Fehrist – Document Indexing Library in Go


TLDR:  Go to Github repo if you’re not within the internals of the lib._

Right now I current you one other library I made in Go language, referred to as, Fehrist

From the Github README:

Fehrist is a pure Go library for indexing various kinds of paperwork. At present, it helps solely CSV and JSON however versatile structure offers you the freedom so as to add extra paperwork. Fehrist(فہرست) is an Urdu phrase for Index. Related terminologies utilized in Arabic(فھرس) and Farsi(فہرست) as nicely.

Fehrist relies on an Inverted Index knowledge construction for indexing functions.

Why did I make it?

It appears I’ve fallen in love with Golang after Python. Go is an opinionated language that doesn’t allow you to get distracted in varied small choices. The rationale for making this explicit lib is nothing however studying about indexing; the way it works and what algorithms can be found. I picked the Inverted Index attributable to its flexibility and comparatively simpler applied than others like B+Bushes. I additionally took inspiration from ElasticSearch for writing and arranging index recordsdata on disk.

The way it Works?

As I discussed that it’s primarily based on an inverted index. All recordsdata, no matter their sort are tokenized after the primary stage the place a DOCID is assigned to every file. A file is a single entry within the case of CSV, JSON, or XML file. After the task of DOCID, it’s then tokenized the place every time period within the file is mapped corresponding DOCID and the file identify. The profitable listed doc(s) can then be searched by offering a key phrase. The output of the search result’s a JSON construction. Golang maps knowledge construction has been used for intermediate knowledge processing and looking. Under is the diagram that may assist you to know all the strategy of indexing.

Fehrist Indexing

In step one, the CSV file was fed to the system which was then break up record-wise and mapped with a DOCID. Within the second step, every file was tokenized into every time period in a manner that it was mapped with the rely of incidence. As an illustration, Jhon was present in paperwork 1.csv and 2.csv, it should create a pipe-delimited construction which is then assigned as a person entry of the map in opposition to its corresponding key. As you’ll be able to within the diagram above, john was present in DOCIDs docid1 and docid2. Go maps have been used for the aim.

Lastly, the information is saved into the disk. I stole the concept from ElasticSearch for the information administration on disk:

As you’ll be able to within the image above, every index file is saved as .idx file. At first it created a folder of the identify you had offered. All related recordsdata are then saved in it(Thanks ElasticSearch for giving me this concept). You might be additionally seeing recordsdata with extension .doc which is definitely initially entries together with their DOCIds. All knowledge is then serialized with the assistance of MessagePack. Under is the code that’s indexing CSV recordsdata.

The library is versatile. Due to polymorphism offered by Go, you’ll be able to prolong this library very simply by implementing a few strategies.

Looking is then simple:

The Search technique takes an index identify as a parameter as a result of a folder was created of the identical identify and all recordsdata have been saved in it. Init() technique was used to drag all the information from paperwork and indices in maps.

As I stated it’s a versatile lib. All you’ll want to add your individual doc sort struct similar to I’ve accomplished for JSON and CSV.


I hope you want this publish and can use this library in your subsequent initiatives. There are some things that it doesn’t cowl like full search. It additionally doesn’t apply stemming and cease phrases both. For now, it returns knowledge primarily based on the precise key phrase discovered. You might be free to reinforce it by forking it. You’ll be able to obtain this lib from Github.

Header Photograph by Maksym Kaharlytskyi on Unsplash

This publish was initially printed here.


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