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DATABASE

🔎 Elasticsearch

Powerful search and analytics for your data

⏱️ 4+ Years
📦 10+ Projects
✓ Available for new projects
Experience at: Anaqua• Workspace InfoTech

🎯 What I Offer

Search Implementation

Build powerful search experiences with Elasticsearch.

Deliverables
  • Index design
  • Query optimization
  • Autocomplete/suggest
  • Faceted search
  • Relevance tuning

Log Analytics (ELK Stack)

Set up log aggregation and analytics with ELK.

Deliverables
  • Logstash configuration
  • Kibana dashboards
  • Alert configuration
  • Retention policies
  • Performance tuning

Elasticsearch Integration

Integrate Elasticsearch with your applications.

Deliverables
  • Data sync pipelines
  • Real-time indexing
  • Search API design
  • Caching strategy
  • Monitoring setup

🔧 Technical Deep Dive

Elasticsearch Architecture

Indices - Collections of documents Documents - JSON records Shards - Distributed storage Replicas - High availability

Key features:

  • Full-text search with analyzers
  • Aggregations for analytics
  • Near real-time indexing
  • Horizontal scaling

Search Patterns

Match Query - Full-text search Term Query - Exact matches Bool Query - Compound queries Function Score - Custom relevance Aggregations - Analytics and facets Suggestions - Autocomplete

📋 Details & Resources

Elasticsearch Query Examples

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// Complex Search Query
{
  "query": {
    "bool": {
      "must": [
        {
          "multi_match": {
            "query": "patent infringement",
            "fields": ["title^2", "abstract", "claims"],
            "type": "best_fields",
            "fuzziness": "AUTO"
          }
        }
      ],
      "filter": [
        { "term": { "status": "active" } },
        { "range": { "filing_date": { "gte": "2020-01-01" } } }
      ],
      "should": [
        { "match": { "category": "software" } }
      ]
    }
  },
  "aggs": {
    "by_category": {
      "terms": { "field": "category.keyword" }
    },
    "by_year": {
      "date_histogram": {
        "field": "filing_date",
        "calendar_interval": "year"
      }
    }
  },
  "highlight": {
    "fields": {
      "abstract": {},
      "claims": {}
    }
  }
}

Index Mapping Design

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{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "english",
        "fields": {
          "keyword": { "type": "keyword" },
          "suggest": { "type": "completion" }
        }
      },
      "content": {
        "type": "text",
        "analyzer": "english"
      },
      "category": {
        "type": "keyword"
      },
      "created_at": {
        "type": "date"
      },
      "embedding": {
        "type": "dense_vector",
        "dims": 1536,
        "index": true,
        "similarity": "cosine"
      }
    }
  }
}

Frequently Asked Questions

What is Elasticsearch development?

Elasticsearch is a distributed search and analytics engine. Elasticsearch development involves designing search schemas, implementing full-text search, building analytics dashboards, log aggregation (ELK stack), and optimizing query performance for large datasets.

How much does Elasticsearch development cost?

Elasticsearch development typically costs $110-160 per hour. A basic search implementation starts around $10,000-20,000, while enterprise deployments with complex aggregations, multi-tenancy, and high availability range from $40,000-120,000+. Infrastructure costs are separate.

Elasticsearch vs PostgreSQL full-text search: which should I use?

Use Elasticsearch for: large-scale search (millions of documents), complex queries with facets, log analytics, or when search is a core feature. Use PostgreSQL for: simple search needs, smaller datasets, or when you want to avoid additional infrastructure.

How do you handle Elasticsearch performance?

I optimize: index mappings for query patterns, shard sizing for data volume, query optimization (filters vs queries), caching strategies, and hardware sizing. Elasticsearch can be fast or slow depending on how it’s configured, I ensure it’s fast.

Do you work with the ELK stack?

Yes. I implement complete observability stacks: Elasticsearch for storage, Logstash for log processing, Kibana for visualization, and Beats for collection. I’ve set up ELK for log aggregation, APM, and security analytics.

💼 Real-World Results

Document Search Platform

Anaqua
Challenge

Search across millions of legal documents with complex queries.

Solution

Elasticsearch with custom analyzers for legal terms, faceted search, and relevance tuning.

Result

Fast, accurate search across patent and trademark documents.

E-commerce Search

Workspace InfoTech
Challenge

Build product search with filters and autocomplete.

Solution

Elasticsearch with synonyms, fuzzy matching, category facets, and suggestion API.

Result

Improved search experience, increased conversions.

⚡ Why Work With Me

  • ✓ 4+ years of production Elasticsearch experience
  • ✓ Full ELK stack expertise
  • ✓ Search relevance tuning specialist
  • ✓ Integration with AI, hybrid search with embeddings
  • ✓ Performance optimization at scale

Build Your Search

Within 24 hours

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