qsv

Table of Contents

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qsv is a command line program for querying, slicing, indexing, analyzing, filtering, enriching, transforming, sorting, validating, joining, formatting & convertingtabular data (CSV, spreadsheets, DBs, parquet, etc). Commands are simple, composable & "blazing fast". * Commands * Installation Options * Whirlwind Tour / Notebooks / Lessons & Exercises * Cookbook * FAQ * Performance Tuning * 👉 Benchmarks 🚀 * Environment Variables * Feature Flags * Goals/Non-goals * Testing * NYC SOD 2022/csv,conf,v8/PyConUS 2025/csv,conf,v9 * Sponsor

Command	Description
apply✨ 📇🚀🧠🤖🔣👆	Apply series of string, date, math & currency transformations to given CSV column/s. It also has some basic NLP functions (similarity, sentiment analysis, profanity, eudex, language & name gender) detection.
applydp✨ 📇🚀🔣👆	applydp is a slimmed-down version of apply with only Datapusher+ relevant subcommands/operations (qsvdp binary variant only).
behead	Drop headers from a CSV.
cat 🗄️	Concatenate CSV files by row or by column.
clipboard✨	Provide input from the clipboard or save output to the clipboard.
count 📇🏎️🐻‍❄️	Count the rows and optionally compile record width statistics of a CSV file. (11.87 seconds for a 15gb, 27m row NYC 311 dataset without an index. Instantaneous with an index.) If the polars feature is enabled, uses Polars' multithreaded, mem-mapped CSV reader for fast counts even without an index
datefmt 📇🚀👆	Formats recognized date fields (19 formats recognized) to a specified date format using strftime date format specifiers.
dedup 🤯🚀👆	Remove duplicate rows (See also extdedup, extsort, sort & sortcheck commands).
describegpt 🌐🤖🪄	Infer a Data Dictionary, Description & Tags or ask questions about a CSV using any OpenAI API-compatible LLM, including local LLMs like Ollama, Jan & LM Studio. (Examples: --all Markdown, JSON; --prompt "What are the top 10 complaint types per community board and borough?" - SQL result)
diff 🚀🪄	Find the difference between two CSVs with ludicrous speed! e.g. compare two CSVs with 1M rows x 9 columns in under 600ms!
edit	Replace the value of a cell specified by its row and column.
enum 👆	Add a new column enumerating rows by adding a column of incremental or uuid identifiers. Can also be used to copy a column or fill a new column with a constant value.
excel 🚀	Exports a specified Excel/ODS sheet to a CSV file.
exclude 📇👆	Removes a set of CSV data from another set based on the specified columns.
explode 🔣👆	Explode rows into multiple ones by splitting a column value based on the given separator.
extdedup 👆	Remove duplicate rows from an arbitrarily large CSV/text file using a memory-mapped, on-disk hash table. Unlike the dedup command, this command does not load the entire file into memory nor does it sort the deduped file.
extsort 🚀📇👆	Sort an arbitrarily large CSV/text file using a multithreaded external merge sort algorithm.
fetch✨ 📇🧠🌐	Send/Fetch data to/from web services for every row using HTTP Get. Comes with HTTP/2 adaptive flow control, jaq JSON query language support, dynamic throttling (RateLimit) & caching with available persistent caching using Redis or a disk-cache.
fetchpost✨ 📇🧠🌐	Similar to fetch, but uses HTTP Post (HTTP GET vs POST methods). Supports HTML form (application/x-www-form-urlencoded), JSON (application/json) and custom content types - with the ability to render payloads using CSV data using the MiniJinja template engine.
fill 👆	Fill empty values.
fixlengths	Force a CSV to have same-length records by either padding or truncating them.
flatten	A flattened view of CSV records. Useful for viewing one record at a time. e.g. qsv slice -i 5 data.csv | qsv flatten.
fmt	Reformat a CSV with different delimiters, record terminators or quoting rules. (Supports ASCII delimited data.)
foreach✨ 📇	Execute a shell command once per record in a given CSV file.
frequency 📇😣🏎️👆🪄	Build frequency tables of each column. Uses multithreading to go faster if an index is present (Examples: CSV JSON).
geocode✨ 📇🧠🌐🚀🔣👆🌎	Geocodes a location against an updatable local copy of the Geonames cities & the Maxmind GeoLite2 databases. With caching and multi-threading, it geocodes up to 360,000 records/sec!
geoconvert✨ 🌎	Convert between various spatial formats and CSV/SVG including GeoJSON, SHP, and more.
headers 🗄️	Show the headers of a CSV. Or show the intersection of all headers between many CSV files.
index	Create an index (📇) for a CSV. This is very quick (even the 15gb, 28m row NYC 311 dataset takes all of 14 seconds to index) & provides constant time indexing/random access into the CSV. With an index, count, sample & slice work instantaneously; random access mode is enabled in luau; and multithreading (🏎️) is enabled for the frequency, split, stats, schema & tojsonl commands.
input	Read CSV data with special commenting, quoting, trimming, line-skipping & non-UTF8 encoding handling rules. Typically used to "normalize" a CSV for further processing with other qsv commands.
join 😣👆	Inner, outer, right, cross, anti & semi joins. Automatically creates a simple, in-memory hash index to make it fast.
joinp✨ 🚀🐻‍❄️🪄	Inner, outer, right, cross, anti, semi, non-equi & asof joins using the Pola.rs engine. Unlike the join command, joinp can process files larger than RAM, is multithreaded, has join key validation, a maintain row order option, pre and post-join filtering, join keys unicode normalization, supports "special" non-equi joins and asof joins (which is particularly useful for time series data) & its output columns can be coalesced.
json 👆	Convert JSON to CSV.
jsonl 🚀🔣	Convert newline-delimited JSON (JSONL/NDJSON) to CSV. See tojsonl command to convert CSV to JSONL.
lens✨ 🐻‍❄️	Interactively view, search & filter a CSV using the csvlens engine.
luau 👑✨ 📇🌐🔣📚	Create multiple new computed columns, filter rows, compute aggregations and build complex data pipelines by executing a Luau 0.690 expression/script for every row of a CSV file (sequential mode), or using random access with an index (random access mode). Can process a single Luau expression or full-fledged data-wrangling scripts using lookup tables with discrete BEGIN, MAIN and END sections. It is not just another qsv command, it is qsv's Domain-specific Language (DSL) with numerous qsv-specific helper functions to build production data pipelines.
partition 👆	Partition a CSV based on a column value.
pivotp✨ 🚀🐻‍❄️🪄	Pivot CSV data. Features "smart" aggregation auto-selection based on data type & stats.
pro	Interact with the qsv pro API.
prompt✨ 🐻‍❄️	Open a file dialog to either pick a file as input or save output to a file.
pseudo 🔣👆	Pseudonymise the value of the given column by replacing them with an incremental identifier.
py✨ 📇🔣	Create a new computed column or filter rows by evaluating a Python expression on every row of a CSV file. Python's f-strings is particularly useful for extended formatting, with the ability to evaluate Python expressions as well. Requires Python 3.8 or greater.
rename	Rename the columns of a CSV efficiently.
replace 📇👆	Replace CSV data using a regex. Applies the regex to each field individually.
reverse 📇🤯	Reverse order of rows in a CSV. Unlike the sort --reverse command, it preserves the order of rows with the same key. If an index is present, it works with constant memory. Otherwise, it will load all the data into memory.
safenames	Modify headers of a CSV to only have "safe" names - guaranteed "database-ready"/"CKAN-ready" names.
sample 📇🌐🏎️	Randomly draw rows (with optional seed) from a CSV using seven different sampling methods - reservoir (default), indexed, bernoulli, systematic, stratified, weighted & cluster sampling. Supports sampling from CSVs on remote URLs.
schema 📇😣🏎️👆🪄🐻‍❄️	Infer either a JSON Schema Validation Draft 2020-12 (Example) or Polars Schema (Example) from CSV data. In JSON Schema Validation mode, it produces a .schema.json file replete with inferred data type & domain/range validation rules derived from stats. Uses multithreading to go faster if an index is present. See validate command to use the generated JSON Schema to validate if similar CSVs comply with the schema. With the --polars option, it produces a .pschema.json file that all polars commands (sqlp, joinp & pivotp) use to determine the data type of each column & to optimize performance. Both schemas are editable and can be fine-tuned. For JSON Schema, to refine the inferred validation rules. For Polars Schema, to change the inferred Polars data types.
search 📇👆	Run a regex over a CSV. Applies the regex to selected fields & shows only matching rows.
searchset 📇👆	Run multiple regexes over a CSV in a single pass. Applies the regexes to each field individually & shows only matching rows.
select 👆	Select, re-order, reverse, duplicate or drop columns.
slice 📇🏎️	Slice rows from any part of a CSV. When an index is present, this only has to parse the rows in the slice (instead of all rows leading up to the start of the slice).
snappy 🚀🌐	Does streaming compression/decompression of the input using Google's Snappy framing format (more info).
sniff 📇🌐	Quickly sniff & infer CSV metadata (delimiter, header row, preamble rows, quote character, flexible, is_utf8, average record length, number of records, content length & estimated number of records if sniffing a CSV on a URL, number of fields, field names & data types). It is also a general mime type detector.
sort 🚀🤯👆	Sorts CSV data in lexicographical, natural, numerical, reverse, unique or random (with optional seed) order (Also see extsort & sortcheck commands).
sortcheck 📇👆	Check if a CSV is sorted. With the --json options, also retrieve record count, sort breaks & duplicate count.
split 📇🏎️	Split one CSV file into many CSV files. It can split by number of rows, number of chunks or file size. Uses multithreading to go faster if an index is present when splitting by rows or chunks.
sqlp✨ 📇🚀🐻‍❄️🗄️🪄	Run Polars SQL (a PostgreSQL dialect) queries against several CSVs, Parquet, JSONL and Arrow files - converting queries to blazing-fast Polars LazyFrame expressions, processing larger than memory CSV files. Query results can be saved in CSV, JSON, JSONL, Parquet, Apache Arrow IPC and Apache Avro formats.
stats 📇🤯🏎️👆🪄	Compute summary statistics (sum, min/max/range, sort order/sortiness, min/max/sum/avg length, mean, standard error of the mean (SEM), geometric/harmonic means, stddev, variance, Coefficient of Variation (CV), nullcount, max precision, sparsity, quartiles, Interquartile Range (IQR), lower/upper fences, skewness, median, mode/s, antimode/s, cardinality & uniqueness ratio) & make GUARANTEED data type inferences (Null, String, Float, Integer, Date, DateTime, Boolean) for each column in a CSV (Example - more info). Uses multithreading to go faster if an index is present (with an index, can compile "streaming" stats on NYC's 311 data (15gb, 28m rows) in less than 7.3 seconds!).
table 🤯	Show aligned output of a CSV using elastic tabstops. To interactively view a CSV, use the lens command.
template 📇🚀🔣📚	Renders a template using CSV data with the MiniJinja template engine (Example).
to✨ 🚀🗄️	Convert CSV files to Parquet, PostgreSQL, SQLite, Excel (XLSX), LibreOffice Calc (ODS) and Data Package.
tojsonl 📇😣🚀🔣🪄	Smartly converts CSV to a newline-delimited JSON (JSONL/NDJSON). By scanning the CSV first, it "smartly" infers the appropriate JSON data type for each column. See jsonl command to convert JSONL to CSV.
transpose 🤯	Transpose rows/columns of a CSV.
validate 📇🚀🌐📚	Validate CSV data blazingly-fast using JSON Schema Validation (Draft 2020-12) (e.g. up to 780,031 rows/second1 using NYC's 311 schema generated by the schema command) & put invalid records into a separate file along with a detailed validation error report. Supports several custom JSON Schema formats & keywords: * currency custom format with ISO-4217 validation * dynamicEnum custom keyword that supports enum validation against a CSV on the filesystem or a URL (http/https/ckan & dathere URL schemes supported) * uniqueCombinedWith custom keyword to validate uniqueness across multiple columns for composite key validation. If no JSON schema file is provided, validates if a CSV conforms to the RFC 4180 standard and is UTF-8 encoded.

✨: enabled by a feature flag.
📇: uses an index when available.
🤯: loads entire CSV into memory, though dedup, stats & transpose have "streaming" modes as well.
😣: uses additional memory proportional to the cardinality of the columns in the CSV.
🧠: expensive operations are memoized with available inter-session Redis/Disk caching for fetch commands.
🗄️: Extended input support.
🐻‍❄️: command powered/accelerated by vectorized query engine.
🤖: command uses Natural Language Processing & General AI techniques.
🏎️: multithreaded and/or faster when an index (📇) is available.
🚀: multithreaded even without an index.
: has CKAN-aware integration options.
🌐: has web-aware options.
🔣: requires UTF-8 encoded input.
👆: has powerful column selector support. See select for syntax.
🪄: "automagical" commands that uses stats and/or frequency tables to work "smarter" & "faster".
📚: has lookup table support, enabling runtime "lookups" against local or remote reference CSVs.
🌎: has geospatial capabilities.

qsv's big brother - qsv pro is additionally available for download from its website and on the Microsoft Store. Apart from a Graphical User Interface, it's superpowered with additional features and capabilities - an API, CKAN integration, a Natural Language interface, no false-positive Windows Defender warnings and more!

Full-featured prebuilt binary variants of the latest qsv version for Linux, macOS & Windows are available for download, including binaries compiled with Rust Nightly (more info). You may click a badge below based on your platform to download a ZIP with pre-built binaries.

These prebuilt binaries are also built with CPU optimizations enabled for x86_64 (e.g. SSE4.2, AVX2, AVX512, etc. on Intel and AMD processors) and Apple Silicon processors (ARM64 SIMD NEON) for even more performance gains. This may prevent some older CPUs from running qsv. If so, "portable" binaries (all CPU optimizations disabled) are also included in the release zip archives (qsv with a "p" suffix - e.g. qsvp, qsvplite qsvpdp).

For Windows, an MSI "Easy installer" for the x86_64 MSVC qsvp binary is also available. After downloading and installing the Easy installer, launch the Easy installer and click "Install qsv" to download the latest qsvp pre-built binary to a folder that is added to your PATH. Afterwards qsv should be installed and you may launch a new terminal to use qsv.

For macOS, "ad-hoc" signatures are used to sign our binaries, so you will need to set appropriate Gatekeeper security settings or run the following command to remove the quarantine attribute from qsv before you run it for the first time:

# replace qsv with qsvlite or qsvdp if you installed those binary variants
xattr -d com.apple.quarantine qsv

An additional benefit of using the prebuilt binaries is that they have the self_update feature enabled, allowing you to quickly update qsv to the latest version with a simple qsv --update. For further security, the self_update feature only fetches releases from this GitHub repo and automatically verifies the signature of the downloaded zip archive before installing the update.

ℹ️ NOTE: The luau feature is not available in musl prebuilt binaries².

All prebuilt binaries zip archives are signed with zipsign with the following public key qsv-zipsign-public.key. To verify the integrity of the downloaded zip archives:

# if you don't have zipsign installed yet
cargo install zipsign

# verify the integrity of the downloaded prebuilt binary zip archive
# after downloading the zip archive and the qsv-zipsign-public.key file.
# replace <PREBUILT-BINARY-ARCHIVE.zip> with the name of the downloaded zip archive
# e.g. zipsign verify zip qsv-0.118.0-aarch64-apple-darwin.zip qsv-zipsign-public.key
zipsign verify zip <PREBUILT-BINARY-ARCHIVE.zip> qsv-zipsign-public.key

qsv is also distributed by several package managers and distros.

Here are the relevant commands for installing qsv using the various package managers and distros:

# Arch Linux AUR (https://aur.archlinux.org/packages/qsv)
yay -S qsv

# Homebrew on macOS/Linux (https://formulae.brew.sh/formula/qsv#default)
brew install qsv

# MacPorts on macOS (https://ports.macports.org/port/qsv/)
sudo port install qsv

# Nixpkgs on Linux/macOS (https://search.nixos.org/packages?channel=unstable&show=qsv&from=0&size=50&sort=relevance&type=packages&query=qsv)
nix-shell -p qsv

# Scoop on Windows (https://scoop.sh/#/apps?q=qsv)
scoop install qsv

# Void Linux (https://voidlinux.org/packages/?arch=x86_64&q=qsv)
sudo xbps-install qsv

# Conda-forge (https://anaconda.org/conda-forge/qsv)
conda install conda-forge::qsv

Note that qsv provided by these package managers/distros enable different features (Homebrew, for instance, only enables the apply and luau features. However, it does automatically install shell completion for bash, fish and zsh shells).

To find out what features are enabled in a package/distro's qsv, run qsv --version (more info).

In the true spirit of open source, these packages are maintained by volunteers who wanted to make qsv easier to install in various environments. They are much appreciated, and we loosely collaborate with the package maintainers through GitHub, but know that these packages are maintained by third-parties.

datHere also maintains a Debian package targeting the latest Ubuntu LTS on x86_64 architecture to make it easier to install qsv with DataPusher+.

To install qsv on Ubuntu/Debian:

wget -O - https://dathere.github.io/qsv-deb-releases/qsv-deb.gpg | sudo gpg --dearmor -o /usr/share/keyrings/qsv-deb.gpg
echo "deb [signed-by=/usr/share/keyrings/qsv-deb.gpg] https://dathere.github.io/qsv-deb-releases ./" | sudo tee /etc/apt/sources.list.d/qsv.list
sudo apt update
sudo apt install qsv

If you have Rust installed, you can also install from source using Rust's cargo command³:

cargo install qsv --locked --features all_features

The binary will be installed in ~/.cargo/bin.

To install different variants and enable optional features, use cargo --features (see Feature Flags for more info):

# to install qsv with all features enabled
cargo install qsv --locked --bin qsv --features feature_capable,apply,fetch,foreach,geocode,luau,polars,python,self_update,to,ui
# or shorthand
cargo install qsv --locked --bin qsv -F all_features
# enable all CPU optimizations for the current CPU (warning: creates non-portable binary)
CARGO_BUILD_RUSTFLAGS='-C target-cpu=native' cargo install qsv --locked --bin qsv -F all_features

# or enable only the apply and polars features
cargo install qsv --locked --bin qsv -F feature_capable,apply,polars

# or to install qsvlite
cargo install qsv --locked --bin qsvlite -F lite

# or to install qsvdp
cargo install qsv --locked --bin qsvdp -F datapusher_plus

ℹ️ NOTE: if you get compilation errors when running cargo install, use Option 4 to compile from source using cargo build. The errors are usually due to cargo install only using the latest release version of qsv's dependencies, and ignoring patch.crates-io entries in our Cargo.toml.

Compiling from source also works similarly³:

git clone https://github.com/dathere/qsv.git
cd qsv
cargo build --release --locked --bin qsv --features all_features

The compiled binary will end up in ./target/release/.

To compile different variants and enable optional features:

# to compile qsv with all features enabled
cargo build --release --locked --bin qsv --features feature_capable,apply,fetch,foreach,geocode,luau,polars,python,self_update,to,ui
# shorthand
cargo build --release --locked --bin qsv -F all_features
# enable all CPU optimizations for the current CPU (warning: creates non-portable binary)
CARGO_BUILD_RUSTFLAGS='-C target-cpu=native' cargo build --release --locked --bin qsv -F all_features

# or build qsv with only the fetch and foreach features enabled
cargo build --release --locked --bin qsv -F feature_capable,fetch,foreach

# for qsvlite
cargo build --release --locked --bin qsvlite -F lite

# for qsvdp
cargo build --release --locked --bin qsvdp -F datapusher_plus

ℹ️ NOTE: To build with Rust nightly, see Nightly Release Builds. The feature_capable, lite and datapusher_plus are MUTUALLY EXCLUSIVE features. See Special Build Features for more info.

There are four binary variants of qsv:

• qsv - feature-capable(✨), with the prebuilt binaries enabling all applicable features except Python ²
• qsvpy - same as qsv but with the Python feature enabled. Four subvariants are available - qsvpy310, qsvpy311, qsvpy312 & qsvpy313 - which are compiled with the latest patch version of Python 3.10, 3.11, 3.12 & 3.13 respectively. We need to have a binary for each Python version as Python is dynamically linked (more info).
• qsvlite - all features disabled (~13% of the size of qsv)
• qsvdp - optimized for use with DataPusher+ with only DataPusher+ relevant commands; an embedded luau interpreter; applydp, a slimmed-down version of the apply feature; the --progressbar option disabled; and the self-update only checking for new releases, requiring an explicit --update (~12% of the the size of qsv).

ℹ️ NOTE: There are "portable" subvariants of qsv available with the "p" suffix - qsvp, qsvplite and qsvpdp. These subvariants are compiled without any CPU features enabled. Use these subvariants if you have an old CPU architecture or getting "Illegal instruction" errors when running the regular qsv binaries.

qsv has extensive, extendable shell completion support. It currently supports the following shells: bash, zsh, powershell, fish, nushell, fig & elvish. You may download a shell completions script for your shell by clicking one of the badges below:

To customize shell completions, see the Shell Completion documentation. If you're using Bash, you can also follow the step-by-step tutorial at 100.dathere.com to learn how to enable the Bash shell completions.

The --select option and several commands (apply, applydp, datefmt, exclude, fetchpost, replace, schema, search, searchset, select, sqlp, stats & validate) allow the user to specify regular expressions. We use the regex crate to parse, compile and execute these expressions. ⁴

Its syntax can be found here and "is similar to other regex engines, but it lacks several features that are not known how to implement efficiently. This includes, but is not limited to, look-around and backreferences. In exchange, all regex searches in this crate have worst case O(m * n) time complexity, where m is proportional to the size of the regex and n is proportional to the size of the string being searched."

If you want to test your regular expressions, regex101 supports the syntax used by the regex crate. Just select the "Rust" flavor.

JSON SCHEMA VALIDATION REGEX NOTE: The schema command, when inferring a JSON Schema Validation file, will derive a regex expression for the selected columns when the --pattern-columns option is used. Though the derived regex is guaranteed to work, it may not be the most efficient.
Before using the generated JSON Schema file in production with the validate command, it is recommended that users inspect and optimize the derived regex as required.
While doing so, note that the validate command in JSON Schema Validation mode, can also support "fancy" regex expressions with look-around and backreferences using the --fancy-regex option.

qsv recognizes UTF-8/ASCII encoded, CSV (.csv), SSV (.ssv) and TSV files (.tsv & .tab). CSV files are assumed to have "," (comma) as a delimiter, SSV files have ";" (semicolon) as a delimiter and TSV files, "\t" (tab) as a delimiter. The delimiter is a single ascii character that can be set either by the --delimiter command-line option or with the QSV_DEFAULT_DELIMITER environment variable or automatically detected when QSV_SNIFF_DELIMITER is set.

When using the --output option, qsv will UTF-8 encode the file & automatically change the delimiter used in the generated file based on the file extension - i.e. comma for .csv, semicolon for .ssv, tab for .tsv & .tab files.

JSON files are recognized & converted to CSV with the json command. JSONL/NDJSON files are also recognized & converted to/from CSV with the jsonl and tojsonl commands respectively.

The fetch & fetchpost commands also produces JSONL files when its invoked without the --new-column option & TSV files with the --report option.

The excel, safenames, sniff, sortcheck & validate commands produce JSON files with their JSON options following the JSON API 1.1 specification, so it can return detailed machine-friendly metadata that can be used by other systems.

The schema command produces a JSON Schema Validation (Draft 7) file with the ".schema.json" file extension, which can be used with the validate command to validate other CSV files with an identical schema.

The excel command recognizes Excel & Open Document Spreadsheet(ODS) files (.xls, .xlsx, .xlsm, .xlsb & .ods files).

Speaking of Excel, if you're having trouble opening qsv-generated CSV files in Excel, set the QSV_OUTPUT_BOM environment variable to add a Byte Order Mark to the beginning of the generated CSV file. This is a workaround for Excel's UTF-8 encoding detection bug.

The to command converts CSVs to Excel .xlsx, LibreOffice/OpenOffice Calc .ods, Parquet & Data Package formats, and populates PostgreSQL and SQLite databases.

The sqlp command returns query results in CSV, JSON, JSONL, Parquet, Apache Arrow IPC & Apache AVRO formats. Polars SQL also supports reading external files directly in various formats with its read_csv, read_ndjson, read_parquet & read_ipc table functions.

The sniff command can also detect the mime type of any file with the --no-infer or --just-mime options, may it be local or remote (http and https schemes supported). It can detect more than 130 file formats, including MS Office/Open Document files, JSON, XML, PDF, PNG, JPEG and specialized geospatial formats like GPX, GML, KML, TML, TMX, TSX, TTML. Click here for a complete list.

ℹ️ NOTE: When the polars feature is enabled, qsv can also natively read .parquet, .ipc, .arrow, .json & .jsonl files.

The cat, headers, sqlp & to commands have extended input support (🗄️). If the input is - or empty, the command will try to use stdin as input. If it's not, it will check if its a directory, and if so, add all the files in the directory as input files.

If its a file, it will first check if it has an .infile-list extension. If it does, it will load the text file and parse each line as an input file path. This is a much faster and convenient way to process a large number of input files, without having to pass them all as separate command-line arguments. Further, the file paths can be anywhere in the file system, even on separate volumes. If an input file path is not fully qualified, it will be treated as relative to the current working directory. Empty lines and lines starting with # are ignored. Invalid file paths will be logged as warnings and skipped.

For both directory and .infile-list input, snappy compressed files with a .sz extension will be automatically decompressed.

Finally, if its just a regular file, it will be treated as a regular input file.

qsv supports automatic compression/decompression using the Snappy frame format. Snappy was chosen instead of more popular compression formats like gzip because it was designed for high-performance streaming compression & decompression (up to 2.58 gb/sec compression, 0.89 gb/sec decompression).

For all commands except the index, extdedup & extsort commands, if the input file has an ".sz" extension, qsv will automatically do streaming decompression as it reads it. Further, if the input file has an extended CSV/TSV ".sz" extension (e.g nyc311.csv.sz/nyc311.tsv.sz/nyc311.tab.sz), qsv will also use the file extension to determine the delimiter to use.

Similarly, if the --output file has an ".sz" extension, qsv will automatically do streaming compression as it writes it. If the output file has an extended CSV/TSV ".sz" extension, qsv will also use the file extension to determine the delimiter to use.

Note however that compressed files cannot be indexed, so index-accelerated commands (frequency, schema, split, stats, tojsonl) will not be multithreaded. Random access is also disabled without an index, so slice will not be instantaneous and luau's random-access mode will not be available.

There is also a dedicated snappy command with four subcommands for direct snappy file operations — a multithreaded compress subcommand (4-5x faster than the built-in, single-threaded auto-compression); a decompress subcommand with detailed compression metadata; a check subcommand to quickly inspect if a file has a Snappy header; and a validate subcommand to confirm if a Snappy file is valid.

The snappy command can be used to compress/decompress ANY file, not just CSV/TSV files.

Using the snappy command, we can compress NYC's 311 data (15gb, 28m rows) to 4.95 gb in 5.77 seconds with the multithreaded compress subcommand - 2.58 gb/sec with a 0.33 (3.01:1) compression ratio. With snappy decompress, we can roundtrip decompress the same file in 16.71 seconds - 0.89 gb/sec.

Compare that to zip 3.0, which compressed the same file to 2.9 gb in 248.3 seconds on the same machine - 43x slower at 0.06 gb/sec with a 0.19 (5.17:1) compression ratio - for just an additional 14% (2.45 gb) of saved space. zip also took 4.3x longer to roundtrip decompress the same file in 72 seconds - 0.20 gb/sec.

ℹ️ NOTE: qsv has additional compression support beyond Snappy:

The sqlp command can:

• Automatically decompress gzip, zstd and zlib compressed input files
• Automatically compress output files when using Arrow, Avro and Parquet formats (via --format and --compression options)

When the polars feature is enabled, qsv can automatically decompress these compressed file formats:

• CSV: .csv.gz, .csv.zst, .csv.zlib
• TSV/TAB: .tsv.gz, .tsv.zst, .tsv.zlib; .tab.gz, .tab.zst, .tab.zlib
• SSV: .ssv.gz, .ssv.zst, .ssv.zlib

qsv follows the RFC 4180 CSV standard. However, in real life, CSV formats vary significantly & qsv is actually not strictly compliant with the specification so it can process "real-world" CSV files. qsv leverages the awesome Rust CSV crate to read/write CSV files.

Click here to find out more about how qsv conforms to the standard using this crate.

When dealing with "atypical" CSV files, you can use the input & fmt commands to normalize them to be RFC 4180-compliant.

qsv requires UTF-8 encoded input (of which ASCII is a subset).

Should you need to re-encode CSV/TSV files, you can use the input command to "lossy save" to UTF-8 - replacing invalid UTF-8 sequences with � (U+FFFD REPLACEMENT CHARACTER).

Alternatively, if you want to truly transcode to UTF-8, there are several utilities like iconv that you can use to do so on Linux/macOS & Windows.

Unlike other modern operating systems, Microsoft Windows' default encoding is UTF16-LE. This will cause problems when redirecting qsv's output to a CSV file in Powershell & trying to open it with Excel - everything will be in the first column, as the UTF16-LE encoded CSV file will not be properly recognized by Excel.

# the following command will produce a UTF16-LE encoded CSV file on Windows
qsv stats wcp.csv > wcpstats.csv

Which is weird, since you'd think Microsoft's own Excel would properly recognize UTF16-LE encoded CSV files. Regardless, to create a properly UTF-8 encoded file on Windows, use the --output option instead:

# so instead of redirecting stdout to a file on Windows
qsv stats wcp.csv > wcpstats.csv

# do this instead, so it will be properly UTF-8 encoded
qsv stats wcp.csv --output wcpstats.csv

Alternatively, qsv can add a Byte Order Mark (BOM) to the beginning of a CSV to indicate it's UTF-8 encoded. You can do this by setting the QSV_OUTPUT_BOM environment variable to 1.

This will allow Excel on Windows to properly recognize the CSV file as UTF-8 encoded.

Note that this is not a problem with Excel on macOS, as macOS (like most other *nixes) uses UTF-8 as its default encoding.

Nor is it a problem with qsv output files produced on other operating systems, as Excel on Windows can properly recognize UTF-8 encoded CSV files.

For complex data-wrangling tasks, you can use Luau and Python scripts.

Luau is recommended over Python for complex data-wrangling tasks as it is faster, more memory-efficient, has no external dependencies and has several data-wrangling helper functions as qsv's DSL.

See Luau vs Python for more info.

Another "interpreter" included with qsv is MiniJinja, which is used in the template and fetchpost commands.

qsv supports three memory allocators - mimalloc (default), jemalloc and the standard allocator.
See Memory Allocator for more info.

It also has Out-of-Memory prevention, with two modes - NORMAL (default) & CONSERVATIVE.
See Out-of-Memory Prevention for more info.

qsv supports an extensive list of environment variables and supports .env files to set them.

For details, see Environment Variables and the dotenv.template.yaml file.

qsv has several feature flags that can be used to enable/disable optional features.

See Features for more info.

qsv's MSRV policy is to require the latest stable Rust version that is supported by Homebrew, currently . qsv itself may upgrade its MSRV, but a new qsv release will only be made once Homebrew supports the latest Rust stable.

QuickSilver's goals, in priority order, are to be:

• As Fast as Possible - To do so, it has frequent releases, an aggressive MSRV policy, takes advantage of CPU features, employs various caching strategies, uses HTTP/2, and is multithreaded when possible and it makes sense. It also uses the latest dependencies when possible, and will use Cargo patch to get unreleased fixes/features from its dependencies. See Performance for more info.
• Able to Process Very Large Files - Most qsv commands are streaming, using constant memory, and can process arbitrarily large CSV files. For those commands that require loading the entire CSV into memory (denoted by 🤯), qsv has Out-of-Memory prevention, batch processing strategies and "ext"ernal commands that use the disk to process larger than memory files. See Memory Management for more info.
• A Complete Data-Wrangling Toolkit - qsv aims to be a comprehensive data-wrangling toolkit that you can use for quick analysis and investigations, but is also robust enough for production data pipelines. Its many commands are targeted towards common data-wrangling tasks and can be combined/composed into complex data-wrangling scripts with its Luau-based DSL.
  Luau will also serve as the backbone of a whole library of qsv recipes - reusable scripts for common tasks (e.g. street-level geocoding, removing PII, data enrichment, etc.) that prompt for easily modifiable parameters.
• Composable/Interoperable - qsv is designed to be composable, with a focus on interoperability with other common CLI tools like 'awk', 'xargs', 'ripgrep', 'sed', etc., and with well known ETL/ELT tools like Airbyte, Airflow, Pentaho Kettle, etc. Its commands can be combined with other tools via pipes, and it supports other common file formats like JSON/JSONL, Parquet, Arrow IPC, Avro, Excel, ODS, PostgreSQL, SQLite, etc. See File Formats for more info.
• As Portable as Possible - qsv is designed to be portable, with installers on several platforms with an integrated self-update mechanism. In preference order, it supports Linux, macOS and Windows. See Installation Options for more info.
• As Easy to Use as Possible - qsv is designed to be easy to use. As easy-to-use that is, as command line interfaces go 🤷. Its commands have numerous options but have sensible defaults. The usage text is written for a data analyst audience, not developers; and there are numerous examples in the usage text, with the tests doubling as examples as well. With qsv pro, it has much expanded functionality while being easier to use with its Graphical User Interface.
• As Secure as Possible - qsv is designed to be secure. It has no external runtime dependencies, is written in Rust, and its codebase is automatically audited for security vulnerabilities with automated DevSkim, "cargo audit" and Codacy Github Actions workflows.
  It uses the latest stable Rust version, with an aggressive MSRV policy and the latest version of all its dependencies. It has an extensive test suite with ~2,000 tests, including several property tests which randomly generate parameters for oft-used commands.
  Its prebuilt binary archives are zipsigned, so you can verify their integrity. Its self-update mechanism automatically verifies the integrity of the prebuilt binaries archive before applying an update. See Security for more info.
• As Easy to Contribute to as Possible - qsv is designed to be easy to contribute to, with a focus on maintainability. It's modular architecture allows the easy addition of self-contained commands gated by feature flags, the source code is heavily commented, the usage text is embedded, and there are helper functions that make it easy to create additional commands and supporting tests. See Features and Contributing for more info.

QuickSilver's non-goals are to be:

• As Small as Possible - qsv is designed to be small, but not at the expense of performance, features, composability, portability, usability, security or maintainability. However, we do have a qsvlite variant that is ~13% of the size of qsv and a qsvdp variant that is ~12% of the size of qsv. Those variants, however, have reduced functionality. Further, several commands are gated behind feature flags, so you can compile qsv with only the features you need.
• Multi-lingual - qsv's usage text and messages are English-only. There are no plans to support other languages. This does not mean it can only process English input files.
  It can process well-formed CSVs in any language so long as its UTF-8 encoded. Further, it supports alternate delimiters/separators other than comma; the apply whatlang operation detects 69 languages; and its apply thousands, currency and eudex operations supports different languages and country conventions for number, currency and date parsing/formatting.
  Finally, though the default Geonames index of the geocode command is English-only, the index can be rebuilt with the geocode index-update subcommand with the --languages option to return place names in multiple languages (with support for 254 languages).

qsv has ~2,000 tests in the tests directory. Each command has its own test suite in a separate file with the convention test_<COMMAND>.rs. Apart from preventing regressions, the tests also serve as good illustrative examples, and are often linked from the usage text of each corresponding command.

To test each binary variant:

# to test qsv
cargo test --features all_features

# to test qsvlite
cargo test --features lite
# to test all tests with "stats" in the name with qsvlite
cargo test stats --features lite

# to test qsvdp
cargo test --features datapusher_plus

# to test a specific command
# here we test only stats and use the
# t alias for test and the -F shortcut for --features
cargo t stats -F all_features

# to test a specific command with a specific feature
# here we test only luau command with the luau feature
cargo t luau -F feature_capable,luau

# to test the count command with multiple features
# we use "test_count" as we don't want to run other tests
# that have "count" in the testname - e.g. test_geocode_countryinfo
cargo t test_count -F feature_capable,luau,polars

# to test using an alternate allocator
# other than the default mimalloc allocator
cargo t --no-default-features -F all_features,jemallocator

Dual-licensed under MIT or the UNLICENSE.

qsv is a fork of the popular xsv utility, merging several pending PRs since xsv 0.13.0's May 2018 release. On top of xsv's 20 commands, it adds numerous new features; 45 additional commands; 4 apply subcommands & 38 operations; 4 to subcommands; 3 cat subcommands; 7 geocode subcommands & 4 index operations; and 4 snappy subcommands. See FAQ for more details.

qsv was made possible by
Standards-based, best-of-breed, open source solutions to make your Data Useful, Usable & Used.

This project is unrelated to Intel's Quick Sync Video.