OpenSearch vs Elasticsearch: When to Choose Each - Complete Comparison 2025

The search engine landscape has evolved significantly with the introduction of OpenSearch as an open-source alternative to Elasticsearch. Understanding the differences between these two powerful search engines is crucial for making informed decisions about your search infrastructure. In this comprehensive comparison, we'll explore features, licensing, performance, and migration strategies to help you choose the right solution.

Understanding the Landscape

What is Elasticsearch?

Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It's widely used for:

• Full-text search - Powerful text search capabilities
• Real-time analytics - Real-time data analysis and visualization
• Log analytics - Centralized logging and log analysis
• Application search - Search functionality for applications
• Security analytics - Security information and event management (SIEM)

What is OpenSearch?

OpenSearch is an open-source search and analytics suite derived from Elasticsearch 7.10.2. It was created by AWS in response to Elasticsearch's licensing changes and includes:

• OpenSearch - The search engine (equivalent to Elasticsearch)
• OpenSearch Dashboards - Visualization and management interface
• OpenSearch plugins - Extensions and integrations
• OpenSearch clients - SDKs for various programming languages

Historical Context

The Elasticsearch Licensing Controversy

In 2021, Elastic changed Elasticsearch's licensing from Apache 2.0 to Elastic License and Server Side Public License (SSPL). This change:

• Restricted commercial use - Limited how companies could use Elasticsearch
• Created uncertainty - Raised concerns about vendor lock-in
• Sparked community response - Led to the creation of OpenSearch

OpenSearch Origins

OpenSearch was created as a community-driven response to address:

• Open-source concerns - Maintain truly open-source search capabilities
• Vendor independence - Reduce dependence on a single vendor
• Community governance - Ensure community-driven development
• Long-term sustainability - Provide a sustainable open-source alternative

Feature Comparison

Core Search Features

Analytics and Aggregations

Security Features

Monitoring and Management

Licensing Comparison

Elasticsearch Licensing

Elastic License:

• Free for development - No cost for development use
• Restricted commercial use - Limited commercial deployment options
• Vendor lock-in - Tied to Elastic's ecosystem
• No redistribution - Cannot redistribute without permission

Server Side Public License (SSPL):

• Copyleft license - Requires open-sourcing derivative works
• Commercial restrictions - Limited commercial use
• Viral nature - Affects downstream users

OpenSearch Licensing

Apache 2.0 License:

• Truly open-source - No commercial restrictions
• Vendor neutral - Not tied to any single vendor
• Redistribution friendly - Can be freely redistributed
• Community driven - Governed by the community

Performance Comparison

Benchmark Results

Performance Analysis

Elasticsearch:

• Slightly higher performance in most benchmarks
• More mature optimization features
• Better integration with Elastic's ecosystem
• Higher resource requirements

OpenSearch:

• Competitive performance with Elasticsearch
• Lower resource requirements
• Active performance optimization
• Growing performance improvements

Use Case Recommendations

Choose Elasticsearch When:

Enterprise Requirements:

• Need enterprise-grade support and SLAs
• Require advanced security features
• Want integrated monitoring and management
• Have complex compliance requirements

Ecosystem Integration:

• Already using Elastic's stack (ELK)
• Need tight integration with Elastic's tools
• Want unified licensing and support
• Require specific Elastic plugins

Example Implementation:

// Elasticsearch integration
public class ElasticsearchService
{
    private readonly ElasticClient _client;
    
    public async Task<ISearchResponse<Document>> SearchAsync(string query)
    {
        var response = await _client.SearchAsync<Document>(s => s
            .Index("documents")
            .Query(q => q
                .MultiMatch(m => m
                    .Query(query)
                    .Fields(f => f
                        .Field(d => d.Title)
                        .Field(d => d.Content)
                    )
                )
            )
        );
        
        return response;
    }
}

Choose OpenSearch When:

Open Source Requirements:

• Need truly open-source solution
• Want vendor independence
• Require Apache 2.0 licensing
• Building open-source projects

Cost Considerations:

• Want to avoid licensing costs
• Need predictable pricing
• Building commercial products
• Require redistribution rights

Example Implementation:

// OpenSearch integration
public class OpenSearchService
{
    private readonly OpenSearchClient _client;
    
    public async Task<SearchResponse<Document>> SearchAsync(string query)
    {
        var request = new SearchRequest
        {
            Index = "documents",
            Query = new MultiMatchQuery
            {
                Query = query,
                Fields = new[] { "title", "content" }
            }
        };
        
        var response = await _client.SearchAsync<Document>(request);
        return response;
    }
}

Migration Strategies

From Elasticsearch to OpenSearch

// Migration from Elasticsearch to OpenSearch
public class ElasticsearchToOpenSearchMigration
{
    private readonly ElasticClient _elasticsearchClient;
    private readonly OpenSearchClient _opensearchClient;
    
    public async Task MigrateIndexAsync(string indexName)
    {
        // 1. Create index in OpenSearch
        await CreateOpenSearchIndexAsync(indexName);
        
        // 2. Migrate data
        await MigrateDataAsync(indexName);
        
        // 3. Verify migration
        await VerifyMigrationAsync(indexName);
    }
    
    private async Task CreateOpenSearchIndexAsync(string indexName)
    {
        var indexSettings = new IndexSettings
        {
            NumberOfShards = 1,
            NumberOfReplicas = 1
        };
        
        var mapping = new TypeMapping
        {
            Properties = new Properties
            {
                { "title", new TextProperty { Analyzer = "standard" } },
                { "content", new TextProperty { Analyzer = "standard" } },
                { "created_at", new DateProperty() }
            }
        };
        
        await _opensearchClient.Indices.CreateAsync(indexName, c => c
            .Settings(indexSettings)
            .Map(mapping)
        );
    }
    
    private async Task MigrateDataAsync(string indexName)
    {
        var scrollResponse = await _elasticsearchClient.SearchAsync<Document>(s => s
            .Index(indexName)
            .Scroll("5m")
            .Size(1000)
        );
        
        while (scrollResponse.Documents.Any())
        {
            var bulkRequest = new BulkRequest
            {
                Operations = scrollResponse.Documents.Select(doc => 
                    new BulkIndexOperation<Document>(doc) { Index = indexName }
                ).Cast<IBulkOperation>().ToList()
            };
            
            await _opensearchClient.BulkAsync(bulkRequest);
            
            scrollResponse = await _elasticsearchClient.ScrollAsync<Document>("5m", 
                scrollResponse.ScrollId);
        }
    }
}

From OpenSearch to Elasticsearch

// Migration from OpenSearch to Elasticsearch
public class OpenSearchToElasticsearchMigration
{
    private readonly OpenSearchClient _opensearchClient;
    private readonly ElasticClient _elasticsearchClient;
    
    public async Task MigrateIndexAsync(string indexName)
    {
        // 1. Create index in Elasticsearch
        await CreateElasticsearchIndexAsync(indexName);
        
        // 2. Migrate data
        await MigrateDataAsync(indexName);
        
        // 3. Verify migration
        await VerifyMigrationAsync(indexName);
    }
    
    private async Task CreateElasticsearchIndexAsync(string indexName)
    {
        var indexSettings = new IndexSettings
        {
            NumberOfShards = 1,
            NumberOfReplicas = 1
        };
        
        var mapping = new TypeMapping
        {
            Properties = new Properties
            {
                { "title", new TextProperty { Analyzer = "standard" } },
                { "content", new TextProperty { Analyzer = "standard" } },
                { "created_at", new DateProperty() }
            }
        };
        
        await _elasticsearchClient.Indices.CreateAsync(indexName, c => c
            .Settings(indexSettings)
            .Map(mapping)
        );
    }
}

Implementation Examples

1. Basic Search Implementation

// Basic search implementation for both engines
public abstract class SearchServiceBase
{
    protected abstract Task<SearchResult> SearchInternalAsync(string query, 
        SearchOptions options);
    
    public async Task<SearchResult> SearchAsync(string query, SearchOptions options = null)
    {
        options ??= new SearchOptions();
        
        // Validate query
        if (string.IsNullOrWhiteSpace(query))
        {
            throw new ArgumentException("Query cannot be empty", nameof(query));
        }
        
        // Perform search
        var result = await SearchInternalAsync(query, options);
        
        // Log search metrics
        LogSearchMetrics(query, result);
        
        return result;
    }
    
    private void LogSearchMetrics(string query, SearchResult result)
    {
        // Implementation for logging search metrics
    }
}

// Elasticsearch implementation
public class ElasticsearchService : SearchServiceBase
{
    private readonly ElasticClient _client;
    
    protected override async Task<SearchResult> SearchInternalAsync(string query, 
        SearchOptions options)
    {
        var response = await _client.SearchAsync<Document>(s => s
            .Index(options.Index)
            .Query(q => q
                .MultiMatch(m => m
                    .Query(query)
                    .Fields(f => f
                        .Field(d => d.Title)
                        .Field(d => d.Content)
                    )
                )
            )
            .From(options.From)
            .Size(options.Size)
            .Sort(sort => sort
                .Field(f => f.Score, SortOrder.Descending)
            )
        );
        
        return new SearchResult
        {
            Documents = response.Documents,
            TotalHits = response.Total,
            Took = response.Took
        };
    }
}

// OpenSearch implementation
public class OpenSearchService : SearchServiceBase
{
    private readonly OpenSearchClient _client;
    
    protected override async Task<SearchResult> SearchInternalAsync(string query, 
        SearchOptions options)
    {
        var request = new SearchRequest
        {
            Index = options.Index,
            Query = new MultiMatchQuery
            {
                Query = query,
                Fields = new[] { "title", "content" }
            },
            From = options.From,
            Size = options.Size,
            Sort = new[]
            {
                new SortField { Field = "_score", Order = SortOrder.Descending }
            }
        };
        
        var response = await _client.SearchAsync<Document>(request);
        
        return new SearchResult
        {
            Documents = response.Documents,
            TotalHits = response.Total,
            Took = response.Took
        };
    }
}

2. Advanced Analytics Implementation

// Advanced analytics implementation
public class AnalyticsService
{
    private readonly ISearchClient _searchClient;
    
    public async Task<AnalyticsResult> GetAnalyticsAsync(AnalyticsRequest request)
    {
        var aggregations = new List<Aggregation>();
        
        // Date histogram aggregation
        if (request.GroupByDate)
        {
            aggregations.Add(new DateHistogramAggregation
            {
                Field = "created_at",
                Interval = request.DateInterval,
                Format = "yyyy-MM-dd"
            });
        }
        
        // Terms aggregation
        if (request.GroupByField != null)
        {
            aggregations.Add(new TermsAggregation
            {
                Field = request.GroupByField,
                Size = request.TopN
            });
        }
        
        // Metrics aggregations
        if (request.Metrics != null)
        {
            foreach (var metric in request.Metrics)
            {
                aggregations.Add(new MetricAggregation
                {
                    Field = metric.Field,
                    Type = metric.Type
                });
            }
        }
        
        var searchRequest = new SearchRequest
        {
            Index = request.Index,
            Query = request.Query,
            Aggregations = aggregations,
            Size = 0 // Only return aggregations
        };
        
        var response = await _searchClient.SearchAsync<Document>(searchRequest);
        
        return new AnalyticsResult
        {
            Aggregations = response.Aggregations,
            TotalHits = response.Total
        };
    }
}

Monitoring and Observability

1. Performance Monitoring

// Performance monitoring for search engines
public class SearchPerformanceMonitor
{
    private readonly ILogger<SearchPerformanceMonitor> _logger;
    private readonly IMetricsCollector _metricsCollector;
    
    public async Task<T> MonitorSearchAsync<T>(string operation, Func<Task<T>> searchOperation)
    {
        var stopwatch = Stopwatch.StartNew();
        
        try
        {
            var result = await searchOperation();
            stopwatch.Stop();
            
            _metricsCollector.RecordHistogram("search.operation.duration", 
                stopwatch.ElapsedMilliseconds, 
                new Dictionary<string, string> { ["operation"] = operation });
                
            _metricsCollector.RecordCounter("search.operations.total", 1,
                new Dictionary<string, string> { ["operation"] = operation });
                
            return result;
        }
        catch (Exception ex)
        {
            stopwatch.Stop();
            
            _logger.LogError(ex, "Search operation failed: {Operation}", operation);
            
            _metricsCollector.RecordCounter("search.operations.failed", 1,
                new Dictionary<string, string> { ["operation"] = operation });
                
            throw;
        }
    }
}

2. Health Checks

// Health checks for search engines
public class SearchHealthChecker
{
    private readonly ISearchClient _searchClient;
    private readonly ILogger<SearchHealthChecker> _logger;
    
    public async Task<HealthStatus> CheckHealthAsync()
    {
        try
        {
            // Check cluster health
            var clusterHealth = await _searchClient.Cluster.HealthAsync();
            
            // Check index health
            var indexHealth = await _searchClient.Indices.HealthAsync();
            
            // Check search functionality
            var searchTest = await _searchClient.SearchAsync<Document>(s => s
                .Index("_all")
                .Query(q => q.MatchAll())
                .Size(1)
            );
            
            return new HealthStatus
            {
                IsHealthy = clusterHealth.Status == "green" && 
                           indexHealth.Status == "green" &&
                           searchTest.IsValid,
                ClusterStatus = clusterHealth.Status,
                IndexStatus = indexHealth.Status,
                SearchTestPassed = searchTest.IsValid,
                Timestamp = DateTime.UtcNow
            };
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "Health check failed");
            
            return new HealthStatus
            {
                IsHealthy = false,
                Error = ex.Message,
                Timestamp = DateTime.UtcNow
            };
        }
    }
}

Best Practices

1. Index Design

// Index design best practices
public class IndexDesignService
{
    public IndexSettings CreateOptimalIndexSettings(int documentCount, 
        int averageDocumentSize)
    {
        var totalSize = documentCount * averageDocumentSize;
        var shardSize = 50 * 1024 * 1024 * 1024; // 50GB per shard
        
        var numberOfShards = Math.Max(1, (int)Math.Ceiling((double)totalSize / shardSize));
        var numberOfReplicas = Math.Min(2, numberOfShards - 1);
        
        return new IndexSettings
        {
            NumberOfShards = numberOfShards,
            NumberOfReplicas = numberOfReplicas,
            RefreshInterval = TimeSpan.FromSeconds(30),
            MaxResultWindow = 10000
        };
    }
    
    public TypeMapping CreateOptimalMapping(DocumentSchema schema)
    {
        var properties = new Properties();
        
        foreach (var field in schema.Fields)
        {
            switch (field.Type)
            {
                case FieldType.Text:
                    properties.Add(field.Name, new TextProperty
                    {
                        Analyzer = field.Analyzer ?? "standard",
                        SearchAnalyzer = field.SearchAnalyzer ?? "standard"
                    });
                    break;
                    
                case FieldType.Keyword:
                    properties.Add(field.Name, new KeywordProperty
                    {
                        Normalizer = field.Normalizer
                    });
                    break;
                    
                case FieldType.Date:
                    properties.Add(field.Name, new DateProperty
                    {
                        Format = field.Format ?? "strict_date_optional_time"
                    });
                    break;
                    
                case FieldType.Number:
                    properties.Add(field.Name, new NumberProperty
                    {
                        Type = field.NumberType ?? NumberType.Long
                    });
                    break;
            }
        }
        
        return new TypeMapping { Properties = properties };
    }
}

2. Query Optimization

// Query optimization best practices
public class QueryOptimizer
{
    public SearchRequest OptimizeQuery(SearchRequest request)
    {
        // Add query caching
        request.Cache = true;
        request.CacheKey = GenerateCacheKey(request);
        
        // Optimize size
        if (request.Size > 10000)
        {
            request.Size = 10000;
        }
        
        // Add timeout
        request.Timeout = TimeSpan.FromSeconds(30);
        
        // Optimize sort
        if (request.Sort == null || !request.Sort.Any())
        {
            request.Sort = new[] { new SortField { Field = "_score", Order = SortOrder.Descending } };
        }
        
        return request;
    }
    
    private string GenerateCacheKey(SearchRequest request)
    {
        var key = $"{request.Index}_{request.Query}_{request.Size}_{request.From}";
        return Convert.ToBase64String(Encoding.UTF8.GetBytes(key));
    }
}

Conclusion

Choosing between OpenSearch and Elasticsearch depends on your specific requirements, licensing needs, and long-term strategy. Both solutions offer powerful search and analytics capabilities, but they differ in licensing, ecosystem, and vendor support.

Key takeaways:

• Elasticsearch - Best for enterprise needs with advanced features and support
• OpenSearch - Ideal for open-source requirements and vendor independence
• Migration - Both solutions support migration with proper planning
• Performance - Both offer competitive performance with slight differences
• Community - Both have active communities and ongoing development

Ready to implement a search solution for your application? Our team at Elysiate can help you choose the right solution and implement it effectively. Contact us to learn more about our search implementation services.

Need help with other search or data analytics challenges? Explore our services to see how we can help your organization succeed.