How does Haystack support multi-threading and parallel processing?

Haystack supports multi-threading and parallel processing by leveraging Python’s native concurrency tools and integrating with external frameworks for distributed workloads. At its core, Haystack pipelines are designed to execute nodes (components like retrievers or classifiers) concurrently when possible. For CPU-bound tasks, such as text processing or embedding generation, Haystack can use Python’s multiprocessing module to bypass the Global Interpreter Lock (GIL) and utilize multiple CPU cores. For I/O-bound operations, like querying external APIs or databases, it employs multi-threading to handle waiting periods efficiently without blocking the main thread. This flexibility ensures developers can optimize performance based on task type.

A key mechanism is Haystack’s support for asynchronous execution. For example, when running a pipeline with multiple retrievers (e.g., a dense retriever and a sparse retriever), these components can process queries in parallel. Developers can also enable sharding, which splits a document corpus into smaller chunks and processes them simultaneously across threads or processes. Additionally, Haystack integrates with distributed computing frameworks like Ray, allowing workloads to scale across clusters. For instance, embedding generation for large datasets can be parallelized using Ray actors, splitting the workload across machines while maintaining a simple API.

Practical implementations include using multiprocessing.Pool in Haystack’s Converter classes to preprocess documents in parallel during indexing. In retrieval-augmented pipelines, nodes like the EmbeddingRetriever can process batches of documents concurrently, reducing latency. Developers can also configure pipelines to run retrievers and readers asynchronously—for example, fetching documents from Elasticsearch while simultaneously initializing a model for answer extraction. By combining Python’s concurrency primitives with distributed frameworks, Haystack balances simplicity and scalability, letting developers handle large-scale data or high-throughput queries without rewriting core logic.