Data Models and Query Language Design in Distributed Systems

1. Problem Description

In distributed systems, data is typically scattered and stored across multiple nodes. How to design data models and query languages so that they can efficiently support distributed queries while hiding the complexity of the underlying data distribution is one of the core challenges in distributed system architecture. For example, users may need to perform join queries (JOINs) across multiple data shards or access heterogeneous data (such as structured and semi-structured data) uniformly.

2. Core Challenges

1. Data Distribution Transparency: Users should not need to be aware of shard locations or replica storage methods.
2. Query Performance: Cross-node queries may cause significant network transmission, requiring optimized query plans.
3. Heterogeneous Data Support: Different data sources (such as relational tables, JSON documents, time-series data) may require a unified query interface.
4. Scalability: Data models and query languages need to adapt to dynamic changes in cluster scale.

3. Key Choices in Data Model Design

3.1 Structured vs. Semi-Structured

• Structured Models (e.g., Relational Model):
  • Advantages: Strong type constraints, ACID transaction support (e.g., distributed database CockroachDB).
  • Disadvantages: Schema changes are difficult; not suitable for flexible data formats.
• Semi-structured Models (e.g., Document Model):
  • Advantages: Dynamic fields, easy to extend (e.g., MongoDB's BSON format).
  • Disadvantages: Query capabilities may be limited (e.g., JOIN operations may need to be implemented at the application layer).

3.2 Data Sharding and Association Methods

• Impact of Sharding Strategy on Queries:
  • If data is sharded by user ID, queries for a specific user's orders can be directed to a single node (local query).
  • If cross-user association queries are needed (e.g., "query the total orders of all users"), aggregation across multiple shards is required (global query).
• Association Design:
  • Embedded associations (e.g., embedding orders in user documents in the document model) are suitable for single-node queries but lead to data redundancy.
  • Referential associations (e.g., foreign keys) require cross-node queries and need distributed JOIN optimization.

4. Query Language Design Principles

4.1 Declarative vs. Imperative

• Declarative Queries (e.g., SQL):
  • Users specify "what" rather than "how to get it."
  • Advantages: The query optimizer automatically selects execution plans (e.g., predicate pushdown, shard pruning).
  • Example: SELECT * FROM orders WHERE user_id IN (SELECT id FROM users WHERE region='Asia')
• Imperative Queries (e.g., NoSQL API):
  • Users need to explicitly define the order of accessing shards (e.g., query the user shard first, then the order shard).
  • Advantages: Fine-grained control but increased complexity.

4.2 Distributed Query Optimization

• Query Rewriting: Transform logical queries into physical execution plans.
  • Example: Rewrite cross-shard JOINs into parallel scans + merge aggregation.
• Predicate Pushdown: Push filtering conditions as close to the data source as possible to reduce network transmission.
  • Example: Push the condition WHERE date > '2023-01-01' down to each shard.
• Locality Awareness: Prioritize processing associated data on the same node (e.g., using co-located shards).

5. Real-World Case: Google Spanner's SQL Layer

1. Data Model: Relational model, supporting global indexes and foreign keys.
2. Query Processing:
   • After query parsing, the optimizer generates a distributed execution plan based on data distribution (e.g., shard locations, indexes).
   • Uses the TrueTime protocol to ensure global consistency for cross-shard transactions.
3. Transparency: Users query via standard SQL without needing to know which continent's shard the data is on.

6. Summary of Design Trade-offs

7. Practical Recommendations

1. Design Shard Keys Based on Query Patterns: High-frequency queries should be localized as much as possible.
2. Limit the Use of Distributed JOINs: Reduce cross-node operations through denormalization or pre-aggregation.
3. Adopt Layered Queries: Perform filtering and aggregation in parallel on each shard first, then summarize the results on a coordinator node.

By following the above steps, the design of data models and query languages in distributed systems can achieve a balance between performance, scalability, and usability.