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APEX: A High-Performance Learned Index on Persistent Memory
Summary: APEX is a learned index for persistent memory, merging ALEX with PM-aware design to enable persistence, concurrency, and instant recovery. On Intel DCPMM, it reduces PM accesses and yields up to 15x faster performance than prior PM indexes, with ~42 ms recovery.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12935
- Venue
- VLDB
- Year
- 2022
- Pagerank
- 6.605467e-05
- Overall Rank
- 3,945 | 72.59%
- DOI
-
10.14778/3494124.3494141
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 27 of 27 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 4,056 |
Are Updatable Learned Indexes Ready? |
2022 |
VLDB |
6.4905689e-05 |
| 5,072 |
Learned Index: A Comprehensive Experimental Evaluation |
2023 |
VLDB |
5.7121108e-05 |
| 5,327 |
DILI: A Distribution-Driven Learned Index |
2023 |
VLDB |
5.5660777e-05 |
| 5,601 |
PLIN: A Persistent Learned Index for Non-Volatile Memory with High Performance and Instant Recovery |
2023 |
VLDB |
5.4158958e-05 |
| 5,655 |
NFL: Robust Learned Index via Distribution Transformation |
2022 |
VLDB |
5.3877506e-05 |
| 5,769 |
Oasis: An Optimal Disjoint Segmented Learned Range Filter |
2024 |
VLDB |
5.3326049e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 7,152 |
Bf-Tree: A Modern Read-Write-Optimized Concurrent Larger-Than-Memory Range Index |
2024 |
VLDB |
4.8126591e-05 |
| 7,389 |
Making In-Memory Learned Indexes Efficient on Disk |
2024 |
SIGMOD |
4.7386163e-05 |
| 7,631 |
Evaluating Persistent Memory Range Indexes: Part Two |
2022 |
VLDB |
4.6878629e-05 |
| 7,870 |
SALI: A Scalable Adaptive Learned Index Framework based on Probability Models |
2023 |
SIGMOD |
4.6271153e-05 |
| 8,079 |
Accelerating String-key Learned Index Structures via Memoization-based Incremental Training |
2024 |
VLDB |
4.5873372e-05 |
| 8,595 |
OptiQL: Robust Optimistic Locking for Memory-Optimized Indexes |
2023 |
SIGMOD |
4.4844188e-05 |
| 8,668 |
Algorithmic Complexity Attacks on Dynamic Learned Indexes |
2024 |
VLDB |
4.4671214e-05 |
| 8,855 |
A Design Space Exploration and Evaluation for Main-Memory Hash Joins in Storage Class Memory |
2023 |
VLDB |
4.4306395e-05 |
| 8,990 |
The Past, Present and Future of Indexing on Persistent Memory |
2022 |
VLDB |
4.4115395e-05 |
| 9,093 |
AirIndex: Versatile Index Tuning Through Data and Storage |
2023 |
SIGMOD |
4.3932886e-05 |
| 9,351 |
Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs |
2024 |
SIGMOD |
4.3490308e-05 |
| 9,618 |
A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach |
2025 |
SIGMOD |
4.3131993e-05 |
| 9,826 |
PLATON: Top-down R-tree Packing with Learned Partition Policy |
2023 |
SIGMOD |
4.2710095e-05 |
| 10,087 |
High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,172 |
HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,281 |
Operation-Aware Hybrid Locking for Modern In-Memory Indexes |
2026 |
VLDB |
4.1905499e-05 |
| 10,331 |
LiBox: A Learned Index as an Array to Minimize Last-Mile Search |
2026 |
VLDB |
4.1905499e-05 |
| 10,571 |
FB+-tree: A Memory-Optimized B+-tree with Latch-Free Update |
2025 |
VLDB |
4.1905499e-05 |
| 10,719 |
DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees |
2025 |
VLDB |
4.1905499e-05 |
| 11,154 |
Data Pipes: Declarative Control over Data Movement |
2023 |
CIDR |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 104 |
Making B+-Trees Cache Conscious in Main Memory |
2000 |
SIGMOD |
0.00049475932 |
| 643 |
FPTree: A Hybrid SCM-DRAM Persistent and Concurrent B-Tree for Storage Class Memory |
2016 |
SIGMOD |
0.00018733394 |
| 809 |
Persistent B+-Trees in Non-Volatile Main Memory |
2015 |
VLDB |
0.00016409797 |
| 819 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016237497 |
| 844 |
The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds |
2020 |
VLDB |
0.00015964123 |
| 1,085 |
HOT: A Height Optimized Trie Index for Main-Memory Database Systems |
2018 |
SIGMOD |
0.00014173956 |
| 1,300 |
Reducing the Storage Overhead of Main-Memory OLTP Databases with Hybrid Indexes |
2016 |
SIGMOD |
0.00012711153 |
| 1,306 |
BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory |
2018 |
VLDB |
0.00012674725 |
| 1,365 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012379754 |
| 1,438 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011965956 |
| 1,464 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.0001184772 |
| 1,608 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011169837 |
| 1,823 |
Dash: Scalable Hashing on Persistent Memory |
2020 |
VLDB |
0.00010403355 |
| 1,887 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010201938 |
| 2,108 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5283642e-05 |
| 2,508 |
LB+-Trees: Optimizing Persistent Index Performance on 3DXPoint Memory |
2020 |
VLDB |
8.6251065e-05 |
| 2,550 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5569576e-05 |
| 2,676 |
Effectively Learning Spatial Indices |
2020 |
VLDB |
8.326321e-05 |
| 2,986 |
DPTree: Differential Indexing for Persistent Memory |
2020 |
VLDB |
7.7757727e-05 |
| 3,341 |
Evaluating Persistent Memory Range Indexes |
2020 |
VLDB |
7.1984492e-05 |
| 6,416 |
Patience is a Virtue: Revisiting Merge and Sort on Modern Processors |
2014 |
SIGMOD |
5.0645953e-05 |
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