I’m working on a project that involves creating a vector search index for a massive dataset consisting of 1.3 trillion tokens. I want to use AutoFAISS in a distributed environment to handle the scale of the data. My current setup involves using PySpark to distribute the indexing tasks across multiple nodes.
Context:
• I’m setting up a distributed environment with multiple nodes using Apache Spark.
• The goal is to build and optimize FAISS indices in parallel across these nodes to manage the large-scale data efficiently.
Problem:
I’m encountering challenges in correctly configuring and implementing the distributed setup using PySpark and AutoFAISS. Specifically:
• How should I configure PySpark and the Spark cluster to ensure efficient parallel processing across nodes?
• What are the best practices for setting up the environment (e.g., memory management, network configuration) to avoid common pitfalls?
• How do I properly handle data shuffling and node communication to optimize the performance of distributed AutoFAISS?
Current Setup: • I’m using Ubuntu 20.04 on all nodes. • Apache Spark 3.2.1 is installed on both the master and worker nodes. • AutoFAISS is downloaded and ready to be executed via PySpark.
Steps Taken:
• I’ve set up the Spark cluster and configured the worker nodes.
• Attempted to run AutoFAISS using a PySpark script, but I’m facing issues with resource allocation and parallel processing efficiency.
Questions:
1. What specific PySpark configurations (e.g., executor memory, cores) are recommended for running AutoFAISS in a distributed environment?
2. Are there any examples or best practices for effectively managing such a large-scale vector indexing task using PySpark?
3. How can I optimize the Spark cluster setup to minimize latency and maximize throughput during the indexing process?
I'm mostly working off of this article: Indexing 1T Vectors (https://github.com/facebookresearch/faiss/wiki/Indexing-1T-vectors). I think the data is too big for AutoFaiss, but I can use that for experiments.
