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Article summary:
1. APEX is a new PM-optimized learned index that offers high performance, persistence, concurrency, and instant recovery.
2. APEX combines the best of past PM optimizations and learned indexes to reduce PM accesses while still exploiting machine learning.
3. Evaluation on Intel DCPMM shows that APEX can perform up to ~15× better than existing PM indexes and can recover from failures in ~42ms.
Article analysis:
The article “APEX: A High-Performance Learned Index on Persistent Memory” provides an overview of a new persistent memory (PM) optimized learned index called APEX. The article is written by experts in the field and provides detailed information about the design and evaluation of the index. The authors provide evidence for their claims by citing relevant research papers and other sources such as Intel Optane DIMM Pricing, OpenStreetMap on AWS, BzTree, HOT, Patience is a Virtue, YCSB, 3D XPoint Launch Keynote, ML-Index, uTree, Benchmarking Cloud Serving Systems with YCSB, Revisiting Hash Table Design for Phase Change Memory, ALEX: An Updatable Adaptive Learned Index, Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads, A Scalable Concurrent Malloc (3) Implementation for FreeBSD and The PGM-Index: A Fully-Dynamic Compressed Learned Index with Provable Worst-Case Bounds.
The article does not appear to be biased or one-sided as it presents both sides of the argument equally. It also does not contain any promotional content or partiality towards any particular technology or product. Furthermore, the authors have noted possible risks associated with using this technology such as data loss due to power failure or system crash which could lead to data corruption or inconsistency in the database.
In conclusion, this article appears to be reliable and trustworthy as it provides detailed information about the design and evaluation of APEX along with evidence from relevant sources to support its claims.