We present the Succinct Range Filter (SuRF), a fast and compact data structure for approximate membership tests. Unlike traditional Bloom filters, SuRF supports both single-key lookups and common range queries: open-range queries, closed-range queries, and range counts. SuRF is based on a new data structure called the Fast Succinct Trie (FST) that matches the point and range query performance of state-of-the-art order-preserving... Read More

The first step towards an autonomous database management system (DBMS) is the ability to model the target application’s workload. This is necessary to allow the system to anticipate future workload needs and select the proper optimizations in a timely manner. Previous forecasting techniques model the resource utilization of the queries. Such metrics, however, change whenever the physical design of the... Read More

SPDK has been successful in enabling a large class of high performance user mode storage applications and appliance. SPDK provides direct access to local NVMe SSDs as well as access to remote storage targets using NVMeoF. SPDK provides a highly concurrent and asynchronous runtime with no locking in the I/O path. High throughput and low latency is realized by directly... Read More

Oracle Database In-Memory dual format was first introduced in 12c in 2013, it optimizes both analytics and mixed workload OLTP, delivering outstanding performance for transactions while simultaneously supporting real-time analytics, business intelligence, and reports. In this talk, I will go over different features in Oracle Database In-Memory, and describe how we accelerate joins and aggregations on In-Memory Database. Read More

Siva Sudhir, Pooja Nilangekar, Bohan Zhang, and Aaron Tian will present their capstone projects. Bohan: OtterTune is really coming:  how to use OtterTune to tune your DBMS automatically Aaron: Fast Durability and Recovery in In-memory Databases Siva: Compilation of User-Defined Functions in Peloton Read More

For partitioned tables, maintaining good clustering properties for frequently accessed dimensions is critical for partition pruning performance. Naive methods of clustering maintenance could be expensive, especially when the clustering dimensions are different from the dimensions with which the data is loaded. On the other hand, approximate clustering is cheaper to maintain while still resulting in good pruning performance. In this... Read More

Current architectures for main-memory online transaction processing (OLTP) database management systems (DBMS) typically use random scheduling to assign transactions to threads. This approach achieves uniform load across threads but it ignores the likelihood of conflicts between transactions. If the DBMS could estimate the potential for transaction conflict and then intelligently schedule transactions to avoid conflicts, then the system could improve... Read More

PipeDream is a new distributed training system for deep neural networks (DNNs) that partitions ranges of DNN layers among machines, and aggressively pipelines computation and communication. Today’s pervasive use of data-parallel training performs well for DNNs of up to 10–20 million model parameters, but inter-machine communication dominates for models that are even 10x larger (e.g., up to 85% of time... Read More

In this seminar - Alok will present Striim, a distributed Streaming platform, and talk about the platform's motivation, distributed architecture, use cases, and open challenges. Read More

In the beginning there was a DBMS, a flexible piece of software that could be used for OLTP and OLAP workloads. When transaction throughput increased and data sizes grew the database needed to be split into two, each instance optimized for a particular workload. And so it has been ever since and the distance between the two systems has increased,... Read More