Badrish Chandramouli

Modern analytical database systems predominantly rely on column-oriented storage, which offers superior compression efficiency due to the nature of the columnar layout. This compression, however, creates challenges in decoding speed during query processing. Previous research has explored predicate pushdown on encoded values to avoid decoding, but these techniques are restricted to specific encoding schemes and predicates, limiting their practical use. In this paper, we propose a generic predicate pushdown approach that supports arbitrary predicates by leveraging selection pushdown to reduce decoding costs. At the core of our approach is a fast select operator capable of directly extracting selected encoded values without decoding, by using Bit Manipulation Instructions, an instruction set extension to the X86 architecture. We empirically evaluate the proposed techniques in the context of Apache Parquet using both micro-benchmarks and the TPC-H benchmark, and show that our techniques improve the query performance of Parquet by up to one order of magnitude with representative scan queries. Further experimentation using Apache Spark demonstrates speed improvements of up to 5.5X even for end-to-end queries involving complex joins.

We present Escapement, a video prototyping tool that introduces a powerful new concept for prototyping screen-based interfaces by flexibly mapping sensor values to dynamic playback control of videos. This recasts the time dimension of video mock-ups as sensor-mediated interaction. This abstraction of time as interaction, which we dub video-escapement prototyping, empowers designers to rapidly explore and viscerally experience direct touch or sensor-mediated interactions across one or more device displays. Our system affords cross-device and bidirectional remote (tele-present) experiences via cloud-based state sharing across multiple devices. This makes Escapement especially potent for exploring multi-device, dual-screen, or remote-work interactions for screen-based applications. We introduce the core concept of sensor-mediated abstraction of time for quickly generating video-based interactive prototypes of screen-based applications, share the results of observations of long-term usage of video-escapement techniques with experienced interaction designers, and articulate design choices for supporting a reflective, iterative, and open-ended creative design process.

Today, businesses rely on efficiently running analytics on large amounts of operational and historical data to gain business insights and competitive advantage. Increasingly, such analytics are run using cloud-based data analytics services, such as Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, and Snowflake. These services persist and process data in compressed, columnar formats, stored in large blocks, each of which contains thousands or millions of records. For these services, disk I/O from (remote) cloud storage is often one of the dominant costs for query processing. To reduce the amount of I/O, services often maintain per-block metadata, such as zone maps, which are used to skip blocks that are irrelevant to the query, leading to lower query execution times. However, the effectiveness of block skipping via zone maps is dependent on how the records are assigned to blocks. Recent work on instance-optimized data layouts aims to maximize block skipping by specializing the block assignment strategy to a specific dataset and workload. However, these existing approaches only optimize the layout for a single table. In this paper, we propose MTO, an instance-optimized data layout framework that determines the blocking strategy for all tables in a multi-table database in the presence of joins, such as in a star or snowflake schema common in real-world workloads. MTO takes advantage of sideways information passing through joins to jointly optimize the layout for all tables, which results in better block skipping and hence reduced query execution times. Experiments on a commercial cloud-based analytics service show that MTO achieves up to 93% reduction in blocks accessed and 75% reduction in end-to-end query times compared to alternative blocking strategies.

Millions of sensors, mobile applications and machines now generate billions of events. Specialized many-core key-value stores (KVSs) can ingest and index these events at high rates (over 100 Mops/s on one machine) if events are generated on the same machine; however, to be practical and cost-effective they must ingest events over the network and scale across cloud resources elastically. We present Shadowfax, a new distributed KVS based on FASTER, that transparently spans DRAM, SSDs, and cloud blob storage while serving 130 Mops/s/VM over commodity Azure VMs using conventional Linux TCP. Beyond high single-VM performance, Shadowfax uses a unique approach to distributed reconfiguration that avoids any server-side key ownership checks or cross-core coordination both during normal operation and migration. Hence, Shadowfax can shift load in 17 s to improve system throughput by 10 Mops/s with little disruption. Compared to the state-of-the-art, it has 8x better throughput (than Seastar+memcached) and avoids costly I/O to move cold data during migration. On 12 machines, Shadowfax retains its high throughput to perform 930 Mops/s, which, to the best of our knowledge, is the highest reported throughput for a distributed KVS used for large-scale data ingestion and indexing.

Because financial applications rely on a continual stream of time-sensitive data, any data management system must be able to process complex queries on the fly. Although many organizations turn to custom solutions, data stream management systems can offer the same low-latency processing with the flexibility to handle a range of applications.