LightDB is a database management system (DBMS) designed to efficiently ingest, store, and deliver virtual reality (VR) content at scale. LightDB currently targets both live and prerecorded spherical panoramic (a.k.a. 360°) and light field VR videos. It persists content as a multidimensional field that includes both spatiotemporal and angular (i.e., orientation) dimensions. ontent delivered through LightDB offers improved throughput, less bandwidth, and scales to many concurrent connections.

See our website for more details.

1. Docker

Install a containerized version of LightDB via DockerHub

docker pull lightdb/core
docker run -it lightdb/core

2. Build from source

To build from source, clone the repository, install dependencies, generate a Makefile, and build:

git clone https://github.com/uwdb/lightdb.git
cat packages.txt | xargs sudo apt-get install
mkdir build
cd build
cmake ..
make

LightDB support declarative queries over temporal light fields (TLF). To obtain a reference to a TLF, scan it from the internal LightDB catalog or load it from disk:

    tlf = Scan("internal-tlf")
    tlf2 = Load("~/video.mp4")

Each of the algebraic operators described in section 3.2 of the LightDB paper may be applied to a TLF. For example, to apply the predictive 360° query described in section 3.5, a user would write the following query:

    query = Scan("internal-tlf")
              .Partition(Dimension::Theta, degrees{90})
              .Partition(Dimension::Phi, degrees{90})
              .Subquery(lambda tlf: tlf.Encode(Codec.hevc, bitrate=b))
              .Store("result")
    Coordinator().execute(query)

Note that this query performs the workload presented in our SIGMOD'16 VideoCloud demonstration and discussed in the video shown below. Note that LightDB's architecture and functionality now extend far beyond this use case; see the paper for more details.

Open an issue with any questions or issues you might have.

If you use LightDB, please cite our VLDB'18 paper:

LightDB: A DBMS for Virtual Reality Video
Brandon Haynes, Amrita Mazumdar, Armin Alaghi, Magdalena Balazinska, Luis Ceze, and Alvin Cheung
VLDB:1192-1205 [PDF]

@article{DBLP:journals/pvldb/HaynesMABCC18,
  author    = {Brandon Haynes and
               Amrita Mazumdar and
               Armin Alaghi and
               Magdalena Balazinska and
               Luis Ceze and
               Alvin Cheung},
  title     = {{LightDB}: {A} {DBMS} for Virtual Reality Video},
  journal   = {{PVLDB}},
  volume    = {11},
  number    = {10},
  pages     = {1192--1205},
  year      = {2018},
  doi       = {10.14778/3231751.3231768},
}

This work is supported by the National Science Foundation through NSF grants CCF-1703051, IIS-1247469, IIS-1546083, CCF-1518703, and CNS-1563788; DARPA award FA8750-16-2-0032; DOE award DE-SC0016260; a Google Faculty Research Award; an award from the University of Washington Reality Lab; and gifts from the Intel Science and Technology Center for Big Data, Intel Corporation, Adobe, Amazon, Facebook, Huawei and Google.