Introduction
In the ever-evolving world of search engines, Elasticsearch, OpenSearch, and Solr have long held the spotlight. However, there are several smaller search platforms that pack a punch and offer compelling alternatives. In this article, we will explore 11 small search platforms, delving into their major features, pros, and cons. Whether you’re seeking lightning-fast performance, advanced customization options, or seamless integration, this comprehensive list will help you optimize your search experience. Join us as we discover the hidden gems in the search engine landscape.
Looking for a one-stop-shop solution? Sematext provides services for Elasticsearch, OpenSearch, and Solr with their entire ecosystem. And if you are curious how these three dominant search engines stack up against each other, we’ve done these comparisons:
Speaking of OpenSearch vs. Elasticsearch, check out this short video comparing the two:
Alternatives to Elasticsearch, OpenSearch, and Solr
1. Algolia
Algolia is a robust and scalable cloud SaaS designed to deliver lightning-fast search performance. This is one of the more forward-looking platforms with respect to AI features than other platforms. Sweetspot would be as a fast catalog style search on smaller data sets.
License SaaS
Pros
- Provides Neural Hashing, a more efficient way to do vector-style similarity search, often used in data science. In simpler use-cases this may be employed in providing suggestions identifying similar question or query phrasing;
- Drop-in Javascript library for autocomplete;
- Frontend widget building blocks for various platforms (React, Vue, Angular, Android, Flutter);
- Geolocation-based search with filtering and ranking;
- Learning-to-Rank;
- Search analytics provided.
Cons
- Pricing can be relatively higher for large-scale or advanced use;
- Per plan limits on maximum index sizing, records size may impact your use case.
2. MeiliSearch
MeiliSearch is an open-source search API inspired by the ideas and algorithms used in Algolia. It supports typo tolerance, stemming, and multi-language search, ensuring accurate and relevant results. MeiliSearch’s simple setup process and developer-friendly SDKs make it a popular choice for quick integration into various applications. The sweet spot would be fast completion-style searching on smaller datasets in a catalog, rather than large, time series style paradigms.
License MIT
Pros
- Self-hosted or cloud based;
- API is specified using OpenAPI;
- RAM based for blazing-fast search performance for rapid results.
- Typo tolerance, stemming, and multi-language support for precise and accurate search results;
- Simple setup and developer-friendly SDKs for easy integration;
- Frontend integrations for Angular, Vue, React and others.
Cons
- Recommended practice is to size data to available RAM;
- Tokenization and analysis uses inbuilt functionality – requires custom development to step outside this;
- Upper limits on number of documents per index and maximum query terms may be a factor in some use cases.
3. Vespa
Vespa is a high-performance, scalable, and versatile search platform developed by Yahoo. It is one of the search engines that lean into AI for data science. It offers distributed indexing and querying capabilities, optimized for enterprise search use-cases and large-scale datasets. Vespa uses customizable ranking models and support for real-time updates. The sweet spot would be for exercising machine-learned models over large data sets.
License Apache License 2.0
Pros
- Scalable architecture to handle large-scale datasets;
- Customizable ranking models;
- Vector Search / ANN;
- Integration into Hugging Face for tokenizers;
- Customizable ranking models for personalized search experiences;
- Support for real-time updates to ensure up-to-date search results;
Cons
- Requires advanced technical expertise.
4. Xapian
Xapian is a versatile and open-source search engine library that provides classic full-text indexing and BM25-based search capabilities. With support for multiple programming languages, Xapian is highly customizable and adaptable to different environments. Its rich query syntax and extensive API support offer developers the flexibility to build robust search applications. Xapian Omega is a customisable CGI web app for searching Xapian databases. Sweet spot would be it’s use as an embedded search engine inside a backend application.
License GPL v2.1
Pros
- Efficient full-text indexing and searching for comprehensive search capabilities.
- Support for multiple programming languages for seamless integration.
- Rich query syntax and extensive API support for advanced search functionalities.
Cons
- Lacks some of the more modern features, e.g. vector data type.
5. Bleve
Bleve is an open-source search library in Go that offers classic indexing and querying capabilities across various data types. It provides advanced features like fuzzy matching, filtering options, and faceted search, enabling users to perform complex searches with ease. The sweet spot would be for simple search use cases within a non-distributed Go application alongside a database, e.g. Couchbase.
License Apache License 2.0
Pros
- Simple Go struct-based indexing (and with struct tags, JSON);
- Multi-language support;
- Console-based playground;
- Doesn’t require a JVM.
Cons
- Limited community support compared to larger platforms.
6. Sphinx
Sphinx is an open-source SQL search engine known for exceptional performance in full-text indexing and searching. It offers flexible indexing options, real-time indexing, and distributed searching capabilities. Sphinx’s support for ranking customization and seamless integration with various databases makes it a versatile choice for search-driven applications. The sweet spot would be alongside a database to extend the search capability, and where index latency is critical.
License GPL v2
Pros
- Flexible tokenization;
- Can use MySQL shell as a playground;
- MySQL, PostgresQL, MS SQL, ODBC integrations as indexing sources;
- Distributed searching capabilities for handling large datasets;
- Customizable ranking options for tailored search experiences.
Cons
- Documentation is lacking in places.
7. Typesense
Typesense is an open-source and cloud based search platform designed for low latency and simplicity of use. It offers features like typo tolerance, filtering, and faceted search, enhancing search accuracy and relevance. Best suited where the dataset can fit in available RAM. Sweetspot would be in use as a fast enterprise search platform.
License GPL v3
Pros
- Fast RAM-based search engine for quick search results;
- Change event based synchronization;
- HA clustering with client-side load balancing and rolling upgrades;
- Multi-tenancy with field-level security;
- Single Sign On;
- Vector / ANN searching;
- Doesn’t require a JVM.
Cons
- Limited to built-in tokenizer;
- Multi-language support
- No support for dynamic synonym suggestions or stop words;
- Search analytics provided by external products.
8. Manticore Search
Manticore Search is a full-text search platform originally forked from Sphinx Search. Known for its real-time indexing and distributed searching capabilities, it offers features like geo-search, highlighting, and query suggestions. Manticore Search’s seamless integration with popular databases and its scalability make it a reliable choice for search-driven applications. Sweetspot would be high volume situations
License GPL v2
Pros
- Both JSON and SQL interfaces;
- MySQL, PostgresQL, MS SQL, ODBC integrations as indexing sources;
- Real-time indexing and distributed searching for up-to-date and efficient search results;
- Flexible tokenization;
- Features like geo-search, highlighted snippet generation, and query suggestions and percolation;
- Many languages supported;
- HA and horizontal scaling;
- JSON/REST interface is specified using OpenAPI enabling client generation.
Cons
- JSON interface limited to search and data modification.
9. Tantivy
Tantivy is an open-source search engine inspired by Lucene and built as a Rust library, with bindings available for use in Python and Ruby. It offers fairly standard full-text indexing and querying capabilities. Tantivy’s low memory footprint and easy integration into Rust applications make it fairly easy to slot into search-driven projects.
Sweetspot would be either as a small embedded full text search inside an app. Alternatively, if ambitious, a scaling management layer could be built on top of Tantivy. In general terms this is equivalent to Elasticsearch forming a distributed management layer over Lucene. Quickwit, Lnx are examples of these types of projects.
License MIT
Pros
- Comparatively faster then other;
- Low memory footprint for optimized resource usage;
- Seamless integration with Rust applications;
- Doesn’t need a JVM.
Cons
- A field can only be indexed one way;
- Limited language support in tokenizers compared to some other search platforms.
- Bespoke tokenizers require development effort.
10. Swirl
Swirl is a enterprise-grade metasearch engine. It adapts queries for each source, sends them out via API integration, asynchronously gathers the results then uses a large language model (LLM) to contextually re-rank the results. It requires no copying, extracting, ingesting, indexing or re-entitling of any content. Swirl is available under the Apache 2.0 license or from Swirl Corporation which provides support, hosting and solutions built on the open source platform.
License FOSS/SaaS/Deployed
Pros
- No copying/indexing of data;
- Uses existing security entitlements including credentials, tokens and OAUTH2;
- Zero-code configuration of connectors, includes Atlassian, Elastic, GitHub, Google BigQuery, HubSpot, Microsoft 365, Miro.com, NLResearch, OpenSearch, PostgreSQL;
- Includes library of processors including date and duplicate detection, entity extraction;
- Open source with commercial support;
- Galaxy Search UI included.
Cons
- No deep faceting;
- Never faster than slowest source.
11. RediSearch
Redisearch is a Redis module that provides full text and geo searching on top of Redis database. Along with the fairly ubiquitous search features it also includes vector searching with KNN similarity. Horizontal scaling is achieved through use of Redis Cluster, resulting in a copy of the dataset synced to each node. Sweetspot would be as a redundant/HA fast search database with a complete copy of your data on each node.
License BSD
Pros
- Comparatively faster than other search databases;
- The benefits of a database with the depth of a search engine;
- Clients for many languages;
- Bulk Indexing;
- Doesn’t need JVM;
- Lots of Redis related tools e.g. visualizers, instrumentation;
- Vector / KNN similarity.
Cons
- Static in-built tokenizer;
- Redis Cluster does not support NAT’d addressing in containers;
- Limited language support in tokenizers compared to some other search platforms;
- Distributed search in Redis Cluster requires that OSS RediSearch Coordinator be compiled in.
Conclusion
In the world of search engines, smaller platforms can offer impressive alternatives to the giants like Elasticsearch, OpenSearch, and Solr. All these alternative search engines and platforms bring their own unique features, customization options, and performance to the table. Whether you prioritize lightning-fast search results, simplicity, or seamless integration, these small search platforms have something to offer. Check out their unique features, together with Sematext: