ArangoSearch vs Solr

Overview

Solr

Stacks805

Followers644

Votes126

ArangoSearch

Stacks7

Followers6

Votes0

ArangoSearch vs Solr: What are the differences?

<Write Introduction here>

Scalability: ArangoSearch is tightly integrated into ArangoDB, enabling it to take advantage of its distributed nature for scalability, while Solr requires the use of external components for distributed operations.
Data Model: ArangoSearch supports a schema-less data model, allowing for flexible and dynamic indexing of data, whereas Solr requires a predefined schema for indexing documents.
Query Language: ArangoSearch leverages the AQL query language for searching and querying data, providing a unified way of querying both document and search indexes, while Solr uses a different query syntax based on Lucene query syntax.
Multi-Model Capabilities: ArangoSearch is part of a multi-model database (ArangoDB) allowing for seamless integration of full-text search with graph and document data models, while Solr is primarily focused on full-text search capabilities.
Transactions: ArangoDB provides multi-document transactions allowing for atomic operations across different data models, including ArangoSearch indexes, while Solr lacks built-in support for transactions.
Customization: ArangoSearch allows for custom analyzers and text processing pipelines to be defined for specific use cases, providing a high level of customization that may not be as straightforward in Solr.

In Summary, ArangoSearch and Solr differ in terms of scalability, data model, query language, multi-model capabilities, transactions, and customization options.

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CLI (Node.js)

Manual

Detailed Comparison

Solr	ArangoSearch
Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.	It is a C++ based full-text search engine including similarity ranking capabilities natively integrated into ArangoDB. It allows users to combine two information retrieval techniques: boolean and generalized ranking retrieval. Search results “approved” by the boolean model can be ranked by relevance to the respective query using the Vector Space Model in conjunction with BM25 or TFIDF weighting schemes.
Advanced full-text search capabilities; Optimized for high volume web traffic; Standards-based open interfaces - XML, JSON and HTTP; Comprehensive HTML administration interfaces; Server statistics exposed over JMX for monitoring; Linearly scalable, auto index replication, auto-failover and recovery; Near real-time indexing; Flexible and adaptable with XML configuration; Extensible plugin architecture	Complex Searches with Boolean Operators; Relevance-Based Matching; Phrase and Prefix Matching; Relevance Tuning on Query-Time; Full combinability of search queries with all supported data models & access patterns; Scalability
Statistics
Stacks 805	Stacks 7
Followers 644	Followers 6
Votes 126	Votes 0
Pros & Cons
Pros 35 Powerful 22 Indexing and searching 20 Scalable 19 Customizable 13 Enterprise Ready	No community feedback yet
Integrations
Lucene	ArangoDB

What are some alternatives to Solr, ArangoSearch?

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Sphinx

It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.

MkDocs

It builds completely static HTML sites that you can host on GitHub pages, Amazon S3, or anywhere else you choose. There's a stack of good looking themes available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes.

Dejavu

dejaVu fits the unmet need of being a hackable data browser for Elasticsearch. Existing browsers were either built with a legacy UI and had a lacking user experience or used server side rendering (I am looking at you, Kibana).

Elassandra

Elassandra is a fork of Elasticsearch modified to run on top of Apache Cassandra in a scalable and resilient peer-to-peer architecture. Elasticsearch code is embedded in Cassanda nodes providing advanced search features on Cassandra tables and Cassandra serve as an Elasticsearch data and configuration store.

Tantivy

It is a full-text search engine library inspired by Apache Lucene and written in Rust. It is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.

Lucene

Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.

Jina

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

Google

Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for.

YugabyteDB

An open-source, high-performance, distributed SQL database built for resilience and scale. Re-uses the upper half of PostgreSQL to offer advanced RDBMS features, architected to be fully distributed like Google Spanner.

Related Comparisons

ArangoSearch vs Solr: What are the differences?

<Write Introduction here>

Scalability: ArangoSearch is tightly integrated into ArangoDB, enabling it to take advantage of its distributed nature for scalability, while Solr requires the use of external components for distributed operations.
Data Model: ArangoSearch supports a schema-less data model, allowing for flexible and dynamic indexing of data, whereas Solr requires a predefined schema for indexing documents.
Query Language: ArangoSearch leverages the AQL query language for searching and querying data, providing a unified way of querying both document and search indexes, while Solr uses a different query syntax based on Lucene query syntax.
Multi-Model Capabilities: ArangoSearch is part of a multi-model database (ArangoDB) allowing for seamless integration of full-text search with graph and document data models, while Solr is primarily focused on full-text search capabilities.
Transactions: ArangoDB provides multi-document transactions allowing for atomic operations across different data models, including ArangoSearch indexes, while Solr lacks built-in support for transactions.
Customization: ArangoSearch allows for custom analyzers and text processing pipelines to be defined for specific use cases, providing a high level of customization that may not be as straightforward in Solr.

In Summary, ArangoSearch and Solr differ in terms of scalability, data model, query language, multi-model capabilities, transactions, and customization options.

ArangoSearch vs Solr

Overview