PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems

Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5x while reducing memory footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 201

SAP HANA Platform

Tato práce pojednává o databázi pracující v paměti nazývané SAP HANA. Detailně popisuje architekturu a nové technologie, které tato databáze využívá. V další části se zabývá porovnáním rychlosti provedení vkládání a vybírání záznamů z databáze se stávající používanou relační databází MaxDB. Pro účely tohoto testování jsem vytvořil jednoduchou aplikaci v jazyce ABAP, která umožňuje testy provádět a zobrazuje jejich výsledky. Ty jsou shrnuty v poslední kapitole a ukazují SAP HANA jako jednoznačně rychlejší ve vybírání dat, avšak srovnatelnou, či pomalejší při vkládání dat do databáze. Přínos mé práce vidím v shrnutí podstatných změn, které s sebou data uložená v paměti přináší a názorné srovnání rychlosti provedení základních typů dotazů.This thesis discusses the in-memory database called SAP HANA. It describes in detail the architecture and new technologies used in this type of database. The next section presents a comparison of speed of the inserting and selecting data from the database with existing relational database MaxDB. For the purposes of this testing I created a simple application in ABAP language, which allows user to perform and display their results. These are summarized in the last chapter and demonstrate SAP HANA as clearly faster during selection of data, but comparable, or slower when inserting data into the database. I see contribution of my work in the summary of significant changes that come with data stored in the main memory and brings comparison of speed of basic types of queries.

Building Efficient Query Engines in a High-Level Language

Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance, instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend. In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes the entire query engine by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other. We evaluate our approach with the TPC-H benchmark and show that: (a) With all optimizations enabled, LegoBase significantly outperforms a commercial database and an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches. (c) The compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for compiling query engines

Database architecture evolution: Mammals flourished long before dinosaurs became extinct

The holy grail for database architecture research is to find a solution that is Scalable & Speedy, to run on anything from small ARM processors up to globally distributed compute clusters, Stable & Secure, to service a broad user community, Small & Simple, to be comprehensible to a small team of programmers, Self-managing, to let it run out-of-the-box without hassle. In this paper, we provide a trip report on this quest, covering both past experiences, ongoing research on hardware-conscious algorithms, and novel ways towards self-management specifically focused on column store solutions