Elasticsearch vs OpenSearch

Pros of Lucene-Solr

• Mature and battle-tested search engine library with a long history
• Highly customizable and extensible architecture
• Strong community support and active development

Cons of Lucene-Solr

• Steeper learning curve compared to Elasticsearch and OpenSearch
• Less out-of-the-box features for distributed search and analytics
• Requires more manual configuration and setup

Code Comparison

Lucene-Solr

IndexWriter writer = new IndexWriter(directory, new StandardAnalyzer(), true);
Document doc = new Document();
doc.add(new Field("content", "Lucene is a powerful search library", Field.Store.YES, Field.Index.ANALYZED));
writer.addDocument(doc);
writer.close();

Elasticsearch

IndexRequest request = new IndexRequest("posts");
request.source(jsonBuilder()
    .startObject()
        .field("content", "Elasticsearch is built on top of Lucene")
    .endObject());
client.index(request, RequestOptions.DEFAULT);

OpenSearch

IndexRequest request = new IndexRequest("posts");
request.source(Map.of("content", "OpenSearch is a fork of Elasticsearch"));
client.index(request, RequestOptions.DEFAULT);

All three projects provide powerful search capabilities, but Elasticsearch and OpenSearch offer more user-friendly APIs and built-in features for distributed search and analytics. Lucene-Solr provides a lower-level library that requires more manual configuration but offers greater flexibility for custom implementations.

Pros of Vespa

• Real-time, low-latency search and serving at scale
• Built-in machine learning capabilities for ranking and recommendations
• Flexible schema-less data model with support for structured and unstructured data

Cons of Vespa

• Steeper learning curve compared to Elasticsearch and OpenSearch
• Smaller community and ecosystem of plugins/integrations
• Less extensive documentation and tutorials for beginners

Code Comparison

Elasticsearch query:

{
  "query": {
    "match": {
      "title": "search engine"
    }
  }
}

OpenSearch query (identical to Elasticsearch):

{
  "query": {
    "match": {
      "title": "search engine"
    }
  }
}

Vespa query:

select * from music where title contains "search engine";

Vespa offers a more SQL-like syntax for querying, while Elasticsearch and OpenSearch use JSON-based query DSL. Vespa's approach may be more intuitive for developers familiar with SQL, but it differs from the widely adopted Elasticsearch query format.

All three engines provide powerful search capabilities, but Vespa stands out with its real-time processing and machine learning features. However, Elasticsearch and OpenSearch benefit from larger communities and more extensive documentation, making them potentially easier to adopt for many users.

Pros of algoliasearch-client-javascript

• Lightweight and focused on client-side search functionality
• Easy integration with JavaScript applications
• Extensive documentation and examples for quick implementation

Cons of algoliasearch-client-javascript

• Limited to Algolia's hosted search service, less flexibility than self-hosted solutions
• May have higher costs for large-scale applications compared to open-source alternatives
• Less customization options for advanced search features

Code Comparison

Elasticsearch (Java):

SearchResponse response = client.search(new SearchRequest("index")
    .source(new SearchSourceBuilder()
        .query(QueryBuilders.matchQuery("field", "value"))));

OpenSearch (Java):

SearchResponse response = client.search(new SearchRequest("index")
    .source(new SearchSourceBuilder()
        .query(QueryBuilders.matchQuery("field", "value"))));

algoliasearch-client-javascript:

const index = client.initIndex('your_index_name');
index.search('query').then(({ hits }) => {
  console.log(hits);
});

The code examples show that Elasticsearch and OpenSearch have similar syntax for performing searches, while algoliasearch-client-javascript offers a more streamlined approach tailored for JavaScript environments. Elasticsearch and OpenSearch provide more granular control over search parameters, whereas Algolia's client focuses on simplicity and ease of use for frontend applications.

Pros of Meilisearch

• Lightweight and easy to set up, with minimal configuration required
• Designed for fast search-as-you-type experiences with typo tolerance
• Provides a simple and intuitive API for integration

Cons of Meilisearch

• Limited advanced features compared to Elasticsearch and OpenSearch
• Smaller ecosystem and community support
• Less suitable for complex, large-scale enterprise search scenarios

Code Comparison

Meilisearch query:

const search = await client.index('movies').search('batman', {
  limit: 10,
  attributesToRetrieve: ['title', 'year']
});

Elasticsearch query:

const result = await client.search({
  index: 'movies',
  body: {
    query: {
      match: { title: 'batman' }
    },
    _source: ['title', 'year'],
    size: 10
  }
});

OpenSearch query:

const response = await client.search({
  index: 'movies',
  body: {
    query: {
      match: { title: 'batman' }
    },
    _source: ['title', 'year'],
    size: 10
  }
});

While Elasticsearch and OpenSearch share similar query structures due to their common origins, Meilisearch offers a more streamlined API for basic search operations. However, Elasticsearch and OpenSearch provide more advanced querying capabilities for complex use cases.

Pros of Typesense

• Simpler setup and configuration compared to Elasticsearch and OpenSearch
• Faster indexing and query performance for certain use cases
• Built-in typo tolerance and geo-search capabilities

Cons of Typesense

• Less mature ecosystem and community support
• Fewer advanced features and customization options
• Limited scalability for extremely large datasets

Code Comparison

Typesense query example:

{
  "q": "harry potter",
  "query_by": "title,author",
  "sort_by": "ratings_count:desc"
}

Elasticsearch query example:

{
  "query": {
    "multi_match": {
      "query": "harry potter",
      "fields": ["title", "author"]
    }
  },
  "sort": [
    { "ratings_count": { "order": "desc" } }
  ]
}

OpenSearch query example:

{
  "query": {
    "multi_match": {
      "query": "harry potter",
      "fields": ["title", "author"]
    }
  },
  "sort": [
    { "ratings_count": { "order": "desc" } }
  ]
}

Note that Elasticsearch and OpenSearch share similar query syntax due to their common origins, while Typesense offers a more simplified query structure.

Pros of Sonic

• Lightweight and fast, designed for speed and efficiency
• Simple to set up and use, with a straightforward API
• Low memory footprint, suitable for resource-constrained environments

Cons of Sonic

• Limited feature set compared to Elasticsearch and OpenSearch
• Less mature ecosystem and community support
• Not as scalable for large-scale, distributed search scenarios

Code Comparison

Sonic query example:

let objects = sonic_channel.query("collection", "bucket", "query", 10, None).await?;

Elasticsearch query example:

SearchResponse<Product> response = client.search(s -> s
    .index("products")
    .query(q -> q
        .match(t -> t
            .field("name")
            .query("bicycle")
        )
    ),
    Product.class
);

OpenSearch query example:

response = client.search(
    index="products",
    body={"query": {"match": {"name": "bicycle"}}}
)

Sonic focuses on simplicity and speed, while Elasticsearch and OpenSearch offer more advanced querying capabilities and features. Elasticsearch and OpenSearch are more suitable for complex, large-scale search applications, whereas Sonic excels in scenarios requiring fast, lightweight search functionality with minimal setup.