HammerSlide

This repo contains the implementation of HammerSlide, an extension of the Two-Stacks algorithm. Hammerslide can be used for incremental window aggregation and requires associative operators and data to arrive in-order (FIFO algorithm).

The algorithm requires external coordination for performing operations on top of windows (e.g., a slicing technique like Panes or Pairs). It is important to perform the insertion/eviction operations based on the window semantics to get correct results.

Implementation

Hammerslide uses a fixed-size circular buffer to store the input data, a variable to store the aggregate values of the back stack and an array for the aggregate values of the front stack. During the swap operation of the Two-Stacks algorithm, Hammerslide reads data from the end to the start of the circular buffer and stores one single aggregate value per window slide in the respective position of the array with the aggregates. In addition, no input values are copied from the back to the front stack. For eviction, only the pointers to the circular buffer and the array are updated and new input data are going to overwrite the old data.

TODO

• Generalize the current solution. Only MIN and SUM aggregate functions are implemented with SIMD instructions.
• Implement time-based windows.
• The current implementation is based on the assumption that the input is of type int and that the CPU supports AVX instructions.

Dependencies

Install the following:

sudo apt install libtbb-dev

and compile with GNU c++ compiler.

API

insert(T)
insert(T *, start, end) // bulk insertion that takes advantage of SIMD instructions
evict(numberOfItems = 1)
query(isSIMD = true)    // perform swap with SIMD instructions or not

How to cite HammerSlide

• [ADMS] Georgios Theodorakis, Alexandros Koliousis, Peter R. Pietzuch, and Holger Pirk. Hammer Slide: Work- and CPU-efficient Streaming Window Aggregation, ADMS, 2018

@inproceedings{Theodorakis2018HammerSW,
  title={Hammer Slide: Work- and CPU-efficient Streaming Window Aggregation},
  author={G. Theodorakis and A. Koliousis and Peter R. Pietzuch and H. Pirk},
  booktitle={ADMS@VLDB},
  year={2018}
}

Other related publications

• [EDBT] Georgios Theodorakis, Peter R. Pietzuch, and Holger Pirk. SlideSide: A fast Incremental Stream Processing Algorithm for Multiple Queries, EDBT, 2020
• [SIGMOD] Alexandros Koliousis, Matthias Weidlich, Raul Castro Fernandez, Alexander Wolf, Paolo Costa, and Peter Pietzuch. Saber: Window-Based Hybrid Stream Processing for Heterogeneous Architectures, SIGMOD, 2016
• [SIGMOD] Georgios Theodorakis, Alexandros Koliousis, Peter R. Pietzuch, and Holger Pirk. LightSaber: Efficient Window Aggregation on Multi-core Processors, SIGMOD, 2020
• [VLDB] Georgios Theodorakis, Fotios Kounelis, Peter R. Pietzuch, and Holger Pirk. Scabbard: Single-Node Fault-Tolerant Stream Processing, VLDB, 2022