Distributed indexing is a technique used to build and maintain an index of information across multiple computers or nodes, rather than on a single machine. In a distributed system, the index is partitioned and distributed among different nodes or machines, which allows the index to be built, maintained and searched more efficiently and effectively.
The working of distributed indexing involves the following steps:
- Partitioning: The first step in distributed indexing is to partition the data that needs to be indexed. This partitioning can be done based on any criteria such as geographical location, data type, or any other logical grouping.
- Indexing: Once the data is partitioned, each node in the system builds an index for its own partition of the data. This can be done using any indexing technique such as inverted indexing, hash-based indexing, or B-tree indexing.
- Merging: The indexes built by each node are then merged into a single global index. This can be done in different ways depending on the application and the indexing technique used.
- Distribution: The global index is then distributed back to each node in the system. Each node may receive a subset of the global index or a complete copy of the index.
- Query processing: When a query is issued, it is sent to all the nodes in the system. Each node processes the query using its local index and returns the results to the query coordinator. The coordinator then merges the results from all the nodes and returns the final result to the user.
The advantage of distributed indexing is that it allows for the efficient processing of large-scale data sets by distributing the work across multiple machines. This reduces the amount of time required to build and search the index, and also increases the scalability and fault-tolerance of the system.
Dynamic indexing is a type of distributed indexing that allows for real-time indexing and querying of data as it is added or removed from the system. In dynamic indexing, documents are partitioned into smaller subsets, and each subset is indexed by a separate node in the network. As new documents are added or removed, the system automatically reorganizes the partitions and updates the indexes in real-time, allowing for immediate searchability of the newly added documents. By using dynamic distributed indexing, it is possible to create a highly scalable and efficient system for indexing and searching large collections of documents. This approach allows for real-time updates and immediate searchability of new documents, making it ideal for applications such as search engines, e-commerce sites, and social media platforms.
- Partitioning: The documents are partitioned into smaller subsets, and each subset is assigned to a separate node in the network.
- Indexing: Each node indexes the documents in its subset, creating a local index for that subset.
- Merging: The local indexes are merged periodically to create a global index that includes all the documents in the collection.
- Querying: Queries are sent to the nodes in the network, and each node returns a list of documents that match the query. These results are combined to create a final list of matching documents.
- Updating: When new documents are added or removed from the collection, the system automatically reorganizes the partitions and updates the indexes in real-time.
Distributed indexing based on term-partitioned indexing is a technique used to distribute the indexing workload across multiple machines or nodes in a distributed system. In this approach, the index is partitioned into smaller subsets called term partitions. Each term partition is stored on a separate machine or node in the distributed system. The basic idea behind term-partitioned indexing is to divide the document collection into smaller subsets and create a local index for each subset. A global index is then created by merging the local indexes of each subset. The local indexes are updated incrementally as new documents are added to the document collection. This approach provides scalability and improves the indexing and search performance of the system.
- The document collection is divided into subsets based on some partitioning scheme. For example, a simple scheme could be to divide the documents based on their document ID or some other metadata.
- Each subset of documents is indexed separately using a local index. The local index is stored on a separate machine or node in the distributed system. The local index contains a subset of the terms in the document collection and the frequency of occurrence of each term in the subset of documents.
- Once all the local indexes are created, they are merged to create a global index. The global index contains all the terms in the document collection and the frequency of occurrence of each term in the entire document collection.
- The local indexes are updated incrementally as new documents are added to the document collection. When a new document is added, it is assigned to a subset based on the partitioning scheme used. The local index for the subset is updated with the terms and their frequency of occurrence in the new document. The global index is then updated by merging the updated local indexes.
- When a query is issued, it is broadcast to all the machines or nodes in the distributed system. Each machine or node searches its local index for the query terms and returns the relevant documents. The search results are then merged to provide a global set of search results.
By dividing the indexing workload into smaller subsets and distributing them across multiple machines or nodes, distributed indexing based on term-partitioned indexing provides a scalable solution for indexing and searching large document collections. It also provides fault tolerance and high availability, as the failure of a single machine or node does not affect the entire system.
Distributed indexing is a technique that allows indexing and searching of data across multiple machines or nodes. This technique is widely used in large-scale search engines, as it allows the system to handle a large amount of data and traffic.
There are several approaches to implement distributed indexing, and two commonly used techniques are dynamic indexing and term-partitioned indexing. In this code, we can see the implementation of these techniques in two separate classes: dynamicIndex and termPartitionedIndex.
Dynamic indexing is a technique where the index is updated dynamically as new documents are added to the system. In other words, the index is built on the fly, and it is not necessary to rebuild the entire index each time a new document is added. In this code, the dynamicIndex class implements dynamic indexing.
The dynamicIndex class has two main functions, addDocument() and search(). The addDocument() function adds a new document to the system and updates the inverted index accordingly. The inverted index is a data structure that stores the mapping between terms and the documents that contain them. In this implementation, the inverted index is implemented as a dictionary where each term is mapped to a set of document IDs. When a new document is added, the addDocument() function splits the document text into individual terms and adds the document ID to the corresponding set in the inverted index.
The search() function takes a query as input and returns a list of relevant documents sorted by their relevance score. The relevance score is calculated by counting the number of query terms that appear in each document. To find the relevant documents, the search() function looks up each query term in the inverted index and retrieves the set of documents that contain the term. The function then takes the union of these sets to get a set of relevant documents. Finally, the function calculates the relevance score for each relevant document and returns a list of documents sorted by their relevance scores.
Term-partitioned indexing is a technique where the index is partitioned into multiple parts based on the terms that occur in the documents. In this technique, the documents are distributed across multiple machines based on the terms they contain. Each machine is responsible for indexing and searching the documents that contain a specific subset of terms. In this code, the termPartitionedIndex class implements term-partitioned indexing.
The termPartitionedIndex class has three main functions, __init__(), addDocument(), and search(). The __init__() function initializes the class and creates the inverted index. The inverted index is implemented as a list of dictionaries, where each dictionary represents the inverted index for a subset of terms. The number of dictionaries in the list is equal to the number of machines used in the system.
The addDocument() function adds a new document to the system and updates the inverted index accordingly. To determine which subset of terms the document belongs to, the function hashes each term and uses the hash value to determine the corresponding subset of terms. The document is then added to the inverted index for that subset of terms.
The search() function takes a query as input and returns a list of relevant documents sorted by their relevance score. The function first determines which subset of terms the query belongs to by hashing each query term and using the hash value to determine the corresponding subset of terms. The function then retrieves the set of relevant documents from the inverted index for that subset of terms. Finally, the function calculates the relevance score for each relevant document and returns a list of documents sorted by their relevance scores.
NOZ is a text searching app that implements two types of indexing, dynamic indexing and term-partitioned indexing. With a graphical user interface (GUI), users can scan through text documents by selecting a few documents or scanning an entire directory for text documents. NOZ then searches for a query in these documents and returns the documents in order of relevance. Users can ask for more than one query at a time.
- One of the key features of NOZ is its ability to implement two types of indexing. This is important because different types of indexing work better for different types of search scenarios. For instance, dynamic indexing works best for small to medium-sized datasets while term-partitioned indexing is more suited for larger datasets. By having both types of indexing available, NOZ can handle a wide range of search queries.
- Another important feature of NOZ is its GUI. The GUI makes it easy for users to navigate through the app and search for documents. Users can select individual documents or entire directories for scanning. Once the search is complete, NOZ returns the documents in order of relevance, making it easier for users to find the information they need.
- In addition to the GUI, NOZ has other user-friendly features. For instance, it allows users to search for multiple queries at once. This is particularly useful for users who need to find information across a range of different topics. NOZ also provides users with the ability to sort the search results based on various criteria, such as relevance or date.
NOZ is a powerful text searching app that implements two types of indexing and provides users with a range of user-friendly features. With its GUI, ability to search through different types of documents, and security features, NOZ is a useful tool for anyone who needs to search for information quickly and efficiently.
- os
- re
- sys
- time
- tkinter
- webbrowser
- PyQt6
Run the following commands in your Python terminal to install the mentioned modules
pip install PyQt6pip install repip install tkinterpip install webbrowser
Rest of the modules should come pre-installed. Make sure to store all the files you downloaded in the same folder as available on GitHub. Proceed by running NOZ.py.