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Implementation for Black-Box and Sprint optimizations for Regular Expressions.

Supported Data Structures

We currently support the following data structures:

  1. Suffix Tree (ST)
  2. Suffix Array (SA)
  3. Compressed Suffix Tree (CST)
  4. k-gram Index (kGM)

We also support Sprint optimizations/naive Black Box Algorithms on CSA, but they are more closely integrated with the data structures; the implementation can be found in the Succinct's C++ repository (see the regex sub-module). We also provide a distributed implementation for CSA on Spark, which can be found at the Succinct-Spark repository.


  • C++11 support from the c++ compiler
  • CMake build system
  • Thrift for the multicore implementation

Building and Cleaning up

To build all binaries and benchmarks, run:


To clean-up build files, run:


Constructing datasets

Since pre-processing can be time consuming for most indexes, we provide a tool to construct the indexes and serialize them to disk. Additionally, all our benchmarks require that the indexes be present in the serialized form. To construct and serialize an index, run:

./build/ds-lib/construct/bin/construct [-d data-structure] [file]

after the build step.

The data-structure parameter is an integer, and uses the following mapping:

0   ST
1   CST
2   Plain Suffix Array (no LCP)
3   SA (with LCP)
4   kGM

The file parameter is simply the path to the input data.


./build/ds-lib/construct/bin/construct -d 3 data/

Running Single Core Benchmark

To benchmark the single core performance for black-box or pull-star approaches, run:

./build/bench/bin/rxbench [-m mode] [-q query_file] [-r res_file] [-d data-structure] [-e executor_type] [-b benchmark] [file]

mode specifies whether the data structure should be constructed on file (1) or should should be read from file (0).

query_file specifies the path to the file containing RegEx queries (see queries/ for sample queries).

res_file specifies the path to the output file for the results.

data-structure specifies which index to use (same mapping as described above).

executor_type specifies whether Black-Box (0) or Pull-Star (1) approach should be used.

benchmark specifies the benchmark-type, and should be set to "regex-latency".

file specifies the input, as before. Note that even if reading the index from its serialized version, specify the name of the original input file.


./build/bench/bin/rxbench -m 0 -q queries/queries.regex -r regex-latency.txt -d 3 -e 1 -b "latency-regex" data/

Running Multi-core Benchmark

To benchmark the multi-core performance for black-box or pull-star approaches, we use thrift as the communication system between different processes which run on different cores. To this end, the query engine must be started as a service before running the benchmarks.

Before starting the service, modify parameters in conf/ Note that the DATA\_PATH must point to the directory containing the different shards (each shard would correspond to a different core) encoded with the correct DATA\_STRUCTURE. Each shard must be named $DATA\_PATH/data\_${i}.${suffix}, where ${i} ranges from 0 to NUM_SHARDS-1, and ${suffix} is the suffix assigned to the serialized shard by the construct tool (e.g., .st for Suffix Trees, .cst for Compressed Suffix Trees, etc.).

To start the service, run:


Once the service is started, you can benchmark the multicore performance by running:

./build/bench/bin/rxbench -t [-q query_file] [-r res_file] [-b benchmark]

-t instructs the benchmark tool to run the benchmark in the multi-core mode. Other parameters have the same meaning as the single-core benchmark.

If you have any doubts or would like to report bugs, please contact us at

  • anuragk [at]
  • ragarwal [at]


Sprint Transformations for RegEx queries






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