Practice: Configuring a Distributed Setup

Configuring a distributed setup for vector search systems involves understanding the thought process and the kinds of decisions made during design. This exercise focuses on these design considerations, rather than providing specific commands for a particular database or library. Its primary goal is to solidify the principles of sharding, replication, and query routing.

Scenario Definition

Imagine you are tasked with building the vector search backend for a large e-commerce platform. The requirements are:

1. Dataset Size: Index 1 billion product embeddings (derived from product descriptions and images).
2. Query Load: Support a peak query throughput of 500 Queries Per Second (QPS).
3. Performance: Maintain a 95th percentile (p95) search latency below 100 milliseconds.
4. Relevance: Achieve a recall rate of at least 0.9 for typical semantic search queries.
5. Availability: The system must be fault-tolerant; failure of a single node should not cause significant service disruption.
6. Filtering: Support filtering search results based on product category and brand metadata.

System Components

Based on the discussions in this chapter, our distributed system will likely include:

• Load Balancer: Distributes incoming user requests.
• Query Router (or Coordinator): Receives requests from the load balancer, fans them out to the appropriate index shards, and aggregates the results.
• Index Shards: Nodes (or pods/containers) that hold a partition of the vector index and perform the actual ANN search. Each shard runs an instance of the vector search engine (e.g., using HNSW).
• Metadata Store: A separate system (or integrated capability) to store and query product category and brand information efficiently, enabling filtering.
• Management Plane: Tools for deploying, configuring, monitoring, and updating the system.

Designing the Sharding Strategy

Goal: Distribute the 1 billion vectors across multiple nodes to parallelize search and stay within the resource limits (memory, CPU) of individual nodes.

Assumptions: Let's assume, based on preliminary tests or instance type selection, that a single index node can comfortably handle indexing and searching up to 100 million vectors while meeting the latency requirements for its portion of the QPS.

Calculation: Number of shards ($N_{shards}$) = Total Vectors / Vectors per Shard $N_{shards} = \frac{1,000,000,000}{100,000,000} = 10$

So, we need 10 primary shards.

Sharding Mechanism: We need a way to decide which shard a vector belongs to upon insertion and which shards a query needs to hit. A common approach is consistent hashing based on the vector's unique ID. When a query arrives, the Query Router will typically send it to all 10 primary shards, as semantic similarity doesn't usually map cleanly to specific shards without more complex routing logic.

A view of query routing to multiple index shards.

Designing the Replication Strategy

Goal: Ensure high availability (HA) and potentially improve read throughput by creating copies of each shard.

Configuration: Let's choose a replication_factor of 2. This means each shard's data will exist on two separate nodes. If one node fails, the other can still serve requests for that data partition.

Calculation: Total search nodes ($N_{nodes}$) = $N_{shards} \times replication\_factor$ $N_{nodes} = 10 \times 2 = 20$

We now need 20 nodes in total to host the index data (10 primary shards, 10 replicas).

Query Handling with Replicas: The Query Router can now potentially send queries to either the primary or the replica for a given shard. This can help distribute read load. Strategies include:

• Querying only primaries unless one fails.
• Load balancing reads across primaries and replicas.
• Querying the replica with the lowest current load or latency.

The choice depends on consistency requirements and the specifics of the vector database implementation.

View showing sharding with replication. Queries are sent to each logical shard, potentially hitting either the primary or replica node.

Integrating Metadata Filtering

Requirement: Filter by product category and brand.

Approach: We'll assume pre-filtering is desired for efficiency. This means filtering happens before the expensive ANN search on the shards.

Option A: Router-level Filtering

1. The Query Router receives the query (e.g., "running shoes") and the filter criteria (e.g., category='footwear', brand='ExampleBrand').
2. The Router queries the Metadata Store to get a list of vector IDs that match the filter criteria.
3. The Router sends the search query along with the allowed list of vector IDs to each relevant shard.
4. Shards perform ANN search but only consider vectors whose IDs are in the allowed list.

Option B: Shard-level Filtering

1. Metadata (category, brand) is stored or indexed directly within each index shard alongside the vectors.
2. The Query Router sends the query and filter criteria to all shards.
3. Each shard uses its local index (which might be a composite index supporting both vector search and metadata filtering) to find candidates matching both semantic similarity and the filter criteria.

Choice Rationale: Option B often leads to lower latency if the vector database supports efficient integrated filtering, as it avoids the extra hop to a separate metadata store and potentially large ID list transfers. However, it might increase shard complexity and data duplication. For this exercise, let's assume our vector database supports efficient shard-level pre-filtering (Option B).

Example Configuration Parameters

If we were using a configuration file or management API, we might specify parameters like these:

# --- Cluster Topology ---
num_shards: 10
replication_factor: 2
sharding_strategy: id_hash # Use hash of vector ID

# --- Indexing (Applied per Shard) ---
index_type: HNSW
hnsw_m: 32            # HNSW graph connectivity
hnsw_ef_construction: 500 # Build-time quality/speed trade-off
quantization_type: PQ     # Product Quantization
pq_subspaces: 96        # Number of PQ subspaces
pq_bits: 8              # Bits per subspace code

# --- Querying (Default settings) ---
query_ef_search: 150    # Search-time quality/speed trade-off
query_consistency: quorum # Read consistency level across replicas
default_top_k: 10

# --- Metadata Integration ---
metadata_fields: [category (string, indexed), brand (string, indexed)]
filtering_support: pre_filter_integrated # Shard-level pre-filtering enabled

# --- Operational ---
monitoring_endpoint: /metrics
auto_scaling_policy: cpu_utilization > 70%

Important: These are illustrative placeholders. Real systems have many more detailed parameters for tuning memory usage, indexing speed, compaction behavior, network settings, and more.

Monitoring Notes

In this distributed setup, you would need to monitor:

• Overall: End-to-end query latency (p50, p95, p99), overall QPS, error rates.
• Router: Latency added by routing and aggregation, request queue length.
• Shards: Per-shard QPS, per-shard latency, CPU utilization, memory usage (especially for the index), disk I/O, network traffic, cache hit rates (if applicable).
• Replication: Replication lag (how quickly updates propagate to replicas), status of replica nodes.
• Metadata Store (if separate): Query latency, availability.

Summary of Configuration

This exercise walked through the high-level design decisions for scaling a vector search system:

1. Analyze Requirements: Understand the scale (vectors, QPS), performance needs, and features (filtering).
2. Estimate Shard Count: Based on assumed single-node capacity, calculate the number of partitions needed.
3. Determine Replication: Choose a replication factor for fault tolerance and potentially higher read capacity.
4. Plan Query Routing: Define how queries are distributed to shards and results aggregated.
5. Integrate Filtering: Decide on a pre-filtering or post-filtering strategy and how metadata is accessed.
6. Define Parameters: Outline the types of settings controlling topology, indexing, querying, and operations.
7. Consider Monitoring: Identify the essential metrics across the distributed components.

"This framework provides a foundation for tackling implementations using specific vector database technologies, where you would translate these principles into concrete configurations and perform extensive testing and tuning."