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DPconv: Super-Polynomially Faster Join Ordering

Build

cd src/
mkdir -p build
cd build
cmake ..
make

Clique Benchmark

Generating Fig. 6 and Fig. 8: To obtain the running times for clique queries, we will use ./clique_bench (this will run 5 rounds of query graphs of 3 relations up to $n$).

In the experiments, we used the largest join cardinality $W = 10^8$, as follows:

./clique_bench 24 100000000

All results (even those of individual rounds) are stored in the benchs directory, labeled normal-clique. The average result is stored in a file with the suffix -final.

As regards the actual figures:

  • Fig. 6 uses minmax_dpsub_time and minmax_dpconv_instant_boosted_time.
  • Fig. 8 uses hybrid_capped_dpsub_time, simple_capped_dpsub_time, and minplus_dpsub_time.

JOB & CEB

To obtain Fig. 5, we can use the ./bench binary. We provide the query graphs with the true cardinalities of JOB and CEB directly in queries. The list of pairs represents (bitset, cardinality).

Since the previous clique benchmark does not use DPccp, due to its overhead to generate the ccp's, let's activate the mode to have it.

To this end, change the macro in BenchmarkRunner.cpp to the following configuration:

#define CLIQUE_BENCHMARK 0
#define CAPPED_COUT_BENCHMARK 1

And run make again:

make

Then,

./bench ../../queries/job/

generates the necessary runtimes in benchs, prefixed by cap-cout. Similarly, for CEB run the following (might take a couple of minutes due to the large number of queries):

./bench ../../queries/ceb-imdb-full/

You can also obtain the statistics about the $C_{out}, C_{max}$, and $C_{cap}$ values in cout_cmax_ratios.csv.

Algorithms

All algorithms are in src/algorithms.

  1. DPconv: Its simple and fast algorithm (Sec. 6) is in DPconv.cpp.
  2. DPsub: All variants of DPsub are in DPsub.cpp.
  3. DPccp: All variants of DPccp are in DPccp.cpp.
  4. SubsetConvolution: The heart of our improved layered dynamic programs is include/SubsetConvolution.hpp.

In particular, BooleanFSC implements all our optimizations (Sec. 5.2, Sec. 5.3) and reduces the copy between the DP-table and the actual subset convolution.

In BoostedBooleanFSC, we reduce the number of steps in the last step.