For an overview of our one-time subgraph matching optimizer, check our paper.
We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multi-way intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called intersection-cost. The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work.
DO NOT DISTRIBUTE. USE ONLY FOR ACADEMIC RESEARCH PURPOSES.
- To do a full clean build:
./gradlew clean build installDist - All subsequent builds:
./gradlew build installDist
After building, run the following command in the project root directory:
. ./env.sh
You can now move into the scripts folder to load a dataset and execute queries:
cd scripts
A dataset may consist of two files: (i) a vertex file, where IDs are from 0 to N and each line is of the format (ID,LABEL); and (ii) an edge file where each line is of the format (FROM,TO,LABEL). If the vertex file is omitted, all vertices are assigned the same label. We mainly used datasets from SNAP. The serialize_dataset.py script lets you load datasets from csv files and serialize them to the appropriate format for quick subsequent loading.
To load and serialize a dataset from a single edges files, run the following command in the scripts folder:
python3 serialize_dataset.py /absolute/path/edges.csv /absolute/path/data
The system will assume that all vertices have the same label in this case. The serialized graph will be stored in the data directory. If the dataset consists of an edges file and a vertices file, the following command can be used instead:
python3 serialize_dataset.py /absolute/path/edges.csv /absolute/path/data -v /absolute/path/vertices.csv
After running one of the commands above, a catalog can be generated for the optimizer using the serialize_catalog.py script.
python3 serialize_catalog.py /absolute/path/data
Once a dataset has been prepared, executing a query is as follows:
python3 execute_query.py "(a)->(b),(b)->(c),(c)->(d)" /absolute/path/data
An output example on the dataset of Amazon0601 from SNAP with 1 edge label and 1 verte label is shown below. The dataset loading time, the opimizer run time, the quey execution run time and the query plan with the number of output and intermediate tuples are logged.
Dataset loading run time: 626.713398 (ms)
Optimizer run time: 9.745375 (ms)
Plan initialization before exec run time: 9.745375 (ms)
Query execution run time: 2334.2977 (ms)
Number output tuples: 118175329
Number intermediate tuples: 34971362
Plan: SCAN (a)->(c), Single-Edge-Extend TO (b) From (a[Fwd]), Multi-Edge-Extend TO (d) From (b[Fwd]-c[Fwd])
In order to invoke a multi-threaded execution, one can execute the query above with the following command to use 2 threads.
python3 execute_query.py "(a)->(b),(b)->(c),(c)->(d)" /absolute/path/data -t 2
The query above assigns an arbitrary edge and vertex labels to (a), (b), (c), (a)->(b), and (b)->(c). Use it with unlabeled datasets only. When the dataset has labels, assign labels to each vertex and edge as follows:
python3 execute_query.py "(a:person)-[friendof]->(b:person), (b:person)-[likes]->(c:movie)" /absolute/path/data
Note that the JVM heap by default is allocated a max of 2GB of memory. Changing the JVM heap maximum size can be done by prepending JAVA_OPTS='-Xmx500G' when calling the python scripts:
JAVA_OPTS='-Xmx500G' python3 serialize_catalog.py /absolute/path/data
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