_file='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 bytes
result = p.run(input=b'name,age\nAlice,30\n')

# From file (streamed in 64 KB chunks)
result = p.run(input_file='data.csv')

# Access results
result.output         # bytes
result.output_text    # str (UTF-8 decoded)
result.errors         # bytes (error channel)
result.stats          # bytes (pipeline stats)
result.stats_text     # str
result.samples        # bytes (sample channel)

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

# Callback sink; result.output is empty when collect_output=False
with open('out.csv', 'wb') as out:
    result = p.run(input_file='data.csv', on_output=out.write, collect_output=False)

# Iterator form
for chunk in p.iter_chunks(input_file='data.csv'):
    process(chunk)

# .gz input files are decompressed as streaming sources by default
result = p.run(input_file='events.jsonl.gz')
raw = p.run(input_file='events.jsonl.gz', compression='none')

# Multiple files stream sequentially through one pipeline.
parts = ['part-a.csv', 'part-b.csv']
result = p.run(input_files=parts, source_column='src')
for chunk in p.iter_chunks(input_files=parts, source_column='src'):
    process(chunk)

source_column 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. It does not remove repeated CSV headers from later files; use shards without repeated headers, header=False, or a pre-cleaning step when every file has its own header.

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, batch_size, repair, mode, strict, max_error_bytes, max_record_bytes, nulls, quoted_nulls, skip, n_max, max_rows, comment, trim_ws, skip_empty_rows)`	CSV decoder. `mode='strict'` fails on field-count mismatches; `repair=True` / `mode='repair'` emits repair diagnostics; `max_record_bytes` bounds buffered records; `nulls=['NA']` adds null sentinels; `skip=2`, `n_max=100`, `comment='#'`, `trim_ws=False`, and `skip_empty_rows=True` control row-local parsing
`codec.csv_encode(delimiter)`	CSV encoder
`codec.jsonl(batch_size, on_error, max_error_bytes)`	JSON Lines decoder. `on_error` is `skip`, `fail`, `warn`, or `quarantine`
`codec.jsonl_encode()`	JSON Lines encoder
`codec.text(batch_size)`	Line-oriented text decoder (single `_line` column)
`codec.text_encode()`	Text encoder
`codec.table_encode(max_width, max_rows)`	Pretty-print Markdown table

CSV treats unquoted empty fields as null by default. Pass nulls=['NA', 'NULL'] to add sentinel strings; pass quoted_nulls=False when quoted sentinels like "NA" or "" should remain strings. Default field-count handling is permissive for compatibility. Use mode='strict' or strict=True to fail on row/header width mismatches. Use repair=True or mode='repair' to pad/truncate and collect JSONL diagnostics in result.errors; max_error_bytes bounds raw previews. max_record_bytes defaults to 67108864 bytes, caps the current record buffer before a newline is seen, and raises with a csv_record_too_large diagnostic when exceeded; pass 0 to disable the guard. skip=2 discards preamble records before header/schema discovery; comments are applied after skipped rows. n_max=100 / max_rows=100 keeps at most that many decoded data rows after skip/comment/header handling and uses only one counter; n_max=0 preserves a header-only schema batch. comment='#' removes text after an unquoted marker and skips comment-only rows; quoted markers are preserved. Unquoted spaces/tabs are trimmed by default; pass trim_ws=False to preserve them. Blank physical rows after the header are preserved as all-null rows by default; pass skip_empty_rows=True to drop them.

Malformed JSONL records are skipped by default. Set on_error='warn' or on_error='quarantine' to keep valid rows and collect JSONL diagnostics in result.errors; set on_error='fail' to raise on the first malformed line. max_error_bytes bounds the raw preview stored in diagnostics.

Cross-codec pipelines work naturally:

# CSV in, JSONL out
tf.pipeline([tf.codec.csv(), tf.ops.head(5), tf.codec.jsonl_encode()])

# JSONL in, CSV out
tf.pipeline([tf.codec.jsonl(), tf.ops.sort(['name']), tf.codec.csv_encode()])

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=True)`	Top N by column value
`ops.sample(n)`	Reservoir sampling (uniform random)
`ops.grep(pattern, invert, column, regex)`	Substring/regex filter
`ops.validate(expr=None, rules=None, rules_file=None, audit=False, audit_limit=None, max_failures=None, warn_failure_rate=None, max_failure_rate=None, name=None, message=None)`	Add `_valid` boolean column, keep all rows; supports inline rules or local JSON rule-suite files; step stats include checked/passed/failed/failure-rate counters; optional bounded failure audit records and count/rate thresholds
`ops.assert_(expr=None, action='fail', name='assert', message='', result='_assert', aggregate=None, op=None, value=None, column=None)`	Row-local data-quality rule or finish-time O(1) aggregate assertion; aggregate mode supports `count`, `sum:col`, `avg:col`, `min:col`, `max:col`, `missing:col`, and `non_null:col` with `fail`/`warn`
`ops.quarantine(expr, name=None, message=None)`	Route rows matching expression to `errors` and drop them from main output
`ops.schema(columns=None, required=None, non_null=None, nullable=None, values=None, min=None, max=None, regex=None, mode='fail', result='_schema')`	Row-local table schema contract; fail, warn, filter, quarantine, or annotate
`ops.schema_infer(rows=None)`	Bounded decoded-type/nullability schema report; defaults to 10000 sampled rows
`ops.tee(expr=None, channel='samples', columns=None, limit=1000, every=1, name='tee')`	Preserve main rows and write bounded JSONL row snapshots to a side channel

Column operations

Method	Description
`ops.select(columns)`	Keep and reorder columns
`ops.relocate(columns, before=None, after=None)`	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.source_name(result='_source', default='')`	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(audit=False, audit_limit=None, **mapping)`	Type conversion; optional bounded value/coercion audit records
`ops.trim(columns)`	Strip whitespace
`ops.fill_null(audit=False, audit_limit=None, **mapping)`	Replace nulls: `fill_null(age='0')`
`ops.fill_down(columns)`	Forward-fill nulls
`ops.clip(column, min, max)`	Clamp numeric values
`ops.replace(column, pattern, replacement, regex=False, audit=False, audit_limit=None)`	String find/replace
`ops.hash(columns)`	Add `_hash` column (DJB2)
`ops.bin(column, boundaries)`	Discretize into bins

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: tf.ops.across(['starts_with(score_)'], fn='round') replaces selected numeric columns per row; tf.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(stats_list)`	Column statistics. Stats: `count`, `min`, `max`, `sum`, `avg`, `stddev`, `variance`, `median`, `p25`, `p75`, `p90`, `p99`, `distinct`, `hist`, `sample`
`ops.frequency(columns, max_values=None, max_state_bytes=None, overflow=None, other=None, audit=False, audit_limit=None)`	Value counts; `overflow="other"` can emit bounded category-overflow audit records
`ops.group_agg(group_by, aggs)`	Group by + aggregate. `count` on a column counts non-null values; `column='*'` counts rows.

# Group aggregation
tf.ops.group_agg(['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, result)`	Sliding window: `avg`, `sum`, `min`, `max`
`ops.rolling_sum/mean/min/max(column, size, result)`	Named trailing fixed-row numeric windows
`ops.rolling_any/all(column, size, result, nulls='ignore')`	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, max_keys)`	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)`	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, tag_value)`	Vertically concatenate another CSV file

Date/time

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

Other

Method	Description
`ops.flatten()`	Flatten nested columns
`ops.json_extract(path, result, column='_line', type='string')`	Extract JSON Pointer/simple JSONPath value into a new column
`ops.json_filter(path, op='exists', value=None, column='_line', type='auto')`	Filter rows by JSON Pointer/simple JSONPath predicate
`ops.json_schema(schema, column='_line', mode='annotate', result='_valid')`	Validate JSON text with a supported JSON Schema subset
`ops.json_flatten(fields, column='_line')`	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').

tf.ops.filter(tf.expr("col('age') > 25 and contains(col('name'), 'A')"))
tf.ops.derive({
    'full':  tf.expr("concat(col('first'), ' ', col('last'))"),
    'grade': tf.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.

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

Recipes

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

result = tf.pipeline('preview').run(input_file='data.csv')
result = tf.pipeline('freq').run(input_file='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:

for r in tf.recipes():
    print(f"{r['name']:15} {r['description']}")

Memory policy

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

tf.pipeline('csv | sort age | csv').run(input_file='small.csv', allow_blocking=True)

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

tf.pipeline('csv | unique city max_keys=10000 | csv').run(input_file='data.csv', memory='64MB')

Native Python 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 spill_dir is provided. For large outputs, use on_output=... with collect_output=False or iter_chunks(spill_dir=...); those paths drain finish-time merge output through the C sink callback or tf_pipeline_finish_step() instead of retaining it in the C output buffer. With engine='duckdb', memory maps to DuckDB memory_limit and spill_dir maps to temp_directory.

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.

pip install tranfi[duckdb]

# Run a pipeline via DuckDB
result = tf.pipeline('csv | filter "age > 25" | sort -age | csv', engine='duckdb')
result.run(input_file='data.csv')

# Or with bytes input
result = tf.pipeline('csv | head 10 | csv', engine='duckdb').run(input=csv_bytes)

SQL transpilation

Generate SQL directly from DSL strings:

sql = tf.compile_to_sql('csv | filter "col(age) > 25" | sort -age | head 10 | csv')
print(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

Use this to run queries with your own DuckDB connection, or any SQL engine that supports CTE syntax.

Advanced

DSL compilation

# Compile DSL to JSON plan
json_plan = tf.compile_dsl('csv | filter "col(age) > 25" | sort -age | csv')

# Save / load recipes
tf.save_recipe([tf.codec.csv(), tf.ops.head(10), tf.codec.csv_encode()], 'preview.tranfi')
p = tf.load_recipe('preview.tranfi')
result = p.run(input_file='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

result = p.run(input_file='data.csv')
print(result.stats_text)   # newline-delimited JSON: summary plus step_stats/state_bytes_estimate/warnings

Pipeline from JSON

p = tf.pipeline(recipe='{"steps":[{"op":"codec.csv.decode","args":{}},{"op":"head","args":{"n":5}},{"op":"codec.csv.encode","args":{}}]}')

Architecture

The Python package is a thin ctypes wrapper around libtranfi.so, the same C11 core used by the CLI, Node.js, and WASM targets. Data flows through columnar batches with typed columns (bool, int64, float64, string, date, timestamp) and per-cell null bitmaps. run(input_file=...) streams input in chunks and drains native main output after each push; by default it still collects the final output for convenience. .gz input files are decompressed in the host source adapter with gzip.open(); use compression='none' to force raw bytes or compression='gzip' to force gzip. Use on_output=... with collect_output=False or iter_chunks() for large outputs. Native execution is strict by default: row-local and bounded-state operators stream, blocking operators require allow_blocking=True or an external engine, and capped key-state plans can be checked with memory='64MB'.