Configuring Elasticsearch for Search in Mobile Apps

Configuring Elasticsearch for Search in Mobile Apps

Imagine an online store with 10,000 products. A user types "Adidas sneakers" — the app freezes for 5 seconds and then shows an empty list because of a slow LIKE query. Conversion drops. We see this on every other project. Our team with 5 years of experience in Elasticsearch and over 30 completed projects sets up search turnkey: from data indexing to a ready UI with autocomplete and facets. We guarantee response time <200ms even on a million documents.

Why Elasticsearch Instead of LIKE Search?

LIKE in SQLite (or similar databases) cannot rank results, does not support morphology, and slows down on datasets over a thousand records. Elasticsearch provides full-text search, autocomplete, faceted filters, and relevance based on dozens of factors. Speed difference — up to 50× faster for complex queries, which is critical for e-commerce conversion. For example, a search for "sneakers" will find both "sneaker" and "sneakers" in milliseconds.

Architecture: The Mobile App Does Not Call ES Directly

The mobile client sends a request to the backend: GET /api/search?q=sneakers&category=sport. The backend (Laravel/Node) performs the search in Elasticsearch and returns a paginated response. Reasons:

• Security: index structure and credentials are hidden from the client.
• Caching: the server caches popular queries, reducing load on ES.
• Load control: a thousand simultaneous clients do not overload the cluster.

Configuring the Index

For Russian text, a morphological analyzer is mandatory. We use the built-in russian or the analysis-morphology plugin. Without it, the query "sneakers" won't find "sneaker". Example mapping:

{
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "russian",
        "fields": {
          "keyword": { "type": "keyword" }
        }
      }
    }
  }
}

The keyword field is needed for sorting and aggregations — text is not suitable for that.

Elasticsearch is an industry standard; for Russian content, russian is preferred.

How to Implement Autocomplete and Pagination?

Autocomplete (search-as-you-type) is implemented using a field of type search_as_you_type or the completion suggester. The client sends a request on each keystroke with a 300ms debounce, the server returns up to 7 suggestions. For pagination, we use search_after instead of the standard from/size, which is limited to 10,000 results. This enables infinite scroll without a "next page" button.

How We Configure Search for Clients?

Our process includes five stages:

1. Data analysis: study product structure, determine fields for indexing, normalize attributes.
2. Index design: choose analyzers, set up mapping and auto-updates via Logstash or Kafka.
3. API implementation: create an endpoint with pagination (search_after), facets, and autocomplete. Document in Swagger.
4. Mobile app integration: connect the API, build UI (skeleton, debounce, infinite scroll). Native SDKs (Alamofire, Retrofit) handle requests.
5. Testing and monitoring: check speed (<500ms), enable slowlog, set up alerts in Kibana.

Let's break down a typical case. Client — a marketplace with 50,000 products. Elasticsearch cluster of 3 nodes, indexing via Kafka. After integration, average response time dropped from 3s to 150ms, cart abandonment decreased by 30%. This required configuring the russian analyzer with a custom stop word list and adding boosting by product rating.

What's Included in the Work?

• Documentation on search architecture and query schemas.
• Access to the Elasticsearch server and configured monitoring (Kibana, Grafana).
• Backend controller source code with sample requests (REST, GraphQL).
• Team training: how to update the index, add fields, change relevance.
• One month of support after launch.

Monitoring and Performance

Slow queries (>500ms) are logged via the Elasticsearch slowlog. On the mobile app, we show skeleton placeholders; on a >3s delay — a message "Search taking longer than usual". Baseline metrics: average response time <200ms, error rate <0.5%.

Timelines and Cost

Basic integration (index + API + search UI) takes 1–2 weeks. Full package (facets, autocomplete, offline cache) — 3–4 weeks. Cost is calculated individually after assessing data volume and complexity.

We are ready to take on your project. Contact us — we will prepare an architectural solution and an accurate estimate. Get a consultation by leaving your request.

How to Start Integrating API into a Mobile App?

The request goes out, the response doesn't come, timeout — 30 seconds. The user stares at the spinner. No network — mobile card in the subway. Or the network is there, but the server returns 200 with an HTML error page instead of JSON — and the app crashes on JSONDecoder.decode(). We see such cases on every second project. So integrating API into a mobile app is not just calling an endpoint, but designing a reliable network layer: error handling, caching, offline mode, certificate pinning. Order an audit of your current network layer — we will evaluate the project in 1 day. Our team guarantees a thorough analysis and provides a detailed roadmap.

Standard libraries like URLSession and OkHttp provide basic HTTP clients, but for production you need retries with exponential backoff, status code validation, typed deserialization, and network state monitoring. Without this, the app loses data and users. We have been doing mobile development for 5 years and implemented more than 30 projects with API integration on iOS, Android, and Flutter — from startups to enterprise solutions.

How to Choose a Protocol for API Integration?

REST remains the standard for most projects. But when a profile screen needs 5 fields out of 40, GraphQL eliminates over-fetching and reduces traffic by 30–60%. gRPC is justified for thousands of requests per minute (trading, IoT) — binary serialization is 3–5 times faster than JSON. WebSocket is the only choice for real-time without polling (messages, notifications).

Practical example: For a fintech app, we replaced REST (40 fields) with GraphQL — response size dropped from 12 KB to 2.5 KB, screen render time decreased by 70%. Traffic savings were significant. Our certified iOS and Android developers have deep experience with all these protocols — you can rely on proven solutions.

How to Ensure Reliable Connection and Offline-First?

Users lose network in the subway, elevator, tunnel. A mobile app must work without internet — at least in read-only mode. We implement the offline-first pattern:

1. On screen open, first show data from the local cache (Core Data / Room).
2. Simultaneously perform a network request, update UI after response.
3. If network is unavailable — show cached data and a 'no connection' label.
4. When network is restored, automatically synchronize changes.

For HTTP response caching we use URLCache (iOS) and OkHttp Cache (Android) with Cache-Control support. For structured data — SwiftData / Room. NWPathMonitor / ConnectivityManager.NetworkCallback monitor network state and trigger updates.

REST and Client Library Selection

Alamofire (iOS) — de facto standard for Swift projects. On top of URLSession it adds request chaining, response validation, automatic retry, certificate pinning via ServerTrustManager. AF.request() with .validate() returns an error for any status code outside 200–299. Without .validate(), Alamofire considers 404 and 500 as successful responses. With Swift Concurrency — async version via serializingDecodable.

Retrofit (Android) — annotation-based HTTP client on top of OkHttp. An interface with annotations compiles into implementation. @GET, @POST, @Path, @Query, @Body — declarative API description. OkHttp under the hood: connection pooling, transparent gzip, HTTP/2 multiplex. HttpLoggingInterceptor — logging in debug builds. Authenticator — automatic token refresh on 401.

Ktor (KMM/Flutter) — multiplatform HTTP client. On iOS it works via Darwin engine (URLSession), on Android — via OkHttp. Single code for both platforms with KMM architecture.

GraphQL: When REST Falls Short

REST returns a fixed structure. A profile screen needs name, avatar, email — the server sends 40 fields. Over-fetching. GraphQL solves this: the client requests exactly the needed fields. This is critical for mobile where traffic and parsing time are real constraints. Apollo iOS and Apollo Kotlin generate typed classes from schema: schema.graphql + query files → strict types at compile time. Subscriptions via WebSocket — real-time without polling. Limitation: GraphQL is harder to cache at the HTTP level. Apollo uses a normalized in-memory cache InMemoryNormalizedCache — requests with overlapping data update the cache without duplication.

WebSocket: Real-Time Without Extra Traffic

Polling (setInterval every 5 seconds) — battery and traffic waste. WebSocket is a persistent bidirectional connection. iOS: URLSessionWebSocketTask (native, iOS 13+). Android: OkHttp WebSocket. Mandatory reconnect handling: on onFailure — exponential backoff (1s → 2s → 4s → 8s → max 60s). Socket.IO is an overlay with automatic reconnect, but for new projects native WebSocket is preferable (fewer dependencies).

gRPC: For High-Load Services

gRPC with protobuf — binary serialization: smaller size, faster parsing. grpc-swift for iOS, grpc-kotlin for Android. The protobuf schema compiles to typed classes. Streaming (server-side, client-side, bidirectional) is a native feature. Application threshold: high request frequency (trading, IoT) or critical latency. For regular CRUD, REST is simpler to debug and monitor.

Certificate Pinning and Security

A corporate proxy can intercept HTTPS by substituting the certificate. Certificate pinning prevents this: the app accepts only a specific certificate or public key. Alamofire: ServerTrustManager with PinnedCertificatesTrustEvaluator. OkHttp: CertificatePinner with SHA-256 hash. Apple's App Transport Security documentation recommends pinning certificates for sensitive data. Operational complexity: on certificate rotation, older app versions stop working. Solution — pinning to the CA public key or support multiple pins with a grace period.

What Is Included in the Work

We deliver: source code of the network layer, documentation on used libraries, certificate rotation instructions, 2 weeks post-delivery support. Our experience guarantees that the solution will be stable and maintainable.

Timeline and Cost

Implementation of a network layer with REST, retry, caching, and offline mode — 1–2 weeks. Adding GraphQL or WebSocket — another 1–2 weeks. gRPC — 2–3 weeks, including code generation. The cost is calculated individually after analyzing the API and offline behavior requirements. We will evaluate the project in 1 day — contact us for a consultation. Get a reliable API integration with guaranteed quality.