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Vector Indexing Architecture

Vector Indexing Architecture

6
posts
2022–2023

Pinecone's vector database architecture is purpose-built for vector search, moving beyond single-algorithm approaches. It employs a per-slab adaptive indexing strategy (Ananas, PQFS, and IVF) on a serverless infrastructure, designed to handle the memory, compute, and scale requirements of real-time AI applications. This approach contrasts with 'bolt-on' solutions that integrate algorithms like HNSW into existing architectures, which Pinecone argues are unable to meet production demands due to me. This release further optimizes performance at scale, significantly lowering search latency for large indexes (e.g., 3.4x faster for 100M vectors on s1 pods). It also improves predictability by flattening the impact of scaling on search latency and reducing variance between p50 and p95 latencies. Data ingestion has been made faster and more reliable, with upserts remaining fast even as the index approaches capacity. Deployment options have been expanded with new regions on GCP and AWS for Standard plan users.

2023

Great Algorithms Are Not Enough

10/27/2023

This post explains why a single algorithm like HNSW is insufficient for a production-grade vector database. It details the limitations of 'bolt-on' indexing strategies, particularly concerning memory consumption, parameter tuning complexity, and the operational challenges of updates and deletes with algorithms like HNSW. The post highlights Pinecone's proprietary approach, which involves a purpose-built architecture with per-slab adaptive indexing (Ananas, PQFS, and IVF) on a serverless platform, designed to address these limitations and meet the demands of real-world AI workloads.

2022

High-throughput vector indexes now generally available and free

12/8/2022

This post announces the general availability of p2 pods, which utilize a new graph-based index for significantly improved search speeds (up to 10x faster than p1) and higher throughput (up to 200 QPS per replica). It highlights that p2 pods are now 50% lower in cost, support dot product distance metrics in addition to cosine and euclidean, and are available on the Starter (free) plan. The post also provides guidance on when to consider p2 pods for low-latency (<10ms) and high-throughput (>100 QPS) applications, with sample query latencies and throughput examples for different pod sizes.

Rewriting a high performance vector database in Rust

9/14/2022

This post details the engineering team's experience rewriting Pinecone's vector database from Python and C++ to Rust. It covers the rationale for choosing Rust (performance, memory management, ease of use), the implementation of continuous benchmarking using Criterion to identify performance regressions, and the development of an observability layer with custom macros for metrics (OpenMetrics) and tracing (OpenTelemetry). It also discusses performance optimizations achieved through Rust's access to low-level instruction sets (e.g., AVX-512), memory layout improvements, and strategies for running asynchronous and CPU-intensive tasks using Tokio, spawn_blocking, Rayon, and MPSC channels.

Inside the Pinecone

8/22/2022

This post details the development of Pinecone's in-house, dynamic, and filterable graph index, contrasting it with HNSW and other existing solutions. It also describes the migration from RocksDB to a new bitcask-like storage engine ('memkey') optimized for vector storage, leading to significant performance and cost improvements. Furthermore, the post discusses the company's decision to rewrite its core codebase in Rust and Go, highlighting the unexpected benefits in development velocity and operational stability.

Vector search just got up to 10x faster, easier to set up, and vertically scalable

8/16/2022

Introduced vertical scaling for live indexes with zero downtime, allowing pod capacity to be doubled. Launched public preview of collections for snapshotting and creating new indexes from saved data, serving as a single source of truth for vector data. Introduced p2 pods, a new pod type optimized for high throughput with up to 10x lower latencies and higher throughput than p1 pods, using a new graph-based index that trades off ingestion speed, filter performance, and recall. Also announced ~50% faster queries on s1 and p1 pods, 5x greater capacity on the Starter plan with s1 pods, and updated pricing for new users.

February Release: Performance at Scale, Predictability, and Control

2/16/2022

This post details significant performance improvements in Pinecone's vector search capabilities. Key technical contributions include rewriting core engine parts in Rust, optimizing I/O operations, implementing dynamic caching, and re-configuring storage formats. These efforts resulted in substantially lower search latencies for large indexes, particularly on storage-optimized (s1) pods, making them more practical for real-time applications. The release also addresses predictability by reducing latency variance and improving data ingestion reliability. Additionally, it introduces new deployment options with expanded region availability for Standard plan users.