Cross-shard queries in HLD - Architecture Diagram

System Overview - Cross-shard queries

This system handles queries that need data from multiple database shards. Each shard stores a portion of the data to improve scalability and performance. The system must combine results from different shards and return a unified response quickly and reliably.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Query Coordinator
 /      |       \
Shard 1 Shard 2  Shard N
  |       |        |
Cache   Cache    Cache
  |       |        |
DB 1    DB 2     DB N

Components

User

client

Sends query requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Receives requests and forwards them to the Query Coordinator

Query Coordinator

service

Splits cross-shard queries, sends sub-queries to shards, aggregates results

Shard 1

database_shard

Stores a subset of data and processes queries for that subset

Shard 2

database_shard

Stores a subset of data and processes queries for that subset

Shard N

database_shard

Stores a subset of data and processes queries for that subset

Cache

cache

Stores frequently accessed query results to reduce latency

DB 1

database

Persistent storage for Shard 1

DB 2

database

Persistent storage for Shard 2

DB N

database

Persistent storage for Shard N

Request Flow - 29 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Query Coordinator

Query Coordinator → Cache

Cache → Query Coordinator

Query Coordinator → Shard 1

Query Coordinator → Shard 2

Query Coordinator → Shard N

Shard 1 → Cache

Cache → Shard 1

Shard 1 → DB 1

DB 1 → Shard 1

Shard 1 → Cache

Shard 1 → Query Coordinator

Shard 2 → Cache

Cache → Shard 2

Shard 2 → DB 2

DB 2 → Shard 2

Shard 2 → Cache

Shard 2 → Query Coordinator

Shard N → Cache

Cache → Shard N

Shard N → DB N

DB N → Shard N

Shard N → Cache

Shard N → Query Coordinator

Query Coordinator → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Shard 2

Impact:Queries involving Shard 2 data fail or return incomplete results. Overall query response is delayed or partial.

Mitigation:Use replication for Shard 2 to failover to a replica. Query Coordinator retries on replica. Cache may serve stale data if DB is down.

Architecture Quiz - 3 Questions

Which component is responsible for splitting a cross-shard query into sub-queries?

A Query Coordinator

B Load Balancer

C API Gateway

D Cache

Design Principle

This design shows how to handle queries that span multiple database shards by splitting the query, querying shards in parallel, caching results to reduce latency, and aggregating responses. It balances scalability and performance while handling failures with replication.

Practice

1. What is the main purpose of cross-shard queries in a distributed database system?

easy

A. To encrypt data within a shard

B. To backup data from one shard to another

C. To split a large shard into smaller shards

D. To retrieve data from multiple shards to answer a single complex request

Solution

1. Step 1: Understand what shards are

   Shards are parts of a database split to store data separately for scalability.

2. Step 2: Identify the role of cross-shard queries

   Cross-shard queries combine data from multiple shards to answer complex requests that need data from more than one shard.

3. Final Answer:

   To retrieve data from multiple shards to answer a single complex request -> Option D

4. Quick Check:

   Cross-shard queries = combine data from shards [OK]

Hint: Cross-shard queries combine data from many shards [OK]

Common Mistakes:

• Confusing cross-shard queries with data backup
• Thinking cross-shard queries split shards
• Assuming cross-shard queries encrypt data

2. Which component is responsible for splitting and merging queries in cross-shard query processing?

easy

A. Coordinator

B. Shard manager

C. Cache server

D. Load balancer

Solution

1. Step 1: Identify query processing roles

   The coordinator handles query distribution and result merging across shards.

2. Step 2: Match roles to components

   Shard manager manages shards but does not split/merge queries; cache server stores data temporarily; load balancer distributes traffic but not queries.

3. Final Answer:

   Coordinator -> Option A

4. Quick Check:

   Coordinator splits and merges queries [OK]

Hint: Coordinator splits and merges cross-shard queries [OK]

Common Mistakes:

• Confusing shard manager with coordinator
• Thinking cache server merges queries
• Assuming load balancer handles query splitting

3. Consider a system with 3 shards. A cross-shard query is sent to all shards in parallel. If each shard takes 100ms, 120ms, and 80ms respectively to respond, what is the total time to get the combined result?

medium

A. 300ms

B. 80ms

C. 120ms

D. 100ms

Solution

1. Step 1: Understand parallel query execution

   Queries run in parallel, so total time is the longest shard response time.

2. Step 2: Identify the longest response time

   Shard times are 100ms, 120ms, and 80ms. The longest is 120ms.

3. Final Answer:

   120ms -> Option C

4. Quick Check:

   Parallel time = max shard time = 120ms [OK]

Hint: Total time = longest shard response in parallel [OK]

Common Mistakes:

• Adding all shard times instead of taking max
• Choosing shortest shard time
• Confusing sequential with parallel execution

4. A cross-shard query coordinator is returning incomplete results. Which of the following is the most likely cause?

medium

A. Coordinator failed to merge all shard responses

B. Shard keys are not evenly distributed

C. Cache server is overloaded

D. Load balancer is routing traffic incorrectly

Solution

1. Step 1: Analyze incomplete results cause

   Incomplete results usually mean some shard data was not included in the final output.

2. Step 2: Identify coordinator role in merging

   The coordinator merges shard responses; failure here causes missing data.

3. Final Answer:

   Coordinator failed to merge all shard responses -> Option A

4. Quick Check:

   Incomplete results = merge failure [OK]

Hint: Incomplete results often mean merge failure by coordinator [OK]

Common Mistakes:

• Blaming shard key distribution for missing data
• Assuming cache overload causes missing data
• Thinking load balancer affects query merging

5. You want to reduce latency of cross-shard queries in a large distributed system. Which combination of strategies is best?

hard

A. Run queries sequentially on each shard and use a load balancer to distribute traffic

B. Use a coordinator to parallelize queries and implement caching of frequent results

C. Increase shard size and avoid caching to reduce complexity

D. Remove the coordinator and let clients query shards directly

Solution

1. Step 1: Identify strategies to reduce latency

   Parallel query execution via coordinator reduces wait time; caching avoids repeated work.

2. Step 2: Evaluate options

   Use a coordinator to parallelize queries and implement caching of frequent results uses both parallelism and caching, best for latency. Others increase complexity or remove coordination, causing delays.

3. Final Answer:

   Use a coordinator to parallelize queries and implement caching of frequent results -> Option B

4. Quick Check:

   Parallel + caching = lower latency [OK]

Hint: Parallel queries plus caching reduce latency best [OK]

Common Mistakes:

• Thinking bigger shards reduce latency
• Running queries sequentially increases latency
• Removing coordinator causes uncoordinated delays