Command line utilities for tabular data files
This is a set of command line utilities for manipulating large tabular data files. Files of numeric and text data commonly found in machine learning, data mining, and similar environments. Filtering, sampling, statistical calculations and other operations are supported.
The tools were originally developed in Perl and used for day-to-day work in a large scale data mining environment. One of the tools was re-written in D as an exercise exploring the language. Significant performance gains and agreeable programmer characteristics soon led to writing additional utilities in D. Information on the D programming language is available at dlang.org.
These tools are especially useful when working with large data sets, as they generally run faster than other tools providing similar functionality, often by significant margins.
File an issue if you have problems, questions or suggestions.
In this README:
- Tool reference
- Tips and tricks
- Performance Studies (quick access: 2018 Comparative Benchmarks)
- Comparing TSV and CSV formats
- Building with Link Time Optimization (LTO) and Profile Guided Optimization (PGO)
- About the code
- Other toolkits
Talks and blog posts:
- Faster Command Line Tools in D. May 24, 2017. A blog post showing a few ways to optimize performance in command line tools. Many of the ideas in the post were identified while developing the TSV Utilities.
- Experimenting with Link Time Optimization. Dec 14, 2017. A presentation at the Silicon Valley D Meetup describing experiments using LTO based on eBay's TSV Utilities.
- Exploring D via Benchmarking of eBay's TSV Utilities. May 2, 2018. A presentation at DConf 2018 describing benchmarking studies conducted using eBay's TSV Utilities (slides here).
These tools perform data manipulation and statistical calculations on tab delimited data. They are intended for large files. Larger than ideal for loading entirely in memory in an application like R, but not so big as to necessitate moving to Hadoop or similar distributed compute environments. The features supported are useful both for standalone analysis and for preparing data for use in R, Pandas, and similar toolkits.
The tools work like traditional Unix command line utilities such as
grep, and are intended to complement these tools. Each tool is a standalone executable. They follow common Unix conventions for pipeline programs. Data is read from files or standard input, results are written to standard output. The field separator defaults to TAB, but any character can be used. Input and output is UTF-8, and all operations are Unicode ready, including regular expression match (
tsv-filter). Documentation is available for each tool by invoking it with the
--help option. TSV format is similar to CSV, see Comparing TSV and CSV formats for the differences.
Speed matters when processing large files, these tools are the fastest the author has found. See the 2018 Comparative Benchmarks Update for comparisons with similar tools.
The rest of this section contains a short description of each tool. There is more detail in the tool reference.
- tsv-filter - Filter data file rows via numeric and string comparisons.
- tsv-select - Keep a subset of columns (fields). Like
cut, but with field reordering.
- tsv-summarize - Summary statistics on selected fields, against the full data set or grouped by key.
- tsv-sample - Subsample input lines or randomize their order. Several sampling methods are available.
- tsv-join - Join lines from multiple files using fields as a key.
- tsv-uniq - Filter out duplicate lines using fields as a key.
- tsv-append - Concatenate TSV files. Header-aware; supports source file tracking.
- tsv-pretty - Print TSV data aligned for easier reading on the command-line.
- csv2tsv - Convert CSV files to TSV.
- number-lines - Number the input lines.
- keep-header - Run a shell command in a header-aware fashion.
Filters lines by making numeric and string comparisons against individual fields. Multiple tests can be specified in a single call. A variety of numeric and string comparison operators are available as well as regular expressions. Example:
$ tsv-filter --ge 3:100 --le 3:200 --str-eq 4:red file.tsv
This outputs lines where field 3 satisfies (100 <= fieldval <= 200) and field 4 matches 'red'.
tsv-filter is the most widely applicable of the tools, as dataset pruning is a common task. It is stream oriented, so it can handle arbitrarily large files. It is fast, quite a bit faster than other tools the author has tried. This makes it ideal for preparing data for applications like R and Pandas. It is also convenient for quickly answering simple questions about a dataset. For example, to count the number of records with a non-zero value in field 3, use the command:
$ tsv-filter --ne 3:0 file.tsv | wc -l
See the tsv-filter reference for details.
A version of the Unix
cut utility with the additional ability to re-order the fields. It also helps with header lines by keeping only the header from the first file (
--header option). The following command writes fields [4, 2, 9, 10, 11] from a pair of files to stdout:
$ tsv-select -f 4,2,9-11 file1.tsv file2.tsv
See the tsv-select reference for details.
tsv-summarize performs statistical calculations on fields. For example, generating the sum or median of a field's values. Calculations can be run across the entire input or can be grouped by key fields. Consider the file
color weight red 6 red 5 blue 15 red 4 blue 10
Calculations of the sum and mean of the
weight column is shown below. The first command runs calculations on all values. The second groups them by color.
$ tsv-summarize --header --sum 2 --mean 2 data.tsv weight_sum weight_mean 40 8 $ tsv-summarize --header --group-by 1 --sum 2 --mean 2 data.tsv color weight_sum weight_mean red 15 5 blue 25 12.5
Multiple fields can be used as the
--group-by key. The file's sort order does not matter, there is no need to sort in the
--group-by order first.
See the tsv-summarize reference for the list of statistical and other aggregation operations available.
tsv-sample subsamples input lines or randomizes their order. Several sampling methods are available, including simple random sampling, weighted random sampling, and distinct sampling. Simple random sampling works on any line oriented data format, weighted random sampling and distinct sampling are specific to TSV format data.
Simple random sampling operates in the customary fashion, randomly selecting lines with equal probability. When reordering a file, lines are randomly selected from the entire file and output in the order selected. In streaming mode, a subset of input lines are selected and output. This occurs in the order of the input. Streaming mode operates on arbitrary large inputs.
Weighted random sampling selects input lines in a weighted fashion, using weights from a field in the data. Lines are output in the order selected, reordering the file.
Distinct sampling selects a subset based on a key in data. Consider a query log with records consisting of <user, query, clicked-url> triples. Simple random sampling selects a random subset of all records. Distinct sampling selects all records matching a subset of values from one of fields. For example, all events for ten percent of the users. This is important for certain types of statistical analysis.
tsv-sample is designed for large data sets. Algorithms make one pass over the data, using reservoir sampling and hashing when possible to limit the memory required. By default, a new random order is generated every run, but options are available for using the same randomization order over multiple runs.
See the tsv-sample reference for further details.
Joins lines from multiple files based on a common key. One file, the 'filter' file, contains the records (lines) being matched. The other input files are scanned for matching records. Matching records are written to standard output, along with any designated fields from the filter file. In database parlance this is a hash semi-join. Example:
$ tsv-join --filter-file filter.tsv --key-fields 1,3 --append-fields 5,6 data.tsv
filter.tsv, creating a lookup table keyed on fields 1 and 3.
data.tsv is read, lines with a matching key are written to standard output with fields 5 and 6 from
filter.tsv appended. This is a form of inner-join. Outer-joins and anti-joins can also be done.
Common uses for
tsv-join are to join related datasets or to filter one dataset based on another. Filter file entries are kept in memory, this limits the ultimate size that can be handled effectively. The author has found that filter files up to about 10 million lines are processed effectively, but performance starts to degrade after that.
See the tsv-join reference for details.
Similar in spirit to the Unix
tsv-uniq filters a dataset so there is only one copy of each line.
tsv-uniq goes beyond Unix
uniq in a couple ways. First, data does not need to be sorted. Second, equivalence is based on a subset of fields rather than the full line.
tsv-uniq can also be run in an 'equivalence class identification' mode, where equivalent entries are marked with a unique id rather than being filtered. An example uniq'ing a file on fields 2 and 3:
$ tsv-uniq -f 2,3 data.tsv
tsv-uniq operates on the entire line when no fields are specified. This is a useful alternative to the traditional
sort -u or
sort | uniq paradigms for identifying unique lines in unsorted files, as it is quite a bit faster.
tsv-join, this uses an in-memory lookup table to record unique entries. This ultimately limits the data sizes that can be processed. The author has found that datasets with up to about 10 million unique entries work fine, but performance degrades after that.
See the tsv-uniq reference for details.
tsv-append concatenates multiple TSV files, similar to the Unix
cat utility. It is header-aware, writing the header from only the first file. It also supports source tracking, adding a column indicating the original file to each row.
Concatenation with header support is useful when preparing data for traditional Unix utilities like
sed or applications that read a single file.
Source tracking is useful when creating long/narrow form tabular data. This format is used by many statistics and data mining packages. (See Wide & Long Data - Stanford University or Hadley Wickham's Tidy data for more info.)
In this scenario, files have been used to capture related data sets, the difference between data sets being a condition represented by the file. For example, results from different variants of an experiment might each be recorded in their own files. Retaining the source file as an output column preserves the condition represented by the file. The source values default to the file names, but this can be customized.
See the tsv-append reference for the complete list of options available.
tsv-pretty prints TSV data in an aligned format for better readability when working on the command-line. Text columns are left aligned, numeric columns are right aligned. Floats aligned on the decimal point and precision can be specified. Header lines are detected automatically. If desired, the header line can be repeated at regular intervals. An example, first printed without formatting:
$ cat sample.tsv Color Count Ht Wt Brown 106 202.2 1.5 Canary Yellow 7 106 0.761 Chartreuse 1139 77.02 6.22 Fluorescent Orange 422 1141.7 7.921 Grey 19 140.3 1.03
tsv-pretty, using header underlining and float formatting:
$ tsv-pretty -u -f sample.tsv Color Count Ht Wt ----- ----- -- -- Brown 106 202.20 1.500 Canary Yellow 7 106.00 0.761 Chartreuse 1139 77.02 6.220 Fluorescent Orange 422 1141.70 7.921 Grey 19 140.30 1.030
See the tsv-pretty reference for details.
csv2tsv does what you expect: convert CSV data to TSV. Example:
$ csv2tsv data.csv > data.tsv
A strict delimited format like TSV has many advantages for data processing over an escape oriented format like CSV. However, CSV is a very popular data interchange format and the default export format for many database and spreadsheet programs. Converting CSV files to TSV allows them to be processed reliably by both this toolkit and standard Unix utilities like
Note that many CSV files do not use escapes, and in-fact follow a strict delimited format using comma as the delimiter. Such files can be processed reliably by this toolkit and Unix tools by specifying the delimiter character. However, when there is doubt, using a
csv2tsv converter adds reliability.
csv2tsv converter often has a second benefit: regularizing newlines. CSV files are often exported using Windows newline conventions.
csv2tsv converts all newlines to Unix format.
There are many variations of CSV file format. See the csv2tsv reference for details the format variations supported by this tool.
A simpler version of the Unix
nl program. It prepends a line number to each line read from files or standard input. This tool was written primarily as an example of a simple command line tool. The code structure it uses is the same as followed by all the other tools. Example:
$ number-lines myfile.txt
Despite it's original purpose as a code sample,
number-lines turns out to be quite convenient. It is often useful to add a unique row ID to a file, and this tool does this in a manner that maintains proper TSV formatting.
See the number-lines reference for details.
A convenience utility that runs unix commands in a header-aware fashion. It is especially useful with
sort, which puts the header line wherever it falls in the sort order. Using
keep-header, the header line retains its position as the first line. For example:
$ keep-header myfile.txt -- sort
It is also useful with
sed, similar tools, when the header line should be excluded from the command's action.
Multiple files can be provided, only the header from the first is retained. The command is executed as specified, so additional command options can be provided. See the keep-header reference for more information.
Obtaining and installation
There are several ways to obtain the tools: prebuilt binaries; building from source code; and installing using the DUB package manager. The tools have been tested on Linux and Mac OS X. They have not been tested on Windows, but there are no obvious impediments to running on Windows as well.
Prebuilt binaries are available for Linux and Mac, these can be found on the Github releases page. Download and unpack the tar.gz file. Executables are in the
bin directory. Add the
bin directory or individual tools to the
PATH environment variable. As an example, the 1.2.1 releases for Linux and MacOS can be downloaded and unpacked with these commands:
$ curl -L https://github.com/eBay/tsv-utils/releases/download/v1.2.1/tsv-utils-v1.2.1_linux-x86_64_ldc2.tar.gz | tar xz $ curl -L https://github.com/eBay/tsv-utils/releases/download/v1.2.1/tsv-utils-v1.2.1_osx-x86_64_ldc2.tar.gz | tar xz
See the Github releases page for the latest release.
For some distributions, a package can directly be installed:
Build from source files
Download a D compiler. These tools have been tested with the DMD and LDC compilers, on Mac OSX and Linux. Use DMD version 2.074.1 or later, LDC version 1.4.0 or later.
Clone this repository, select a compiler, and run
make from the top level directory:
$ git clone https://github.com/eBay/tsv-utils.git $ cd tsv-utils $ make # For LDC: make DCOMPILER=ldc2
Executables are written to
tsv-utils/bin, place this directory or the executables in the PATH. The compiler defaults to DMD, this can be changed on the make command line (e.g.
make DCOMPILER=ldc2). DMD is the reference compiler, but LDC produces faster executables. (For some tools LDC is quite a bit faster than DMD.)
The makefile supports other typical development tasks such as unit tests and code coverage reports. See Building and makefile for more details.
For fastest performance, use LDC with Link Time Optimization enabled (LTO). See Building with Link Time Optimization for instructions. The prebuilt binaries are built using LTO, but it must be explicitly enabled when building from source. Several tools are configured to be built using Profile Guided Optimization (PGO). See Building with Link Time Optimization for instructions turning this on.
Install using DUB
If you are a D user you likely use DUB, the D package manager. DUB comes packaged with DMD starting with DMD 2.072. You can install and build using DUB as follows (replace
1.1.13 with the current version):
$ dub fetch tsv-utils --cache=local $ cd tsv-utils-1.2.0/tsv-utils $ dub run # For LDC: dub run -- --compiler=ldc2
dub run command compiles all the tools. The executables are written to
tsv-utils/bin. Add this directory or individual executables to the PATH.
See Building and makefile for more information about the DUB setup.
The applications can be built with Link Time Optimization (LTO) enabled when source code is fetched by DUB. However, the DUB build system does not support this.
make must be used instead. Building with Link Time Optimization.
There are a number of simple ways to ways to improve the utility of these tools, these are listed on the Tips and tricks page. Bash aliases, Unix sort command customization, and bash completion are especially useful.