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Typeahead Autocomplete - Trie vs Prefix Hash vs Elasticsearch

HLDmediumInterview freq: common
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ArchitectureTraceChallenge
▶
Request Trace
Step 1/6
Load Balancer

Receives HTTP prefix query from client

Load Balancer forwards query to Query API Servers

Protocol:HTTP
BeforeClient has typed prefix query and sent HTTP request
AfterQuery API Servers receive prefix query request
Query API Servers

Forwards prefix query to Autocomplete Index Store

Sends query using gRPC to Index Store for matching prefixes

Protocol:gRPC
BeforeQuery API Servers have received client query
AfterAutocomplete Index Store receives prefix query
Autocomplete Index Store

Searches prefix matches using selected index (Trie, Prefix Hash, or Elasticsearch)

Returns matched autocomplete candidates to Query API Servers

Protocol:gRPC
BeforeIndex Store has received prefix query
AfterIndex Store returns candidate autocomplete results
Ranking Service

Ranks autocomplete candidates by relevance

Receives candidates from Query API Servers and returns ranked list

Protocol:gRPC
BeforeQuery API Servers have candidate results from Index Store
AfterRanking Service returns ranked autocomplete results
Query API Servers

Sends ranked autocomplete results back to client

Responds over HTTP with ranked suggestions

Protocol:HTTP
BeforeQuery API Servers have ranked results from Ranking Service
AfterClient receives ranked autocomplete suggestions
Update Processor

Processes index update events from data pipeline

Sends index update commands to Autocomplete Index Store via Kafka

Protocol:Kafka
BeforeUpdate Processor receives new data for autocomplete index
AfterAutocomplete Index Store applies index updates
🏗️
Architecture Diagram
Architecture Diagram
          ┌───────────────┐
          │ Load Balancer │
          └───────┬───────┘
                  │ HTTP Req
                  ▼
          ┌───────────────────┐
          │ Query API Servers │
          └───────┬───────────┘
                  │ Query (gRPC)
                  ▼
          ┌────────────────────────┐
          │ Autocomplete Index Store│
          │ (Trie / Prefix Hash /  │
          │    Elasticsearch)      │
          └───────┬────────────────┘
                  │ Results (gRPC)
                  ▼
          ┌────────────────┐
          │ Ranking Service│
          └───────┬────────┘
                  │ Ranked Results (gRPC)
                  ▼
          ┌───────────────────┐
          │ Query API Servers │
          └───────┬───────────┘
                  │ HTTP Resp
                  ▼
          ┌───────────────┐
          │    Client     │
          └───────────────┘

          ┌───────────────────┐
          │ Update Processor  │
          └───────┬───────────┘
                  │ Index Update (Kafka)
                  ▼
          ┌────────────────────────┐
          │ Autocomplete Index Store│
          └────────────────────────┘
Design Rationale

This architecture supports typeahead autocomplete by routing client prefix queries through a Load Balancer to Query API Servers. The servers query the Autocomplete Index Store, which may use Trie, Prefix Hash, or Elasticsearch indexes to find prefix matches. Results are then sent to the Ranking Service to order suggestions by relevance before returning them to the client. Meanwhile, the Update Processor asynchronously processes index updates via Kafka to keep the Autocomplete Index Store current. This design cleanly separates query serving from index updating, enabling low-latency lookups and scalable index maintenance.

Primary Focus

✓

Practice

(1/5)
1. In an Active-Passive failover system, at step 3 the passive node receives a heartbeat timeout from the active node. What is the passive node's immediate state transition?
easy
A. Passive node continues waiting for active node recovery
B. Passive node promotes itself to active and starts accepting traffic
C. Passive node sends a failback request to active node
D. Passive node shuts down to avoid split-brain

Solution

  1. Step 1: Understand heartbeat role

    Heartbeat timeout signals active node failure, triggering failover.

  2. Step 2: Passive node failover action

    Passive node must promote itself to active to maintain availability and accept traffic.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Heartbeat timeout -> passive promotes to active [check]
Hint: Heartbeat lost -> passive promotes active [check]
Common Mistakes:
2. A chat system must handle 1 million concurrent user requests, each requiring low-latency token streaming from a large language model. Which component is best suited to buffer and stream tokens efficiently to clients?
easy
A. A relational database with row-level locking to ensure token order
B. A batch processing system that aggregates tokens before sending
C. An in-memory message queue with backpressure support like Redis Streams
D. A distributed file system to store token chunks for streaming

Solution

  1. Step 1: Identify token streaming requirements

    Token streaming demands low latency and ordered delivery with backpressure to avoid overwhelming clients.

  2. Step 2: Evaluate component suitability

    Relational DBs (A) add latency and locking overhead; distributed file systems (C) are too slow for real-time streaming; batch processing (D) adds unacceptable delay. Redis Streams (B) supports in-memory buffering with backpressure and ordered delivery.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Redis Streams supports ordered, low-latency token streaming with backpressure [check]
Hint: In-memory queues handle streaming better than DBs or batch [check]
Common Mistakes:
3. If Redis becomes unavailable in a centralized rate limiting system, what is the immediate failure mode for incoming requests?
medium
A. API servers allow all requests without rate limiting, risking overload
B. Requests are queued locally until Redis recovers, causing increased latency
C. API servers reject all requests immediately to avoid exceeding limits
D. API servers switch to a backup Redis instance automatically without delay

Solution

  1. Step 1: Understand Redis unavailability impact

    Without Redis, the centralized counter is unreachable, so rate limiting cannot be enforced.

  2. Step 2: Check fallback behavior

    Most implementations allow requests to proceed without rate limiting to avoid blocking, risking overload.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Redis down -> no rate limiting -> requests allowed unchecked [check]
Hint: No Redis means no rate limiting, requests pass unchecked [check]
Common Mistakes:
4. For a high-throughput system requiring SSL termination and HTTP header manipulation, which architecture is more appropriate and why?
medium
A. Use a Reverse Proxy for SSL termination and header manipulation because it operates at the HTTP layer with fine-grained control
B. Use an API Gateway for SSL termination and header manipulation because it is optimized for high throughput
C. Use a Load Balancer for SSL termination and header manipulation because it handles traffic efficiently
D. Use DNS load balancing to distribute SSL termination across multiple servers

Solution

  1. Step 1: Identify component capabilities

    Load Balancers typically operate at transport layer and may support SSL termination but lack detailed HTTP header manipulation; API Gateways add overhead and are less optimized for raw throughput; DNS load balancing does not handle SSL termination.

  2. Step 2: Match requirement to component

    Reverse Proxy operates at HTTP layer, supports SSL termination and header manipulation efficiently, suitable for high throughput.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Reverse Proxy = HTTP layer control + SSL termination [check]
Hint: Reverse Proxy = SSL termination + HTTP header control [check]
Common Mistakes:
5. At 10x scale, which component in a server-side service discovery architecture is most likely to fail first and why?
hard
A. Service registry, due to centralized state and high query volume causing latency spikes
B. Individual microservices, as they cannot handle increased load without horizontal scaling
C. API Gateway, because it cannot cache service endpoints and must query registry on every request
D. Load balancers, since they become overwhelmed by the increased number of service instances

Solution

  1. Step 1: Identify bottlenecks in server-side discovery

    API Gateway queries registry per request without caching, causing query amplification at scale.

  2. Step 2: Compare other components

    Registry is centralized but can be scaled with sharding; microservices scale horizontally; load balancers handle instance scaling better than API Gateway query load.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    API Gateway query volume grows linearly causing first failure [check]
Hint: API Gateway query volume grows fastest causing first failure [check]
Common Mistakes:
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