Summary

A very efficient library for diffing two sorted streams of tabular data, such as CSV files.

Keywords

table
tabular data
CSV
TSV
diff
difference
delta
changes
comparison

Table of content

Why another lib?
Features
Points of interest
Algorithm complexity
Usage
Documentation
Development
Roadmap

Why another lib?

Most of the diffing libraries either load all the data in memory for comparison or would at least load the keys and store some hash on the data. This is fine for a lot of scenarios but it doesn't scale with huge files and puts a risk that the data would'nt fit in memory. Also, those strategies require a two-pass approach for diffing which is more expensive.

This library requires that the submitted files are already sorted by some primary key to compare the two streams in a single pass, while loading at most two rows of data in memory.

If your data is not already sorted, you can use my other lib https://github.com/livetocode/huge-csv-sorter, which can sort a huge file very efficiently thanks to SQLite.

This allows us to diff two 600MB files containing 2.6 millions of rows and 37 columns in 18 seconds on my MacBook Pro. Or two 250 MB files containing 4 millions of rows and 7 columns in 10 seconds.

Features

very fast
memory efficient
multiple input formats
multiple output formats
input files can have different column sets, in different order
input files can have different formats
compact JSON output format (field names are not repeated)
highly configurable and customizable
change stats
new and old values available

Points of interest

single pass algorithm offering O(n) performance
100% code coverage
no external dependency
small composable objects
async streams
async iterator for enumerating the changes
generic input format using an async generator function

Algorithm complexity

Assuming that n is the number of rows in the old source and m the number of rows in the new source:

Min complexity is O(max(n, m))
Max complexity is O(n+m) (when old source contains only deleted rows and new source only new rows)

The average complexity, assuming a low rate of additions or deletions, should be linear and based on the input files.

Usage

Install the library

npm i tabular-data-differ

Examples

Diff 2 CSV files on the console

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: ['id'],
}).to('console');
console.log(stats);

Diff 2 CSV files on the console when the key column is in descending order

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: [{
        name: 'id',
        sortDirection: 'DESC',
    }],
}).to('console');
console.log(stats);

Diff 2 CSV files on the console with a multi-column primary key, including a number

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: [
        'code',
        {
            name: 'version',
            comparer: 'number',
        }
    ],
}).to('console');
console.log(stats);

Diff 2 CSV files on the console with a single case insensitive primary key (using a custom comparer)

import { diff, CellValue, cellComparer, stringComparer } from 'tabular-data-differ';

function caseInsensitiveCompare((a: CellValue, b: CellValue): number {
    if (typeof a === 'string' && typeof b === 'string') {
        return stringComparer(a.toLowerCase(), b.toLowerCase());
    }
    return cellComparer(a, b);
}

const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: [
        {
            name: 'id',
            comparer: caseInsensitiveCompare,
        }
    ],
}).to('console');
console.log(stats);

Diff 2 CSV files and only get the stats

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: ['id'],
}).to('null');
console.log(stats);

Diff 2 CSV files and produce a CSV file

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: ['id'],
}).to('./temp/delta.csv');
console.log(stats);

Diff 2 CSV files and produce a CSV file using HTTP transport

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: new URL('https://some.server.org/tests/a.csv'),
    newSource: new URL('https://some.server.org/tests/b.csv'),
    keys: ['id'],
}).to(new URL('https://some.server.org/temp/delta.csv'));
console.log(stats);

Note that you can provide the username/password in the URL object if you need basic authentication.

Diff 2 CSV files and produce a JSON file

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: ['id'],
}).to({
    destination: {
        format: 'json',
        stream: './temp/delta.json',
    },
});
console.log(stats);

Diff 2 CSV files and produce a TSV file

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: ['id'],
}).to({
    destination: {
        format: 'tsv',
        stream: './temp/delta.tsv',
    }
});
console.log(stats);

Diff one CSV and one TSV and produce a JSON file

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: {
        format: 'tsv',
        stream: './tests/b.tsv',
    },
    keys: ['id'],
}).to({
    destination: {
        format: 'json',
        stream: './temp/delta.json',
    },
});
console.log(stats);

Diff two string arrays and enumerate the changes

import { diff, ArrayInputStream } from 'tabular-data-differ';
const ctx = await diff({
    oldSource: {
        format: 'csv',
        stream: new ArrayInputStream([
            'id,name',
            '1,john',
            '2,mary',
        ]),
    },
    newSource: {
        format: 'csv',
        stream: new ArrayInputStream([
            'id,name',
            '1,johnny',
            '3,sarah',
        ]),
    },
    keys: ['id'],
}).start();
console.log('columns:', ctx.columns);
const idIdx = ctx.columns.indexOf('id);
assert(idIdx >= 0, 'could not find id column');
const nameIdx = ctx.columns.indexOf('name);
assert(nameIdx >= 0, 'could not find name column');
for await (const rowDiff of ctx.diffs()) {
    if (rowDiff.status === 'modified') {
        const id = rowDiff.newRow[idIdx];
        const oldName = rowDiff.oldRow[nameIdx];
        const newName = rowDiff.newRow[nameIdx];
        if (oldName !== newName) {
            console.log('In record ', id, ', name changed from', oldName, 'to', newName);
        }
    }
}
console.log('stats:', ctx.stats);

Diff 2 CSV files on the console and ignore deleted rows

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a.csv',
    newSource: './tests/b.csv',
    keys: ['id'],
}).to({
    destination: 'console',
    filter: (rowDiff) => rowDiff.status !== 'deleted',
});
console.log(stats);

Diff 2 CSV files on the console but select only some categories of rows

import { diff } from 'tabular-data-differ';
const ctx = await diff({
    oldSource: './tests/c.csv',
    newSource: './tests/d.csv',
    keys: [
        'code',
        {
            name: 'version',
            comparer: 'number',
        }
    ],
}).start();
const catIdx = ctx.columns.indexOf('CATEGORY');
assert(catIdx >= 0, 'could not find CATEGORY column');
const stats = await ctx.to({
    destination: 'console',
    filter: (rowDiff) => ['Fruit', 'Meat'].includes(rowDiff.newRow?.[catIdx]?.toString() ?? rowDiff.oldRow?.[catIdx]?.toString() ?? ''),
});
console.log(stats);

Duplicate key handling

If your data sources contain duplicate keys, then the diffing will fail by default, but you can configure this behavior using the duplicateKeyHandling option.

You can resolve the conflict by keeping the first or last row of the duplicates:

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a2.csv',
    newSource: './tests/b2.csv',
    keys: ['id'],
    duplicateKeyHandling: 'keepFirstRow', // or 'keepLastRow'
}).to('console');
console.log(stats);

Or, if you need more control in the row selection, then you can provide your own handler:

import { diff } from 'tabular-data-differ';
const stats = await diff({
    oldSource: './tests/a2.csv',
    newSource: './tests/b2.csv',
    keys: ['id'],
    duplicateKeyHandling: (rows) => rows[0], // same as 'keepFirstRow'
    duplicateRowBufferSize: 2000,
}).to('null');
console.log(stats);

Note that you can specify the size of the buffer if you know that it cannot exceed this quantity, otherwise you can enable the duplicateRowBufferOverflow option, which will remove the first entries when it exceeds the allocated capacity, to avoid any failure.

Finally, you can inspect the source stats to check the duplication metrics:

import { diff } from 'tabular-data-differ';
const ctx = await diff({
    oldSource: './tests/a2.csv',
    newSource: './tests/b2.csv',
    keys: ['id'],
    duplicateKeyHandling: 'keepFirstRow', // or 'keepLastRow'
}).start();
const stats = await ctx.to('null');
console.log(stats);
console.log(ctx.oldStats);
console.log(ctx.newStats);

Order 2 CSV files and diff them on the console

Don't forget to install first my other lib: npm i huge-csv-sorter.

import { diff } from 'tabular-data-differ';
import { sort } from 'huge-csv-sorter';

await sort({
    source: './tests/a.csv',
    destination: './tests/a.sorted.csv',
    orderBy: ['id'],
});

await sort({
    source: './tests/b.csv',
    destination: './tests/b.sorted.csv',
    orderBy: ['id'],
});

const stats = await diff({
    oldSource: './tests/a.sorted.csv',
    newSource: './tests/b.sorted.csv',
    keys: ['id'],
}).to('console');
console.log(stats);

Auto-correct unordered CSV files and retry diff

Don't forget to install first my other lib: npm i huge-csv-sorter.

import { diff } from 'tabular-data-differ';
import { sort } from 'huge-csv-sorter';

try {
    // try diff
    const stats = await diff({
        oldSource: './tests/a.csv',
        newSource: './tests/b.csv',
        keys: ['id'],
    }).to('./tests/diff.csv');
    console.log(stats);
} catch(err) {
    // catch unordered exception
    if (err instanceof UnorderedStreamsError) {
        // sort files
        await sort({
            source: './tests/a.csv',
            destination: './tests/a.sorted.csv',
            orderBy: ['id'],
        });

        await sort({
            source: './tests/b.csv',
            destination: './tests/b.sorted.csv',
            orderBy: ['id'],
        });
        // retry diff
        const stats = await diff({
            oldSource: './tests/a.sorted.csv',
            newSource: './tests/b.sorted.csv',
            keys: ['id'],
        }).to('./tests/diff.csv');
        console.log(stats);
    } else {
        throw err;
    }
} finally {
    // delete sorted files...
}

async iterable source

You can easily plug any kind of data source by leveraging nodejs async generator functions, which would allow you to fetch the data from a database or from a REST API endpoint!

Here's a simplistic example:

import { diff } from 'tabular-data-differ';

const stats = await diff({
    oldSource: {
        format: 'iterable',
        provider: someAsyncSource,
    },
    newSource: {
        format: 'iterable',
        provider: () => someAsyncSource(2),
    },
    keys: ['id'],
}).to('./output/files/output.csv');
console.log(stats);


async function *someAsyncSource(limit?: number) {
    let items = [
        {
            id: 1,
            name: 'John',
            age: 33,
        },
        {
            id: 2,
            name: 'Mary',
            age: 22,
        },
        {
            id: 3,
            name: 'Cindy',
            age: 44,
        },
    ];  
    if (limit !== undefined){
        items = items.slice(0, limit);
    }
    for (const item of items) {
        yield item;
    }
}

Documentation

API
File formats

API

Source options

CSV

Name	Required	Default value	Description
format	yes		You must specify 'csv' to select the CSV format
stream	yes		either a string filename, a URL or an instance of an InputStream (like FileInputStream).
delimiter	no	,	the char used to delimit fields within a row.

TSV

Name	Required	Default value	Description
format	yes		You must specify 'tsv' to select the TSV format
stream	yes		either a string filename, a URL or an instance of an InputStream (like FileInputStream).
delimiter	no	\t	the char used to delimit fields within a row.

JSON

Name	Required	Default value	Description
format	yes		You must specify 'json' to select the JSON format
stream	yes		either a string filename, a URL or an instance of an InputStream (like FileInputStream).

Iterable

Name	Required	Default value	Description
format	yes		You must specify 'iterable' to select the Iterable format
provider	yes		a function that must return an instance of an async iterable object (see Async generator functions)

See Example

Custom

Name	Required	Default value	Description
format	yes		You must specify 'custom' to select the custom format
reader	yes		an instance of a FormatReader

Destination options

CSV

Name	Required	Default value	Description
format	yes		You must specify 'csv' to select the CSV format
stream	yes		either a string filename, a URL or an instance of an OutputStream (like FileOutputStream).
delimiter	no	,	the char used to delimit fields within a row.
keepOldValues	no	false	specifies if the destination should contain both the old and new values for each row.

TSV

Name	Required	Default value	Description
format	yes		You must specify 'tsv' to select the TSV format
stream	yes		either a string filename, a URL or an instance of an OutputStream (like FileOutputStream).
delimiter	no	\t	the char used to delimit fields within a row.
keepOldValues	no	false	specifies if the destination should contain both the old and new values for each row.

JSON

Name	Required	Default value	Description
format	yes		You must specify 'json' to select the JSON format
stream	yes		either a string filename, a URL or an instance of an OutputStream (like FileOutputStream).
keepOldValues	no	false	specifies if the destination should contain both the old and new values for each row.

Custom

Name	Required	Default value	Description
format	yes		You must specify 'custom' to select the custom format
writer	yes		an instance of a FormatWriter

OutputOptions

Name	Required	Default value	Description
destination	yes		either a standard output (console, null), a string filename, a URL or an instance of an InputStream (like FileInputStream).
filter	no		a filter to select which changes should be sent to the output stream.
keepSameRows	no	false	specifies if the output should also contain the rows that haven't changed.
changeLimit	no		specifies a maximum number of differences that should be outputted.
labels	no		a dictionary of key/value pairs that can provide custom metadata to the generated file.

Key options (ColumnDefinition)

Name	Required	Default value	Description
name	yes		the name of the column.
comparer	no	string	either a standard comparer ('string' or 'number') or a custom comparer.
sortDirection	no	ASC	specifies if the column is sorted in ascending (ASC) or descending (DESC) order.

Differ options

Name	Required	Default value	Description
oldSource	yes		either a string filename, a URL or a SourceOptions
newSource	yes		either a string filename, a URL or a SourceOptions
keys	yes		the list of columns that form the primary key. This is required for comparing the rows. A key can be a string name or a {ColumnDefinition}
includedColumns	no		the list of columns to keep from the input sources. If not specified, all columns are selected.
excludedColumns	no		the list of columns to exclude from the input sources.
rowComparer	no		specifies a custom row comparer.
duplicateKeyHandling	no	fail	specifies how to handle duplicate rows in a source. It will fail by default and throw a UniqueKeyViolationError exception. But you can ignore, keep the first or last row, or even provide your own function that will receive the duplicates and select the best candidate.
duplicateRowBufferSize	no	1000	specifies the maximum size of the buffer used to accumulate duplicate rows.
duplicateRowBufferOverflow	no	false	specifies if we can remove the first entries of the buffer to continue adding new duplicate entries when reaching maximum capacity, to avoir throwing an error and halting the process.

diff function

Creates a Differ object from the submitted DifferOptions.

Differ methods

start

returns a new DifferContext object with the input streams open and columns initialized.

You must call start to get an iterator (DifferContext.diffs) or if you need the columns prior to sending the diffs to the output with the "to" method.

to

Initiates the comparison between the old and new sources and sends the diffs to the specified output.

This returns the change stats once completed.

The options parameter can be either a standard output (console, null), a string filename, a URL or an OutputOptions.

Note that it can throw the UnorderedStreamsError exception if it detects that the streams are not properly ordered by the specified keys. Note that it can throw the UniqueKeyViolationError exception if it detects that a stream has duplicate keys which violates the primary keys specified in the options.

DifferContext methods

close

Closes all open streams.

Note that the methods "to" or "diffs" will automatically close the streams.

columns

Returns the current column names.

stats

Returns the currents stats.

oldSourceStats

Returns the stats accumulated while parsing the old source.

newSourceStats

Returns the stats accumulated while parsing the new source.

to

Initiates the comparison between the old and new sources and sends the diffs to the specified output.

This returns the change stats once completed.

The options parameter can be either a standard output (console, null), a string filename, a URL or an OutputOptions.

Note that it can throw the UnorderedStreamsError exception if it detects that the streams are not properly ordered by the specified keys. Note that it can throw the UniqueKeyViolationError exception if it detects that a stream has duplicate keys which violates the primary keys specified in the options.

diffs

Enumerates the differences between the old and new sources.

Note that it can throw the UnorderedStreamsError exception if it detects that the streams are not properly ordered by the specified keys. Note that it can throw the UniqueKeyViolationError exception if it detects that a stream has duplicate keys which violates the primary keys specified in the options.

JSON input format

This library implements a simplistic JSON parser with a couple of assumptions:

each JSON object should be saved on a distinct line
the JSON file should only contain an array of objects
each object should be flat (no nested JSON objects)
all objects should share the same properties
the lines can be indented
each object can have either a preceding or a trailing comma
the array start ([) and end (]) can be inlined with the first/last object or their own separate line

Examples

[
    {"id": "01","a":"a1","b":"b1","c":"c1"},
    {"id": "02","a":"a2","b":"b2","c":"c2"},
    {"id": "03","a":"a3","b":"b3","c":"c3"}
]

[{"id": "01","a":"a1","b":"b1","c":"c1"},
{"id": "02","a":"a2","b":"b2","c":"c2"},
{"id": "03","a":"a3","b":"b3","c":"c3"}]

[
    {"id": "01","a":"a1","b":"b1","c":"c1"}
    ,{"id": "02","a":"a2","b":"b2","c":"c2"}
    ,{"id": "03","a":"a3","b":"b3","c":"c3"}
]

File formats

CSV output format

This is a standard CSV format, using the specified character for delimiting fields or the default one (comma).

Note that there is an additional column named DIFF_STATUS that will tell if the row was added, deleted, modified.

DIFF_STATUS,id,a,b,c
deleted,01,a1,b1,c1
modified,04,aa4,bb4,cc4
deleted,05,a5,b5,c5
deleted,06,a6,b6,c6
added,10,a10,b10,c10
added,11,a11,b11,c11

Note that if you set the "OutputOptions.keepOldValues" property to true, you'll get additional columns prefixed by 'OLD_':

DIFF_STATUS,id,a,b,c,OLD_id,OLD_a,OLD_b,OLD_c
deleted,,,,,01,a1,b1,c1
modified,04,aa4,bb4,cc4,04,a4,b4,c4
deleted,,,,,05,a5,b5,c5
deleted,,,,,06,a6,b6,c6
added,10,a10,b10,c10,,,,
added,11,a11,b11,c11,,,,

JSON output format

The schema is made of 3 parts:

the header
the items
the footer

{
    "header": {},
    "items": [...],
    "footer": {}
}

Header

The header contains a mandatory list of columns and an optional dictionary of key/value pairs named labels.

{
    "columns": ["col1", "col2", "col3"]
}

or

{
    "columns": ["col1", "col2", "col3"],
    "labels": {
        "key1": "val1",
        "key2": "val2",
    }
}

Items

A list of RowDiff objects, which can have two distinct layouts based on the "OutputOptions.keepOldValues" property.

keepOldValues is false or undefined

{"status":"deleted","data":["01","a1","b1","c1"]},
{"status":"same","data":["02","a2","b2","c2"]},
{"status":"modified","data":["04","aa4","bb4","cc4"]},
{"status":"added","data":["10","a10","b10","c10"]},

keepOldValues is true

{"status":"deleted","old":["01","a1","b1","c1"]},
{"status":"same","new":["02","a2","b2","c2"],"old":["02","a2","b2","c2"]},
{"status":"modified","new":["04","aa4","bb4","cc4"],"old":["04","a4","b4","c4"]},
{"status":"added","new":["10","a10","b10","c10"]},

Footer

The footer will simply contain a stats section summarizing the types of changes in the file.

{
    "stats" : {
        "totalComparisons": 11,
        "totalChanges": 6,
        "changePercent": 54.55,
        "added": 2,
        "deleted": 3,
        "modified": 1,
        "same": 5
    }
}

Development

Install

git clone [email protected]:livetocode/tabular-data-differ.git
cd tabular-data-differ
npm i

Tests

Tests are implemented with Jest and can be run with: npm t

You can also look at the coverage with: npm run show-coverage

Roadmap

If you manifest some interest in this project, we could add new streams:

S3, allowing you to use an external storage capacity such as AWS S3
HTTP, allowing you to provide custom headers for authentication

And we could add more formats:

XML
protobuff
SQL, allowing you to diff two database tables between two separate databases

But with the async iterable sources feature, you should be able to easily plug any kind of data source you need!

Name		Name	Last commit message	Last commit date
Latest commit History 41 Commits
.github/workflows		.github/workflows
src		src
tests		tests
.gitignore		.gitignore
README.md		README.md
jest.config.cjs		jest.config.cjs
package-lock.json		package-lock.json
package.json		package.json
tsconfig.json		tsconfig.json

livetocode/tabular-data-differ

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Summary

Keywords

Table of content

Why another lib?

Features

Points of interest

Algorithm complexity

Usage

Install the library

Examples

Diff 2 CSV files on the console

Diff 2 CSV files on the console when the key column is in descending order

Diff 2 CSV files on the console with a multi-column primary key, including a number

Diff 2 CSV files on the console with a single case insensitive primary key (using a custom comparer)

Diff 2 CSV files and only get the stats

Diff 2 CSV files and produce a CSV file

Diff 2 CSV files and produce a CSV file using HTTP transport

Diff 2 CSV files and produce a JSON file

Diff 2 CSV files and produce a TSV file

Diff one CSV and one TSV and produce a JSON file

Diff two string arrays and enumerate the changes

Diff 2 CSV files on the console and ignore deleted rows

Diff 2 CSV files on the console but select only some categories of rows

Duplicate key handling

Order 2 CSV files and diff them on the console

Auto-correct unordered CSV files and retry diff

async iterable source

Documentation

API

Source options

CSV

TSV

JSON

Iterable

Custom

Destination options

CSV

TSV

JSON

Custom

OutputOptions

Key options (ColumnDefinition)

Differ options

diff function

Differ methods

start

to

DifferContext methods

close

columns

stats

oldSourceStats

newSourceStats

to

diffs

JSON input format

Examples

File formats

CSV output format

JSON output format

Header

Items

keepOldValues is false or undefined

keepOldValues is true

Footer

Development

Install

Tests

Roadmap

About

Topics

Resources

Stars

Watchers

Forks

Packages