An official website of the United States government
Data-intensive analytical applications need to support both efficient reads and writes. However, what is usually a good data layout for an update-heavy workload, is not well-suited for a read-mostly one and vice versa. Modern analytical data systems rely on columnar layouts and employ delta stores to inject new data and updates. We show that for hybrid workloads we can achieve close to one order of magnitude better performance by tailoring the column layout design to the data and query workload. Our approach navigates the possible design space of the physical layout: it organizes each column’s data by determining the number of partitions, their corresponding sizes and ranges, and the amount of buffer space and how it is allocated. We frame these design decisions as an optimization problem that, given workload knowledge and performance requirements, provides an optimal physical layout for the workload at hand. To evaluate this work, we build an in-memory storage engine, Casper, and we show that it outperforms state-of-the-art data layouts of analytical systems for hybrid workloads. Casper delivers up to 2.32x higher throughput for update-intensive workloads and up to 2.14x higher throughput for hybrid workloads. We further show how to make data layout decisions robust to workload variation by carefully selecting the input of the optimization.
more »
« less
TASM: A Tile-Based Storage Manager for Video Analytics
Modern video data management systems store videos as a single encoded file, which significantly limits possible storage level optimizations. We design, implement, and evaluate TASM, a new tile-based storage manager for video data. TASM uses a feature in modern video codecs called "tiles" that enables spatial random access into encoded videos. TASM physically tunes stored videos by optimizing their tile layouts given the video content and a query workload. Additionally, TASM dynamically tunes that layout in response to changes in the query workload or if the query workload and video contents are incrementally discovered. Finally, TASM also produces efficient initial tile layouts for newly ingested videos. We demonstrate that TASM can speed up subframe selection queries by an average of over 50% and up to 94%. TASM can also improve the throughput of the full scan phase of object detection queries by up to 2×.
more »
« less
- Award ID(s):
- 1703051
- PAR ID:
- 10257065
- Date Published:
- Journal Name:
- 2021 IEEE 37th International Conference on Data Engineering (ICDE)
- Page Range / eLocation ID:
- 1775 to 1786
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Query driven cardinality estimation models learn from a historical log of queries. They are lightweight, having low storage requirements, fast inference and training, and are easily adaptable for any kind of query. Unfortunately, such models can suffer unpredictably bad performance under workload drift, i.e., if the query pattern or data changes. This makes them unreliable and hard to deploy. We analyze the reasons why models become unpredictable due to workload drift, and introduce modifications to the query representation and neural network training techniques to make query-driven models robust to the effects of workload drift. First, we emulate workload drift in queries involving some unseen tables or columns by randomly masking out some table or column features during training. This forces the model to make predictions with missing query information, relying more on robust features based on up-to-date DBMS statistics that are useful even when query or data drift happens. Second, we introduce join bitmaps, which extends sampling-based features to be consistent across joins using ideas from sideways information passing. Finally, we show how both of these ideas can be adapted to handle data updates. We show significantly greater generalization than past works across different workloads and databases. For instance, a model trained with our techniques on a simple workload (JOBLight-train), with 40ksynthetically generated queries of at most 3 tables each, is able to generalize to the much more complex Join Order Benchmark, which include queries with up to 16 tables, and improve query runtimes by 2× over PostgreSQL. We show similar robustness results with data updates, and across other workloads. We discuss the situations where we expect, and see, improvements, as well as more challenging workload drift scenarios where these techniques do not improve much over PostgreSQL. However, even in the most challenging scenarios, our models never perform worse than PostgreSQL, while standard query driven models can get much worse than PostgreSQL.more » « less
-
Querying video data has become increasingly popular and useful. Video queries can be complex, ranging from retrieval tasks (“find me the top videos that have … ”), to analytics (“how many videos contained object X per day?”), to excerpting tasks (“highlight and zoom into scenes with object X near object Y”), or combinations thereof. Results for video queries are still typically shown as either relational data or a primitive collection of clickable thumbnails on a web page. Presenting query results in this form is an impedance mismatch with the video medium: they are cumbersome to skim through and are in a different modality and information density compared to the source data. We describe V2V, a system to efficiently synthesize video results for video queries. V2V returns a fully-edited video, allowing the user to consume results in the same manner as the source videos. A key challenge is that synthesizing video results from a collection of videos is computationally intensive, especially within interactive query response times. To address this, V2V features a grammar to express video transformations in a declarative manner and a heuristic optimizer that improves the efficiency of V2V processing in a manner similar to how databases execute relational queries. Experiments show that our V2V optimizer enables video synthesis to run 3x faster.more » « less
-
Large volumes of videos are continuously recorded from cameras deployed for traffic control and surveillance with the goal of answering “after the fact” queries: identify video frames with objects of certain classes (cars, bags) from many days of recorded video. Current systems for processing such queries on large video datasets incur either high cost at video ingest time or high latency at query time. We present Focus, a system providing both low-cost and low-latency querying on large video datasets. Focus’s architecture flexibly and effectively divides the query processing work between ingest time and query time. At ingest time (on live videos), Focus uses cheap convolutional network classifiers (CNNs) to construct an approximate index of all possible object classes in each frame (to handle queries for any class in the future). At query time, Focus leverages this approximate index to provide low latency, but compensates for the lower accuracy of the cheap CNNs through the judicious use of an expensive CNN. Experiments on commercial video streams show that Focus is 48× (up to 92×) cheaper than using expensive CNNs for ingestion, and provides 125× (up to 607×) lower query latency than a state-of-the-art video querying system (NoScope).more » « less
-
Recent advancements in deep learning techniques facilitate intelligent-query support in diverse applications, such as content-based image retrieval and audio texturing. Unlike conventional key-based queries, these intelligent queries lack efficient indexing and require complex compute operations for feature matching. To achieve high-performance intelligent querying against massive datasets, modern computing systems employ GPUs in-conjunction with solid-state drives (SSDs) for fast data access and parallel data processing. However, our characterization with various intelligent-query workloads developed with deep neural networks (DNNs), shows that the storage I/O bandwidth is still the major bottleneck that contributes 56%--90% of the query execution time. To this end, we present DeepStore, an in-storage accelerator architecture for intelligent queries. It consists of (1) energy-efficient in-storage accelerators designed specifically for supporting DNN-based intelligent queries, under the resource constraints in modern SSD controllers; (2) a similarity-based in-storage query cache to exploit the temporal locality of user queries for further performance improvement; and (3) a lightweight in-storage runtime system working as the query engine, which provides a simple software abstraction to support different types of intelligent queries. DeepStore exploits SSD parallelisms with design space exploration for achieving the maximal energy efficiency for in-storage accelerators. We validate DeepStore design with an SSD simulator, and evaluate it with a variety of vision, text, and audio based intelligent queries. Compared with the state-of-the-art GPU+SSD approach, DeepStore improves the query performance by up to 17.7×, and energy-efficiency by up to 78.6×.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDE51399.2021.00156
